2023-11-02 18:43:57,215 - mmseg - INFO - Multi-processing start method is `None` 2023-11-02 18:43:57,221 - mmseg - INFO - OpenCV num_threads is `128 2023-11-02 18:43:57,221 - mmseg - INFO - OMP num threads is 1 2023-11-02 18:43:57,292 - mmseg - INFO - Environment info: ------------------------------------------------------------ sys.platform: linux Python: 3.8.15 (default, Nov 4 2022, 20:59:55) [GCC 11.2.0] CUDA available: True GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB CUDA_HOME: /mnt/petrelfs/wangwenhai/miniconda3/envs/mmdetseg NVCC: Cuda compilation tools, release 11.7, V11.7.99 GCC: gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44) PyTorch: 1.13.0 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201402 - Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 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 11.7 - 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_61,code=sm_61;-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;-gencode;arch=compute_37,code=compute_37 - CuDNN 8.5 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -fabi-version=11 -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 -Werror=non-virtual-dtor -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -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=1.13.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, TorchVision: 0.14.0 OpenCV: 4.8.0 MMCV: 1.7.0 MMCV Compiler: GCC 7.3 MMCV CUDA Compiler: 11.7 MMSegmentation: 0.27.0+ ------------------------------------------------------------ 2023-11-02 18:43:57,292 - mmseg - INFO - Distributed training: True 2023-11-02 18:43:57,565 - mmseg - INFO - Config: checkpoint = 'https://download.openmmlab.com/mmsegmentation/v0.5/pretrain/segmenter/vit_base_p16_384_20220308-96dfe169.pth' backbone_norm_cfg = dict(type='LN', eps=1e-06, requires_grad=True) model = dict( type='EncoderDecoder', pretrained= './pretrained/intern_vit_6b_224px.pth', backbone=dict( type='InternViT6B', pretrain_size=224, img_size=504, patch_size=14, embed_dim=3200, depth=48, num_heads=25, mlp_ratio=4.0, qkv_bias=False, drop_path_rate=0.4, init_values=0.1, with_cp=True, use_flash_attn=True, qk_normalization=True, layerscale_no_force_fp32=True, freeze_vit=False, out_indices=[47]), decode_head=dict( type='FCNHead', in_channels=3200, channels=3200, num_convs=0, dropout_ratio=0.0, concat_input=False, num_classes=150, with_norm=True, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), test_cfg=dict(mode='slide', crop_size=(504, 504), stride=(322, 322))) 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 = (504, 504) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2016, 504), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(504, 504), 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=(504, 504), 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=(2016, 504), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='ResizeToMultiple', size_divisor=14), 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', max_image_num=10105, pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations', reduce_zero_label=True), dict(type='Resize', img_scale=(2016, 504), ratio_range=(0.5, 2.0)), dict(type='RandomCrop', crop_size=(504, 504), 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=(504, 504), 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=(2016, 504), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='ResizeToMultiple', size_divisor=14), 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=(2016, 504), flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='ResizeToMultiple', size_divisor=14), 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)) optimizer_config = dict() lr_config = dict( policy='poly', warmup='linear', warmup_iters=800, warmup_ratio=1e-06, power=1.0, min_lr=0.0, by_epoch=False) runner = dict(type='IterBasedRunner', max_iters=40000) checkpoint_config = dict( by_epoch=False, interval=1000, deepspeed=True, max_keep_ckpts=2) evaluation = dict( interval=1000, metric='mIoU', pre_eval=True, save_best='auto') deepspeed = True deepspeed_config = 'zero_configs/adam_zero1_bf16.json' pretrained = './pretrained/intern_vit_6b_224px.pth' custom_hooks = [dict(type='ToBFloat16Hook', priority=49)] work_dir = './work_dirs/segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2' gpu_ids = range(0, 8) auto_resume = False 2023-11-02 18:44:01,979 - mmseg - INFO - Set random seed to 54129263, deterministic: False 2023-11-02 18:45:17,703 - mmseg - INFO - 2023-11-02 18:45:33,254 - mmseg - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} Name of parameter - Initialization information backbone.pos_embed - torch.Size([1, 1297, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.cls_token - torch.Size([1, 1, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.patch_embed.proj.weight - torch.Size([3200, 3, 14, 14]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.patch_embed.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.0.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.1.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.2.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.3.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.4.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.5.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.6.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.7.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.8.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.9.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.10.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.11.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.12.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.13.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.14.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.15.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.16.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.17.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.18.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.19.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.20.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.21.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.22.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.23.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.24.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.25.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.26.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.27.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.28.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.29.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.30.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.31.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.32.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.33.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.34.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.35.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.36.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.37.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.38.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.39.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.40.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.41.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.42.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.43.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.44.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.45.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.46.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.norm1.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.attn.qkv.weight - torch.Size([9600, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.attn.proj.weight - torch.Size([3200, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.attn.proj.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.attn.q_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.attn.k_norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.ls1.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.norm2.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.mlp.fc1.weight - torch.Size([12800, 3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.mlp.fc1.bias - torch.Size([12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.mlp.fc2.weight - torch.Size([3200, 12800]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.mlp.fc2.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder backbone.blocks.47.ls2.gamma - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.conv_seg.weight - torch.Size([150, 3200, 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.norm.weight - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.norm.bias - torch.Size([3200]): The value is the same before and after calling `init_weights` of EncoderDecoder 2023-11-02 18:45:33,263 - mmseg - INFO - EncoderDecoder( (backbone): InternViT6B( (patch_embed): PatchEmbed( (proj): Conv2d(3, 3200, kernel_size=(14, 14), stride=(14, 14)) (norm): Identity() ) (pos_drop): Identity() (blocks): ModuleList( (0): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): Identity() (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): Identity() ) (1): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.009) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.009) ) (2): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.017) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.017) ) (3): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.026) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.026) ) (4): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.034) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.034) ) (5): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.043) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.043) ) (6): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.051) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.051) ) (7): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.060) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.060) ) (8): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.068) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.068) ) (9): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.077) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.077) ) (10): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.085) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.085) ) (11): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.094) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.094) ) (12): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.102) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.102) ) (13): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.111) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.111) ) (14): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.119) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.119) ) (15): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.128) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.128) ) (16): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.136) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.136) ) (17): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.145) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.145) ) (18): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.153) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.153) ) (19): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.162) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.162) ) (20): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.170) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.170) ) (21): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.179) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.179) ) (22): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.187) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.187) ) (23): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.196) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.196) ) (24): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.204) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.204) ) (25): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.213) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.213) ) (26): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.221) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.221) ) (27): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.230) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.230) ) (28): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.238) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.238) ) (29): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.247) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.247) ) (30): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.255) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.255) ) (31): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.264) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.264) ) (32): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.272) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.272) ) (33): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.281) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.281) ) (34): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.289) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.289) ) (35): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.298) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.298) ) (36): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.306) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.306) ) (37): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.315) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.315) ) (38): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.323) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.323) ) (39): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.332) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.332) ) (40): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.340) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.340) ) (41): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.349) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.349) ) (42): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.357) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.357) ) (43): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.366) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.366) ) (44): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.374) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.374) ) (45): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.383) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.383) ) (46): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.391) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.391) ) (47): Block( (norm1): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (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): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (k_norm): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) ) (ls1): LayerScale() (drop_path1): DropPath(drop_prob=0.400) (norm2): FusedRMSNorm(torch.Size([3200]), eps=1e-06, elementwise_affine=True) (mlp): Mlp( (fc1): Linear(in_features=3200, out_features=12800, bias=True) (act): GELU(approximate='none') (drop1): Dropout(p=0.0, inplace=False) (fc2): Linear(in_features=12800, out_features=3200, bias=True) (drop2): Dropout(p=0.0, inplace=False) ) (ls2): LayerScale() (drop_path2): DropPath(drop_prob=0.400) ) ) ) (decode_head): FCNHead( input_transform=None, ignore_index=255, align_corners=False (loss_decode): CrossEntropyLoss(avg_non_ignore=False) (conv_seg): Conv2d(3200, 150, kernel_size=(1, 1), stride=(1, 1)) (convs): Identity() (norm): SyncBatchNorm(3200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} ) 2023-11-02 18:45:33,828 - mmseg - INFO - Loaded 20210 images 2023-11-02 18:45:33,837 - mmseg - INFO - Randomly select 10105 images 2023-11-02 18:45:35,206 - mmseg - INFO - {'num_layers': 48, 'layer_decay_rate': 0.95} 2023-11-02 18:45:35,206 - mmseg - INFO - Build LayerDecayOptimizerConstructor 0.950000 - 50 2023-11-02 18:45:35,211 - mmseg - INFO - Param groups = { "layer_0_decay": { "param_names": [ "backbone.pos_embed", "backbone.cls_token", "backbone.patch_embed.proj.weight" ], "lr_scale": 0.0809947108175928, "lr": 3.2397884327037123e-06, "weight_decay": 0.05 }, "layer_0_no_decay": { "param_names": [ "backbone.patch_embed.proj.bias" ], "lr_scale": 0.0809947108175928, "lr": 3.2397884327037123e-06, "weight_decay": 0.0 }, "layer_1_no_decay": { "param_names": [ "backbone.blocks.0.norm1.weight", "backbone.blocks.0.attn.proj.bias", "backbone.blocks.0.attn.q_norm.weight", "backbone.blocks.0.attn.k_norm.weight", "backbone.blocks.0.ls1.gamma", "backbone.blocks.0.norm2.weight", "backbone.blocks.0.mlp.fc1.bias", "backbone.blocks.0.mlp.fc2.bias", "backbone.blocks.0.ls2.gamma" ], "lr_scale": 0.0852575903343082, "lr": 3.4103036133723282e-06, "weight_decay": 0.0 }, "layer_1_decay": { "param_names": [ "backbone.blocks.0.attn.qkv.weight", "backbone.blocks.0.attn.proj.weight", "backbone.blocks.0.mlp.fc1.weight", "backbone.blocks.0.mlp.fc2.weight" ], "lr_scale": 0.0852575903343082, "lr": 3.4103036133723282e-06, "weight_decay": 0.05 }, "layer_2_no_decay": { "param_names": [ "backbone.blocks.1.norm1.weight", "backbone.blocks.1.attn.proj.bias", "backbone.blocks.1.attn.q_norm.weight", "backbone.blocks.1.attn.k_norm.weight", "backbone.blocks.1.ls1.gamma", "backbone.blocks.1.norm2.weight", "backbone.blocks.1.mlp.fc1.bias", "backbone.blocks.1.mlp.fc2.bias", "backbone.blocks.1.ls2.gamma" ], "lr_scale": 0.08974483193085075, "lr": 3.5897932772340305e-06, "weight_decay": 0.0 }, "layer_2_decay": { "param_names": [ "backbone.blocks.1.attn.qkv.weight", "backbone.blocks.1.attn.proj.weight", "backbone.blocks.1.mlp.fc1.weight", "backbone.blocks.1.mlp.fc2.weight" ], "lr_scale": 0.08974483193085075, "lr": 3.5897932772340305e-06, "weight_decay": 0.05 }, "layer_3_no_decay": { "param_names": [ "backbone.blocks.2.norm1.weight", "backbone.blocks.2.attn.proj.bias", "backbone.blocks.2.attn.q_norm.weight", "backbone.blocks.2.attn.k_norm.weight", "backbone.blocks.2.ls1.gamma", "backbone.blocks.2.norm2.weight", "backbone.blocks.2.mlp.fc1.bias", "backbone.blocks.2.mlp.fc2.bias", "backbone.blocks.2.ls2.gamma" ], "lr_scale": 0.09446824413773763, "lr": 3.7787297655095058e-06, "weight_decay": 0.0 }, "layer_3_decay": { "param_names": [ "backbone.blocks.2.attn.qkv.weight", "backbone.blocks.2.attn.proj.weight", "backbone.blocks.2.mlp.fc1.weight", "backbone.blocks.2.mlp.fc2.weight" ], "lr_scale": 0.09446824413773763, "lr": 3.7787297655095058e-06, "weight_decay": 0.05 }, "layer_4_no_decay": { "param_names": [ "backbone.blocks.3.norm1.weight", "backbone.blocks.3.attn.proj.bias", "backbone.blocks.3.attn.q_norm.weight", "backbone.blocks.3.attn.k_norm.weight", "backbone.blocks.3.ls1.gamma", "backbone.blocks.3.norm2.weight", "backbone.blocks.3.mlp.fc1.bias", "backbone.blocks.3.mlp.fc2.bias", "backbone.blocks.3.ls2.gamma" ], "lr_scale": 0.09944025698709225, "lr": 3.97761027948369e-06, "weight_decay": 0.0 }, "layer_4_decay": { "param_names": [ "backbone.blocks.3.attn.qkv.weight", "backbone.blocks.3.attn.proj.weight", "backbone.blocks.3.mlp.fc1.weight", "backbone.blocks.3.mlp.fc2.weight" ], "lr_scale": 0.09944025698709225, "lr": 3.97761027948369e-06, "weight_decay": 0.05 }, "layer_5_no_decay": { "param_names": [ "backbone.blocks.4.norm1.weight", "backbone.blocks.4.attn.proj.bias", "backbone.blocks.4.attn.q_norm.weight", "backbone.blocks.4.attn.k_norm.weight", "backbone.blocks.4.ls1.gamma", "backbone.blocks.4.norm2.weight", "backbone.blocks.4.mlp.fc1.bias", "backbone.blocks.4.mlp.fc2.bias", "backbone.blocks.4.ls2.gamma" ], "lr_scale": 0.10467395472325501, "lr": 4.186958188930201e-06, "weight_decay": 0.0 }, "layer_5_decay": { "param_names": [ "backbone.blocks.4.attn.qkv.weight", "backbone.blocks.4.attn.proj.weight", "backbone.blocks.4.mlp.fc1.weight", "backbone.blocks.4.mlp.fc2.weight" ], "lr_scale": 0.10467395472325501, "lr": 4.186958188930201e-06, "weight_decay": 0.05 }, "layer_6_no_decay": { "param_names": [ "backbone.blocks.5.norm1.weight", "backbone.blocks.5.attn.proj.bias", "backbone.blocks.5.attn.q_norm.weight", "backbone.blocks.5.attn.k_norm.weight", "backbone.blocks.5.ls1.gamma", "backbone.blocks.5.norm2.weight", "backbone.blocks.5.mlp.fc1.bias", "backbone.blocks.5.mlp.fc2.bias", "backbone.blocks.5.ls2.gamma" ], "lr_scale": 0.11018311023500528, "lr": 4.407324409400211e-06, "weight_decay": 0.0 }, "layer_6_decay": { "param_names": [ "backbone.blocks.5.attn.qkv.weight", "backbone.blocks.5.attn.proj.weight", 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"backbone.blocks.46.attn.proj.weight", "backbone.blocks.46.mlp.fc1.weight", "backbone.blocks.46.mlp.fc2.weight" ], "lr_scale": 0.9025, "lr": 3.61e-05, "weight_decay": 0.05 }, "layer_48_no_decay": { "param_names": [ "backbone.blocks.47.norm1.weight", "backbone.blocks.47.attn.proj.bias", "backbone.blocks.47.attn.q_norm.weight", "backbone.blocks.47.attn.k_norm.weight", "backbone.blocks.47.ls1.gamma", "backbone.blocks.47.norm2.weight", "backbone.blocks.47.mlp.fc1.bias", "backbone.blocks.47.mlp.fc2.bias", "backbone.blocks.47.ls2.gamma" ], "lr_scale": 0.95, "lr": 3.8e-05, "weight_decay": 0.0 }, "layer_48_decay": { "param_names": [ "backbone.blocks.47.attn.qkv.weight", "backbone.blocks.47.attn.proj.weight", "backbone.blocks.47.mlp.fc1.weight", "backbone.blocks.47.mlp.fc2.weight" ], "lr_scale": 0.95, "lr": 3.8e-05, "weight_decay": 0.05 }, "layer_49_decay": { "param_names": [ "decode_head.conv_seg.weight" ], "lr_scale": 1.0, "lr": 4e-05, "weight_decay": 0.05 }, "layer_49_no_decay": { "param_names": [ "decode_head.conv_seg.bias", "decode_head.norm.weight", "decode_head.norm.bias" ], "lr_scale": 1.0, "lr": 4e-05, "weight_decay": 0.0 } } 2023-11-02 18:46:12,288 - mmseg - INFO - trainable parameters: 5906608150 2023-11-02 18:46:12,289 - mmseg - INFO - total parameters: 5906608150 2023-11-02 18:46:12,334 - mmseg - INFO - Loaded 2000 images 2023-11-02 18:46:12,335 - mmseg - INFO - Randomly select 2000 images 2023-11-02 18:46:12,335 - mmseg - INFO - Start running, host: wangwenhai@SH-IDC1-10-140-37-51, work_dir: /mnt/petrelfs/wangwenhai/workspace/ViTDetection/mmsegmentation/work_dirs/segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2 2023-11-02 18:46:12,335 - 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 -------------------- 2023-11-02 18:46:12,335 - mmseg - INFO - workflow: [('train', 1)], max: 40000 iters 2023-11-02 18:46:12,343 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/wangwenhai/workspace/ViTDetection/mmsegmentation/work_dirs/segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2 by HardDiskBackend. 2023-11-02 18:48:15,107 - mmseg - INFO - Iter [50/40000] lr: 1.982e-07, eta: 14:11:51, time: 1.279, data_time: 0.008, memory: 38534, decode.loss_ce: 4.1557, decode.acc_seg: 0.7854, loss: 4.1557 2023-11-02 18:49:17,310 - mmseg - INFO - Iter [100/40000] lr: 3.999e-07, eta: 13:59:02, time: 1.244, data_time: 0.038, memory: 38534, decode.loss_ce: 4.0301, decode.acc_seg: 2.5216, loss: 4.0301 2023-11-02 18:50:17,941 - mmseg - INFO - Iter [150/40000] lr: 6.012e-07, eta: 13:47:06, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 3.5998, decode.acc_seg: 21.4281, loss: 3.5998 2023-11-02 18:51:18,567 - mmseg - INFO - Iter [200/40000] lr: 8.019e-07, eta: 13:40:37, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 2.5255, decode.acc_seg: 44.2931, loss: 2.5255 2023-11-02 18:52:19,161 - mmseg - INFO - Iter [250/40000] lr: 1.002e-06, eta: 13:36:15, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 1.7943, decode.acc_seg: 57.4016, loss: 1.7943 2023-11-02 18:53:19,755 - mmseg - INFO - Iter [300/40000] lr: 1.202e-06, eta: 13:32:59, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 1.5546, decode.acc_seg: 61.3504, loss: 1.5546 2023-11-02 18:54:20,383 - mmseg - INFO - Iter [350/40000] lr: 1.401e-06, eta: 13:30:26, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 1.3489, decode.acc_seg: 63.4579, loss: 1.3489 2023-11-02 18:55:21,096 - mmseg - INFO - Iter [400/40000] lr: 1.600e-06, eta: 13:28:25, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 1.1833, decode.acc_seg: 65.6719, loss: 1.1833 2023-11-02 18:56:21,826 - mmseg - INFO - Iter [450/40000] lr: 1.798e-06, eta: 13:26:38, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 1.1529, decode.acc_seg: 66.3451, loss: 1.1529 2023-11-02 18:57:22,593 - mmseg - INFO - Iter [500/40000] lr: 1.996e-06, eta: 13:25:04, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 1.1312, decode.acc_seg: 67.0684, loss: 1.1312 2023-11-02 18:58:23,326 - mmseg - INFO - Iter [550/40000] lr: 2.193e-06, eta: 13:23:33, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.9882, decode.acc_seg: 70.0283, loss: 0.9882 2023-11-02 18:59:23,988 - mmseg - INFO - Iter [600/40000] lr: 2.389e-06, eta: 13:22:03, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.9374, decode.acc_seg: 71.2020, loss: 0.9374 2023-11-02 19:00:27,051 - mmseg - INFO - Iter [650/40000] lr: 2.586e-06, eta: 13:23:02, time: 1.261, data_time: 0.054, memory: 38534, decode.loss_ce: 0.8871, decode.acc_seg: 73.0608, loss: 0.8871 2023-11-02 19:01:27,754 - mmseg - INFO - Iter [700/40000] lr: 2.781e-06, eta: 13:21:32, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.8135, decode.acc_seg: 74.0146, loss: 0.8135 2023-11-02 19:02:28,511 - mmseg - INFO - Iter [750/40000] lr: 2.976e-06, eta: 13:20:08, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.8557, decode.acc_seg: 73.4025, loss: 0.8557 2023-11-02 19:03:29,258 - mmseg - INFO - Iter [800/40000] lr: 3.171e-06, eta: 13:18:47, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.7686, decode.acc_seg: 75.2319, loss: 0.7686 2023-11-02 19:04:29,980 - mmseg - INFO - Iter [850/40000] lr: 3.171e-06, eta: 13:17:27, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.8116, decode.acc_seg: 73.7426, loss: 0.8116 2023-11-02 19:05:30,674 - mmseg - INFO - Iter [900/40000] lr: 3.167e-06, eta: 13:16:08, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.7651, decode.acc_seg: 75.0734, loss: 0.7651 2023-11-02 19:06:31,399 - mmseg - INFO - Iter [950/40000] lr: 3.163e-06, eta: 13:14:52, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.7670, decode.acc_seg: 75.0386, loss: 0.7670 2023-11-02 19:07:32,105 - mmseg - INFO - Saving checkpoint at 1000 iterations 2023-11-02 19:08:23,543 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 19:08:23,543 - mmseg - INFO - Iter [1000/40000] lr: 3.159e-06, eta: 13:47:03, time: 2.243, data_time: 0.007, memory: 38534, decode.loss_ce: 0.7258, decode.acc_seg: 75.3294, loss: 0.7258 2023-11-02 19:10:19,670 - mmseg - INFO - per class results: 2023-11-02 19:10:19,676 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 73.68 | 83.95 | | building | 81.33 | 91.92 | | sky | 92.87 | 95.55 | | floor | 76.4 | 89.75 | | tree | 72.09 | 87.58 | | ceiling | 80.56 | 92.0 | | road | 81.12 | 85.5 | | bed | 86.59 | 95.85 | | windowpane | 59.8 | 81.16 | | grass | 65.4 | 85.26 | | cabinet | 58.84 | 70.32 | | sidewalk | 60.35 | 87.9 | | person | 78.01 | 90.87 | | earth | 34.94 | 51.22 | | door | 43.63 | 66.02 | | table | 56.0 | 70.25 | | mountain | 54.35 | 75.08 | | plant | 51.94 | 62.33 | | curtain | 68.58 | 77.54 | | chair | 46.23 | 55.17 | | car | 81.06 | 91.41 | | water | 32.15 | 38.33 | | painting | 61.25 | 87.68 | | sofa | 66.73 | 82.19 | | shelf | 33.68 | 61.21 | | house | 34.63 | 39.64 | | sea | 55.32 | 67.77 | | mirror | 59.45 | 67.04 | | rug | 20.79 | 21.56 | | field | 21.29 | 27.34 | | armchair | 42.57 | 72.93 | | seat | 58.18 | 83.03 | | fence | 30.46 | 45.33 | | desk | 36.48 | 66.39 | | rock | 35.93 | 47.34 | | wardrobe | 48.78 | 74.91 | | lamp | 52.53 | 64.11 | | bathtub | 71.0 | 79.85 | | railing | 33.65 | 43.55 | | cushion | 53.6 | 68.9 | | base | 8.93 | 13.01 | | box | 16.35 | 19.04 | | column | 42.42 | 58.12 | | signboard | 32.37 | 58.33 | | chest of drawers | 37.5 | 44.91 | | counter | 39.67 | 66.2 | | sand | 48.24 | 53.41 | | sink | 67.97 | 73.25 | | skyscraper | 51.4 | 75.46 | | fireplace | 64.64 | 93.28 | | refrigerator | 63.61 | 86.56 | | grandstand | 34.46 | 81.72 | | path | 13.34 | 15.44 | | stairs | 9.87 | 10.52 | | runway | 65.51 | 96.23 | | case | 51.1 | 69.02 | | pool table | 86.66 | 95.01 | | pillow | 51.65 | 59.34 | | screen door | 41.4 | 44.05 | | stairway | 31.19 | 55.74 | | river | 12.09 | 78.75 | | bridge | 61.63 | 82.81 | | bookcase | 26.68 | 53.23 | | blind | 18.28 | 18.99 | | coffee table | 59.7 | 78.37 | | toilet | 80.16 | 90.57 | | flower | 27.23 | 40.46 | | book | 41.95 | 68.18 | | hill | 4.38 | 5.21 | | bench | 42.42 | 54.44 | | countertop | 58.56 | 69.58 | | stove | 68.18 | 80.28 | | palm | 45.61 | 76.79 | | kitchen island | 34.05 | 65.57 | | computer | 65.31 | 88.87 | | swivel chair | 32.72 | 83.2 | | boat | 56.63 | 90.97 | | bar | 53.49 | 58.31 | | arcade machine | 69.51 | 82.48 | | hovel | 19.36 | 21.8 | | bus | 66.88 | 94.96 | | towel | 56.45 | 62.86 | | light | 35.03 | 39.85 | | truck | 27.84 | 38.17 | | tower | 24.98 | 37.81 | | chandelier | 53.35 | 85.93 | | awning | 30.16 | 36.61 | | streetlight | 16.05 | 19.56 | | booth | 11.23 | 15.32 | | television receiver | 57.36 | 86.09 | | airplane | 49.5 | 67.83 | | dirt track | 0.0 | 0.0 | | apparel | 32.19 | 44.57 | | pole | 15.09 | 17.73 | | land | 0.0 | 0.0 | | bannister | 0.0 | 0.0 | | escalator | 48.24 | 58.95 | | ottoman | 20.51 | 22.52 | | bottle | 18.46 | 25.98 | | buffet | 33.1 | 35.16 | | poster | 0.03 | 0.03 | | stage | 11.28 | 21.89 | | van | 40.73 | 48.99 | | ship | 0.07 | 0.07 | | fountain | 26.57 | 27.32 | | conveyer belt | 70.38 | 94.49 | | canopy | 3.44 | 3.54 | | washer | 67.24 | 80.66 | | plaything | 0.0 | 0.0 | | swimming pool | 5.02 | 5.02 | | stool | 34.11 | 48.91 | | barrel | 20.97 | 59.3 | | basket | 30.02 | 37.67 | | waterfall | 29.35 | 33.75 | | tent | 91.64 | 97.99 | | bag | 5.21 | 5.31 | | minibike | 60.28 | 75.36 | | cradle | 75.19 | 96.28 | | oven | 42.98 | 62.03 | | ball | 0.0 | 0.0 | | food | 45.3 | 70.0 | | step | 0.0 | 0.0 | | tank | 32.32 | 33.63 | | trade name | 8.42 | 8.76 | | microwave | 64.79 | 70.47 | | pot | 38.73 | 41.97 | | animal | 73.71 | 89.94 | | bicycle | 54.69 | 69.44 | | lake | 0.0 | 0.0 | | dishwasher | 49.08 | 60.01 | | screen | 65.49 | 87.98 | | blanket | 1.27 | 1.51 | | sculpture | 0.0 | 0.0 | | hood | 56.89 | 65.79 | | sconce | 15.9 | 17.53 | | vase | 22.61 | 26.96 | | traffic light | 8.95 | 9.25 | | tray | 0.56 | 0.58 | | ashcan | 15.46 | 15.56 | | fan | 47.5 | 61.8 | | pier | 29.74 | 35.78 | | crt screen | 0.0 | 0.0 | | plate | 44.09 | 68.45 | | monitor | 5.49 | 5.8 | | bulletin board | 0.55 | 0.55 | | shower | 0.0 | 0.0 | | radiator | 60.13 | 64.81 | | glass | 0.83 | 0.83 | | clock | 12.36 | 12.79 | | flag | 9.35 | 9.42 | +---------------------+-------+-------+ 2023-11-02 19:10:19,676 - mmseg - INFO - Summary: 2023-11-02 19:10:19,676 - mmseg - INFO - +------+-------+-------+ | aAcc | mIoU | mAcc | +------+-------+-------+ | 80.0 | 39.76 | 52.01 | +------+-------+-------+ 2023-11-02 19:10:19,677 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 19:10:19,677 - mmseg - INFO - Iter(val) [250] aAcc: 0.8000, mIoU: 0.3976, mAcc: 0.5201, IoU.wall: 0.7368, IoU.building: 0.8133, IoU.sky: 0.9287, IoU.floor: 0.7640, IoU.tree: 0.7209, IoU.ceiling: 0.8056, IoU.road: 0.8112, IoU.bed : 0.8659, IoU.windowpane: 0.5980, IoU.grass: 0.6540, IoU.cabinet: 0.5884, IoU.sidewalk: 0.6035, IoU.person: 0.7801, IoU.earth: 0.3494, IoU.door: 0.4363, IoU.table: 0.5600, IoU.mountain: 0.5435, IoU.plant: 0.5194, IoU.curtain: 0.6858, IoU.chair: 0.4623, IoU.car: 0.8106, IoU.water: 0.3215, IoU.painting: 0.6125, IoU.sofa: 0.6673, IoU.shelf: 0.3368, IoU.house: 0.3463, IoU.sea: 0.5532, IoU.mirror: 0.5945, IoU.rug: 0.2079, IoU.field: 0.2129, IoU.armchair: 0.4257, IoU.seat: 0.5818, IoU.fence: 0.3046, IoU.desk: 0.3648, IoU.rock: 0.3593, IoU.wardrobe: 0.4878, IoU.lamp: 0.5253, IoU.bathtub: 0.7100, IoU.railing: 0.3365, IoU.cushion: 0.5360, IoU.base: 0.0893, IoU.box: 0.1635, IoU.column: 0.4242, IoU.signboard: 0.3237, IoU.chest of drawers: 0.3750, IoU.counter: 0.3967, IoU.sand: 0.4824, IoU.sink: 0.6797, IoU.skyscraper: 0.5140, IoU.fireplace: 0.6464, IoU.refrigerator: 0.6361, IoU.grandstand: 0.3446, IoU.path: 0.1334, IoU.stairs: 0.0987, IoU.runway: 0.6551, IoU.case: 0.5110, IoU.pool table: 0.8666, IoU.pillow: 0.5165, IoU.screen door: 0.4140, IoU.stairway: 0.3119, IoU.river: 0.1209, IoU.bridge: 0.6163, IoU.bookcase: 0.2668, IoU.blind: 0.1828, IoU.coffee table: 0.5970, IoU.toilet: 0.8016, IoU.flower: 0.2723, IoU.book: 0.4195, IoU.hill: 0.0438, IoU.bench: 0.4242, IoU.countertop: 0.5856, IoU.stove: 0.6818, IoU.palm: 0.4561, IoU.kitchen island: 0.3405, IoU.computer: 0.6531, IoU.swivel chair: 0.3272, IoU.boat: 0.5663, IoU.bar: 0.5349, IoU.arcade machine: 0.6951, IoU.hovel: 0.1936, IoU.bus: 0.6688, IoU.towel: 0.5645, IoU.light: 0.3503, IoU.truck: 0.2784, IoU.tower: 0.2498, IoU.chandelier: 0.5335, IoU.awning: 0.3016, IoU.streetlight: 0.1605, IoU.booth: 0.1123, IoU.television receiver: 0.5736, IoU.airplane: 0.4950, IoU.dirt track: 0.0000, IoU.apparel: 0.3219, IoU.pole: 0.1509, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.4824, IoU.ottoman: 0.2051, IoU.bottle: 0.1846, IoU.buffet: 0.3310, IoU.poster: 0.0003, IoU.stage: 0.1128, IoU.van: 0.4073, IoU.ship: 0.0007, IoU.fountain: 0.2657, IoU.conveyer belt: 0.7038, IoU.canopy: 0.0344, IoU.washer: 0.6724, IoU.plaything: 0.0000, IoU.swimming pool: 0.0502, IoU.stool: 0.3411, IoU.barrel: 0.2097, IoU.basket: 0.3002, IoU.waterfall: 0.2935, IoU.tent: 0.9164, IoU.bag: 0.0521, IoU.minibike: 0.6028, IoU.cradle: 0.7519, IoU.oven: 0.4298, IoU.ball: 0.0000, IoU.food: 0.4530, IoU.step: 0.0000, IoU.tank: 0.3232, IoU.trade name: 0.0842, IoU.microwave: 0.6479, IoU.pot: 0.3873, IoU.animal: 0.7371, IoU.bicycle: 0.5469, IoU.lake: 0.0000, IoU.dishwasher: 0.4908, IoU.screen: 0.6549, IoU.blanket: 0.0127, IoU.sculpture: 0.0000, IoU.hood: 0.5689, IoU.sconce: 0.1590, IoU.vase: 0.2261, IoU.traffic light: 0.0895, IoU.tray: 0.0056, IoU.ashcan: 0.1546, IoU.fan: 0.4750, IoU.pier: 0.2974, IoU.crt screen: 0.0000, IoU.plate: 0.4409, IoU.monitor: 0.0549, IoU.bulletin board: 0.0055, IoU.shower: 0.0000, IoU.radiator: 0.6013, IoU.glass: 0.0083, IoU.clock: 0.1236, IoU.flag: 0.0935, Acc.wall: 0.8395, Acc.building: 0.9192, Acc.sky: 0.9555, Acc.floor: 0.8975, Acc.tree: 0.8758, Acc.ceiling: 0.9200, Acc.road: 0.8550, Acc.bed : 0.9585, Acc.windowpane: 0.8116, Acc.grass: 0.8526, Acc.cabinet: 0.7032, Acc.sidewalk: 0.8790, Acc.person: 0.9087, Acc.earth: 0.5122, Acc.door: 0.6602, Acc.table: 0.7025, Acc.mountain: 0.7508, Acc.plant: 0.6233, Acc.curtain: 0.7754, Acc.chair: 0.5517, Acc.car: 0.9141, Acc.water: 0.3833, Acc.painting: 0.8768, Acc.sofa: 0.8219, Acc.shelf: 0.6121, Acc.house: 0.3964, Acc.sea: 0.6777, Acc.mirror: 0.6704, Acc.rug: 0.2156, Acc.field: 0.2734, Acc.armchair: 0.7293, Acc.seat: 0.8303, Acc.fence: 0.4533, Acc.desk: 0.6639, Acc.rock: 0.4734, Acc.wardrobe: 0.7491, Acc.lamp: 0.6411, Acc.bathtub: 0.7985, Acc.railing: 0.4355, Acc.cushion: 0.6890, Acc.base: 0.1301, Acc.box: 0.1904, Acc.column: 0.5812, Acc.signboard: 0.5833, Acc.chest of drawers: 0.4491, Acc.counter: 0.6620, Acc.sand: 0.5341, Acc.sink: 0.7325, Acc.skyscraper: 0.7546, Acc.fireplace: 0.9328, Acc.refrigerator: 0.8656, Acc.grandstand: 0.8172, Acc.path: 0.1544, Acc.stairs: 0.1052, Acc.runway: 0.9623, Acc.case: 0.6902, Acc.pool table: 0.9501, Acc.pillow: 0.5934, Acc.screen door: 0.4405, Acc.stairway: 0.5574, Acc.river: 0.7875, Acc.bridge: 0.8281, Acc.bookcase: 0.5323, Acc.blind: 0.1899, Acc.coffee table: 0.7837, Acc.toilet: 0.9057, Acc.flower: 0.4046, Acc.book: 0.6818, Acc.hill: 0.0521, Acc.bench: 0.5444, Acc.countertop: 0.6958, Acc.stove: 0.8028, Acc.palm: 0.7679, Acc.kitchen island: 0.6557, Acc.computer: 0.8887, Acc.swivel chair: 0.8320, Acc.boat: 0.9097, Acc.bar: 0.5831, Acc.arcade machine: 0.8248, Acc.hovel: 0.2180, Acc.bus: 0.9496, Acc.towel: 0.6286, Acc.light: 0.3985, Acc.truck: 0.3817, Acc.tower: 0.3781, Acc.chandelier: 0.8593, Acc.awning: 0.3661, Acc.streetlight: 0.1956, Acc.booth: 0.1532, Acc.television receiver: 0.8609, Acc.airplane: 0.6783, Acc.dirt track: 0.0000, Acc.apparel: 0.4457, Acc.pole: 0.1773, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.5895, Acc.ottoman: 0.2252, Acc.bottle: 0.2598, Acc.buffet: 0.3516, Acc.poster: 0.0003, Acc.stage: 0.2189, Acc.van: 0.4899, Acc.ship: 0.0007, Acc.fountain: 0.2732, Acc.conveyer belt: 0.9449, Acc.canopy: 0.0354, Acc.washer: 0.8066, Acc.plaything: 0.0000, Acc.swimming pool: 0.0502, Acc.stool: 0.4891, Acc.barrel: 0.5930, Acc.basket: 0.3767, Acc.waterfall: 0.3375, Acc.tent: 0.9799, Acc.bag: 0.0531, Acc.minibike: 0.7536, Acc.cradle: 0.9628, Acc.oven: 0.6203, Acc.ball: 0.0000, Acc.food: 0.7000, Acc.step: 0.0000, Acc.tank: 0.3363, Acc.trade name: 0.0876, Acc.microwave: 0.7047, Acc.pot: 0.4197, Acc.animal: 0.8994, Acc.bicycle: 0.6944, Acc.lake: 0.0000, Acc.dishwasher: 0.6001, Acc.screen: 0.8798, Acc.blanket: 0.0151, Acc.sculpture: 0.0000, Acc.hood: 0.6579, Acc.sconce: 0.1753, Acc.vase: 0.2696, Acc.traffic light: 0.0925, Acc.tray: 0.0058, Acc.ashcan: 0.1556, Acc.fan: 0.6180, Acc.pier: 0.3578, Acc.crt screen: 0.0000, Acc.plate: 0.6845, Acc.monitor: 0.0580, Acc.bulletin board: 0.0055, Acc.shower: 0.0000, Acc.radiator: 0.6481, Acc.glass: 0.0083, Acc.clock: 0.1279, Acc.flag: 0.0942 2023-11-02 19:11:20,482 - mmseg - INFO - Iter [1050/40000] lr: 3.155e-06, eta: 14:56:03, time: 3.539, data_time: 2.331, memory: 38534, decode.loss_ce: 0.7307, decode.acc_seg: 76.0802, loss: 0.7307 2023-11-02 19:12:21,218 - mmseg - INFO - Iter [1100/40000] lr: 3.151e-06, eta: 14:50:01, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.7291, decode.acc_seg: 75.4573, loss: 0.7291 2023-11-02 19:13:21,992 - mmseg - INFO - Iter [1150/40000] lr: 3.147e-06, eta: 14:44:27, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.7768, decode.acc_seg: 75.0236, loss: 0.7768 2023-11-02 19:14:22,758 - mmseg - INFO - Iter [1200/40000] lr: 3.143e-06, eta: 14:39:15, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.7004, decode.acc_seg: 76.1961, loss: 0.7004 2023-11-02 19:15:23,536 - mmseg - INFO - Iter [1250/40000] lr: 3.139e-06, eta: 14:34:23, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.7175, decode.acc_seg: 76.2270, loss: 0.7175 2023-11-02 19:16:26,637 - mmseg - INFO - Iter [1300/40000] lr: 3.135e-06, eta: 14:30:59, time: 1.262, data_time: 0.053, memory: 38534, decode.loss_ce: 0.6840, decode.acc_seg: 77.0821, loss: 0.6840 2023-11-02 19:17:27,432 - mmseg - INFO - Iter [1350/40000] lr: 3.131e-06, eta: 14:26:39, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.6375, decode.acc_seg: 78.0411, loss: 0.6375 2023-11-02 19:18:28,187 - mmseg - INFO - Iter [1400/40000] lr: 3.126e-06, eta: 14:22:32, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5774, decode.acc_seg: 79.4017, loss: 0.5774 2023-11-02 19:19:28,946 - mmseg - INFO - Iter [1450/40000] lr: 3.122e-06, eta: 14:18:38, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.6151, decode.acc_seg: 78.3189, loss: 0.6151 2023-11-02 19:20:29,758 - mmseg - INFO - Iter [1500/40000] lr: 3.118e-06, eta: 14:14:57, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.6452, decode.acc_seg: 77.4897, loss: 0.6452 2023-11-02 19:21:30,522 - mmseg - INFO - Iter [1550/40000] lr: 3.114e-06, eta: 14:11:25, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.6881, decode.acc_seg: 75.6978, loss: 0.6881 2023-11-02 19:22:31,285 - mmseg - INFO - Iter [1600/40000] lr: 3.110e-06, eta: 14:08:03, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5975, decode.acc_seg: 78.9187, loss: 0.5975 2023-11-02 19:23:32,006 - mmseg - INFO - Iter [1650/40000] lr: 3.106e-06, eta: 14:04:48, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.6139, decode.acc_seg: 77.6581, loss: 0.6139 2023-11-02 19:24:32,758 - mmseg - INFO - Iter [1700/40000] lr: 3.102e-06, eta: 14:01:41, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.6226, decode.acc_seg: 78.3201, loss: 0.6226 2023-11-02 19:25:33,526 - mmseg - INFO - Iter [1750/40000] lr: 3.098e-06, eta: 13:58:43, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.6649, decode.acc_seg: 77.8289, loss: 0.6649 2023-11-02 19:26:34,320 - mmseg - INFO - Iter [1800/40000] lr: 3.094e-06, eta: 13:55:51, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5891, decode.acc_seg: 78.6719, loss: 0.5891 2023-11-02 19:27:35,043 - mmseg - INFO - Iter [1850/40000] lr: 3.090e-06, eta: 13:53:04, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5856, decode.acc_seg: 78.8113, loss: 0.5856 2023-11-02 19:28:38,212 - mmseg - INFO - Iter [1900/40000] lr: 3.086e-06, eta: 13:51:11, time: 1.263, data_time: 0.056, memory: 38534, decode.loss_ce: 0.5876, decode.acc_seg: 79.1061, loss: 0.5876 2023-11-02 19:29:38,950 - mmseg - INFO - Iter [1950/40000] lr: 3.082e-06, eta: 13:48:34, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5559, decode.acc_seg: 79.9199, loss: 0.5559 2023-11-02 19:30:39,730 - mmseg - INFO - Saving checkpoint at 2000 iterations 2023-11-02 19:31:32,104 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 19:31:32,105 - mmseg - INFO - Iter [2000/40000] lr: 3.078e-06, eta: 14:02:37, time: 2.263, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5490, decode.acc_seg: 79.9242, loss: 0.5490 2023-11-02 19:32:30,374 - mmseg - INFO - per class results: 2023-11-02 19:32:30,380 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 76.18 | 85.05 | | building | 81.25 | 92.02 | | sky | 92.97 | 97.7 | | floor | 81.25 | 88.44 | | tree | 73.29 | 84.88 | | ceiling | 82.76 | 91.58 | | road | 81.08 | 92.32 | | bed | 89.27 | 96.15 | | windowpane | 61.57 | 79.79 | | grass | 67.43 | 87.04 | | cabinet | 60.67 | 68.5 | | sidewalk | 60.69 | 69.56 | | person | 79.81 | 91.56 | | earth | 38.54 | 51.81 | | door | 50.82 | 69.71 | | table | 62.31 | 75.0 | | mountain | 58.79 | 79.17 | | plant | 55.28 | 68.83 | | curtain | 73.49 | 87.43 | | chair | 56.79 | 71.53 | | car | 81.05 | 93.69 | | water | 56.29 | 72.71 | | painting | 69.39 | 87.12 | | sofa | 68.81 | 88.42 | | shelf | 40.23 | 53.52 | | house | 44.97 | 61.12 | | sea | 66.18 | 81.81 | | mirror | 69.07 | 81.05 | | rug | 68.45 | 74.25 | | field | 26.98 | 33.73 | | armchair | 45.89 | 58.73 | | seat | 68.28 | 85.95 | | fence | 45.34 | 57.64 | | desk | 42.8 | 60.81 | | rock | 43.57 | 49.27 | | wardrobe | 49.24 | 72.99 | | lamp | 58.11 | 76.42 | | bathtub | 79.4 | 91.0 | | railing | 38.95 | 60.66 | | cushion | 55.88 | 64.56 | | base | 32.91 | 51.27 | | box | 29.59 | 42.37 | | column | 44.2 | 51.44 | | signboard | 35.66 | 49.16 | | chest of drawers | 48.62 | 65.41 | | counter | 49.94 | 63.7 | | sand | 68.33 | 85.98 | | sink | 70.17 | 76.15 | | skyscraper | 41.88 | 70.96 | | fireplace | 68.75 | 90.98 | | refrigerator | 68.12 | 87.03 | | grandstand | 33.74 | 85.96 | | path | 19.48 | 33.1 | | stairs | 39.82 | 49.79 | | runway | 68.38 | 87.33 | | case | 47.04 | 88.8 | | pool table | 84.83 | 98.32 | | pillow | 57.97 | 72.01 | | screen door | 68.56 | 88.29 | | stairway | 37.06 | 43.32 | | river | 11.73 | 23.91 | | bridge | 48.32 | 61.57 | | bookcase | 34.67 | 48.9 | | blind | 35.91 | 39.33 | | coffee table | 62.79 | 84.32 | | toilet | 84.33 | 92.25 | | flower | 35.85 | 68.85 | | book | 46.21 | 74.72 | | hill | 4.36 | 4.76 | | bench | 57.61 | 73.21 | | countertop | 50.32 | 72.29 | | stove | 75.6 | 85.7 | | palm | 48.41 | 80.22 | | kitchen island | 35.93 | 50.64 | | computer | 64.05 | 84.96 | | swivel chair | 44.35 | 87.57 | | boat | 53.18 | 75.85 | | bar | 61.56 | 74.17 | | arcade machine | 85.67 | 97.53 | | hovel | 30.02 | 35.63 | | bus | 87.22 | 94.09 | | towel | 64.5 | 84.95 | | light | 41.12 | 49.36 | | truck | 27.51 | 29.5 | | tower | 4.35 | 5.31 | | chandelier | 61.79 | 85.2 | | awning | 32.85 | 42.74 | | streetlight | 19.49 | 33.52 | | booth | 36.31 | 66.64 | | television receiver | 68.51 | 84.05 | | airplane | 57.84 | 66.59 | | dirt track | 4.41 | 5.48 | | apparel | 38.6 | 60.94 | | pole | 13.81 | 16.11 | | land | 0.0 | 0.0 | | bannister | 3.19 | 3.46 | | escalator | 52.32 | 67.09 | | ottoman | 48.84 | 65.47 | | bottle | 21.93 | 30.62 | | buffet | 50.58 | 88.0 | | poster | 25.01 | 32.8 | | stage | 18.78 | 44.28 | | van | 19.2 | 23.65 | | ship | 10.12 | 11.97 | | fountain | 22.46 | 23.12 | | conveyer belt | 70.83 | 96.82 | | canopy | 27.61 | 42.1 | | washer | 75.28 | 85.04 | | plaything | 18.65 | 21.97 | | swimming pool | 52.35 | 85.35 | | stool | 38.75 | 54.69 | | barrel | 48.66 | 59.3 | | basket | 35.53 | 47.77 | | waterfall | 44.75 | 51.18 | | tent | 81.13 | 98.77 | | bag | 10.35 | 10.56 | | minibike | 65.93 | 88.24 | | cradle | 75.35 | 97.64 | | oven | 57.26 | 76.97 | | ball | 50.61 | 55.65 | | food | 53.11 | 58.59 | | step | 0.0 | 0.0 | | tank | 50.56 | 65.55 | | trade name | 30.23 | 39.5 | | microwave | 81.16 | 88.86 | | pot | 47.77 | 55.35 | | animal | 77.58 | 88.29 | | bicycle | 58.17 | 77.94 | | lake | 0.0 | 0.0 | | dishwasher | 59.62 | 78.13 | | screen | 54.27 | 90.53 | | blanket | 10.99 | 12.84 | | sculpture | 57.63 | 63.26 | | hood | 64.0 | 83.54 | | sconce | 38.07 | 58.95 | | vase | 32.4 | 52.72 | | traffic light | 23.27 | 34.79 | | tray | 10.42 | 12.44 | | ashcan | 44.01 | 54.64 | | fan | 55.59 | 68.61 | | pier | 35.81 | 39.73 | | crt screen | 0.01 | 0.02 | | plate | 55.27 | 73.48 | | monitor | 21.89 | 35.18 | | bulletin board | 54.4 | 66.93 | | shower | 0.0 | 0.0 | | radiator | 65.15 | 81.07 | | glass | 13.13 | 13.97 | | clock | 21.07 | 22.03 | | flag | 59.95 | 69.61 | +---------------------+-------+-------+ 2023-11-02 19:32:30,380 - mmseg - INFO - Summary: 2023-11-02 19:32:30,380 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 82.69 | 48.32 | 62.03 | +-------+-------+-------+ 2023-11-02 19:32:30,381 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 19:32:30,381 - mmseg - INFO - Iter(val) [250] aAcc: 0.8269, mIoU: 0.4832, mAcc: 0.6203, IoU.wall: 0.7618, IoU.building: 0.8125, IoU.sky: 0.9297, IoU.floor: 0.8125, IoU.tree: 0.7329, IoU.ceiling: 0.8276, IoU.road: 0.8108, IoU.bed : 0.8927, IoU.windowpane: 0.6157, IoU.grass: 0.6743, IoU.cabinet: 0.6067, IoU.sidewalk: 0.6069, IoU.person: 0.7981, IoU.earth: 0.3854, IoU.door: 0.5082, IoU.table: 0.6231, IoU.mountain: 0.5879, IoU.plant: 0.5528, IoU.curtain: 0.7349, IoU.chair: 0.5679, IoU.car: 0.8105, IoU.water: 0.5629, IoU.painting: 0.6939, IoU.sofa: 0.6881, IoU.shelf: 0.4023, IoU.house: 0.4497, IoU.sea: 0.6618, IoU.mirror: 0.6907, IoU.rug: 0.6845, IoU.field: 0.2698, IoU.armchair: 0.4589, IoU.seat: 0.6828, IoU.fence: 0.4534, IoU.desk: 0.4280, IoU.rock: 0.4357, IoU.wardrobe: 0.4924, IoU.lamp: 0.5811, IoU.bathtub: 0.7940, IoU.railing: 0.3895, IoU.cushion: 0.5588, IoU.base: 0.3291, IoU.box: 0.2959, IoU.column: 0.4420, IoU.signboard: 0.3566, IoU.chest of drawers: 0.4862, IoU.counter: 0.4994, IoU.sand: 0.6833, IoU.sink: 0.7017, IoU.skyscraper: 0.4188, IoU.fireplace: 0.6875, IoU.refrigerator: 0.6812, IoU.grandstand: 0.3374, IoU.path: 0.1948, IoU.stairs: 0.3982, IoU.runway: 0.6838, IoU.case: 0.4704, IoU.pool table: 0.8483, IoU.pillow: 0.5797, IoU.screen door: 0.6856, IoU.stairway: 0.3706, IoU.river: 0.1173, IoU.bridge: 0.4832, IoU.bookcase: 0.3467, IoU.blind: 0.3591, IoU.coffee table: 0.6279, IoU.toilet: 0.8433, IoU.flower: 0.3585, IoU.book: 0.4621, IoU.hill: 0.0436, IoU.bench: 0.5761, IoU.countertop: 0.5032, IoU.stove: 0.7560, IoU.palm: 0.4841, IoU.kitchen island: 0.3593, IoU.computer: 0.6405, IoU.swivel chair: 0.4435, IoU.boat: 0.5318, IoU.bar: 0.6156, IoU.arcade machine: 0.8567, IoU.hovel: 0.3002, IoU.bus: 0.8722, IoU.towel: 0.6450, IoU.light: 0.4112, IoU.truck: 0.2751, IoU.tower: 0.0435, IoU.chandelier: 0.6179, IoU.awning: 0.3285, IoU.streetlight: 0.1949, IoU.booth: 0.3631, IoU.television receiver: 0.6851, IoU.airplane: 0.5784, IoU.dirt track: 0.0441, IoU.apparel: 0.3860, IoU.pole: 0.1381, IoU.land: 0.0000, IoU.bannister: 0.0319, IoU.escalator: 0.5232, IoU.ottoman: 0.4884, IoU.bottle: 0.2193, IoU.buffet: 0.5058, IoU.poster: 0.2501, IoU.stage: 0.1878, IoU.van: 0.1920, IoU.ship: 0.1012, IoU.fountain: 0.2246, IoU.conveyer belt: 0.7083, IoU.canopy: 0.2761, IoU.washer: 0.7528, IoU.plaything: 0.1865, IoU.swimming pool: 0.5235, IoU.stool: 0.3875, IoU.barrel: 0.4866, IoU.basket: 0.3553, IoU.waterfall: 0.4475, IoU.tent: 0.8113, IoU.bag: 0.1035, IoU.minibike: 0.6593, IoU.cradle: 0.7535, IoU.oven: 0.5726, IoU.ball: 0.5061, IoU.food: 0.5311, IoU.step: 0.0000, IoU.tank: 0.5056, IoU.trade name: 0.3023, IoU.microwave: 0.8116, IoU.pot: 0.4777, IoU.animal: 0.7758, IoU.bicycle: 0.5817, IoU.lake: 0.0000, IoU.dishwasher: 0.5962, IoU.screen: 0.5427, IoU.blanket: 0.1099, IoU.sculpture: 0.5763, IoU.hood: 0.6400, IoU.sconce: 0.3807, IoU.vase: 0.3240, IoU.traffic light: 0.2327, IoU.tray: 0.1042, IoU.ashcan: 0.4401, IoU.fan: 0.5559, IoU.pier: 0.3581, IoU.crt screen: 0.0001, IoU.plate: 0.5527, IoU.monitor: 0.2189, IoU.bulletin board: 0.5440, IoU.shower: 0.0000, IoU.radiator: 0.6515, IoU.glass: 0.1313, IoU.clock: 0.2107, IoU.flag: 0.5995, Acc.wall: 0.8505, Acc.building: 0.9202, Acc.sky: 0.9770, Acc.floor: 0.8844, Acc.tree: 0.8488, Acc.ceiling: 0.9158, Acc.road: 0.9232, Acc.bed : 0.9615, Acc.windowpane: 0.7979, Acc.grass: 0.8704, Acc.cabinet: 0.6850, Acc.sidewalk: 0.6956, Acc.person: 0.9156, Acc.earth: 0.5181, Acc.door: 0.6971, Acc.table: 0.7500, Acc.mountain: 0.7917, Acc.plant: 0.6883, Acc.curtain: 0.8743, Acc.chair: 0.7153, Acc.car: 0.9369, Acc.water: 0.7271, Acc.painting: 0.8712, Acc.sofa: 0.8842, Acc.shelf: 0.5352, Acc.house: 0.6112, Acc.sea: 0.8181, Acc.mirror: 0.8105, Acc.rug: 0.7425, Acc.field: 0.3373, Acc.armchair: 0.5873, Acc.seat: 0.8595, Acc.fence: 0.5764, Acc.desk: 0.6081, Acc.rock: 0.4927, Acc.wardrobe: 0.7299, Acc.lamp: 0.7642, Acc.bathtub: 0.9100, Acc.railing: 0.6066, Acc.cushion: 0.6456, Acc.base: 0.5127, Acc.box: 0.4237, Acc.column: 0.5144, Acc.signboard: 0.4916, Acc.chest of drawers: 0.6541, Acc.counter: 0.6370, Acc.sand: 0.8598, Acc.sink: 0.7615, Acc.skyscraper: 0.7096, Acc.fireplace: 0.9098, Acc.refrigerator: 0.8703, Acc.grandstand: 0.8596, Acc.path: 0.3310, Acc.stairs: 0.4979, Acc.runway: 0.8733, Acc.case: 0.8880, Acc.pool table: 0.9832, Acc.pillow: 0.7201, Acc.screen door: 0.8829, Acc.stairway: 0.4332, Acc.river: 0.2391, Acc.bridge: 0.6157, Acc.bookcase: 0.4890, Acc.blind: 0.3933, Acc.coffee table: 0.8432, Acc.toilet: 0.9225, Acc.flower: 0.6885, Acc.book: 0.7472, Acc.hill: 0.0476, Acc.bench: 0.7321, Acc.countertop: 0.7229, Acc.stove: 0.8570, Acc.palm: 0.8022, Acc.kitchen island: 0.5064, Acc.computer: 0.8496, Acc.swivel chair: 0.8757, Acc.boat: 0.7585, Acc.bar: 0.7417, Acc.arcade machine: 0.9753, Acc.hovel: 0.3563, Acc.bus: 0.9409, Acc.towel: 0.8495, Acc.light: 0.4936, Acc.truck: 0.2950, Acc.tower: 0.0531, Acc.chandelier: 0.8520, Acc.awning: 0.4274, Acc.streetlight: 0.3352, Acc.booth: 0.6664, Acc.television receiver: 0.8405, Acc.airplane: 0.6659, Acc.dirt track: 0.0548, Acc.apparel: 0.6094, Acc.pole: 0.1611, Acc.land: 0.0000, Acc.bannister: 0.0346, Acc.escalator: 0.6709, Acc.ottoman: 0.6547, Acc.bottle: 0.3062, Acc.buffet: 0.8800, Acc.poster: 0.3280, Acc.stage: 0.4428, Acc.van: 0.2365, Acc.ship: 0.1197, Acc.fountain: 0.2312, Acc.conveyer belt: 0.9682, Acc.canopy: 0.4210, Acc.washer: 0.8504, Acc.plaything: 0.2197, Acc.swimming pool: 0.8535, Acc.stool: 0.5469, Acc.barrel: 0.5930, Acc.basket: 0.4777, Acc.waterfall: 0.5118, Acc.tent: 0.9877, Acc.bag: 0.1056, Acc.minibike: 0.8824, Acc.cradle: 0.9764, Acc.oven: 0.7697, Acc.ball: 0.5565, Acc.food: 0.5859, Acc.step: 0.0000, Acc.tank: 0.6555, Acc.trade name: 0.3950, Acc.microwave: 0.8886, Acc.pot: 0.5535, Acc.animal: 0.8829, Acc.bicycle: 0.7794, Acc.lake: 0.0000, Acc.dishwasher: 0.7813, Acc.screen: 0.9053, Acc.blanket: 0.1284, Acc.sculpture: 0.6326, Acc.hood: 0.8354, Acc.sconce: 0.5895, Acc.vase: 0.5272, Acc.traffic light: 0.3479, Acc.tray: 0.1244, Acc.ashcan: 0.5464, Acc.fan: 0.6861, Acc.pier: 0.3973, Acc.crt screen: 0.0002, Acc.plate: 0.7348, Acc.monitor: 0.3518, Acc.bulletin board: 0.6693, Acc.shower: 0.0000, Acc.radiator: 0.8107, Acc.glass: 0.1397, Acc.clock: 0.2203, Acc.flag: 0.6961 2023-11-02 19:33:31,188 - mmseg - INFO - Iter [2050/40000] lr: 3.074e-06, eta: 14:17:44, time: 2.382, data_time: 1.174, memory: 38534, decode.loss_ce: 0.5340, decode.acc_seg: 80.5262, loss: 0.5340 2023-11-02 19:34:31,925 - mmseg - INFO - Iter [2100/40000] lr: 3.070e-06, eta: 14:14:28, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5717, decode.acc_seg: 79.3010, loss: 0.5717 2023-11-02 19:35:32,684 - mmseg - INFO - Iter [2150/40000] lr: 3.066e-06, eta: 14:11:20, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5045, decode.acc_seg: 82.1135, loss: 0.5045 2023-11-02 19:36:33,429 - mmseg - INFO - Iter [2200/40000] lr: 3.062e-06, eta: 14:08:17, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5543, decode.acc_seg: 79.7686, loss: 0.5543 2023-11-02 19:37:34,149 - mmseg - INFO - Iter [2250/40000] lr: 3.058e-06, eta: 14:05:18, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5647, decode.acc_seg: 79.4311, loss: 0.5647 2023-11-02 19:38:34,899 - mmseg - INFO - Iter [2300/40000] lr: 3.054e-06, eta: 14:02:26, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5530, decode.acc_seg: 79.6731, loss: 0.5530 2023-11-02 19:39:35,623 - mmseg - INFO - Iter [2350/40000] lr: 3.050e-06, eta: 13:59:38, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5235, decode.acc_seg: 80.6551, loss: 0.5235 2023-11-02 19:40:36,419 - mmseg - INFO - Iter [2400/40000] lr: 3.045e-06, eta: 13:56:55, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5238, decode.acc_seg: 81.4688, loss: 0.5238 2023-11-02 19:41:37,147 - mmseg - INFO - Iter [2450/40000] lr: 3.041e-06, eta: 13:54:16, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5327, decode.acc_seg: 80.9468, loss: 0.5327 2023-11-02 19:42:37,858 - mmseg - INFO - Iter [2500/40000] lr: 3.037e-06, eta: 13:51:40, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5315, decode.acc_seg: 79.7907, loss: 0.5315 2023-11-02 19:43:40,979 - mmseg - INFO - Iter [2550/40000] lr: 3.033e-06, eta: 13:49:43, time: 1.262, data_time: 0.054, memory: 38534, decode.loss_ce: 0.5274, decode.acc_seg: 81.0856, loss: 0.5274 2023-11-02 19:44:41,691 - mmseg - INFO - Iter [2600/40000] lr: 3.029e-06, eta: 13:47:14, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5132, decode.acc_seg: 81.5131, loss: 0.5132 2023-11-02 19:45:42,401 - mmseg - INFO - Iter [2650/40000] lr: 3.025e-06, eta: 13:44:48, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4787, decode.acc_seg: 82.5055, loss: 0.4787 2023-11-02 19:46:43,145 - mmseg - INFO - Iter [2700/40000] lr: 3.021e-06, eta: 13:42:26, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.5164, decode.acc_seg: 81.6351, loss: 0.5164 2023-11-02 19:47:43,883 - mmseg - INFO - Iter [2750/40000] lr: 3.017e-06, eta: 13:40:06, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4800, decode.acc_seg: 82.5235, loss: 0.4800 2023-11-02 19:48:44,626 - mmseg - INFO - Iter [2800/40000] lr: 3.013e-06, eta: 13:37:50, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.4867, decode.acc_seg: 81.5445, loss: 0.4867 2023-11-02 19:49:45,378 - mmseg - INFO - Iter [2850/40000] lr: 3.009e-06, eta: 13:35:36, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5116, decode.acc_seg: 81.2676, loss: 0.5116 2023-11-02 19:50:46,088 - mmseg - INFO - Iter [2900/40000] lr: 3.005e-06, eta: 13:33:24, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4841, decode.acc_seg: 81.7386, loss: 0.4841 2023-11-02 19:51:46,842 - mmseg - INFO - Iter [2950/40000] lr: 3.001e-06, eta: 13:31:15, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4903, decode.acc_seg: 82.1702, loss: 0.4903 2023-11-02 19:52:47,566 - mmseg - INFO - Saving checkpoint at 3000 iterations 2023-11-02 19:53:43,321 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 19:53:43,322 - mmseg - INFO - Iter [3000/40000] lr: 2.997e-06, eta: 13:40:36, time: 2.330, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4888, decode.acc_seg: 81.7487, loss: 0.4888 2023-11-02 19:54:43,987 - mmseg - INFO - per class results: 2023-11-02 19:54:43,993 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 76.1 | 83.84 | | building | 80.3 | 93.89 | | sky | 93.5 | 96.53 | | floor | 81.74 | 88.25 | | tree | 74.06 | 89.36 | | ceiling | 82.18 | 89.03 | | road | 82.86 | 87.6 | | bed | 90.56 | 95.81 | | windowpane | 63.09 | 81.19 | | grass | 69.42 | 88.94 | | cabinet | 63.38 | 74.55 | | sidewalk | 65.03 | 88.47 | | person | 80.22 | 90.73 | | earth | 34.42 | 45.35 | | door | 51.55 | 70.88 | | table | 62.49 | 78.21 | | mountain | 55.34 | 83.61 | | plant | 53.27 | 62.83 | | curtain | 70.91 | 89.77 | | chair | 56.48 | 73.13 | | car | 82.04 | 92.65 | | water | 62.0 | 81.3 | | painting | 68.18 | 85.96 | | sofa | 71.3 | 90.13 | | shelf | 41.68 | 54.89 | | house | 12.07 | 12.46 | | sea | 71.87 | 90.8 | | mirror | 71.33 | 79.63 | | rug | 69.07 | 79.99 | | field | 30.97 | 47.59 | | armchair | 48.86 | 67.34 | | seat | 60.62 | 89.85 | | fence | 46.08 | 56.14 | | desk | 46.39 | 57.89 | | rock | 41.96 | 50.53 | | wardrobe | 53.88 | 70.78 | | lamp | 59.55 | 74.82 | | bathtub | 86.83 | 92.13 | | railing | 42.09 | 61.59 | | cushion | 57.31 | 76.88 | | base | 22.32 | 76.74 | | box | 24.6 | 32.53 | | column | 47.67 | 60.35 | | signboard | 35.36 | 45.51 | | chest of drawers | 39.9 | 63.16 | | counter | 41.5 | 60.32 | | sand | 71.75 | 76.18 | | sink | 72.14 | 79.48 | | skyscraper | 47.92 | 71.53 | | fireplace | 70.52 | 89.33 | | refrigerator | 72.39 | 76.07 | | grandstand | 37.7 | 87.92 | | path | 21.15 | 25.23 | | stairs | 30.19 | 37.01 | | runway | 70.31 | 96.8 | | case | 41.35 | 43.39 | | pool table | 91.83 | 96.17 | | pillow | 53.37 | 59.62 | | screen door | 69.67 | 92.8 | | stairway | 33.76 | 42.82 | | river | 34.02 | 42.37 | | bridge | 70.54 | 82.49 | | bookcase | 36.78 | 49.56 | | blind | 36.79 | 39.8 | | coffee table | 63.16 | 83.88 | | toilet | 85.66 | 91.57 | | flower | 33.06 | 52.4 | | book | 49.7 | 72.66 | | hill | 5.97 | 7.49 | | bench | 50.87 | 56.13 | | countertop | 59.41 | 71.89 | | stove | 77.35 | 85.01 | | palm | 52.05 | 72.13 | | kitchen island | 43.09 | 80.54 | | computer | 72.51 | 90.35 | | swivel chair | 47.74 | 70.83 | | boat | 41.96 | 44.97 | | bar | 62.64 | 67.19 | | arcade machine | 54.3 | 55.52 | | hovel | 21.72 | 22.86 | | bus | 88.08 | 95.5 | | towel | 67.57 | 80.88 | | light | 42.43 | 48.77 | | truck | 47.76 | 56.57 | | tower | 28.41 | 42.38 | | chandelier | 63.68 | 85.0 | | awning | 35.72 | 43.72 | | streetlight | 22.1 | 30.02 | | booth | 49.36 | 64.32 | | television receiver | 73.09 | 84.09 | | airplane | 67.94 | 85.09 | | dirt track | 8.53 | 9.58 | | apparel | 33.89 | 39.34 | | pole | 17.22 | 20.55 | | land | 12.04 | 12.19 | | bannister | 4.5 | 5.6 | | escalator | 61.03 | 80.68 | | ottoman | 52.11 | 68.11 | | bottle | 25.86 | 34.62 | | buffet | 52.78 | 75.24 | | poster | 29.13 | 50.37 | | stage | 19.39 | 77.75 | | van | 30.16 | 33.92 | | ship | 43.98 | 91.51 | | fountain | 25.46 | 26.19 | | conveyer belt | 78.36 | 96.75 | | canopy | 15.97 | 26.0 | | washer | 73.78 | 78.77 | | plaything | 21.31 | 42.62 | | swimming pool | 54.47 | 84.3 | | stool | 20.98 | 21.5 | | barrel | 52.19 | 73.25 | | basket | 34.14 | 44.37 | | waterfall | 46.49 | 57.93 | | tent | 94.32 | 94.76 | | bag | 12.27 | 13.26 | | minibike | 63.32 | 72.59 | | cradle | 84.67 | 95.93 | | oven | 60.33 | 69.82 | | ball | 8.92 | 9.03 | | food | 58.24 | 67.89 | | step | 3.98 | 4.59 | | tank | 52.39 | 65.04 | | trade name | 23.23 | 25.3 | | microwave | 84.3 | 91.51 | | pot | 50.74 | 57.47 | | animal | 77.75 | 83.15 | | bicycle | 50.13 | 72.46 | | lake | 0.0 | 0.0 | | dishwasher | 62.88 | 76.87 | | screen | 51.61 | 86.94 | | blanket | 34.53 | 48.78 | | sculpture | 59.76 | 69.44 | | hood | 60.22 | 63.58 | | sconce | 36.83 | 45.47 | | vase | 37.32 | 47.26 | | traffic light | 27.72 | 42.65 | | tray | 10.34 | 13.02 | | ashcan | 47.01 | 55.68 | | fan | 58.61 | 71.74 | | pier | 43.38 | 54.53 | | crt screen | 10.73 | 17.28 | | plate | 54.72 | 70.85 | | monitor | 48.95 | 66.18 | | bulletin board | 46.03 | 50.02 | | shower | 0.0 | 0.0 | | radiator | 64.94 | 78.22 | | glass | 12.67 | 13.46 | | clock | 25.33 | 27.99 | | flag | 65.05 | 72.99 | +---------------------+-------+-------+ 2023-11-02 19:54:43,993 - mmseg - INFO - Summary: 2023-11-02 19:54:43,993 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 82.81 | 49.93 | 62.54 | +-------+-------+-------+ 2023-11-02 19:54:43,994 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 19:54:43,994 - mmseg - INFO - Iter(val) [250] aAcc: 0.8281, mIoU: 0.4993, mAcc: 0.6254, IoU.wall: 0.7610, IoU.building: 0.8030, IoU.sky: 0.9350, IoU.floor: 0.8174, IoU.tree: 0.7406, IoU.ceiling: 0.8218, IoU.road: 0.8286, IoU.bed : 0.9056, IoU.windowpane: 0.6309, IoU.grass: 0.6942, IoU.cabinet: 0.6338, IoU.sidewalk: 0.6503, IoU.person: 0.8022, IoU.earth: 0.3442, IoU.door: 0.5155, IoU.table: 0.6249, IoU.mountain: 0.5534, IoU.plant: 0.5327, IoU.curtain: 0.7091, IoU.chair: 0.5648, IoU.car: 0.8204, IoU.water: 0.6200, IoU.painting: 0.6818, IoU.sofa: 0.7130, IoU.shelf: 0.4168, IoU.house: 0.1207, IoU.sea: 0.7187, IoU.mirror: 0.7133, IoU.rug: 0.6907, IoU.field: 0.3097, IoU.armchair: 0.4886, IoU.seat: 0.6062, IoU.fence: 0.4608, IoU.desk: 0.4639, IoU.rock: 0.4196, IoU.wardrobe: 0.5388, IoU.lamp: 0.5955, IoU.bathtub: 0.8683, IoU.railing: 0.4209, IoU.cushion: 0.5731, IoU.base: 0.2232, IoU.box: 0.2460, IoU.column: 0.4767, IoU.signboard: 0.3536, IoU.chest of drawers: 0.3990, IoU.counter: 0.4150, IoU.sand: 0.7175, IoU.sink: 0.7214, IoU.skyscraper: 0.4792, IoU.fireplace: 0.7052, IoU.refrigerator: 0.7239, IoU.grandstand: 0.3770, IoU.path: 0.2115, IoU.stairs: 0.3019, IoU.runway: 0.7031, IoU.case: 0.4135, IoU.pool table: 0.9183, IoU.pillow: 0.5337, IoU.screen door: 0.6967, IoU.stairway: 0.3376, IoU.river: 0.3402, IoU.bridge: 0.7054, IoU.bookcase: 0.3678, IoU.blind: 0.3679, IoU.coffee table: 0.6316, IoU.toilet: 0.8566, IoU.flower: 0.3306, IoU.book: 0.4970, IoU.hill: 0.0597, IoU.bench: 0.5087, IoU.countertop: 0.5941, IoU.stove: 0.7735, IoU.palm: 0.5205, IoU.kitchen island: 0.4309, IoU.computer: 0.7251, IoU.swivel chair: 0.4774, IoU.boat: 0.4196, IoU.bar: 0.6264, IoU.arcade machine: 0.5430, IoU.hovel: 0.2172, IoU.bus: 0.8808, IoU.towel: 0.6757, IoU.light: 0.4243, IoU.truck: 0.4776, IoU.tower: 0.2841, IoU.chandelier: 0.6368, IoU.awning: 0.3572, IoU.streetlight: 0.2210, IoU.booth: 0.4936, IoU.television receiver: 0.7309, IoU.airplane: 0.6794, IoU.dirt track: 0.0853, IoU.apparel: 0.3389, IoU.pole: 0.1722, IoU.land: 0.1204, IoU.bannister: 0.0450, IoU.escalator: 0.6103, IoU.ottoman: 0.5211, IoU.bottle: 0.2586, IoU.buffet: 0.5278, IoU.poster: 0.2913, IoU.stage: 0.1939, IoU.van: 0.3016, IoU.ship: 0.4398, IoU.fountain: 0.2546, IoU.conveyer belt: 0.7836, IoU.canopy: 0.1597, IoU.washer: 0.7378, IoU.plaything: 0.2131, IoU.swimming pool: 0.5447, IoU.stool: 0.2098, IoU.barrel: 0.5219, IoU.basket: 0.3414, IoU.waterfall: 0.4649, IoU.tent: 0.9432, IoU.bag: 0.1227, IoU.minibike: 0.6332, IoU.cradle: 0.8467, IoU.oven: 0.6033, IoU.ball: 0.0892, IoU.food: 0.5824, IoU.step: 0.0398, IoU.tank: 0.5239, IoU.trade name: 0.2323, IoU.microwave: 0.8430, IoU.pot: 0.5074, IoU.animal: 0.7775, IoU.bicycle: 0.5013, IoU.lake: 0.0000, IoU.dishwasher: 0.6288, IoU.screen: 0.5161, IoU.blanket: 0.3453, IoU.sculpture: 0.5976, IoU.hood: 0.6022, IoU.sconce: 0.3683, IoU.vase: 0.3732, IoU.traffic light: 0.2772, IoU.tray: 0.1034, IoU.ashcan: 0.4701, IoU.fan: 0.5861, IoU.pier: 0.4338, IoU.crt screen: 0.1073, IoU.plate: 0.5472, IoU.monitor: 0.4895, IoU.bulletin board: 0.4603, IoU.shower: 0.0000, IoU.radiator: 0.6494, IoU.glass: 0.1267, IoU.clock: 0.2533, IoU.flag: 0.6505, Acc.wall: 0.8384, Acc.building: 0.9389, Acc.sky: 0.9653, Acc.floor: 0.8825, Acc.tree: 0.8936, Acc.ceiling: 0.8903, Acc.road: 0.8760, Acc.bed : 0.9581, Acc.windowpane: 0.8119, Acc.grass: 0.8894, Acc.cabinet: 0.7455, Acc.sidewalk: 0.8847, Acc.person: 0.9073, Acc.earth: 0.4535, Acc.door: 0.7088, Acc.table: 0.7821, Acc.mountain: 0.8361, Acc.plant: 0.6283, Acc.curtain: 0.8977, Acc.chair: 0.7313, Acc.car: 0.9265, Acc.water: 0.8130, Acc.painting: 0.8596, Acc.sofa: 0.9013, Acc.shelf: 0.5489, Acc.house: 0.1246, Acc.sea: 0.9080, Acc.mirror: 0.7963, Acc.rug: 0.7999, Acc.field: 0.4759, Acc.armchair: 0.6734, Acc.seat: 0.8985, Acc.fence: 0.5614, Acc.desk: 0.5789, Acc.rock: 0.5053, Acc.wardrobe: 0.7078, Acc.lamp: 0.7482, Acc.bathtub: 0.9213, Acc.railing: 0.6159, Acc.cushion: 0.7688, Acc.base: 0.7674, Acc.box: 0.3253, Acc.column: 0.6035, Acc.signboard: 0.4551, Acc.chest of drawers: 0.6316, Acc.counter: 0.6032, Acc.sand: 0.7618, Acc.sink: 0.7948, Acc.skyscraper: 0.7153, Acc.fireplace: 0.8933, Acc.refrigerator: 0.7607, Acc.grandstand: 0.8792, Acc.path: 0.2523, Acc.stairs: 0.3701, Acc.runway: 0.9680, Acc.case: 0.4339, Acc.pool table: 0.9617, Acc.pillow: 0.5962, Acc.screen door: 0.9280, Acc.stairway: 0.4282, Acc.river: 0.4237, Acc.bridge: 0.8249, Acc.bookcase: 0.4956, Acc.blind: 0.3980, Acc.coffee table: 0.8388, Acc.toilet: 0.9157, Acc.flower: 0.5240, Acc.book: 0.7266, Acc.hill: 0.0749, Acc.bench: 0.5613, Acc.countertop: 0.7189, Acc.stove: 0.8501, Acc.palm: 0.7213, Acc.kitchen island: 0.8054, Acc.computer: 0.9035, Acc.swivel chair: 0.7083, Acc.boat: 0.4497, Acc.bar: 0.6719, Acc.arcade machine: 0.5552, Acc.hovel: 0.2286, Acc.bus: 0.9550, Acc.towel: 0.8088, Acc.light: 0.4877, Acc.truck: 0.5657, Acc.tower: 0.4238, Acc.chandelier: 0.8500, Acc.awning: 0.4372, Acc.streetlight: 0.3002, Acc.booth: 0.6432, Acc.television receiver: 0.8409, Acc.airplane: 0.8509, Acc.dirt track: 0.0958, Acc.apparel: 0.3934, Acc.pole: 0.2055, Acc.land: 0.1219, Acc.bannister: 0.0560, Acc.escalator: 0.8068, Acc.ottoman: 0.6811, Acc.bottle: 0.3462, Acc.buffet: 0.7524, Acc.poster: 0.5037, Acc.stage: 0.7775, Acc.van: 0.3392, Acc.ship: 0.9151, Acc.fountain: 0.2619, Acc.conveyer belt: 0.9675, Acc.canopy: 0.2600, Acc.washer: 0.7877, Acc.plaything: 0.4262, Acc.swimming pool: 0.8430, Acc.stool: 0.2150, Acc.barrel: 0.7325, Acc.basket: 0.4437, Acc.waterfall: 0.5793, Acc.tent: 0.9476, Acc.bag: 0.1326, Acc.minibike: 0.7259, Acc.cradle: 0.9593, Acc.oven: 0.6982, Acc.ball: 0.0903, Acc.food: 0.6789, Acc.step: 0.0459, Acc.tank: 0.6504, Acc.trade name: 0.2530, Acc.microwave: 0.9151, Acc.pot: 0.5747, Acc.animal: 0.8315, Acc.bicycle: 0.7246, Acc.lake: 0.0000, Acc.dishwasher: 0.7687, Acc.screen: 0.8694, Acc.blanket: 0.4878, Acc.sculpture: 0.6944, Acc.hood: 0.6358, Acc.sconce: 0.4547, Acc.vase: 0.4726, Acc.traffic light: 0.4265, Acc.tray: 0.1302, Acc.ashcan: 0.5568, Acc.fan: 0.7174, Acc.pier: 0.5453, Acc.crt screen: 0.1728, Acc.plate: 0.7085, Acc.monitor: 0.6618, Acc.bulletin board: 0.5002, Acc.shower: 0.0000, Acc.radiator: 0.7822, Acc.glass: 0.1346, Acc.clock: 0.2799, Acc.flag: 0.7299 2023-11-02 19:55:44,795 - mmseg - INFO - Iter [3050/40000] lr: 2.993e-06, eta: 13:50:35, time: 2.429, data_time: 1.222, memory: 38534, decode.loss_ce: 0.5155, decode.acc_seg: 81.8990, loss: 0.5155 2023-11-02 19:56:45,522 - mmseg - INFO - Iter [3100/40000] lr: 2.989e-06, eta: 13:48:08, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5203, decode.acc_seg: 80.4435, loss: 0.5203 2023-11-02 19:57:46,198 - mmseg - INFO - Iter [3150/40000] lr: 2.985e-06, eta: 13:45:42, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.5036, decode.acc_seg: 81.4785, loss: 0.5036 2023-11-02 19:58:49,304 - mmseg - INFO - Iter [3200/40000] lr: 2.981e-06, eta: 13:43:48, time: 1.262, data_time: 0.055, memory: 38534, decode.loss_ce: 0.4575, decode.acc_seg: 83.1134, loss: 0.4575 2023-11-02 19:59:50,000 - mmseg - INFO - Iter [3250/40000] lr: 2.977e-06, eta: 13:41:28, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4722, decode.acc_seg: 82.2130, loss: 0.4722 2023-11-02 20:00:50,709 - mmseg - INFO - Iter [3300/40000] lr: 2.973e-06, eta: 13:39:10, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4500, decode.acc_seg: 82.6815, loss: 0.4500 2023-11-02 20:01:51,453 - mmseg - INFO - Iter [3350/40000] lr: 2.969e-06, eta: 13:36:55, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4557, decode.acc_seg: 82.5067, loss: 0.4557 2023-11-02 20:02:52,198 - mmseg - INFO - Iter [3400/40000] lr: 2.964e-06, eta: 13:34:42, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4443, decode.acc_seg: 83.4433, loss: 0.4443 2023-11-02 20:03:52,971 - mmseg - INFO - Iter [3450/40000] lr: 2.960e-06, eta: 13:32:32, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4249, decode.acc_seg: 83.5211, loss: 0.4249 2023-11-02 20:04:53,707 - mmseg - INFO - Iter [3500/40000] lr: 2.956e-06, eta: 13:30:23, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4711, decode.acc_seg: 82.3272, loss: 0.4711 2023-11-02 20:05:54,387 - mmseg - INFO - Iter [3550/40000] lr: 2.952e-06, eta: 13:28:15, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.4491, decode.acc_seg: 83.1364, loss: 0.4491 2023-11-02 20:06:55,050 - mmseg - INFO - Iter [3600/40000] lr: 2.948e-06, eta: 13:26:10, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4614, decode.acc_seg: 83.2141, loss: 0.4614 2023-11-02 20:07:55,718 - mmseg - INFO - Iter [3650/40000] lr: 2.944e-06, eta: 13:24:06, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4843, decode.acc_seg: 81.5232, loss: 0.4843 2023-11-02 20:08:56,394 - mmseg - INFO - Iter [3700/40000] lr: 2.940e-06, eta: 13:22:04, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4697, decode.acc_seg: 82.2635, loss: 0.4697 2023-11-02 20:09:57,156 - mmseg - INFO - Iter [3750/40000] lr: 2.936e-06, eta: 13:20:04, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4976, decode.acc_seg: 81.6128, loss: 0.4976 2023-11-02 20:11:00,435 - mmseg - INFO - Iter [3800/40000] lr: 2.932e-06, eta: 13:18:30, time: 1.266, data_time: 0.058, memory: 38534, decode.loss_ce: 0.4631, decode.acc_seg: 82.8927, loss: 0.4631 2023-11-02 20:12:01,197 - mmseg - INFO - Iter [3850/40000] lr: 2.928e-06, eta: 13:16:33, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4320, decode.acc_seg: 83.7637, loss: 0.4320 2023-11-02 20:13:01,947 - mmseg - INFO - Iter [3900/40000] lr: 2.924e-06, eta: 13:14:37, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4715, decode.acc_seg: 82.9469, loss: 0.4715 2023-11-02 20:14:02,667 - mmseg - INFO - Iter [3950/40000] lr: 2.920e-06, eta: 13:12:42, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4222, decode.acc_seg: 84.4925, loss: 0.4222 2023-11-02 20:15:03,397 - mmseg - INFO - Saving checkpoint at 4000 iterations 2023-11-02 20:16:04,899 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 20:16:04,899 - mmseg - INFO - Iter [4000/40000] lr: 2.916e-06, eta: 13:20:03, time: 2.445, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4407, decode.acc_seg: 83.4305, loss: 0.4407 2023-11-02 20:17:05,634 - mmseg - INFO - per class results: 2023-11-02 20:17:05,639 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 77.92 | 89.02 | | building | 81.77 | 94.53 | | sky | 93.49 | 97.0 | | floor | 82.18 | 89.56 | | tree | 73.5 | 81.09 | | ceiling | 84.1 | 92.31 | | road | 84.22 | 91.17 | | bed | 89.93 | 95.09 | | windowpane | 61.58 | 82.69 | | grass | 65.04 | 87.85 | | cabinet | 62.48 | 70.38 | | sidewalk | 66.72 | 82.79 | | person | 81.01 | 91.81 | | earth | 36.49 | 49.65 | | door | 50.96 | 63.61 | | table | 62.47 | 75.64 | | mountain | 64.44 | 79.9 | | plant | 54.78 | 72.11 | | curtain | 74.75 | 85.89 | | chair | 55.91 | 67.12 | | car | 83.3 | 92.6 | | water | 58.3 | 67.87 | | painting | 71.92 | 86.14 | | sofa | 75.77 | 90.95 | | shelf | 45.3 | 62.05 | | house | 42.88 | 48.87 | | sea | 68.59 | 90.57 | | mirror | 71.15 | 78.98 | | rug | 66.78 | 83.43 | | field | 25.96 | 37.96 | | armchair | 50.77 | 64.41 | | seat | 63.88 | 86.81 | | fence | 37.48 | 44.75 | | desk | 45.82 | 72.93 | | rock | 60.13 | 66.85 | | wardrobe | 55.41 | 74.74 | | lamp | 60.26 | 74.49 | | bathtub | 85.56 | 90.37 | | railing | 36.39 | 49.21 | | cushion | 57.14 | 72.56 | | base | 32.22 | 43.98 | | box | 31.9 | 38.84 | | column | 48.23 | 58.72 | | signboard | 36.49 | 46.06 | | chest of drawers | 45.35 | 66.85 | | counter | 44.53 | 60.11 | | sand | 52.88 | 73.18 | | sink | 71.84 | 77.22 | | skyscraper | 46.15 | 51.71 | | fireplace | 70.21 | 85.67 | | refrigerator | 71.91 | 89.85 | | grandstand | 48.55 | 81.36 | | path | 23.95 | 32.46 | | stairs | 35.93 | 42.26 | | runway | 67.87 | 87.63 | | case | 63.37 | 84.61 | | pool table | 91.15 | 97.35 | | pillow | 55.91 | 62.74 | | screen door | 73.75 | 84.41 | | stairway | 36.03 | 42.21 | | river | 23.15 | 50.65 | | bridge | 75.02 | 82.37 | | bookcase | 38.56 | 52.66 | | blind | 28.01 | 28.69 | | coffee table | 59.57 | 89.84 | | toilet | 86.02 | 91.32 | | flower | 40.37 | 61.34 | | book | 48.71 | 70.88 | | hill | 5.93 | 6.34 | | bench | 50.84 | 57.83 | | countertop | 59.19 | 69.94 | | stove | 76.01 | 85.43 | | palm | 44.47 | 75.04 | | kitchen island | 42.02 | 87.73 | | computer | 71.96 | 85.9 | | swivel chair | 48.56 | 78.33 | | boat | 61.27 | 91.02 | | bar | 57.51 | 59.75 | | arcade machine | 72.14 | 76.76 | | hovel | 18.32 | 19.21 | | bus | 90.74 | 93.89 | | towel | 65.3 | 81.1 | | light | 44.2 | 52.69 | | truck | 42.15 | 53.15 | | tower | 26.07 | 47.24 | | chandelier | 64.66 | 81.73 | | awning | 31.21 | 39.1 | | streetlight | 22.12 | 28.46 | | booth | 36.32 | 42.35 | | television receiver | 66.7 | 83.38 | | airplane | 58.7 | 68.22 | | dirt track | 15.66 | 15.94 | | apparel | 41.34 | 49.2 | | pole | 15.41 | 18.41 | | land | 0.05 | 0.06 | | bannister | 11.44 | 15.4 | | escalator | 51.5 | 62.41 | | ottoman | 51.11 | 73.18 | | bottle | 31.62 | 37.39 | | buffet | 53.36 | 78.51 | | poster | 26.06 | 33.65 | | stage | 21.13 | 31.71 | | van | 43.72 | 55.97 | | ship | 43.65 | 44.96 | | fountain | 28.24 | 28.9 | | conveyer belt | 85.4 | 87.39 | | canopy | 20.88 | 34.57 | | washer | 87.27 | 93.33 | | plaything | 24.0 | 37.01 | | swimming pool | 52.95 | 84.95 | | stool | 42.8 | 63.07 | | barrel | 66.43 | 81.35 | | basket | 38.84 | 50.89 | | waterfall | 52.31 | 69.03 | | tent | 90.52 | 98.42 | | bag | 23.28 | 27.07 | | minibike | 65.51 | 76.57 | | cradle | 82.67 | 96.74 | | oven | 58.15 | 71.03 | | ball | 35.86 | 37.94 | | food | 57.28 | 66.81 | | step | 5.5 | 6.44 | | tank | 54.69 | 66.86 | | trade name | 11.12 | 11.7 | | microwave | 85.09 | 90.7 | | pot | 46.59 | 53.28 | | animal | 76.43 | 82.75 | | bicycle | 55.52 | 76.25 | | lake | 47.93 | 56.3 | | dishwasher | 59.01 | 60.38 | | screen | 54.83 | 69.0 | | blanket | 24.31 | 28.75 | | sculpture | 61.76 | 67.72 | | hood | 64.12 | 70.25 | | sconce | 44.1 | 58.37 | | vase | 37.79 | 51.91 | | traffic light | 28.33 | 50.35 | | tray | 9.23 | 11.7 | | ashcan | 46.45 | 59.55 | | fan | 55.51 | 62.68 | | pier | 36.39 | 43.44 | | crt screen | 8.44 | 22.95 | | plate | 55.58 | 74.35 | | monitor | 6.85 | 8.15 | | bulletin board | 53.07 | 60.2 | | shower | 0.0 | 0.0 | | radiator | 65.98 | 69.31 | | glass | 13.62 | 14.27 | | clock | 19.64 | 20.52 | | flag | 62.31 | 68.52 | +---------------------+-------+-------+ 2023-11-02 20:17:05,640 - mmseg - INFO - Summary: 2023-11-02 20:17:05,640 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 83.71 | 51.19 | 62.76 | +-------+-------+-------+ 2023-11-02 20:17:05,641 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 20:17:05,641 - mmseg - INFO - Iter(val) [250] aAcc: 0.8371, mIoU: 0.5119, mAcc: 0.6276, IoU.wall: 0.7792, IoU.building: 0.8177, IoU.sky: 0.9349, IoU.floor: 0.8218, IoU.tree: 0.7350, IoU.ceiling: 0.8410, IoU.road: 0.8422, IoU.bed : 0.8993, IoU.windowpane: 0.6158, IoU.grass: 0.6504, IoU.cabinet: 0.6248, IoU.sidewalk: 0.6672, IoU.person: 0.8101, IoU.earth: 0.3649, IoU.door: 0.5096, IoU.table: 0.6247, IoU.mountain: 0.6444, IoU.plant: 0.5478, IoU.curtain: 0.7475, IoU.chair: 0.5591, IoU.car: 0.8330, IoU.water: 0.5830, IoU.painting: 0.7192, IoU.sofa: 0.7577, IoU.shelf: 0.4530, IoU.house: 0.4288, IoU.sea: 0.6859, IoU.mirror: 0.7115, IoU.rug: 0.6678, IoU.field: 0.2596, IoU.armchair: 0.5077, IoU.seat: 0.6388, IoU.fence: 0.3748, IoU.desk: 0.4582, IoU.rock: 0.6013, IoU.wardrobe: 0.5541, IoU.lamp: 0.6026, IoU.bathtub: 0.8556, IoU.railing: 0.3639, IoU.cushion: 0.5714, IoU.base: 0.3222, IoU.box: 0.3190, IoU.column: 0.4823, IoU.signboard: 0.3649, IoU.chest of drawers: 0.4535, IoU.counter: 0.4453, IoU.sand: 0.5288, IoU.sink: 0.7184, IoU.skyscraper: 0.4615, IoU.fireplace: 0.7021, IoU.refrigerator: 0.7191, IoU.grandstand: 0.4855, IoU.path: 0.2395, IoU.stairs: 0.3593, IoU.runway: 0.6787, IoU.case: 0.6337, IoU.pool table: 0.9115, IoU.pillow: 0.5591, IoU.screen door: 0.7375, IoU.stairway: 0.3603, IoU.river: 0.2315, IoU.bridge: 0.7502, IoU.bookcase: 0.3856, IoU.blind: 0.2801, IoU.coffee table: 0.5957, IoU.toilet: 0.8602, IoU.flower: 0.4037, IoU.book: 0.4871, IoU.hill: 0.0593, IoU.bench: 0.5084, IoU.countertop: 0.5919, IoU.stove: 0.7601, IoU.palm: 0.4447, IoU.kitchen island: 0.4202, IoU.computer: 0.7196, IoU.swivel chair: 0.4856, IoU.boat: 0.6127, IoU.bar: 0.5751, IoU.arcade machine: 0.7214, IoU.hovel: 0.1832, IoU.bus: 0.9074, IoU.towel: 0.6530, IoU.light: 0.4420, IoU.truck: 0.4215, IoU.tower: 0.2607, IoU.chandelier: 0.6466, IoU.awning: 0.3121, IoU.streetlight: 0.2212, IoU.booth: 0.3632, IoU.television receiver: 0.6670, IoU.airplane: 0.5870, IoU.dirt track: 0.1566, IoU.apparel: 0.4134, IoU.pole: 0.1541, IoU.land: 0.0005, IoU.bannister: 0.1144, IoU.escalator: 0.5150, IoU.ottoman: 0.5111, IoU.bottle: 0.3162, IoU.buffet: 0.5336, IoU.poster: 0.2606, IoU.stage: 0.2113, IoU.van: 0.4372, IoU.ship: 0.4365, IoU.fountain: 0.2824, IoU.conveyer belt: 0.8540, IoU.canopy: 0.2088, IoU.washer: 0.8727, IoU.plaything: 0.2400, IoU.swimming pool: 0.5295, IoU.stool: 0.4280, IoU.barrel: 0.6643, IoU.basket: 0.3884, IoU.waterfall: 0.5231, IoU.tent: 0.9052, IoU.bag: 0.2328, IoU.minibike: 0.6551, IoU.cradle: 0.8267, IoU.oven: 0.5815, IoU.ball: 0.3586, IoU.food: 0.5728, IoU.step: 0.0550, IoU.tank: 0.5469, IoU.trade name: 0.1112, IoU.microwave: 0.8509, IoU.pot: 0.4659, IoU.animal: 0.7643, IoU.bicycle: 0.5552, IoU.lake: 0.4793, IoU.dishwasher: 0.5901, IoU.screen: 0.5483, IoU.blanket: 0.2431, IoU.sculpture: 0.6176, IoU.hood: 0.6412, IoU.sconce: 0.4410, IoU.vase: 0.3779, IoU.traffic light: 0.2833, IoU.tray: 0.0923, IoU.ashcan: 0.4645, IoU.fan: 0.5551, IoU.pier: 0.3639, IoU.crt screen: 0.0844, IoU.plate: 0.5558, IoU.monitor: 0.0685, IoU.bulletin board: 0.5307, IoU.shower: 0.0000, IoU.radiator: 0.6598, IoU.glass: 0.1362, IoU.clock: 0.1964, IoU.flag: 0.6231, Acc.wall: 0.8902, Acc.building: 0.9453, Acc.sky: 0.9700, Acc.floor: 0.8956, Acc.tree: 0.8109, Acc.ceiling: 0.9231, Acc.road: 0.9117, Acc.bed : 0.9509, Acc.windowpane: 0.8269, Acc.grass: 0.8785, Acc.cabinet: 0.7038, Acc.sidewalk: 0.8279, Acc.person: 0.9181, Acc.earth: 0.4965, Acc.door: 0.6361, Acc.table: 0.7564, Acc.mountain: 0.7990, Acc.plant: 0.7211, Acc.curtain: 0.8589, Acc.chair: 0.6712, Acc.car: 0.9260, Acc.water: 0.6787, Acc.painting: 0.8614, Acc.sofa: 0.9095, Acc.shelf: 0.6205, Acc.house: 0.4887, Acc.sea: 0.9057, Acc.mirror: 0.7898, Acc.rug: 0.8343, Acc.field: 0.3796, Acc.armchair: 0.6441, Acc.seat: 0.8681, Acc.fence: 0.4475, Acc.desk: 0.7293, Acc.rock: 0.6685, Acc.wardrobe: 0.7474, Acc.lamp: 0.7449, Acc.bathtub: 0.9037, Acc.railing: 0.4921, Acc.cushion: 0.7256, Acc.base: 0.4398, Acc.box: 0.3884, Acc.column: 0.5872, Acc.signboard: 0.4606, Acc.chest of drawers: 0.6685, Acc.counter: 0.6011, Acc.sand: 0.7318, Acc.sink: 0.7722, Acc.skyscraper: 0.5171, Acc.fireplace: 0.8567, Acc.refrigerator: 0.8985, Acc.grandstand: 0.8136, Acc.path: 0.3246, Acc.stairs: 0.4226, Acc.runway: 0.8763, Acc.case: 0.8461, Acc.pool table: 0.9735, Acc.pillow: 0.6274, Acc.screen door: 0.8441, Acc.stairway: 0.4221, Acc.river: 0.5065, Acc.bridge: 0.8237, Acc.bookcase: 0.5266, Acc.blind: 0.2869, Acc.coffee table: 0.8984, Acc.toilet: 0.9132, Acc.flower: 0.6134, Acc.book: 0.7088, Acc.hill: 0.0634, Acc.bench: 0.5783, Acc.countertop: 0.6994, Acc.stove: 0.8543, Acc.palm: 0.7504, Acc.kitchen island: 0.8773, Acc.computer: 0.8590, Acc.swivel chair: 0.7833, Acc.boat: 0.9102, Acc.bar: 0.5975, Acc.arcade machine: 0.7676, Acc.hovel: 0.1921, Acc.bus: 0.9389, Acc.towel: 0.8110, Acc.light: 0.5269, Acc.truck: 0.5315, Acc.tower: 0.4724, Acc.chandelier: 0.8173, Acc.awning: 0.3910, Acc.streetlight: 0.2846, Acc.booth: 0.4235, Acc.television receiver: 0.8338, Acc.airplane: 0.6822, Acc.dirt track: 0.1594, Acc.apparel: 0.4920, Acc.pole: 0.1841, Acc.land: 0.0006, Acc.bannister: 0.1540, Acc.escalator: 0.6241, Acc.ottoman: 0.7318, Acc.bottle: 0.3739, Acc.buffet: 0.7851, Acc.poster: 0.3365, Acc.stage: 0.3171, Acc.van: 0.5597, Acc.ship: 0.4496, Acc.fountain: 0.2890, Acc.conveyer belt: 0.8739, Acc.canopy: 0.3457, Acc.washer: 0.9333, Acc.plaything: 0.3701, Acc.swimming pool: 0.8495, Acc.stool: 0.6307, Acc.barrel: 0.8135, Acc.basket: 0.5089, Acc.waterfall: 0.6903, Acc.tent: 0.9842, Acc.bag: 0.2707, Acc.minibike: 0.7657, Acc.cradle: 0.9674, Acc.oven: 0.7103, Acc.ball: 0.3794, Acc.food: 0.6681, Acc.step: 0.0644, Acc.tank: 0.6686, Acc.trade name: 0.1170, Acc.microwave: 0.9070, Acc.pot: 0.5328, Acc.animal: 0.8275, Acc.bicycle: 0.7625, Acc.lake: 0.5630, Acc.dishwasher: 0.6038, Acc.screen: 0.6900, Acc.blanket: 0.2875, Acc.sculpture: 0.6772, Acc.hood: 0.7025, Acc.sconce: 0.5837, Acc.vase: 0.5191, Acc.traffic light: 0.5035, Acc.tray: 0.1170, Acc.ashcan: 0.5955, Acc.fan: 0.6268, Acc.pier: 0.4344, Acc.crt screen: 0.2295, Acc.plate: 0.7435, Acc.monitor: 0.0815, Acc.bulletin board: 0.6020, Acc.shower: 0.0000, Acc.radiator: 0.6931, Acc.glass: 0.1427, Acc.clock: 0.2052, Acc.flag: 0.6852 2023-11-02 20:18:06,445 - mmseg - INFO - Iter [4050/40000] lr: 2.912e-06, eta: 13:27:03, time: 2.431, data_time: 1.223, memory: 38534, decode.loss_ce: 0.4592, decode.acc_seg: 83.1999, loss: 0.4592 2023-11-02 20:19:07,134 - mmseg - INFO - Iter [4100/40000] lr: 2.908e-06, eta: 13:24:58, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4646, decode.acc_seg: 82.7290, loss: 0.4646 2023-11-02 20:20:07,794 - mmseg - INFO - Iter [4150/40000] lr: 2.904e-06, eta: 13:22:53, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4309, decode.acc_seg: 84.2047, loss: 0.4309 2023-11-02 20:21:08,507 - mmseg - INFO - Iter [4200/40000] lr: 2.900e-06, eta: 13:20:51, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4281, decode.acc_seg: 83.5175, loss: 0.4281 2023-11-02 20:22:09,214 - mmseg - INFO - Iter [4250/40000] lr: 2.896e-06, eta: 13:18:50, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4138, decode.acc_seg: 84.2295, loss: 0.4138 2023-11-02 20:23:09,962 - mmseg - INFO - Iter [4300/40000] lr: 2.892e-06, eta: 13:16:51, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4683, decode.acc_seg: 83.1477, loss: 0.4683 2023-11-02 20:24:10,691 - mmseg - INFO - Iter [4350/40000] lr: 2.888e-06, eta: 13:14:53, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4285, decode.acc_seg: 84.4556, loss: 0.4285 2023-11-02 20:25:11,366 - mmseg - INFO - Iter [4400/40000] lr: 2.883e-06, eta: 13:12:55, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4230, decode.acc_seg: 83.2038, loss: 0.4230 2023-11-02 20:26:14,424 - mmseg - INFO - Iter [4450/40000] lr: 2.879e-06, eta: 13:11:19, time: 1.261, data_time: 0.054, memory: 38534, decode.loss_ce: 0.3997, decode.acc_seg: 84.3554, loss: 0.3997 2023-11-02 20:27:15,149 - mmseg - INFO - Iter [4500/40000] lr: 2.875e-06, eta: 13:09:24, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3937, decode.acc_seg: 85.2910, loss: 0.3937 2023-11-02 20:28:15,932 - mmseg - INFO - Iter [4550/40000] lr: 2.871e-06, eta: 13:07:31, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3811, decode.acc_seg: 85.2824, loss: 0.3811 2023-11-02 20:29:16,646 - mmseg - INFO - Iter [4600/40000] lr: 2.867e-06, eta: 13:05:39, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4142, decode.acc_seg: 84.4526, loss: 0.4142 2023-11-02 20:30:17,424 - mmseg - INFO - Iter [4650/40000] lr: 2.863e-06, eta: 13:03:48, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4132, decode.acc_seg: 84.7215, loss: 0.4132 2023-11-02 20:31:18,183 - mmseg - INFO - Iter [4700/40000] lr: 2.859e-06, eta: 13:01:58, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3865, decode.acc_seg: 85.2910, loss: 0.3865 2023-11-02 20:32:18,913 - mmseg - INFO - Iter [4750/40000] lr: 2.855e-06, eta: 13:00:09, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3978, decode.acc_seg: 85.1302, loss: 0.3978 2023-11-02 20:33:19,636 - mmseg - INFO - Iter [4800/40000] lr: 2.851e-06, eta: 12:58:21, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3993, decode.acc_seg: 84.5579, loss: 0.3993 2023-11-02 20:34:20,345 - mmseg - INFO - Iter [4850/40000] lr: 2.847e-06, eta: 12:56:34, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.4197, decode.acc_seg: 84.2226, loss: 0.4197 2023-11-02 20:35:21,042 - mmseg - INFO - Iter [4900/40000] lr: 2.843e-06, eta: 12:54:48, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.4264, decode.acc_seg: 84.2223, loss: 0.4264 2023-11-02 20:36:21,710 - mmseg - INFO - Iter [4950/40000] lr: 2.839e-06, eta: 12:53:02, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.4101, decode.acc_seg: 83.6705, loss: 0.4101 2023-11-02 20:37:22,463 - mmseg - INFO - Saving checkpoint at 5000 iterations 2023-11-02 20:38:22,874 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 20:38:22,874 - mmseg - INFO - Iter [5000/40000] lr: 2.835e-06, eta: 12:58:21, time: 2.423, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4291, decode.acc_seg: 83.9168, loss: 0.4291 2023-11-02 20:39:24,786 - mmseg - INFO - per class results: 2023-11-02 20:39:24,796 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 77.52 | 87.8 | | building | 82.28 | 93.76 | | sky | 93.49 | 97.63 | | floor | 82.87 | 90.23 | | tree | 74.77 | 83.41 | | ceiling | 84.25 | 91.45 | | road | 83.84 | 89.49 | | bed | 90.69 | 94.68 | | windowpane | 63.31 | 77.73 | | grass | 60.22 | 91.31 | | cabinet | 62.28 | 71.12 | | sidewalk | 66.41 | 86.6 | | person | 81.18 | 90.92 | | earth | 30.33 | 36.41 | | door | 52.18 | 75.52 | | table | 63.64 | 78.98 | | mountain | 61.75 | 70.92 | | plant | 52.85 | 68.4 | | curtain | 70.41 | 89.03 | | chair | 58.35 | 71.65 | | car | 83.56 | 92.95 | | water | 48.93 | 55.1 | | painting | 73.29 | 84.89 | | sofa | 74.33 | 85.39 | | shelf | 44.57 | 64.86 | | house | 37.41 | 45.92 | | sea | 63.02 | 95.93 | | mirror | 72.23 | 80.42 | | rug | 69.67 | 81.76 | | field | 17.87 | 26.82 | | armchair | 51.27 | 59.64 | | seat | 61.99 | 76.97 | | fence | 38.49 | 43.21 | | desk | 46.69 | 58.36 | | rock | 59.94 | 78.4 | | wardrobe | 49.55 | 72.26 | | lamp | 61.07 | 73.98 | | bathtub | 85.47 | 89.65 | | railing | 41.7 | 59.33 | | cushion | 58.63 | 80.16 | | base | 35.19 | 49.24 | | box | 31.09 | 45.81 | | column | 49.04 | 63.25 | | signboard | 34.55 | 44.35 | | chest of drawers | 49.2 | 63.44 | | counter | 41.71 | 52.52 | | sand | 57.83 | 85.07 | | sink | 72.4 | 79.98 | | skyscraper | 47.88 | 64.77 | | fireplace | 66.94 | 73.84 | | refrigerator | 80.0 | 86.43 | | grandstand | 49.71 | 79.04 | | path | 14.36 | 16.47 | | stairs | 24.09 | 27.66 | | runway | 66.92 | 86.87 | | case | 61.13 | 87.64 | | pool table | 90.81 | 96.58 | | pillow | 60.12 | 71.29 | | screen door | 75.55 | 83.15 | | stairway | 32.99 | 53.97 | | river | 20.47 | 54.25 | | bridge | 75.03 | 84.22 | | bookcase | 38.12 | 56.32 | | blind | 44.56 | 53.08 | | coffee table | 66.0 | 86.05 | | toilet | 86.56 | 90.0 | | flower | 38.39 | 65.01 | | book | 49.22 | 69.78 | | hill | 8.53 | 20.47 | | bench | 60.77 | 73.4 | | countertop | 61.0 | 67.19 | | stove | 77.66 | 83.08 | | palm | 47.21 | 77.01 | | kitchen island | 40.94 | 82.21 | | computer | 66.2 | 73.94 | | swivel chair | 46.39 | 70.23 | | boat | 62.65 | 89.16 | | bar | 63.35 | 79.79 | | arcade machine | 77.56 | 82.6 | | hovel | 31.92 | 35.93 | | bus | 90.43 | 95.48 | | towel | 65.37 | 82.13 | | light | 46.53 | 58.01 | | truck | 40.46 | 49.33 | | tower | 30.06 | 43.93 | | chandelier | 64.49 | 79.6 | | awning | 31.49 | 36.9 | | streetlight | 26.86 | 37.68 | | booth | 34.72 | 37.94 | | television receiver | 73.72 | 85.34 | | airplane | 62.53 | 69.74 | | dirt track | 9.77 | 11.95 | | apparel | 44.58 | 59.28 | | pole | 21.66 | 30.22 | | land | 0.32 | 0.38 | | bannister | 10.41 | 12.99 | | escalator | 60.1 | 74.5 | | ottoman | 50.79 | 70.44 | | bottle | 22.47 | 32.94 | | buffet | 53.91 | 88.71 | | poster | 27.63 | 35.82 | | stage | 21.54 | 29.07 | | van | 40.17 | 49.77 | | ship | 8.84 | 9.25 | | fountain | 24.45 | 24.75 | | conveyer belt | 81.09 | 94.49 | | canopy | 8.22 | 11.4 | | washer | 80.02 | 84.48 | | plaything | 20.98 | 41.03 | | swimming pool | 52.47 | 87.79 | | stool | 46.15 | 65.08 | | barrel | 46.69 | 55.89 | | basket | 37.07 | 63.7 | | waterfall | 50.95 | 58.61 | | tent | 91.39 | 98.1 | | bag | 24.43 | 32.33 | | minibike | 68.26 | 85.8 | | cradle | 84.39 | 95.77 | | oven | 63.23 | 75.2 | | ball | 7.21 | 7.25 | | food | 51.06 | 55.86 | | step | 20.49 | 28.74 | | tank | 52.93 | 66.44 | | trade name | 29.83 | 36.79 | | microwave | 85.7 | 93.43 | | pot | 49.85 | 58.7 | | animal | 76.06 | 83.95 | | bicycle | 60.03 | 80.11 | | lake | 60.46 | 62.8 | | dishwasher | 61.68 | 80.42 | | screen | 51.06 | 95.56 | | blanket | 24.42 | 27.23 | | sculpture | 70.25 | 77.49 | | hood | 63.76 | 69.05 | | sconce | 41.7 | 49.11 | | vase | 39.55 | 51.3 | | traffic light | 30.5 | 53.12 | | tray | 10.32 | 11.17 | | ashcan | 46.21 | 61.72 | | fan | 60.31 | 77.49 | | pier | 37.04 | 42.9 | | crt screen | 0.46 | 0.76 | | plate | 56.93 | 76.38 | | monitor | 30.41 | 54.41 | | bulletin board | 43.25 | 52.33 | | shower | 0.0 | 0.0 | | radiator | 66.13 | 76.58 | | glass | 13.53 | 14.21 | | clock | 30.49 | 32.56 | | flag | 61.45 | 80.94 | +---------------------+-------+-------+ 2023-11-02 20:39:24,796 - mmseg - INFO - Summary: 2023-11-02 20:39:24,796 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 83.41 | 51.25 | 63.75 | +-------+-------+-------+ 2023-11-02 20:39:24,797 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 20:39:24,798 - mmseg - INFO - Iter(val) [250] aAcc: 0.8341, mIoU: 0.5125, mAcc: 0.6375, IoU.wall: 0.7752, IoU.building: 0.8228, IoU.sky: 0.9349, IoU.floor: 0.8287, IoU.tree: 0.7477, IoU.ceiling: 0.8425, IoU.road: 0.8384, IoU.bed : 0.9069, IoU.windowpane: 0.6331, IoU.grass: 0.6022, IoU.cabinet: 0.6228, IoU.sidewalk: 0.6641, IoU.person: 0.8118, IoU.earth: 0.3033, IoU.door: 0.5218, IoU.table: 0.6364, IoU.mountain: 0.6175, IoU.plant: 0.5285, IoU.curtain: 0.7041, IoU.chair: 0.5835, IoU.car: 0.8356, IoU.water: 0.4893, IoU.painting: 0.7329, IoU.sofa: 0.7433, IoU.shelf: 0.4457, IoU.house: 0.3741, IoU.sea: 0.6302, IoU.mirror: 0.7223, IoU.rug: 0.6967, IoU.field: 0.1787, IoU.armchair: 0.5127, IoU.seat: 0.6199, IoU.fence: 0.3849, IoU.desk: 0.4669, IoU.rock: 0.5994, IoU.wardrobe: 0.4955, IoU.lamp: 0.6107, IoU.bathtub: 0.8547, IoU.railing: 0.4170, IoU.cushion: 0.5863, IoU.base: 0.3519, IoU.box: 0.3109, IoU.column: 0.4904, IoU.signboard: 0.3455, IoU.chest of drawers: 0.4920, IoU.counter: 0.4171, IoU.sand: 0.5783, IoU.sink: 0.7240, IoU.skyscraper: 0.4788, IoU.fireplace: 0.6694, IoU.refrigerator: 0.8000, IoU.grandstand: 0.4971, IoU.path: 0.1436, IoU.stairs: 0.2409, IoU.runway: 0.6692, IoU.case: 0.6113, IoU.pool table: 0.9081, IoU.pillow: 0.6012, IoU.screen door: 0.7555, IoU.stairway: 0.3299, IoU.river: 0.2047, IoU.bridge: 0.7503, IoU.bookcase: 0.3812, IoU.blind: 0.4456, IoU.coffee table: 0.6600, IoU.toilet: 0.8656, IoU.flower: 0.3839, IoU.book: 0.4922, IoU.hill: 0.0853, IoU.bench: 0.6077, IoU.countertop: 0.6100, IoU.stove: 0.7766, IoU.palm: 0.4721, IoU.kitchen island: 0.4094, IoU.computer: 0.6620, IoU.swivel chair: 0.4639, IoU.boat: 0.6265, IoU.bar: 0.6335, IoU.arcade machine: 0.7756, IoU.hovel: 0.3192, IoU.bus: 0.9043, IoU.towel: 0.6537, IoU.light: 0.4653, IoU.truck: 0.4046, IoU.tower: 0.3006, IoU.chandelier: 0.6449, IoU.awning: 0.3149, IoU.streetlight: 0.2686, IoU.booth: 0.3472, IoU.television receiver: 0.7372, IoU.airplane: 0.6253, IoU.dirt track: 0.0977, IoU.apparel: 0.4458, IoU.pole: 0.2166, IoU.land: 0.0032, IoU.bannister: 0.1041, IoU.escalator: 0.6010, IoU.ottoman: 0.5079, IoU.bottle: 0.2247, IoU.buffet: 0.5391, IoU.poster: 0.2763, IoU.stage: 0.2154, IoU.van: 0.4017, IoU.ship: 0.0884, IoU.fountain: 0.2445, IoU.conveyer belt: 0.8109, IoU.canopy: 0.0822, IoU.washer: 0.8002, IoU.plaything: 0.2098, IoU.swimming pool: 0.5247, IoU.stool: 0.4615, IoU.barrel: 0.4669, IoU.basket: 0.3707, IoU.waterfall: 0.5095, IoU.tent: 0.9139, IoU.bag: 0.2443, IoU.minibike: 0.6826, IoU.cradle: 0.8439, IoU.oven: 0.6323, IoU.ball: 0.0721, IoU.food: 0.5106, IoU.step: 0.2049, IoU.tank: 0.5293, IoU.trade name: 0.2983, IoU.microwave: 0.8570, IoU.pot: 0.4985, IoU.animal: 0.7606, IoU.bicycle: 0.6003, IoU.lake: 0.6046, IoU.dishwasher: 0.6168, IoU.screen: 0.5106, IoU.blanket: 0.2442, IoU.sculpture: 0.7025, IoU.hood: 0.6376, IoU.sconce: 0.4170, IoU.vase: 0.3955, IoU.traffic light: 0.3050, IoU.tray: 0.1032, IoU.ashcan: 0.4621, IoU.fan: 0.6031, IoU.pier: 0.3704, IoU.crt screen: 0.0046, IoU.plate: 0.5693, IoU.monitor: 0.3041, IoU.bulletin board: 0.4325, IoU.shower: 0.0000, IoU.radiator: 0.6613, IoU.glass: 0.1353, IoU.clock: 0.3049, IoU.flag: 0.6145, Acc.wall: 0.8780, Acc.building: 0.9376, Acc.sky: 0.9763, Acc.floor: 0.9023, Acc.tree: 0.8341, Acc.ceiling: 0.9145, Acc.road: 0.8949, Acc.bed : 0.9468, Acc.windowpane: 0.7773, Acc.grass: 0.9131, Acc.cabinet: 0.7112, Acc.sidewalk: 0.8660, Acc.person: 0.9092, Acc.earth: 0.3641, Acc.door: 0.7552, Acc.table: 0.7898, Acc.mountain: 0.7092, Acc.plant: 0.6840, Acc.curtain: 0.8903, Acc.chair: 0.7165, Acc.car: 0.9295, Acc.water: 0.5510, Acc.painting: 0.8489, Acc.sofa: 0.8539, Acc.shelf: 0.6486, Acc.house: 0.4592, Acc.sea: 0.9593, Acc.mirror: 0.8042, Acc.rug: 0.8176, Acc.field: 0.2682, Acc.armchair: 0.5964, Acc.seat: 0.7697, Acc.fence: 0.4321, Acc.desk: 0.5836, Acc.rock: 0.7840, Acc.wardrobe: 0.7226, Acc.lamp: 0.7398, Acc.bathtub: 0.8965, Acc.railing: 0.5933, Acc.cushion: 0.8016, Acc.base: 0.4924, Acc.box: 0.4581, Acc.column: 0.6325, Acc.signboard: 0.4435, Acc.chest of drawers: 0.6344, Acc.counter: 0.5252, Acc.sand: 0.8507, Acc.sink: 0.7998, Acc.skyscraper: 0.6477, Acc.fireplace: 0.7384, Acc.refrigerator: 0.8643, Acc.grandstand: 0.7904, Acc.path: 0.1647, Acc.stairs: 0.2766, Acc.runway: 0.8687, Acc.case: 0.8764, Acc.pool table: 0.9658, Acc.pillow: 0.7129, Acc.screen door: 0.8315, Acc.stairway: 0.5397, Acc.river: 0.5425, Acc.bridge: 0.8422, Acc.bookcase: 0.5632, Acc.blind: 0.5308, Acc.coffee table: 0.8605, Acc.toilet: 0.9000, Acc.flower: 0.6501, Acc.book: 0.6978, Acc.hill: 0.2047, Acc.bench: 0.7340, Acc.countertop: 0.6719, Acc.stove: 0.8308, Acc.palm: 0.7701, Acc.kitchen island: 0.8221, Acc.computer: 0.7394, Acc.swivel chair: 0.7023, Acc.boat: 0.8916, Acc.bar: 0.7979, Acc.arcade machine: 0.8260, Acc.hovel: 0.3593, Acc.bus: 0.9548, Acc.towel: 0.8213, Acc.light: 0.5801, Acc.truck: 0.4933, Acc.tower: 0.4393, Acc.chandelier: 0.7960, Acc.awning: 0.3690, Acc.streetlight: 0.3768, Acc.booth: 0.3794, Acc.television receiver: 0.8534, Acc.airplane: 0.6974, Acc.dirt track: 0.1195, Acc.apparel: 0.5928, Acc.pole: 0.3022, Acc.land: 0.0038, Acc.bannister: 0.1299, Acc.escalator: 0.7450, Acc.ottoman: 0.7044, Acc.bottle: 0.3294, Acc.buffet: 0.8871, Acc.poster: 0.3582, Acc.stage: 0.2907, Acc.van: 0.4977, Acc.ship: 0.0925, Acc.fountain: 0.2475, Acc.conveyer belt: 0.9449, Acc.canopy: 0.1140, Acc.washer: 0.8448, Acc.plaything: 0.4103, Acc.swimming pool: 0.8779, Acc.stool: 0.6508, Acc.barrel: 0.5589, Acc.basket: 0.6370, Acc.waterfall: 0.5861, Acc.tent: 0.9810, Acc.bag: 0.3233, Acc.minibike: 0.8580, Acc.cradle: 0.9577, Acc.oven: 0.7520, Acc.ball: 0.0725, Acc.food: 0.5586, Acc.step: 0.2874, Acc.tank: 0.6644, Acc.trade name: 0.3679, Acc.microwave: 0.9343, Acc.pot: 0.5870, Acc.animal: 0.8395, Acc.bicycle: 0.8011, Acc.lake: 0.6280, Acc.dishwasher: 0.8042, Acc.screen: 0.9556, Acc.blanket: 0.2723, Acc.sculpture: 0.7749, Acc.hood: 0.6905, Acc.sconce: 0.4911, Acc.vase: 0.5130, Acc.traffic light: 0.5312, Acc.tray: 0.1117, Acc.ashcan: 0.6172, Acc.fan: 0.7749, Acc.pier: 0.4290, Acc.crt screen: 0.0076, Acc.plate: 0.7638, Acc.monitor: 0.5441, Acc.bulletin board: 0.5233, Acc.shower: 0.0000, Acc.radiator: 0.7658, Acc.glass: 0.1421, Acc.clock: 0.3256, Acc.flag: 0.8094 2023-11-02 20:40:25,647 - mmseg - INFO - Iter [5050/40000] lr: 2.831e-06, eta: 13:03:42, time: 2.455, data_time: 1.247, memory: 38534, decode.loss_ce: 0.4073, decode.acc_seg: 84.4676, loss: 0.4073 2023-11-02 20:41:28,727 - mmseg - INFO - Iter [5100/40000] lr: 2.827e-06, eta: 13:02:06, time: 1.262, data_time: 0.056, memory: 38534, decode.loss_ce: 0.3769, decode.acc_seg: 85.6160, loss: 0.3769 2023-11-02 20:42:29,390 - mmseg - INFO - Iter [5150/40000] lr: 2.823e-06, eta: 13:00:15, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3907, decode.acc_seg: 85.0296, loss: 0.3907 2023-11-02 20:43:30,074 - mmseg - INFO - Iter [5200/40000] lr: 2.819e-06, eta: 12:58:24, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3720, decode.acc_seg: 85.4621, loss: 0.3720 2023-11-02 20:44:30,694 - mmseg - INFO - Iter [5250/40000] lr: 2.815e-06, eta: 12:56:34, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3928, decode.acc_seg: 85.7584, loss: 0.3928 2023-11-02 20:45:31,353 - mmseg - INFO - Iter [5300/40000] lr: 2.811e-06, eta: 12:54:45, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3799, decode.acc_seg: 85.2930, loss: 0.3799 2023-11-02 20:46:32,011 - mmseg - INFO - Iter [5350/40000] lr: 2.807e-06, eta: 12:52:57, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3729, decode.acc_seg: 85.9050, loss: 0.3729 2023-11-02 20:47:32,726 - mmseg - INFO - Iter [5400/40000] lr: 2.802e-06, eta: 12:51:11, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3952, decode.acc_seg: 85.1875, loss: 0.3952 2023-11-02 20:48:33,484 - mmseg - INFO - Iter [5450/40000] lr: 2.798e-06, eta: 12:49:25, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3864, decode.acc_seg: 85.0778, loss: 0.3864 2023-11-02 20:49:34,187 - mmseg - INFO - Iter [5500/40000] lr: 2.794e-06, eta: 12:47:40, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4296, decode.acc_seg: 84.1389, loss: 0.4296 2023-11-02 20:50:34,946 - mmseg - INFO - Iter [5550/40000] lr: 2.790e-06, eta: 12:45:56, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4004, decode.acc_seg: 84.2928, loss: 0.4004 2023-11-02 20:51:35,597 - mmseg - INFO - Iter [5600/40000] lr: 2.786e-06, eta: 12:44:12, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3791, decode.acc_seg: 85.2205, loss: 0.3791 2023-11-02 20:52:36,282 - mmseg - INFO - Iter [5650/40000] lr: 2.782e-06, eta: 12:42:29, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3920, decode.acc_seg: 84.7805, loss: 0.3920 2023-11-02 20:53:39,337 - mmseg - INFO - Iter [5700/40000] lr: 2.778e-06, eta: 12:41:01, time: 1.261, data_time: 0.052, memory: 38534, decode.loss_ce: 0.3769, decode.acc_seg: 85.1481, loss: 0.3769 2023-11-02 20:54:40,050 - mmseg - INFO - Iter [5750/40000] lr: 2.774e-06, eta: 12:39:20, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3625, decode.acc_seg: 85.7783, loss: 0.3625 2023-11-02 20:55:40,808 - mmseg - INFO - Iter [5800/40000] lr: 2.770e-06, eta: 12:37:39, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3931, decode.acc_seg: 85.4545, loss: 0.3931 2023-11-02 20:56:41,517 - mmseg - INFO - Iter [5850/40000] lr: 2.766e-06, eta: 12:35:59, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.4102, decode.acc_seg: 84.8062, loss: 0.4102 2023-11-02 20:57:42,235 - mmseg - INFO - Iter [5900/40000] lr: 2.762e-06, eta: 12:34:20, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3493, decode.acc_seg: 86.6774, loss: 0.3493 2023-11-02 20:58:42,970 - mmseg - INFO - Iter [5950/40000] lr: 2.758e-06, eta: 12:32:42, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3419, decode.acc_seg: 86.1906, loss: 0.3419 2023-11-02 20:59:43,583 - mmseg - INFO - Saving checkpoint at 6000 iterations 2023-11-02 21:00:41,802 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 21:00:41,802 - mmseg - INFO - Iter [6000/40000] lr: 2.754e-06, eta: 12:36:33, time: 2.377, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3736, decode.acc_seg: 85.8851, loss: 0.3736 2023-11-02 21:01:39,272 - mmseg - INFO - per class results: 2023-11-02 21:01:39,277 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.88 | 87.32 | | building | 82.76 | 91.16 | | sky | 93.71 | 96.75 | | floor | 83.6 | 90.13 | | tree | 74.12 | 86.6 | | ceiling | 83.39 | 92.48 | | road | 85.16 | 90.85 | | bed | 90.12 | 97.02 | | windowpane | 61.53 | 76.56 | | grass | 65.74 | 86.03 | | cabinet | 62.85 | 74.56 | | sidewalk | 67.27 | 80.19 | | person | 81.75 | 91.04 | | earth | 35.52 | 48.31 | | door | 53.65 | 70.88 | | table | 65.29 | 78.39 | | mountain | 60.52 | 71.1 | | plant | 55.62 | 66.58 | | curtain | 71.88 | 88.44 | | chair | 58.41 | 71.24 | | car | 84.65 | 91.94 | | water | 52.52 | 62.69 | | painting | 74.94 | 88.13 | | sofa | 75.95 | 89.67 | | shelf | 45.55 | 64.55 | | house | 50.54 | 88.57 | | sea | 63.38 | 75.36 | | mirror | 73.27 | 84.75 | | rug | 70.39 | 83.09 | | field | 25.34 | 39.56 | | armchair | 53.34 | 71.1 | | seat | 62.11 | 88.58 | | fence | 48.11 | 62.26 | | desk | 52.88 | 73.61 | | rock | 54.91 | 75.08 | | wardrobe | 52.72 | 76.26 | | lamp | 63.0 | 76.02 | | bathtub | 86.44 | 93.08 | | railing | 41.31 | 51.73 | | cushion | 58.63 | 73.75 | | base | 33.45 | 61.55 | | box | 31.09 | 42.4 | | column | 47.46 | 61.24 | | signboard | 38.26 | 51.94 | | chest of drawers | 38.17 | 48.37 | | counter | 50.24 | 71.67 | | sand | 60.32 | 83.33 | | sink | 72.66 | 77.77 | | skyscraper | 48.1 | 64.37 | | fireplace | 72.63 | 94.86 | | refrigerator | 80.01 | 87.82 | | grandstand | 44.13 | 76.74 | | path | 22.02 | 30.83 | | stairs | 36.26 | 54.75 | | runway | 70.69 | 93.29 | | case | 58.47 | 92.17 | | pool table | 90.86 | 97.1 | | pillow | 56.17 | 62.6 | | screen door | 31.97 | 35.12 | | stairway | 42.27 | 47.44 | | river | 20.61 | 63.48 | | bridge | 75.43 | 87.55 | | bookcase | 39.86 | 49.08 | | blind | 45.79 | 53.14 | | coffee table | 67.53 | 85.36 | | toilet | 87.84 | 92.7 | | flower | 36.12 | 49.11 | | book | 51.15 | 68.53 | | hill | 6.37 | 12.71 | | bench | 51.34 | 56.69 | | countertop | 60.84 | 72.69 | | stove | 79.4 | 86.16 | | palm | 44.97 | 81.53 | | kitchen island | 39.8 | 85.78 | | computer | 72.78 | 91.06 | | swivel chair | 45.98 | 81.0 | | boat | 28.54 | 58.01 | | bar | 64.39 | 79.33 | | arcade machine | 80.75 | 85.77 | | hovel | 34.84 | 43.54 | | bus | 88.32 | 96.38 | | towel | 67.9 | 78.09 | | light | 45.41 | 52.38 | | truck | 39.9 | 55.24 | | tower | 31.8 | 52.61 | | chandelier | 65.12 | 84.42 | | awning | 30.74 | 39.34 | | streetlight | 25.5 | 35.18 | | booth | 65.57 | 73.42 | | television receiver | 72.1 | 88.17 | | airplane | 75.89 | 85.3 | | dirt track | 5.9 | 24.36 | | apparel | 48.22 | 71.12 | | pole | 21.12 | 27.84 | | land | 3.96 | 6.46 | | bannister | 14.29 | 17.89 | | escalator | 61.52 | 85.16 | | ottoman | 53.03 | 71.64 | | bottle | 23.31 | 30.3 | | buffet | 47.85 | 54.33 | | poster | 29.19 | 40.76 | | stage | 20.81 | 39.97 | | van | 48.38 | 69.43 | | ship | 28.18 | 43.28 | | fountain | 29.42 | 30.11 | | conveyer belt | 81.92 | 94.41 | | canopy | 27.34 | 40.61 | | washer | 85.3 | 91.47 | | plaything | 26.46 | 33.96 | | swimming pool | 52.25 | 86.38 | | stool | 45.07 | 49.43 | | barrel | 54.35 | 76.22 | | basket | 38.79 | 53.43 | | waterfall | 53.3 | 59.81 | | tent | 94.55 | 97.88 | | bag | 28.64 | 34.56 | | minibike | 70.8 | 85.23 | | cradle | 84.79 | 96.06 | | oven | 63.09 | 73.76 | | ball | 10.08 | 10.52 | | food | 39.7 | 40.78 | | step | 13.68 | 17.01 | | tank | 49.98 | 66.74 | | trade name | 34.29 | 48.53 | | microwave | 85.51 | 94.27 | | pot | 46.18 | 51.84 | | animal | 74.11 | 77.09 | | bicycle | 57.99 | 82.67 | | lake | 60.72 | 62.86 | | dishwasher | 65.3 | 77.24 | | screen | 65.82 | 86.52 | | blanket | 20.87 | 22.7 | | sculpture | 73.5 | 81.76 | | hood | 69.14 | 75.95 | | sconce | 43.29 | 53.94 | | vase | 41.63 | 57.6 | | traffic light | 30.59 | 59.8 | | tray | 12.57 | 16.98 | | ashcan | 44.96 | 67.16 | | fan | 60.91 | 78.49 | | pier | 34.01 | 42.51 | | crt screen | 8.01 | 8.76 | | plate | 54.74 | 73.41 | | monitor | 59.31 | 64.66 | | bulletin board | 53.18 | 57.87 | | shower | 0.04 | 0.59 | | radiator | 64.24 | 77.21 | | glass | 17.03 | 18.6 | | clock | 29.72 | 31.95 | | flag | 61.86 | 74.03 | +---------------------+-------+-------+ 2023-11-02 21:01:39,277 - mmseg - INFO - Summary: 2023-11-02 21:01:39,277 - mmseg - INFO - +-------+-------+------+ | aAcc | mIoU | mAcc | +-------+-------+------+ | 83.88 | 52.63 | 65.8 | +-------+-------+------+ 2023-11-02 21:01:39,278 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 21:01:39,279 - mmseg - INFO - Iter(val) [250] aAcc: 0.8388, mIoU: 0.5263, mAcc: 0.6580, IoU.wall: 0.7888, IoU.building: 0.8276, IoU.sky: 0.9371, IoU.floor: 0.8360, IoU.tree: 0.7412, IoU.ceiling: 0.8339, IoU.road: 0.8516, IoU.bed : 0.9012, IoU.windowpane: 0.6153, IoU.grass: 0.6574, IoU.cabinet: 0.6285, IoU.sidewalk: 0.6727, IoU.person: 0.8175, IoU.earth: 0.3552, IoU.door: 0.5365, IoU.table: 0.6529, IoU.mountain: 0.6052, IoU.plant: 0.5562, IoU.curtain: 0.7188, IoU.chair: 0.5841, IoU.car: 0.8465, IoU.water: 0.5252, IoU.painting: 0.7494, IoU.sofa: 0.7595, IoU.shelf: 0.4555, IoU.house: 0.5054, IoU.sea: 0.6338, IoU.mirror: 0.7327, IoU.rug: 0.7039, IoU.field: 0.2534, IoU.armchair: 0.5334, IoU.seat: 0.6211, IoU.fence: 0.4811, IoU.desk: 0.5288, IoU.rock: 0.5491, IoU.wardrobe: 0.5272, IoU.lamp: 0.6300, IoU.bathtub: 0.8644, IoU.railing: 0.4131, IoU.cushion: 0.5863, IoU.base: 0.3345, IoU.box: 0.3109, IoU.column: 0.4746, IoU.signboard: 0.3826, IoU.chest of drawers: 0.3817, IoU.counter: 0.5024, IoU.sand: 0.6032, IoU.sink: 0.7266, IoU.skyscraper: 0.4810, IoU.fireplace: 0.7263, IoU.refrigerator: 0.8001, IoU.grandstand: 0.4413, IoU.path: 0.2202, IoU.stairs: 0.3626, IoU.runway: 0.7069, IoU.case: 0.5847, IoU.pool table: 0.9086, IoU.pillow: 0.5617, IoU.screen door: 0.3197, IoU.stairway: 0.4227, IoU.river: 0.2061, IoU.bridge: 0.7543, IoU.bookcase: 0.3986, IoU.blind: 0.4579, IoU.coffee table: 0.6753, IoU.toilet: 0.8784, IoU.flower: 0.3612, IoU.book: 0.5115, IoU.hill: 0.0637, IoU.bench: 0.5134, IoU.countertop: 0.6084, IoU.stove: 0.7940, IoU.palm: 0.4497, IoU.kitchen island: 0.3980, IoU.computer: 0.7278, IoU.swivel chair: 0.4598, IoU.boat: 0.2854, IoU.bar: 0.6439, IoU.arcade machine: 0.8075, IoU.hovel: 0.3484, IoU.bus: 0.8832, IoU.towel: 0.6790, IoU.light: 0.4541, IoU.truck: 0.3990, IoU.tower: 0.3180, IoU.chandelier: 0.6512, IoU.awning: 0.3074, IoU.streetlight: 0.2550, IoU.booth: 0.6557, IoU.television receiver: 0.7210, IoU.airplane: 0.7589, IoU.dirt track: 0.0590, IoU.apparel: 0.4822, IoU.pole: 0.2112, IoU.land: 0.0396, IoU.bannister: 0.1429, IoU.escalator: 0.6152, IoU.ottoman: 0.5303, IoU.bottle: 0.2331, IoU.buffet: 0.4785, IoU.poster: 0.2919, IoU.stage: 0.2081, IoU.van: 0.4838, IoU.ship: 0.2818, IoU.fountain: 0.2942, IoU.conveyer belt: 0.8192, IoU.canopy: 0.2734, IoU.washer: 0.8530, IoU.plaything: 0.2646, IoU.swimming pool: 0.5225, IoU.stool: 0.4507, IoU.barrel: 0.5435, IoU.basket: 0.3879, IoU.waterfall: 0.5330, IoU.tent: 0.9455, IoU.bag: 0.2864, IoU.minibike: 0.7080, IoU.cradle: 0.8479, IoU.oven: 0.6309, IoU.ball: 0.1008, IoU.food: 0.3970, IoU.step: 0.1368, IoU.tank: 0.4998, IoU.trade name: 0.3429, IoU.microwave: 0.8551, IoU.pot: 0.4618, IoU.animal: 0.7411, IoU.bicycle: 0.5799, IoU.lake: 0.6072, IoU.dishwasher: 0.6530, IoU.screen: 0.6582, IoU.blanket: 0.2087, IoU.sculpture: 0.7350, IoU.hood: 0.6914, IoU.sconce: 0.4329, IoU.vase: 0.4163, IoU.traffic light: 0.3059, IoU.tray: 0.1257, IoU.ashcan: 0.4496, IoU.fan: 0.6091, IoU.pier: 0.3401, IoU.crt screen: 0.0801, IoU.plate: 0.5474, IoU.monitor: 0.5931, IoU.bulletin board: 0.5318, IoU.shower: 0.0004, IoU.radiator: 0.6424, IoU.glass: 0.1703, IoU.clock: 0.2972, IoU.flag: 0.6186, Acc.wall: 0.8732, Acc.building: 0.9116, Acc.sky: 0.9675, Acc.floor: 0.9013, Acc.tree: 0.8660, Acc.ceiling: 0.9248, Acc.road: 0.9085, Acc.bed : 0.9702, Acc.windowpane: 0.7656, Acc.grass: 0.8603, Acc.cabinet: 0.7456, Acc.sidewalk: 0.8019, Acc.person: 0.9104, Acc.earth: 0.4831, Acc.door: 0.7088, Acc.table: 0.7839, Acc.mountain: 0.7110, Acc.plant: 0.6658, Acc.curtain: 0.8844, Acc.chair: 0.7124, Acc.car: 0.9194, Acc.water: 0.6269, Acc.painting: 0.8813, Acc.sofa: 0.8967, Acc.shelf: 0.6455, Acc.house: 0.8857, Acc.sea: 0.7536, Acc.mirror: 0.8475, Acc.rug: 0.8309, Acc.field: 0.3956, Acc.armchair: 0.7110, Acc.seat: 0.8858, Acc.fence: 0.6226, Acc.desk: 0.7361, Acc.rock: 0.7508, Acc.wardrobe: 0.7626, Acc.lamp: 0.7602, Acc.bathtub: 0.9308, Acc.railing: 0.5173, Acc.cushion: 0.7375, Acc.base: 0.6155, Acc.box: 0.4240, Acc.column: 0.6124, Acc.signboard: 0.5194, Acc.chest of drawers: 0.4837, Acc.counter: 0.7167, Acc.sand: 0.8333, Acc.sink: 0.7777, Acc.skyscraper: 0.6437, Acc.fireplace: 0.9486, Acc.refrigerator: 0.8782, Acc.grandstand: 0.7674, Acc.path: 0.3083, Acc.stairs: 0.5475, Acc.runway: 0.9329, Acc.case: 0.9217, Acc.pool table: 0.9710, Acc.pillow: 0.6260, Acc.screen door: 0.3512, Acc.stairway: 0.4744, Acc.river: 0.6348, Acc.bridge: 0.8755, Acc.bookcase: 0.4908, Acc.blind: 0.5314, Acc.coffee table: 0.8536, Acc.toilet: 0.9270, Acc.flower: 0.4911, Acc.book: 0.6853, Acc.hill: 0.1271, Acc.bench: 0.5669, Acc.countertop: 0.7269, Acc.stove: 0.8616, Acc.palm: 0.8153, Acc.kitchen island: 0.8578, Acc.computer: 0.9106, Acc.swivel chair: 0.8100, Acc.boat: 0.5801, Acc.bar: 0.7933, Acc.arcade machine: 0.8577, Acc.hovel: 0.4354, Acc.bus: 0.9638, Acc.towel: 0.7809, Acc.light: 0.5238, Acc.truck: 0.5524, Acc.tower: 0.5261, Acc.chandelier: 0.8442, Acc.awning: 0.3934, Acc.streetlight: 0.3518, Acc.booth: 0.7342, Acc.television receiver: 0.8817, Acc.airplane: 0.8530, Acc.dirt track: 0.2436, Acc.apparel: 0.7112, Acc.pole: 0.2784, Acc.land: 0.0646, Acc.bannister: 0.1789, Acc.escalator: 0.8516, Acc.ottoman: 0.7164, Acc.bottle: 0.3030, Acc.buffet: 0.5433, Acc.poster: 0.4076, Acc.stage: 0.3997, Acc.van: 0.6943, Acc.ship: 0.4328, Acc.fountain: 0.3011, Acc.conveyer belt: 0.9441, Acc.canopy: 0.4061, Acc.washer: 0.9147, Acc.plaything: 0.3396, Acc.swimming pool: 0.8638, Acc.stool: 0.4943, Acc.barrel: 0.7622, Acc.basket: 0.5343, Acc.waterfall: 0.5981, Acc.tent: 0.9788, Acc.bag: 0.3456, Acc.minibike: 0.8523, Acc.cradle: 0.9606, Acc.oven: 0.7376, Acc.ball: 0.1052, Acc.food: 0.4078, Acc.step: 0.1701, Acc.tank: 0.6674, Acc.trade name: 0.4853, Acc.microwave: 0.9427, Acc.pot: 0.5184, Acc.animal: 0.7709, Acc.bicycle: 0.8267, Acc.lake: 0.6286, Acc.dishwasher: 0.7724, Acc.screen: 0.8652, Acc.blanket: 0.2270, Acc.sculpture: 0.8176, Acc.hood: 0.7595, Acc.sconce: 0.5394, Acc.vase: 0.5760, Acc.traffic light: 0.5980, Acc.tray: 0.1698, Acc.ashcan: 0.6716, Acc.fan: 0.7849, Acc.pier: 0.4251, Acc.crt screen: 0.0876, Acc.plate: 0.7341, Acc.monitor: 0.6466, Acc.bulletin board: 0.5787, Acc.shower: 0.0059, Acc.radiator: 0.7721, Acc.glass: 0.1860, Acc.clock: 0.3195, Acc.flag: 0.7403 2023-11-02 21:02:40,087 - mmseg - INFO - Iter [6050/40000] lr: 2.750e-06, eta: 12:40:15, time: 2.366, data_time: 1.157, memory: 38534, decode.loss_ce: 0.3570, decode.acc_seg: 86.1811, loss: 0.3570 2023-11-02 21:03:40,901 - mmseg - INFO - Iter [6100/40000] lr: 2.746e-06, eta: 12:38:33, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3713, decode.acc_seg: 85.9532, loss: 0.3713 2023-11-02 21:04:41,602 - mmseg - INFO - Iter [6150/40000] lr: 2.742e-06, eta: 12:36:50, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3730, decode.acc_seg: 86.0087, loss: 0.3730 2023-11-02 21:05:42,341 - mmseg - INFO - Iter [6200/40000] lr: 2.738e-06, eta: 12:35:09, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3815, decode.acc_seg: 85.7688, loss: 0.3815 2023-11-02 21:06:43,140 - mmseg - INFO - Iter [6250/40000] lr: 2.734e-06, eta: 12:33:28, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3763, decode.acc_seg: 85.8088, loss: 0.3763 2023-11-02 21:07:43,877 - mmseg - INFO - Iter [6300/40000] lr: 2.730e-06, eta: 12:31:48, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3757, decode.acc_seg: 85.4283, loss: 0.3757 2023-11-02 21:08:50,875 - mmseg - INFO - Iter [6350/40000] lr: 2.726e-06, eta: 12:30:41, time: 1.340, data_time: 0.130, memory: 38534, decode.loss_ce: 0.3444, decode.acc_seg: 86.5119, loss: 0.3444 2023-11-02 21:09:51,621 - mmseg - INFO - Iter [6400/40000] lr: 2.722e-06, eta: 12:29:02, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3542, decode.acc_seg: 86.2891, loss: 0.3542 2023-11-02 21:10:52,374 - mmseg - INFO - Iter [6450/40000] lr: 2.717e-06, eta: 12:27:23, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3385, decode.acc_seg: 86.8459, loss: 0.3385 2023-11-02 21:11:53,135 - mmseg - INFO - Iter [6500/40000] lr: 2.713e-06, eta: 12:25:45, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3555, decode.acc_seg: 86.5295, loss: 0.3555 2023-11-02 21:12:53,874 - mmseg - INFO - Iter [6550/40000] lr: 2.709e-06, eta: 12:24:07, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3349, decode.acc_seg: 87.0977, loss: 0.3349 2023-11-02 21:13:54,629 - mmseg - INFO - Iter [6600/40000] lr: 2.705e-06, eta: 12:22:30, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3409, decode.acc_seg: 86.8974, loss: 0.3409 2023-11-02 21:14:55,352 - mmseg - INFO - Iter [6650/40000] lr: 2.701e-06, eta: 12:20:53, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3382, decode.acc_seg: 86.8955, loss: 0.3382 2023-11-02 21:15:56,139 - mmseg - INFO - Iter [6700/40000] lr: 2.697e-06, eta: 12:19:18, time: 1.216, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3587, decode.acc_seg: 86.0322, loss: 0.3587 2023-11-02 21:16:56,909 - mmseg - INFO - Iter [6750/40000] lr: 2.693e-06, eta: 12:17:42, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3474, decode.acc_seg: 86.4797, loss: 0.3474 2023-11-02 21:17:57,676 - mmseg - INFO - Iter [6800/40000] lr: 2.689e-06, eta: 12:16:07, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3584, decode.acc_seg: 86.1756, loss: 0.3584 2023-11-02 21:18:58,447 - mmseg - INFO - Iter [6850/40000] lr: 2.685e-06, eta: 12:14:33, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3760, decode.acc_seg: 85.8892, loss: 0.3760 2023-11-02 21:19:59,173 - mmseg - INFO - Iter [6900/40000] lr: 2.681e-06, eta: 12:12:59, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3617, decode.acc_seg: 86.5722, loss: 0.3617 2023-11-02 21:20:59,950 - mmseg - INFO - Iter [6950/40000] lr: 2.677e-06, eta: 12:11:26, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3518, decode.acc_seg: 86.5603, loss: 0.3518 2023-11-02 21:22:03,077 - mmseg - INFO - Saving checkpoint at 7000 iterations 2023-11-02 21:22:59,628 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 21:22:59,628 - mmseg - INFO - Iter [7000/40000] lr: 2.673e-06, eta: 12:14:31, time: 2.394, data_time: 0.053, memory: 38534, decode.loss_ce: 0.3447, decode.acc_seg: 86.4290, loss: 0.3447 2023-11-02 21:23:57,710 - mmseg - INFO - per class results: 2023-11-02 21:23:57,716 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.09 | 87.62 | | building | 83.39 | 92.97 | | sky | 93.83 | 97.17 | | floor | 83.04 | 89.74 | | tree | 74.83 | 86.9 | | ceiling | 84.71 | 93.96 | | road | 84.51 | 88.32 | | bed | 90.79 | 96.85 | | windowpane | 61.68 | 84.14 | | grass | 68.65 | 80.21 | | cabinet | 63.62 | 72.18 | | sidewalk | 67.25 | 83.21 | | person | 81.3 | 93.52 | | earth | 37.49 | 50.29 | | door | 53.89 | 67.51 | | table | 65.54 | 79.97 | | mountain | 60.84 | 75.13 | | plant | 53.95 | 69.25 | | curtain | 76.37 | 84.6 | | chair | 56.7 | 68.39 | | car | 83.74 | 93.09 | | water | 56.03 | 69.33 | | painting | 73.97 | 87.21 | | sofa | 76.13 | 88.5 | | shelf | 44.38 | 61.63 | | house | 44.64 | 58.18 | | sea | 62.74 | 79.09 | | mirror | 71.74 | 78.93 | | rug | 69.5 | 81.75 | | field | 31.41 | 63.32 | | armchair | 51.93 | 72.19 | | seat | 63.96 | 88.25 | | fence | 48.0 | 62.76 | | desk | 47.72 | 67.76 | | rock | 47.28 | 60.53 | | wardrobe | 50.61 | 69.27 | | lamp | 63.06 | 74.36 | | bathtub | 84.9 | 87.54 | | railing | 35.6 | 50.93 | | cushion | 61.08 | 75.64 | | base | 26.17 | 60.91 | | box | 31.54 | 41.85 | | column | 48.19 | 60.42 | | signboard | 38.92 | 51.47 | | chest of drawers | 42.3 | 63.29 | | counter | 45.14 | 52.75 | | sand | 67.65 | 78.74 | | sink | 73.88 | 79.94 | | skyscraper | 48.06 | 59.78 | | fireplace | 71.53 | 92.22 | | refrigerator | 80.0 | 88.67 | | grandstand | 44.4 | 71.1 | | path | 25.14 | 53.51 | | stairs | 23.89 | 29.41 | | runway | 72.74 | 94.76 | | case | 58.93 | 75.18 | | pool table | 91.36 | 96.78 | | pillow | 61.38 | 72.46 | | screen door | 66.77 | 83.15 | | stairway | 31.86 | 48.99 | | river | 23.68 | 59.79 | | bridge | 78.37 | 86.72 | | bookcase | 42.71 | 53.67 | | blind | 38.56 | 43.42 | | coffee table | 62.61 | 87.65 | | toilet | 86.65 | 89.47 | | flower | 39.1 | 60.59 | | book | 49.27 | 75.99 | | hill | 8.8 | 12.89 | | bench | 51.1 | 57.2 | | countertop | 64.41 | 78.11 | | stove | 76.53 | 86.03 | | palm | 47.24 | 79.45 | | kitchen island | 40.01 | 61.44 | | computer | 76.05 | 88.57 | | swivel chair | 46.66 | 75.1 | | boat | 37.66 | 85.89 | | bar | 63.42 | 84.33 | | arcade machine | 87.66 | 92.83 | | hovel | 36.35 | 58.09 | | bus | 90.64 | 94.83 | | towel | 65.94 | 85.72 | | light | 39.48 | 42.95 | | truck | 31.05 | 40.36 | | tower | 22.76 | 43.04 | | chandelier | 65.46 | 85.79 | | awning | 43.23 | 53.87 | | streetlight | 23.11 | 33.18 | | booth | 55.27 | 58.62 | | television receiver | 75.53 | 88.05 | | airplane | 69.69 | 93.84 | | dirt track | 11.59 | 11.7 | | apparel | 53.37 | 73.63 | | pole | 14.25 | 16.33 | | land | 3.97 | 7.24 | | bannister | 13.5 | 18.95 | | escalator | 62.06 | 79.39 | | ottoman | 53.97 | 69.3 | | bottle | 15.48 | 16.84 | | buffet | 49.62 | 85.05 | | poster | 31.31 | 35.68 | | stage | 19.21 | 42.18 | | van | 36.62 | 47.77 | | ship | 8.75 | 9.5 | | fountain | 43.52 | 44.32 | | conveyer belt | 83.74 | 93.99 | | canopy | 33.78 | 35.35 | | washer | 82.38 | 87.13 | | plaything | 27.39 | 38.07 | | swimming pool | 52.24 | 89.49 | | stool | 47.78 | 64.74 | | barrel | 52.37 | 64.88 | | basket | 39.12 | 57.43 | | waterfall | 54.68 | 68.36 | | tent | 95.89 | 97.58 | | bag | 20.12 | 22.3 | | minibike | 69.31 | 82.86 | | cradle | 85.41 | 96.59 | | oven | 63.9 | 73.27 | | ball | 54.63 | 60.2 | | food | 32.22 | 34.56 | | step | 9.71 | 12.43 | | tank | 52.8 | 62.0 | | trade name | 27.29 | 32.25 | | microwave | 86.0 | 92.02 | | pot | 42.17 | 46.14 | | animal | 77.29 | 83.13 | | bicycle | 56.23 | 78.94 | | lake | 62.57 | 62.6 | | dishwasher | 64.04 | 74.98 | | screen | 63.39 | 87.08 | | blanket | 25.04 | 28.27 | | sculpture | 71.99 | 86.95 | | hood | 70.54 | 81.68 | | sconce | 51.12 | 67.57 | | vase | 41.27 | 59.89 | | traffic light | 30.6 | 53.29 | | tray | 13.13 | 16.84 | | ashcan | 47.32 | 64.23 | | fan | 57.5 | 66.78 | | pier | 34.94 | 43.0 | | crt screen | 8.08 | 9.33 | | plate | 56.5 | 74.06 | | monitor | 61.88 | 76.64 | | bulletin board | 48.37 | 64.95 | | shower | 0.0 | 0.0 | | radiator | 66.87 | 76.53 | | glass | 17.81 | 19.42 | | clock | 27.99 | 30.64 | | flag | 66.93 | 75.77 | +---------------------+-------+-------+ 2023-11-02 21:23:57,716 - mmseg - INFO - Summary: 2023-11-02 21:23:57,716 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.06 | 52.87 | 65.71 | +-------+-------+-------+ 2023-11-02 21:23:57,717 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 21:23:57,717 - mmseg - INFO - Iter(val) [250] aAcc: 0.8406, mIoU: 0.5287, mAcc: 0.6571, IoU.wall: 0.7909, IoU.building: 0.8339, IoU.sky: 0.9383, IoU.floor: 0.8304, IoU.tree: 0.7483, IoU.ceiling: 0.8471, IoU.road: 0.8451, IoU.bed : 0.9079, IoU.windowpane: 0.6168, IoU.grass: 0.6865, IoU.cabinet: 0.6362, IoU.sidewalk: 0.6725, IoU.person: 0.8130, IoU.earth: 0.3749, IoU.door: 0.5389, IoU.table: 0.6554, IoU.mountain: 0.6084, IoU.plant: 0.5395, IoU.curtain: 0.7637, IoU.chair: 0.5670, IoU.car: 0.8374, IoU.water: 0.5603, IoU.painting: 0.7397, IoU.sofa: 0.7613, IoU.shelf: 0.4438, IoU.house: 0.4464, IoU.sea: 0.6274, IoU.mirror: 0.7174, IoU.rug: 0.6950, IoU.field: 0.3141, IoU.armchair: 0.5193, IoU.seat: 0.6396, IoU.fence: 0.4800, IoU.desk: 0.4772, IoU.rock: 0.4728, IoU.wardrobe: 0.5061, IoU.lamp: 0.6306, IoU.bathtub: 0.8490, IoU.railing: 0.3560, IoU.cushion: 0.6108, IoU.base: 0.2617, IoU.box: 0.3154, IoU.column: 0.4819, IoU.signboard: 0.3892, IoU.chest of drawers: 0.4230, IoU.counter: 0.4514, IoU.sand: 0.6765, IoU.sink: 0.7388, IoU.skyscraper: 0.4806, IoU.fireplace: 0.7153, IoU.refrigerator: 0.8000, IoU.grandstand: 0.4440, IoU.path: 0.2514, IoU.stairs: 0.2389, IoU.runway: 0.7274, IoU.case: 0.5893, IoU.pool table: 0.9136, IoU.pillow: 0.6138, IoU.screen door: 0.6677, IoU.stairway: 0.3186, IoU.river: 0.2368, IoU.bridge: 0.7837, IoU.bookcase: 0.4271, IoU.blind: 0.3856, IoU.coffee table: 0.6261, IoU.toilet: 0.8665, IoU.flower: 0.3910, IoU.book: 0.4927, IoU.hill: 0.0880, IoU.bench: 0.5110, IoU.countertop: 0.6441, IoU.stove: 0.7653, IoU.palm: 0.4724, IoU.kitchen island: 0.4001, IoU.computer: 0.7605, IoU.swivel chair: 0.4666, IoU.boat: 0.3766, IoU.bar: 0.6342, IoU.arcade machine: 0.8766, IoU.hovel: 0.3635, IoU.bus: 0.9064, IoU.towel: 0.6594, IoU.light: 0.3948, IoU.truck: 0.3105, IoU.tower: 0.2276, IoU.chandelier: 0.6546, IoU.awning: 0.4323, IoU.streetlight: 0.2311, IoU.booth: 0.5527, IoU.television receiver: 0.7553, IoU.airplane: 0.6969, IoU.dirt track: 0.1159, IoU.apparel: 0.5337, IoU.pole: 0.1425, IoU.land: 0.0397, IoU.bannister: 0.1350, IoU.escalator: 0.6206, IoU.ottoman: 0.5397, IoU.bottle: 0.1548, IoU.buffet: 0.4962, IoU.poster: 0.3131, IoU.stage: 0.1921, IoU.van: 0.3662, IoU.ship: 0.0875, IoU.fountain: 0.4352, IoU.conveyer belt: 0.8374, IoU.canopy: 0.3378, IoU.washer: 0.8238, IoU.plaything: 0.2739, IoU.swimming pool: 0.5224, IoU.stool: 0.4778, IoU.barrel: 0.5237, IoU.basket: 0.3912, IoU.waterfall: 0.5468, IoU.tent: 0.9589, IoU.bag: 0.2012, IoU.minibike: 0.6931, IoU.cradle: 0.8541, IoU.oven: 0.6390, IoU.ball: 0.5463, IoU.food: 0.3222, IoU.step: 0.0971, IoU.tank: 0.5280, IoU.trade name: 0.2729, IoU.microwave: 0.8600, IoU.pot: 0.4217, IoU.animal: 0.7729, IoU.bicycle: 0.5623, IoU.lake: 0.6257, IoU.dishwasher: 0.6404, IoU.screen: 0.6339, IoU.blanket: 0.2504, IoU.sculpture: 0.7199, IoU.hood: 0.7054, IoU.sconce: 0.5112, IoU.vase: 0.4127, IoU.traffic light: 0.3060, IoU.tray: 0.1313, IoU.ashcan: 0.4732, IoU.fan: 0.5750, IoU.pier: 0.3494, IoU.crt screen: 0.0808, IoU.plate: 0.5650, IoU.monitor: 0.6188, IoU.bulletin board: 0.4837, IoU.shower: 0.0000, IoU.radiator: 0.6687, IoU.glass: 0.1781, IoU.clock: 0.2799, IoU.flag: 0.6693, Acc.wall: 0.8762, Acc.building: 0.9297, Acc.sky: 0.9717, Acc.floor: 0.8974, Acc.tree: 0.8690, Acc.ceiling: 0.9396, Acc.road: 0.8832, Acc.bed : 0.9685, Acc.windowpane: 0.8414, Acc.grass: 0.8021, Acc.cabinet: 0.7218, Acc.sidewalk: 0.8321, Acc.person: 0.9352, Acc.earth: 0.5029, Acc.door: 0.6751, Acc.table: 0.7997, Acc.mountain: 0.7513, Acc.plant: 0.6925, Acc.curtain: 0.8460, Acc.chair: 0.6839, Acc.car: 0.9309, Acc.water: 0.6933, Acc.painting: 0.8721, Acc.sofa: 0.8850, Acc.shelf: 0.6163, Acc.house: 0.5818, Acc.sea: 0.7909, Acc.mirror: 0.7893, Acc.rug: 0.8175, Acc.field: 0.6332, Acc.armchair: 0.7219, Acc.seat: 0.8825, Acc.fence: 0.6276, Acc.desk: 0.6776, Acc.rock: 0.6053, Acc.wardrobe: 0.6927, Acc.lamp: 0.7436, Acc.bathtub: 0.8754, Acc.railing: 0.5093, Acc.cushion: 0.7564, Acc.base: 0.6091, Acc.box: 0.4185, Acc.column: 0.6042, Acc.signboard: 0.5147, Acc.chest of drawers: 0.6329, Acc.counter: 0.5275, Acc.sand: 0.7874, Acc.sink: 0.7994, Acc.skyscraper: 0.5978, Acc.fireplace: 0.9222, Acc.refrigerator: 0.8867, Acc.grandstand: 0.7110, Acc.path: 0.5351, Acc.stairs: 0.2941, Acc.runway: 0.9476, Acc.case: 0.7518, Acc.pool table: 0.9678, Acc.pillow: 0.7246, Acc.screen door: 0.8315, Acc.stairway: 0.4899, Acc.river: 0.5979, Acc.bridge: 0.8672, Acc.bookcase: 0.5367, Acc.blind: 0.4342, Acc.coffee table: 0.8765, Acc.toilet: 0.8947, Acc.flower: 0.6059, Acc.book: 0.7599, Acc.hill: 0.1289, Acc.bench: 0.5720, Acc.countertop: 0.7811, Acc.stove: 0.8603, Acc.palm: 0.7945, Acc.kitchen island: 0.6144, Acc.computer: 0.8857, Acc.swivel chair: 0.7510, Acc.boat: 0.8589, Acc.bar: 0.8433, Acc.arcade machine: 0.9283, Acc.hovel: 0.5809, Acc.bus: 0.9483, Acc.towel: 0.8572, Acc.light: 0.4295, Acc.truck: 0.4036, Acc.tower: 0.4304, Acc.chandelier: 0.8579, Acc.awning: 0.5387, Acc.streetlight: 0.3318, Acc.booth: 0.5862, Acc.television receiver: 0.8805, Acc.airplane: 0.9384, Acc.dirt track: 0.1170, Acc.apparel: 0.7363, Acc.pole: 0.1633, Acc.land: 0.0724, Acc.bannister: 0.1895, Acc.escalator: 0.7939, Acc.ottoman: 0.6930, Acc.bottle: 0.1684, Acc.buffet: 0.8505, Acc.poster: 0.3568, Acc.stage: 0.4218, Acc.van: 0.4777, Acc.ship: 0.0950, Acc.fountain: 0.4432, Acc.conveyer belt: 0.9399, Acc.canopy: 0.3535, Acc.washer: 0.8713, Acc.plaything: 0.3807, Acc.swimming pool: 0.8949, Acc.stool: 0.6474, Acc.barrel: 0.6488, Acc.basket: 0.5743, Acc.waterfall: 0.6836, Acc.tent: 0.9758, Acc.bag: 0.2230, Acc.minibike: 0.8286, Acc.cradle: 0.9659, Acc.oven: 0.7327, Acc.ball: 0.6020, Acc.food: 0.3456, Acc.step: 0.1243, Acc.tank: 0.6200, Acc.trade name: 0.3225, Acc.microwave: 0.9202, Acc.pot: 0.4614, Acc.animal: 0.8313, Acc.bicycle: 0.7894, Acc.lake: 0.6260, Acc.dishwasher: 0.7498, Acc.screen: 0.8708, Acc.blanket: 0.2827, Acc.sculpture: 0.8695, Acc.hood: 0.8168, Acc.sconce: 0.6757, Acc.vase: 0.5989, Acc.traffic light: 0.5329, Acc.tray: 0.1684, Acc.ashcan: 0.6423, Acc.fan: 0.6678, Acc.pier: 0.4300, Acc.crt screen: 0.0933, Acc.plate: 0.7406, Acc.monitor: 0.7664, Acc.bulletin board: 0.6495, Acc.shower: 0.0000, Acc.radiator: 0.7653, Acc.glass: 0.1942, Acc.clock: 0.3064, Acc.flag: 0.7577 2023-11-02 21:24:58,501 - mmseg - INFO - Iter [7050/40000] lr: 2.669e-06, eta: 12:17:27, time: 2.377, data_time: 1.169, memory: 38534, decode.loss_ce: 0.3438, decode.acc_seg: 87.1777, loss: 0.3438 2023-11-02 21:25:59,226 - mmseg - INFO - Iter [7100/40000] lr: 2.665e-06, eta: 12:15:50, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3551, decode.acc_seg: 86.0094, loss: 0.3551 2023-11-02 21:26:59,935 - mmseg - INFO - Iter [7150/40000] lr: 2.661e-06, eta: 12:14:14, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3428, decode.acc_seg: 86.6454, loss: 0.3428 2023-11-02 21:28:00,647 - mmseg - INFO - Iter [7200/40000] lr: 2.657e-06, eta: 12:12:38, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3234, decode.acc_seg: 87.1842, loss: 0.3234 2023-11-02 21:29:01,357 - mmseg - INFO - Iter [7250/40000] lr: 2.653e-06, eta: 12:11:02, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3423, decode.acc_seg: 87.4179, loss: 0.3423 2023-11-02 21:30:02,112 - mmseg - INFO - Iter [7300/40000] lr: 2.649e-06, eta: 12:09:28, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3187, decode.acc_seg: 87.7580, loss: 0.3187 2023-11-02 21:31:02,827 - mmseg - INFO - Iter [7350/40000] lr: 2.645e-06, eta: 12:07:53, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3331, decode.acc_seg: 87.0367, loss: 0.3331 2023-11-02 21:32:07,368 - mmseg - INFO - Iter [7400/40000] lr: 2.641e-06, eta: 12:06:36, time: 1.291, data_time: 0.081, memory: 38534, decode.loss_ce: 0.3326, decode.acc_seg: 87.1046, loss: 0.3326 2023-11-02 21:33:08,055 - mmseg - INFO - Iter [7450/40000] lr: 2.636e-06, eta: 12:05:02, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3694, decode.acc_seg: 85.9694, loss: 0.3694 2023-11-02 21:34:08,718 - mmseg - INFO - Iter [7500/40000] lr: 2.632e-06, eta: 12:03:29, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3654, decode.acc_seg: 86.0952, loss: 0.3654 2023-11-02 21:35:09,428 - mmseg - INFO - Iter [7550/40000] lr: 2.628e-06, eta: 12:01:56, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3382, decode.acc_seg: 86.5723, loss: 0.3382 2023-11-02 21:36:12,393 - mmseg - INFO - Iter [7600/40000] lr: 2.624e-06, eta: 12:00:33, time: 1.259, data_time: 0.052, memory: 38534, decode.loss_ce: 0.3611, decode.acc_seg: 86.2347, loss: 0.3611 2023-11-02 21:37:13,135 - mmseg - INFO - Iter [7650/40000] lr: 2.620e-06, eta: 11:59:01, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3185, decode.acc_seg: 87.7908, loss: 0.3185 2023-11-02 21:38:13,896 - mmseg - INFO - Iter [7700/40000] lr: 2.616e-06, eta: 11:57:29, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3327, decode.acc_seg: 87.2960, loss: 0.3327 2023-11-02 21:39:14,630 - mmseg - INFO - Iter [7750/40000] lr: 2.612e-06, eta: 11:55:58, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2941, decode.acc_seg: 87.8344, loss: 0.2941 2023-11-02 21:40:15,376 - mmseg - INFO - Iter [7800/40000] lr: 2.608e-06, eta: 11:54:27, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3017, decode.acc_seg: 87.8214, loss: 0.3017 2023-11-02 21:41:16,110 - mmseg - INFO - Iter [7850/40000] lr: 2.604e-06, eta: 11:52:57, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3218, decode.acc_seg: 87.5212, loss: 0.3218 2023-11-02 21:42:16,837 - mmseg - INFO - Iter [7900/40000] lr: 2.600e-06, eta: 11:51:27, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3219, decode.acc_seg: 87.3705, loss: 0.3219 2023-11-02 21:43:17,557 - mmseg - INFO - Iter [7950/40000] lr: 2.596e-06, eta: 11:49:57, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3246, decode.acc_seg: 87.4781, loss: 0.3246 2023-11-02 21:44:18,244 - mmseg - INFO - Saving checkpoint at 8000 iterations 2023-11-02 21:45:16,832 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 21:45:16,832 - mmseg - INFO - Iter [8000/40000] lr: 2.592e-06, eta: 11:52:22, time: 2.385, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3286, decode.acc_seg: 87.2264, loss: 0.3286 2023-11-02 21:46:16,270 - mmseg - INFO - per class results: 2023-11-02 21:46:16,276 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 78.92 | 89.16 | | building | 83.75 | 91.33 | | sky | 94.03 | 97.13 | | floor | 83.25 | 90.16 | | tree | 75.29 | 89.61 | | ceiling | 83.75 | 92.24 | | road | 84.02 | 92.88 | | bed | 91.44 | 96.39 | | windowpane | 62.2 | 79.03 | | grass | 67.47 | 82.48 | | cabinet | 63.19 | 76.6 | | sidewalk | 65.54 | 75.02 | | person | 82.11 | 91.47 | | earth | 32.95 | 40.69 | | door | 54.44 | 71.06 | | table | 67.48 | 78.2 | | mountain | 60.18 | 74.72 | | plant | 55.73 | 72.24 | | curtain | 74.25 | 88.34 | | chair | 58.57 | 73.87 | | car | 83.67 | 93.74 | | water | 64.36 | 78.6 | | painting | 75.74 | 86.48 | | sofa | 74.71 | 91.55 | | shelf | 43.84 | 56.12 | | house | 50.33 | 73.57 | | sea | 67.21 | 86.51 | | mirror | 67.88 | 74.32 | | rug | 68.89 | 80.7 | | field | 34.46 | 58.54 | | armchair | 51.2 | 64.3 | | seat | 63.43 | 85.92 | | fence | 44.73 | 58.16 | | desk | 49.07 | 74.78 | | rock | 48.64 | 61.36 | | wardrobe | 46.21 | 57.99 | | lamp | 63.57 | 77.43 | | bathtub | 86.13 | 89.81 | | railing | 41.22 | 67.31 | | cushion | 60.88 | 74.18 | | base | 30.9 | 51.5 | | box | 34.46 | 44.59 | | column | 50.23 | 65.4 | | signboard | 36.66 | 48.49 | | chest of drawers | 39.9 | 73.06 | | counter | 45.37 | 54.56 | | sand | 62.72 | 90.02 | | sink | 73.49 | 78.61 | | skyscraper | 54.85 | 75.75 | | fireplace | 74.93 | 87.87 | | refrigerator | 79.87 | 87.27 | | grandstand | 46.95 | 80.57 | | path | 26.04 | 37.58 | | stairs | 32.5 | 41.09 | | runway | 68.75 | 89.54 | | case | 64.38 | 89.2 | | pool table | 90.56 | 97.44 | | pillow | 63.04 | 76.08 | | screen door | 53.83 | 80.51 | | stairway | 32.15 | 44.45 | | river | 31.66 | 38.27 | | bridge | 74.58 | 82.78 | | bookcase | 42.26 | 53.96 | | blind | 39.09 | 44.97 | | coffee table | 65.45 | 86.99 | | toilet | 87.73 | 91.75 | | flower | 43.92 | 62.55 | | book | 50.94 | 71.22 | | hill | 10.25 | 17.14 | | bench | 53.9 | 61.79 | | countertop | 63.75 | 75.42 | | stove | 78.78 | 88.65 | | palm | 48.85 | 83.79 | | kitchen island | 39.73 | 59.36 | | computer | 64.21 | 70.43 | | swivel chair | 43.86 | 63.69 | | boat | 46.87 | 60.38 | | bar | 64.74 | 69.43 | | arcade machine | 84.53 | 93.18 | | hovel | 4.22 | 4.26 | | bus | 89.02 | 96.77 | | towel | 71.09 | 80.16 | | light | 45.82 | 52.88 | | truck | 43.64 | 55.43 | | tower | 23.27 | 47.3 | | chandelier | 65.72 | 80.69 | | awning | 26.38 | 30.13 | | streetlight | 22.9 | 31.53 | | booth | 40.13 | 43.19 | | television receiver | 77.23 | 88.61 | | airplane | 62.91 | 70.49 | | dirt track | 3.57 | 8.08 | | apparel | 53.92 | 79.81 | | pole | 18.13 | 22.13 | | land | 0.01 | 0.02 | | bannister | 15.11 | 21.58 | | escalator | 61.36 | 76.4 | | ottoman | 53.32 | 74.71 | | bottle | 26.79 | 33.07 | | buffet | 45.52 | 60.83 | | poster | 28.97 | 40.78 | | stage | 25.48 | 36.48 | | van | 37.64 | 48.15 | | ship | 8.45 | 11.88 | | fountain | 26.33 | 26.5 | | conveyer belt | 72.64 | 95.24 | | canopy | 18.72 | 20.22 | | washer | 88.04 | 95.02 | | plaything | 29.32 | 39.19 | | swimming pool | 51.85 | 87.0 | | stool | 50.21 | 63.73 | | barrel | 50.29 | 65.47 | | basket | 33.01 | 40.58 | | waterfall | 62.92 | 75.8 | | tent | 93.2 | 97.46 | | bag | 28.17 | 33.79 | | minibike | 70.09 | 86.15 | | cradle | 82.28 | 98.62 | | oven | 62.3 | 77.86 | | ball | 57.81 | 69.79 | | food | 61.65 | 69.86 | | step | 14.1 | 16.39 | | tank | 52.2 | 65.39 | | trade name | 11.2 | 11.78 | | microwave | 86.91 | 93.42 | | pot | 47.23 | 52.36 | | animal | 75.25 | 79.24 | | bicycle | 58.64 | 82.66 | | lake | 62.86 | 63.31 | | dishwasher | 64.93 | 71.03 | | screen | 63.43 | 89.61 | | blanket | 28.12 | 33.45 | | sculpture | 74.5 | 86.85 | | hood | 58.41 | 61.4 | | sconce | 45.23 | 53.74 | | vase | 41.91 | 58.72 | | traffic light | 30.55 | 59.18 | | tray | 13.87 | 17.65 | | ashcan | 49.36 | 61.74 | | fan | 55.92 | 63.96 | | pier | 39.24 | 42.94 | | crt screen | 9.56 | 23.83 | | plate | 57.46 | 76.69 | | monitor | 57.56 | 68.27 | | bulletin board | 44.76 | 54.36 | | shower | 0.0 | 0.0 | | radiator | 68.08 | 76.01 | | glass | 17.75 | 19.26 | | clock | 37.36 | 43.17 | | flag | 64.4 | 79.43 | +---------------------+-------+-------+ 2023-11-02 21:46:16,276 - mmseg - INFO - Summary: 2023-11-02 21:46:16,276 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.29 | 52.81 | 64.98 | +-------+-------+-------+ 2023-11-02 21:46:16,277 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 21:46:16,277 - mmseg - INFO - Iter(val) [250] aAcc: 0.8429, mIoU: 0.5281, mAcc: 0.6498, IoU.wall: 0.7892, IoU.building: 0.8375, IoU.sky: 0.9403, IoU.floor: 0.8325, IoU.tree: 0.7529, IoU.ceiling: 0.8375, IoU.road: 0.8402, IoU.bed : 0.9144, IoU.windowpane: 0.6220, IoU.grass: 0.6747, IoU.cabinet: 0.6319, IoU.sidewalk: 0.6554, IoU.person: 0.8211, IoU.earth: 0.3295, IoU.door: 0.5444, IoU.table: 0.6748, IoU.mountain: 0.6018, IoU.plant: 0.5573, IoU.curtain: 0.7425, IoU.chair: 0.5857, IoU.car: 0.8367, IoU.water: 0.6436, IoU.painting: 0.7574, IoU.sofa: 0.7471, IoU.shelf: 0.4384, IoU.house: 0.5033, IoU.sea: 0.6721, IoU.mirror: 0.6788, IoU.rug: 0.6889, IoU.field: 0.3446, IoU.armchair: 0.5120, IoU.seat: 0.6343, IoU.fence: 0.4473, IoU.desk: 0.4907, IoU.rock: 0.4864, IoU.wardrobe: 0.4621, IoU.lamp: 0.6357, IoU.bathtub: 0.8613, IoU.railing: 0.4122, IoU.cushion: 0.6088, IoU.base: 0.3090, IoU.box: 0.3446, IoU.column: 0.5023, IoU.signboard: 0.3666, IoU.chest of drawers: 0.3990, IoU.counter: 0.4537, IoU.sand: 0.6272, IoU.sink: 0.7349, IoU.skyscraper: 0.5485, IoU.fireplace: 0.7493, IoU.refrigerator: 0.7987, IoU.grandstand: 0.4695, IoU.path: 0.2604, IoU.stairs: 0.3250, IoU.runway: 0.6875, IoU.case: 0.6438, IoU.pool table: 0.9056, IoU.pillow: 0.6304, IoU.screen door: 0.5383, IoU.stairway: 0.3215, IoU.river: 0.3166, IoU.bridge: 0.7458, IoU.bookcase: 0.4226, IoU.blind: 0.3909, IoU.coffee table: 0.6545, IoU.toilet: 0.8773, IoU.flower: 0.4392, IoU.book: 0.5094, IoU.hill: 0.1025, IoU.bench: 0.5390, IoU.countertop: 0.6375, IoU.stove: 0.7878, IoU.palm: 0.4885, IoU.kitchen island: 0.3973, IoU.computer: 0.6421, IoU.swivel chair: 0.4386, IoU.boat: 0.4687, IoU.bar: 0.6474, IoU.arcade machine: 0.8453, IoU.hovel: 0.0422, IoU.bus: 0.8902, IoU.towel: 0.7109, IoU.light: 0.4582, IoU.truck: 0.4364, IoU.tower: 0.2327, IoU.chandelier: 0.6572, IoU.awning: 0.2638, IoU.streetlight: 0.2290, IoU.booth: 0.4013, IoU.television receiver: 0.7723, IoU.airplane: 0.6291, IoU.dirt track: 0.0357, IoU.apparel: 0.5392, IoU.pole: 0.1813, IoU.land: 0.0001, IoU.bannister: 0.1511, IoU.escalator: 0.6136, IoU.ottoman: 0.5332, IoU.bottle: 0.2679, IoU.buffet: 0.4552, IoU.poster: 0.2897, IoU.stage: 0.2548, IoU.van: 0.3764, IoU.ship: 0.0845, IoU.fountain: 0.2633, IoU.conveyer belt: 0.7264, IoU.canopy: 0.1872, IoU.washer: 0.8804, IoU.plaything: 0.2932, IoU.swimming pool: 0.5185, IoU.stool: 0.5021, IoU.barrel: 0.5029, IoU.basket: 0.3301, IoU.waterfall: 0.6292, IoU.tent: 0.9320, IoU.bag: 0.2817, IoU.minibike: 0.7009, IoU.cradle: 0.8228, IoU.oven: 0.6230, IoU.ball: 0.5781, IoU.food: 0.6165, IoU.step: 0.1410, IoU.tank: 0.5220, IoU.trade name: 0.1120, IoU.microwave: 0.8691, IoU.pot: 0.4723, IoU.animal: 0.7525, IoU.bicycle: 0.5864, IoU.lake: 0.6286, IoU.dishwasher: 0.6493, IoU.screen: 0.6343, IoU.blanket: 0.2812, IoU.sculpture: 0.7450, IoU.hood: 0.5841, IoU.sconce: 0.4523, IoU.vase: 0.4191, IoU.traffic light: 0.3055, IoU.tray: 0.1387, IoU.ashcan: 0.4936, IoU.fan: 0.5592, IoU.pier: 0.3924, IoU.crt screen: 0.0956, IoU.plate: 0.5746, IoU.monitor: 0.5756, IoU.bulletin board: 0.4476, IoU.shower: 0.0000, IoU.radiator: 0.6808, IoU.glass: 0.1775, IoU.clock: 0.3736, IoU.flag: 0.6440, Acc.wall: 0.8916, Acc.building: 0.9133, Acc.sky: 0.9713, Acc.floor: 0.9016, Acc.tree: 0.8961, Acc.ceiling: 0.9224, Acc.road: 0.9288, Acc.bed : 0.9639, Acc.windowpane: 0.7903, Acc.grass: 0.8248, Acc.cabinet: 0.7660, Acc.sidewalk: 0.7502, Acc.person: 0.9147, Acc.earth: 0.4069, Acc.door: 0.7106, Acc.table: 0.7820, Acc.mountain: 0.7472, Acc.plant: 0.7224, Acc.curtain: 0.8834, Acc.chair: 0.7387, Acc.car: 0.9374, Acc.water: 0.7860, Acc.painting: 0.8648, Acc.sofa: 0.9155, Acc.shelf: 0.5612, Acc.house: 0.7357, Acc.sea: 0.8651, Acc.mirror: 0.7432, Acc.rug: 0.8070, Acc.field: 0.5854, Acc.armchair: 0.6430, Acc.seat: 0.8592, Acc.fence: 0.5816, Acc.desk: 0.7478, Acc.rock: 0.6136, Acc.wardrobe: 0.5799, Acc.lamp: 0.7743, Acc.bathtub: 0.8981, Acc.railing: 0.6731, Acc.cushion: 0.7418, Acc.base: 0.5150, Acc.box: 0.4459, Acc.column: 0.6540, Acc.signboard: 0.4849, Acc.chest of drawers: 0.7306, Acc.counter: 0.5456, Acc.sand: 0.9002, Acc.sink: 0.7861, Acc.skyscraper: 0.7575, Acc.fireplace: 0.8787, Acc.refrigerator: 0.8727, Acc.grandstand: 0.8057, Acc.path: 0.3758, Acc.stairs: 0.4109, Acc.runway: 0.8954, Acc.case: 0.8920, Acc.pool table: 0.9744, Acc.pillow: 0.7608, Acc.screen door: 0.8051, Acc.stairway: 0.4445, Acc.river: 0.3827, Acc.bridge: 0.8278, Acc.bookcase: 0.5396, Acc.blind: 0.4497, Acc.coffee table: 0.8699, Acc.toilet: 0.9175, Acc.flower: 0.6255, Acc.book: 0.7122, Acc.hill: 0.1714, Acc.bench: 0.6179, Acc.countertop: 0.7542, Acc.stove: 0.8865, Acc.palm: 0.8379, Acc.kitchen island: 0.5936, Acc.computer: 0.7043, Acc.swivel chair: 0.6369, Acc.boat: 0.6038, Acc.bar: 0.6943, Acc.arcade machine: 0.9318, Acc.hovel: 0.0426, Acc.bus: 0.9677, Acc.towel: 0.8016, Acc.light: 0.5288, Acc.truck: 0.5543, Acc.tower: 0.4730, Acc.chandelier: 0.8069, Acc.awning: 0.3013, Acc.streetlight: 0.3153, Acc.booth: 0.4319, Acc.television receiver: 0.8861, Acc.airplane: 0.7049, Acc.dirt track: 0.0808, Acc.apparel: 0.7981, Acc.pole: 0.2213, Acc.land: 0.0002, Acc.bannister: 0.2158, Acc.escalator: 0.7640, Acc.ottoman: 0.7471, Acc.bottle: 0.3307, Acc.buffet: 0.6083, Acc.poster: 0.4078, Acc.stage: 0.3648, Acc.van: 0.4815, Acc.ship: 0.1188, Acc.fountain: 0.2650, Acc.conveyer belt: 0.9524, Acc.canopy: 0.2022, Acc.washer: 0.9502, Acc.plaything: 0.3919, Acc.swimming pool: 0.8700, Acc.stool: 0.6373, Acc.barrel: 0.6547, Acc.basket: 0.4058, Acc.waterfall: 0.7580, Acc.tent: 0.9746, Acc.bag: 0.3379, Acc.minibike: 0.8615, Acc.cradle: 0.9862, Acc.oven: 0.7786, Acc.ball: 0.6979, Acc.food: 0.6986, Acc.step: 0.1639, Acc.tank: 0.6539, Acc.trade name: 0.1178, Acc.microwave: 0.9342, Acc.pot: 0.5236, Acc.animal: 0.7924, Acc.bicycle: 0.8266, Acc.lake: 0.6331, Acc.dishwasher: 0.7103, Acc.screen: 0.8961, Acc.blanket: 0.3345, Acc.sculpture: 0.8685, Acc.hood: 0.6140, Acc.sconce: 0.5374, Acc.vase: 0.5872, Acc.traffic light: 0.5918, Acc.tray: 0.1765, Acc.ashcan: 0.6174, Acc.fan: 0.6396, Acc.pier: 0.4294, Acc.crt screen: 0.2383, Acc.plate: 0.7669, Acc.monitor: 0.6827, Acc.bulletin board: 0.5436, Acc.shower: 0.0000, Acc.radiator: 0.7601, Acc.glass: 0.1926, Acc.clock: 0.4317, Acc.flag: 0.7943 2023-11-02 21:47:16,975 - mmseg - INFO - Iter [8050/40000] lr: 2.588e-06, eta: 11:54:47, time: 2.403, data_time: 1.196, memory: 38534, decode.loss_ce: 0.3342, decode.acc_seg: 86.8743, loss: 0.3342 2023-11-02 21:48:17,680 - mmseg - INFO - Iter [8100/40000] lr: 2.584e-06, eta: 11:53:14, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3279, decode.acc_seg: 87.2546, loss: 0.3279 2023-11-02 21:49:18,367 - mmseg - INFO - Iter [8150/40000] lr: 2.580e-06, eta: 11:51:42, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3412, decode.acc_seg: 86.6532, loss: 0.3412 2023-11-02 21:50:19,044 - mmseg - INFO - Iter [8200/40000] lr: 2.576e-06, eta: 11:50:11, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3349, decode.acc_seg: 87.1996, loss: 0.3349 2023-11-02 21:51:22,076 - mmseg - INFO - Iter [8250/40000] lr: 2.572e-06, eta: 11:48:48, time: 1.261, data_time: 0.051, memory: 38534, decode.loss_ce: 0.3291, decode.acc_seg: 87.3673, loss: 0.3291 2023-11-02 21:52:22,753 - mmseg - INFO - Iter [8300/40000] lr: 2.568e-06, eta: 11:47:17, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3050, decode.acc_seg: 87.9858, loss: 0.3050 2023-11-02 21:53:23,410 - mmseg - INFO - Iter [8350/40000] lr: 2.564e-06, eta: 11:45:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2889, decode.acc_seg: 88.6483, loss: 0.2889 2023-11-02 21:54:24,072 - mmseg - INFO - Iter [8400/40000] lr: 2.560e-06, eta: 11:44:16, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2837, decode.acc_seg: 88.7569, loss: 0.2837 2023-11-02 21:55:24,757 - mmseg - INFO - Iter [8450/40000] lr: 2.555e-06, eta: 11:42:46, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3000, decode.acc_seg: 88.2093, loss: 0.3000 2023-11-02 21:56:25,502 - mmseg - INFO - Iter [8500/40000] lr: 2.551e-06, eta: 11:41:17, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3086, decode.acc_seg: 87.9561, loss: 0.3086 2023-11-02 21:57:26,214 - mmseg - INFO - Iter [8550/40000] lr: 2.547e-06, eta: 11:39:48, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3210, decode.acc_seg: 87.6305, loss: 0.3210 2023-11-02 21:58:26,960 - mmseg - INFO - Iter [8600/40000] lr: 2.543e-06, eta: 11:38:19, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3199, decode.acc_seg: 87.6876, loss: 0.3199 2023-11-02 21:59:27,726 - mmseg - INFO - Iter [8650/40000] lr: 2.539e-06, eta: 11:36:51, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3031, decode.acc_seg: 88.0898, loss: 0.3031 2023-11-02 22:00:28,483 - mmseg - INFO - Iter [8700/40000] lr: 2.535e-06, eta: 11:35:23, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3174, decode.acc_seg: 87.2607, loss: 0.3174 2023-11-02 22:01:29,264 - mmseg - INFO - Iter [8750/40000] lr: 2.531e-06, eta: 11:33:55, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.3210, decode.acc_seg: 87.7245, loss: 0.3210 2023-11-02 22:02:29,989 - mmseg - INFO - Iter [8800/40000] lr: 2.527e-06, eta: 11:32:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3204, decode.acc_seg: 87.5604, loss: 0.3204 2023-11-02 22:03:33,482 - mmseg - INFO - Iter [8850/40000] lr: 2.523e-06, eta: 11:31:10, time: 1.270, data_time: 0.057, memory: 38534, decode.loss_ce: 0.3268, decode.acc_seg: 87.6272, loss: 0.3268 2023-11-02 22:04:34,126 - mmseg - INFO - Iter [8900/40000] lr: 2.519e-06, eta: 11:29:43, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2984, decode.acc_seg: 88.5029, loss: 0.2984 2023-11-02 22:05:34,782 - mmseg - INFO - Iter [8950/40000] lr: 2.515e-06, eta: 11:28:16, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2885, decode.acc_seg: 88.5212, loss: 0.2885 2023-11-02 22:06:35,453 - mmseg - INFO - Saving checkpoint at 9000 iterations 2023-11-02 22:07:36,729 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 22:07:36,729 - mmseg - INFO - Iter [9000/40000] lr: 2.511e-06, eta: 11:30:21, time: 2.439, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2870, decode.acc_seg: 88.7049, loss: 0.2870 2023-11-02 22:08:35,974 - mmseg - INFO - per class results: 2023-11-02 22:08:35,980 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.68 | 89.91 | | building | 83.43 | 93.03 | | sky | 94.08 | 97.36 | | floor | 82.97 | 91.93 | | tree | 74.88 | 85.68 | | ceiling | 84.77 | 92.54 | | road | 84.99 | 89.62 | | bed | 90.96 | 96.26 | | windowpane | 64.06 | 75.46 | | grass | 68.73 | 83.41 | | cabinet | 63.38 | 74.33 | | sidewalk | 67.82 | 84.74 | | person | 81.78 | 91.36 | | earth | 34.04 | 45.21 | | door | 56.22 | 72.63 | | table | 66.77 | 77.58 | | mountain | 56.9 | 80.6 | | plant | 55.0 | 66.77 | | curtain | 74.97 | 86.98 | | chair | 59.32 | 71.05 | | car | 84.3 | 91.39 | | water | 65.18 | 83.11 | | painting | 75.5 | 85.39 | | sofa | 72.86 | 90.84 | | shelf | 42.67 | 54.12 | | house | 54.18 | 74.51 | | sea | 71.64 | 80.23 | | mirror | 70.13 | 77.04 | | rug | 70.38 | 81.65 | | field | 32.04 | 57.45 | | armchair | 50.0 | 61.99 | | seat | 65.22 | 87.5 | | fence | 48.46 | 63.35 | | desk | 52.09 | 70.5 | | rock | 33.06 | 39.21 | | wardrobe | 53.63 | 75.56 | | lamp | 65.77 | 82.44 | | bathtub | 87.46 | 91.7 | | railing | 41.84 | 61.78 | | cushion | 58.82 | 70.86 | | base | 35.13 | 61.83 | | box | 35.83 | 49.14 | | column | 50.05 | 58.73 | | signboard | 38.23 | 52.65 | | chest of drawers | 36.39 | 45.67 | | counter | 52.17 | 60.26 | | sand | 61.43 | 89.41 | | sink | 73.48 | 81.29 | | skyscraper | 47.02 | 59.42 | | fireplace | 75.68 | 90.87 | | refrigerator | 78.94 | 88.28 | | grandstand | 48.5 | 77.29 | | path | 26.89 | 36.28 | | stairs | 37.19 | 49.2 | | runway | 69.48 | 90.41 | | case | 60.58 | 76.06 | | pool table | 91.08 | 97.37 | | pillow | 62.46 | 74.84 | | screen door | 57.09 | 58.76 | | stairway | 35.06 | 44.15 | | river | 22.76 | 33.93 | | bridge | 70.37 | 77.1 | | bookcase | 41.53 | 54.93 | | blind | 45.11 | 52.69 | | coffee table | 66.03 | 85.18 | | toilet | 88.67 | 92.95 | | flower | 39.52 | 67.12 | | book | 49.3 | 72.06 | | hill | 5.09 | 6.42 | | bench | 59.42 | 70.7 | | countertop | 63.39 | 71.69 | | stove | 75.66 | 80.33 | | palm | 46.91 | 85.64 | | kitchen island | 44.96 | 74.37 | | computer | 63.47 | 70.47 | | swivel chair | 45.99 | 62.08 | | boat | 52.83 | 64.67 | | bar | 65.16 | 74.64 | | arcade machine | 79.49 | 84.78 | | hovel | 38.85 | 47.37 | | bus | 90.99 | 96.03 | | towel | 69.04 | 77.29 | | light | 49.05 | 61.29 | | truck | 47.97 | 64.71 | | tower | 27.05 | 42.7 | | chandelier | 66.53 | 80.72 | | awning | 32.79 | 37.24 | | streetlight | 25.74 | 32.1 | | booth | 74.47 | 80.92 | | television receiver | 72.81 | 91.37 | | airplane | 83.04 | 94.45 | | dirt track | 3.88 | 19.03 | | apparel | 43.75 | 66.44 | | pole | 23.96 | 32.69 | | land | 2.81 | 3.21 | | bannister | 9.83 | 13.63 | | escalator | 56.38 | 68.3 | | ottoman | 52.34 | 73.01 | | bottle | 25.21 | 33.57 | | buffet | 53.41 | 66.44 | | poster | 30.38 | 36.37 | | stage | 20.54 | 30.07 | | van | 51.87 | 67.97 | | ship | 8.59 | 11.06 | | fountain | 34.52 | 35.29 | | conveyer belt | 86.74 | 94.87 | | canopy | 44.81 | 51.97 | | washer | 82.84 | 88.23 | | plaything | 30.53 | 37.2 | | swimming pool | 53.8 | 78.72 | | stool | 46.24 | 58.74 | | barrel | 49.65 | 65.66 | | basket | 38.73 | 50.75 | | waterfall | 53.93 | 64.62 | | tent | 86.32 | 98.03 | | bag | 22.83 | 25.33 | | minibike | 70.9 | 85.46 | | cradle | 79.86 | 97.58 | | oven | 57.1 | 76.12 | | ball | 27.45 | 29.08 | | food | 63.55 | 73.22 | | step | 16.36 | 20.37 | | tank | 50.91 | 66.6 | | trade name | 22.87 | 25.91 | | microwave | 87.45 | 92.24 | | pot | 51.12 | 57.86 | | animal | 75.72 | 79.46 | | bicycle | 59.81 | 75.6 | | lake | 61.98 | 62.32 | | dishwasher | 67.54 | 78.81 | | screen | 58.69 | 70.08 | | blanket | 26.15 | 31.19 | | sculpture | 74.52 | 84.1 | | hood | 57.61 | 65.8 | | sconce | 48.28 | 60.23 | | vase | 41.34 | 57.16 | | traffic light | 29.6 | 59.12 | | tray | 15.69 | 25.87 | | ashcan | 50.65 | 65.47 | | fan | 61.53 | 76.13 | | pier | 38.34 | 45.32 | | crt screen | 8.48 | 23.69 | | plate | 56.26 | 70.17 | | monitor | 57.24 | 69.37 | | bulletin board | 47.1 | 54.61 | | shower | 0.0 | 0.0 | | radiator | 64.16 | 77.28 | | glass | 19.01 | 21.36 | | clock | 34.9 | 40.12 | | flag | 66.9 | 73.02 | +---------------------+-------+-------+ 2023-11-02 22:08:35,980 - mmseg - INFO - Summary: 2023-11-02 22:08:35,980 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.58 | 53.73 | 65.46 | +-------+-------+-------+ 2023-11-02 22:08:35,981 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 22:08:35,981 - mmseg - INFO - Iter(val) [250] aAcc: 0.8458, mIoU: 0.5373, mAcc: 0.6546, IoU.wall: 0.7968, IoU.building: 0.8343, IoU.sky: 0.9408, IoU.floor: 0.8297, IoU.tree: 0.7488, IoU.ceiling: 0.8477, IoU.road: 0.8499, IoU.bed : 0.9096, IoU.windowpane: 0.6406, IoU.grass: 0.6873, IoU.cabinet: 0.6338, IoU.sidewalk: 0.6782, IoU.person: 0.8178, IoU.earth: 0.3404, IoU.door: 0.5622, IoU.table: 0.6677, IoU.mountain: 0.5690, IoU.plant: 0.5500, IoU.curtain: 0.7497, IoU.chair: 0.5932, IoU.car: 0.8430, IoU.water: 0.6518, IoU.painting: 0.7550, IoU.sofa: 0.7286, IoU.shelf: 0.4267, IoU.house: 0.5418, IoU.sea: 0.7164, IoU.mirror: 0.7013, IoU.rug: 0.7038, IoU.field: 0.3204, IoU.armchair: 0.5000, IoU.seat: 0.6522, IoU.fence: 0.4846, IoU.desk: 0.5209, IoU.rock: 0.3306, IoU.wardrobe: 0.5363, IoU.lamp: 0.6577, IoU.bathtub: 0.8746, IoU.railing: 0.4184, IoU.cushion: 0.5882, IoU.base: 0.3513, IoU.box: 0.3583, IoU.column: 0.5005, IoU.signboard: 0.3823, IoU.chest of drawers: 0.3639, IoU.counter: 0.5217, IoU.sand: 0.6143, IoU.sink: 0.7348, IoU.skyscraper: 0.4702, IoU.fireplace: 0.7568, IoU.refrigerator: 0.7894, IoU.grandstand: 0.4850, IoU.path: 0.2689, IoU.stairs: 0.3719, IoU.runway: 0.6948, IoU.case: 0.6058, IoU.pool table: 0.9108, IoU.pillow: 0.6246, IoU.screen door: 0.5709, IoU.stairway: 0.3506, IoU.river: 0.2276, IoU.bridge: 0.7037, IoU.bookcase: 0.4153, IoU.blind: 0.4511, IoU.coffee table: 0.6603, IoU.toilet: 0.8867, IoU.flower: 0.3952, IoU.book: 0.4930, IoU.hill: 0.0509, IoU.bench: 0.5942, IoU.countertop: 0.6339, IoU.stove: 0.7566, IoU.palm: 0.4691, IoU.kitchen island: 0.4496, IoU.computer: 0.6347, IoU.swivel chair: 0.4599, IoU.boat: 0.5283, IoU.bar: 0.6516, IoU.arcade machine: 0.7949, IoU.hovel: 0.3885, IoU.bus: 0.9099, IoU.towel: 0.6904, IoU.light: 0.4905, IoU.truck: 0.4797, IoU.tower: 0.2705, IoU.chandelier: 0.6653, IoU.awning: 0.3279, IoU.streetlight: 0.2574, IoU.booth: 0.7447, IoU.television receiver: 0.7281, IoU.airplane: 0.8304, IoU.dirt track: 0.0388, IoU.apparel: 0.4375, IoU.pole: 0.2396, IoU.land: 0.0281, IoU.bannister: 0.0983, IoU.escalator: 0.5638, IoU.ottoman: 0.5234, IoU.bottle: 0.2521, IoU.buffet: 0.5341, IoU.poster: 0.3038, IoU.stage: 0.2054, IoU.van: 0.5187, IoU.ship: 0.0859, IoU.fountain: 0.3452, IoU.conveyer belt: 0.8674, IoU.canopy: 0.4481, IoU.washer: 0.8284, IoU.plaything: 0.3053, IoU.swimming pool: 0.5380, IoU.stool: 0.4624, IoU.barrel: 0.4965, IoU.basket: 0.3873, IoU.waterfall: 0.5393, IoU.tent: 0.8632, IoU.bag: 0.2283, IoU.minibike: 0.7090, IoU.cradle: 0.7986, IoU.oven: 0.5710, IoU.ball: 0.2745, IoU.food: 0.6355, IoU.step: 0.1636, IoU.tank: 0.5091, IoU.trade name: 0.2287, IoU.microwave: 0.8745, IoU.pot: 0.5112, IoU.animal: 0.7572, IoU.bicycle: 0.5981, IoU.lake: 0.6198, IoU.dishwasher: 0.6754, IoU.screen: 0.5869, IoU.blanket: 0.2615, IoU.sculpture: 0.7452, IoU.hood: 0.5761, IoU.sconce: 0.4828, IoU.vase: 0.4134, IoU.traffic light: 0.2960, IoU.tray: 0.1569, IoU.ashcan: 0.5065, IoU.fan: 0.6153, IoU.pier: 0.3834, IoU.crt screen: 0.0848, IoU.plate: 0.5626, IoU.monitor: 0.5724, IoU.bulletin board: 0.4710, IoU.shower: 0.0000, IoU.radiator: 0.6416, IoU.glass: 0.1901, IoU.clock: 0.3490, IoU.flag: 0.6690, Acc.wall: 0.8991, Acc.building: 0.9303, Acc.sky: 0.9736, Acc.floor: 0.9193, Acc.tree: 0.8568, Acc.ceiling: 0.9254, Acc.road: 0.8962, Acc.bed : 0.9626, Acc.windowpane: 0.7546, Acc.grass: 0.8341, Acc.cabinet: 0.7433, Acc.sidewalk: 0.8474, Acc.person: 0.9136, Acc.earth: 0.4521, Acc.door: 0.7263, Acc.table: 0.7758, Acc.mountain: 0.8060, Acc.plant: 0.6677, Acc.curtain: 0.8698, Acc.chair: 0.7105, Acc.car: 0.9139, Acc.water: 0.8311, Acc.painting: 0.8539, Acc.sofa: 0.9084, Acc.shelf: 0.5412, Acc.house: 0.7451, Acc.sea: 0.8023, Acc.mirror: 0.7704, Acc.rug: 0.8165, Acc.field: 0.5745, Acc.armchair: 0.6199, Acc.seat: 0.8750, Acc.fence: 0.6335, Acc.desk: 0.7050, Acc.rock: 0.3921, Acc.wardrobe: 0.7556, Acc.lamp: 0.8244, Acc.bathtub: 0.9170, Acc.railing: 0.6178, Acc.cushion: 0.7086, Acc.base: 0.6183, Acc.box: 0.4914, Acc.column: 0.5873, Acc.signboard: 0.5265, Acc.chest of drawers: 0.4567, Acc.counter: 0.6026, Acc.sand: 0.8941, Acc.sink: 0.8129, Acc.skyscraper: 0.5942, Acc.fireplace: 0.9087, Acc.refrigerator: 0.8828, Acc.grandstand: 0.7729, Acc.path: 0.3628, Acc.stairs: 0.4920, Acc.runway: 0.9041, Acc.case: 0.7606, Acc.pool table: 0.9737, Acc.pillow: 0.7484, Acc.screen door: 0.5876, Acc.stairway: 0.4415, Acc.river: 0.3393, Acc.bridge: 0.7710, Acc.bookcase: 0.5493, Acc.blind: 0.5269, Acc.coffee table: 0.8518, Acc.toilet: 0.9295, Acc.flower: 0.6712, Acc.book: 0.7206, Acc.hill: 0.0642, Acc.bench: 0.7070, Acc.countertop: 0.7169, Acc.stove: 0.8033, Acc.palm: 0.8564, Acc.kitchen island: 0.7437, Acc.computer: 0.7047, Acc.swivel chair: 0.6208, Acc.boat: 0.6467, Acc.bar: 0.7464, Acc.arcade machine: 0.8478, Acc.hovel: 0.4737, Acc.bus: 0.9603, Acc.towel: 0.7729, Acc.light: 0.6129, Acc.truck: 0.6471, Acc.tower: 0.4270, Acc.chandelier: 0.8072, Acc.awning: 0.3724, Acc.streetlight: 0.3210, Acc.booth: 0.8092, Acc.television receiver: 0.9137, Acc.airplane: 0.9445, Acc.dirt track: 0.1903, Acc.apparel: 0.6644, Acc.pole: 0.3269, Acc.land: 0.0321, Acc.bannister: 0.1363, Acc.escalator: 0.6830, Acc.ottoman: 0.7301, Acc.bottle: 0.3357, Acc.buffet: 0.6644, Acc.poster: 0.3637, Acc.stage: 0.3007, Acc.van: 0.6797, Acc.ship: 0.1106, Acc.fountain: 0.3529, Acc.conveyer belt: 0.9487, Acc.canopy: 0.5197, Acc.washer: 0.8823, Acc.plaything: 0.3720, Acc.swimming pool: 0.7872, Acc.stool: 0.5874, Acc.barrel: 0.6566, Acc.basket: 0.5075, Acc.waterfall: 0.6462, Acc.tent: 0.9803, Acc.bag: 0.2533, Acc.minibike: 0.8546, Acc.cradle: 0.9758, Acc.oven: 0.7612, Acc.ball: 0.2908, Acc.food: 0.7322, Acc.step: 0.2037, Acc.tank: 0.6660, Acc.trade name: 0.2591, Acc.microwave: 0.9224, Acc.pot: 0.5786, Acc.animal: 0.7946, Acc.bicycle: 0.7560, Acc.lake: 0.6232, Acc.dishwasher: 0.7881, Acc.screen: 0.7008, Acc.blanket: 0.3119, Acc.sculpture: 0.8410, Acc.hood: 0.6580, Acc.sconce: 0.6023, Acc.vase: 0.5716, Acc.traffic light: 0.5912, Acc.tray: 0.2587, Acc.ashcan: 0.6547, Acc.fan: 0.7613, Acc.pier: 0.4532, Acc.crt screen: 0.2369, Acc.plate: 0.7017, Acc.monitor: 0.6937, Acc.bulletin board: 0.5461, Acc.shower: 0.0000, Acc.radiator: 0.7728, Acc.glass: 0.2136, Acc.clock: 0.4012, Acc.flag: 0.7302 2023-11-02 22:09:36,711 - mmseg - INFO - Iter [9050/40000] lr: 2.507e-06, eta: 11:32:16, time: 2.400, data_time: 1.193, memory: 38534, decode.loss_ce: 0.2900, decode.acc_seg: 88.6729, loss: 0.2900 2023-11-02 22:10:37,416 - mmseg - INFO - Iter [9100/40000] lr: 2.503e-06, eta: 11:30:47, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3101, decode.acc_seg: 88.1375, loss: 0.3101 2023-11-02 22:11:38,055 - mmseg - INFO - Iter [9150/40000] lr: 2.499e-06, eta: 11:29:18, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3168, decode.acc_seg: 87.7141, loss: 0.3168 2023-11-02 22:12:38,752 - mmseg - INFO - Iter [9200/40000] lr: 2.495e-06, eta: 11:27:50, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3018, decode.acc_seg: 88.1993, loss: 0.3018 2023-11-02 22:13:39,364 - mmseg - INFO - Iter [9250/40000] lr: 2.491e-06, eta: 11:26:22, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2858, decode.acc_seg: 88.5276, loss: 0.2858 2023-11-02 22:14:40,007 - mmseg - INFO - Iter [9300/40000] lr: 2.487e-06, eta: 11:24:54, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3053, decode.acc_seg: 88.0564, loss: 0.3053 2023-11-02 22:15:40,673 - mmseg - INFO - Iter [9350/40000] lr: 2.483e-06, eta: 11:23:26, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3052, decode.acc_seg: 87.8163, loss: 0.3052 2023-11-02 22:16:41,300 - mmseg - INFO - Iter [9400/40000] lr: 2.479e-06, eta: 11:21:59, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2926, decode.acc_seg: 88.3399, loss: 0.2926 2023-11-02 22:17:41,961 - mmseg - INFO - Iter [9450/40000] lr: 2.474e-06, eta: 11:20:32, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3157, decode.acc_seg: 87.9267, loss: 0.3157 2023-11-02 22:18:44,996 - mmseg - INFO - Iter [9500/40000] lr: 2.470e-06, eta: 11:19:13, time: 1.261, data_time: 0.051, memory: 38534, decode.loss_ce: 0.3022, decode.acc_seg: 88.3663, loss: 0.3022 2023-11-02 22:19:45,627 - mmseg - INFO - Iter [9550/40000] lr: 2.466e-06, eta: 11:17:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2761, decode.acc_seg: 88.8680, loss: 0.2761 2023-11-02 22:20:46,286 - mmseg - INFO - Iter [9600/40000] lr: 2.462e-06, eta: 11:16:20, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2974, decode.acc_seg: 88.3457, loss: 0.2974 2023-11-02 22:21:46,994 - mmseg - INFO - Iter [9650/40000] lr: 2.458e-06, eta: 11:14:55, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3105, decode.acc_seg: 88.2920, loss: 0.3105 2023-11-02 22:22:47,674 - mmseg - INFO - Iter [9700/40000] lr: 2.454e-06, eta: 11:13:29, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2886, decode.acc_seg: 88.5568, loss: 0.2886 2023-11-02 22:23:48,359 - mmseg - INFO - Iter [9750/40000] lr: 2.450e-06, eta: 11:12:04, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2897, decode.acc_seg: 88.9411, loss: 0.2897 2023-11-02 22:24:49,048 - mmseg - INFO - Iter [9800/40000] lr: 2.446e-06, eta: 11:10:39, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2761, decode.acc_seg: 88.9719, loss: 0.2761 2023-11-02 22:25:49,722 - mmseg - INFO - Iter [9850/40000] lr: 2.442e-06, eta: 11:09:14, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2782, decode.acc_seg: 88.9634, loss: 0.2782 2023-11-02 22:26:50,365 - mmseg - INFO - Iter [9900/40000] lr: 2.438e-06, eta: 11:07:49, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3183, decode.acc_seg: 88.0811, loss: 0.3183 2023-11-02 22:27:51,031 - mmseg - INFO - Iter [9950/40000] lr: 2.434e-06, eta: 11:06:25, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.3094, decode.acc_seg: 87.9964, loss: 0.3094 2023-11-02 22:28:51,716 - mmseg - INFO - Saving checkpoint at 10000 iterations 2023-11-02 22:29:48,889 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 22:29:48,889 - mmseg - INFO - Iter [10000/40000] lr: 2.430e-06, eta: 11:07:52, time: 2.357, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2978, decode.acc_seg: 88.0152, loss: 0.2978 2023-11-02 22:30:50,369 - mmseg - INFO - per class results: 2023-11-02 22:30:50,374 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.33 | 88.82 | | building | 82.66 | 92.58 | | sky | 93.82 | 96.18 | | floor | 82.76 | 91.56 | | tree | 75.14 | 88.98 | | ceiling | 84.35 | 93.5 | | road | 84.15 | 90.45 | | bed | 91.09 | 96.23 | | windowpane | 63.21 | 76.39 | | grass | 66.82 | 85.12 | | cabinet | 65.32 | 74.67 | | sidewalk | 66.39 | 81.89 | | person | 81.57 | 91.78 | | earth | 32.74 | 44.09 | | door | 56.16 | 74.48 | | table | 63.83 | 77.02 | | mountain | 60.27 | 73.25 | | plant | 55.42 | 69.04 | | curtain | 74.94 | 86.91 | | chair | 61.01 | 75.57 | | car | 84.9 | 93.67 | | water | 58.58 | 71.23 | | painting | 74.09 | 88.74 | | sofa | 74.71 | 91.25 | | shelf | 47.82 | 71.37 | | house | 35.92 | 42.7 | | sea | 57.83 | 65.82 | | mirror | 71.78 | 78.59 | | rug | 65.76 | 74.13 | | field | 30.58 | 50.15 | | armchair | 47.19 | 53.74 | | seat | 62.97 | 84.77 | | fence | 44.37 | 57.32 | | desk | 48.32 | 76.44 | | rock | 55.16 | 79.53 | | wardrobe | 49.89 | 56.66 | | lamp | 64.74 | 79.01 | | bathtub | 88.22 | 91.04 | | railing | 40.86 | 54.59 | | cushion | 59.6 | 69.28 | | base | 31.32 | 54.26 | | box | 33.26 | 42.31 | | column | 52.13 | 64.53 | | signboard | 36.85 | 48.19 | | chest of drawers | 39.78 | 64.24 | | counter | 43.46 | 61.16 | | sand | 67.26 | 88.92 | | sink | 72.83 | 84.21 | | skyscraper | 49.28 | 70.24 | | fireplace | 71.03 | 80.82 | | refrigerator | 81.89 | 87.83 | | grandstand | 46.0 | 82.18 | | path | 17.38 | 20.47 | | stairs | 36.64 | 46.92 | | runway | 66.74 | 88.84 | | case | 66.01 | 81.62 | | pool table | 90.98 | 97.65 | | pillow | 62.36 | 76.72 | | screen door | 82.41 | 91.23 | | stairway | 33.44 | 39.3 | | river | 17.53 | 54.86 | | bridge | 73.79 | 81.26 | | bookcase | 41.06 | 48.95 | | blind | 38.12 | 40.75 | | coffee table | 64.68 | 84.46 | | toilet | 88.14 | 91.74 | | flower | 39.69 | 59.09 | | book | 51.29 | 73.8 | | hill | 8.88 | 15.21 | | bench | 52.91 | 61.14 | | countertop | 63.04 | 71.34 | | stove | 79.12 | 83.78 | | palm | 51.7 | 77.43 | | kitchen island | 36.22 | 67.19 | | computer | 77.03 | 88.8 | | swivel chair | 46.44 | 64.63 | | boat | 55.99 | 75.24 | | bar | 48.06 | 50.3 | | arcade machine | 85.67 | 97.88 | | hovel | 27.12 | 39.2 | | bus | 90.4 | 96.7 | | towel | 72.11 | 83.84 | | light | 40.01 | 43.44 | | truck | 44.76 | 61.3 | | tower | 23.85 | 48.3 | | chandelier | 64.99 | 76.04 | | awning | 31.65 | 38.71 | | streetlight | 26.05 | 32.57 | | booth | 48.44 | 62.07 | | television receiver | 74.55 | 90.25 | | airplane | 63.6 | 70.03 | | dirt track | 4.17 | 24.34 | | apparel | 49.93 | 73.97 | | pole | 25.53 | 38.22 | | land | 7.12 | 15.26 | | bannister | 9.29 | 12.6 | | escalator | 63.8 | 82.14 | | ottoman | 55.1 | 69.33 | | bottle | 25.38 | 32.28 | | buffet | 58.25 | 79.54 | | poster | 26.14 | 39.79 | | stage | 25.0 | 42.17 | | van | 55.68 | 77.92 | | ship | 8.61 | 10.37 | | fountain | 43.67 | 44.73 | | conveyer belt | 77.11 | 95.64 | | canopy | 39.38 | 45.03 | | washer | 80.61 | 85.07 | | plaything | 30.27 | 41.43 | | swimming pool | 56.44 | 82.37 | | stool | 48.71 | 56.8 | | barrel | 55.16 | 66.56 | | basket | 35.21 | 49.27 | | waterfall | 59.59 | 72.76 | | tent | 94.17 | 96.57 | | bag | 24.57 | 28.6 | | minibike | 71.04 | 84.12 | | cradle | 84.05 | 96.88 | | oven | 61.01 | 78.79 | | ball | 46.5 | 51.02 | | food | 65.07 | 72.68 | | step | 15.19 | 17.24 | | tank | 51.83 | 66.76 | | trade name | 26.66 | 30.48 | | microwave | 87.7 | 92.53 | | pot | 49.03 | 55.83 | | animal | 74.91 | 77.58 | | bicycle | 60.64 | 81.67 | | lake | 61.56 | 63.44 | | dishwasher | 69.3 | 79.2 | | screen | 68.2 | 86.64 | | blanket | 31.01 | 37.52 | | sculpture | 75.8 | 83.67 | | hood | 63.18 | 71.26 | | sconce | 45.49 | 52.5 | | vase | 42.52 | 58.61 | | traffic light | 29.41 | 58.13 | | tray | 17.9 | 26.88 | | ashcan | 49.35 | 60.37 | | fan | 58.99 | 68.81 | | pier | 36.21 | 46.6 | | crt screen | 17.14 | 19.27 | | plate | 55.04 | 65.75 | | monitor | 56.76 | 77.01 | | bulletin board | 54.89 | 59.29 | | shower | 0.0 | 0.0 | | radiator | 65.88 | 70.75 | | glass | 15.29 | 15.94 | | clock | 32.73 | 35.02 | | flag | 62.0 | 83.13 | +---------------------+-------+-------+ 2023-11-02 22:30:50,374 - mmseg - INFO - Summary: 2023-11-02 22:30:50,374 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.18 | 53.69 | 65.79 | +-------+-------+-------+ 2023-11-02 22:30:50,375 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 22:30:50,375 - mmseg - INFO - Iter(val) [250] aAcc: 0.8418, mIoU: 0.5369, mAcc: 0.6579, IoU.wall: 0.7933, IoU.building: 0.8266, IoU.sky: 0.9382, IoU.floor: 0.8276, IoU.tree: 0.7514, IoU.ceiling: 0.8435, IoU.road: 0.8415, IoU.bed : 0.9109, IoU.windowpane: 0.6321, IoU.grass: 0.6682, IoU.cabinet: 0.6532, IoU.sidewalk: 0.6639, IoU.person: 0.8157, IoU.earth: 0.3274, IoU.door: 0.5616, IoU.table: 0.6383, IoU.mountain: 0.6027, IoU.plant: 0.5542, IoU.curtain: 0.7494, IoU.chair: 0.6101, IoU.car: 0.8490, IoU.water: 0.5858, IoU.painting: 0.7409, IoU.sofa: 0.7471, IoU.shelf: 0.4782, IoU.house: 0.3592, IoU.sea: 0.5783, IoU.mirror: 0.7178, IoU.rug: 0.6576, IoU.field: 0.3058, IoU.armchair: 0.4719, IoU.seat: 0.6297, IoU.fence: 0.4437, IoU.desk: 0.4832, IoU.rock: 0.5516, IoU.wardrobe: 0.4989, IoU.lamp: 0.6474, IoU.bathtub: 0.8822, IoU.railing: 0.4086, IoU.cushion: 0.5960, IoU.base: 0.3132, IoU.box: 0.3326, IoU.column: 0.5213, IoU.signboard: 0.3685, IoU.chest of drawers: 0.3978, IoU.counter: 0.4346, IoU.sand: 0.6726, IoU.sink: 0.7283, IoU.skyscraper: 0.4928, IoU.fireplace: 0.7103, IoU.refrigerator: 0.8189, IoU.grandstand: 0.4600, IoU.path: 0.1738, IoU.stairs: 0.3664, IoU.runway: 0.6674, IoU.case: 0.6601, IoU.pool table: 0.9098, IoU.pillow: 0.6236, IoU.screen door: 0.8241, IoU.stairway: 0.3344, IoU.river: 0.1753, IoU.bridge: 0.7379, IoU.bookcase: 0.4106, IoU.blind: 0.3812, IoU.coffee table: 0.6468, IoU.toilet: 0.8814, IoU.flower: 0.3969, IoU.book: 0.5129, IoU.hill: 0.0888, IoU.bench: 0.5291, IoU.countertop: 0.6304, IoU.stove: 0.7912, IoU.palm: 0.5170, IoU.kitchen island: 0.3622, IoU.computer: 0.7703, IoU.swivel chair: 0.4644, IoU.boat: 0.5599, IoU.bar: 0.4806, IoU.arcade machine: 0.8567, IoU.hovel: 0.2712, IoU.bus: 0.9040, IoU.towel: 0.7211, IoU.light: 0.4001, IoU.truck: 0.4476, IoU.tower: 0.2385, IoU.chandelier: 0.6499, IoU.awning: 0.3165, IoU.streetlight: 0.2605, IoU.booth: 0.4844, IoU.television receiver: 0.7455, IoU.airplane: 0.6360, IoU.dirt track: 0.0417, IoU.apparel: 0.4993, IoU.pole: 0.2553, IoU.land: 0.0712, IoU.bannister: 0.0929, IoU.escalator: 0.6380, IoU.ottoman: 0.5510, IoU.bottle: 0.2538, IoU.buffet: 0.5825, IoU.poster: 0.2614, IoU.stage: 0.2500, IoU.van: 0.5568, IoU.ship: 0.0861, IoU.fountain: 0.4367, IoU.conveyer belt: 0.7711, IoU.canopy: 0.3938, IoU.washer: 0.8061, IoU.plaything: 0.3027, IoU.swimming pool: 0.5644, IoU.stool: 0.4871, IoU.barrel: 0.5516, IoU.basket: 0.3521, IoU.waterfall: 0.5959, IoU.tent: 0.9417, IoU.bag: 0.2457, IoU.minibike: 0.7104, IoU.cradle: 0.8405, IoU.oven: 0.6101, IoU.ball: 0.4650, IoU.food: 0.6507, IoU.step: 0.1519, IoU.tank: 0.5183, IoU.trade name: 0.2666, IoU.microwave: 0.8770, IoU.pot: 0.4903, IoU.animal: 0.7491, IoU.bicycle: 0.6064, IoU.lake: 0.6156, IoU.dishwasher: 0.6930, IoU.screen: 0.6820, IoU.blanket: 0.3101, IoU.sculpture: 0.7580, IoU.hood: 0.6318, IoU.sconce: 0.4549, IoU.vase: 0.4252, IoU.traffic light: 0.2941, IoU.tray: 0.1790, IoU.ashcan: 0.4935, IoU.fan: 0.5899, IoU.pier: 0.3621, IoU.crt screen: 0.1714, IoU.plate: 0.5504, IoU.monitor: 0.5676, IoU.bulletin board: 0.5489, IoU.shower: 0.0000, IoU.radiator: 0.6588, IoU.glass: 0.1529, IoU.clock: 0.3273, IoU.flag: 0.6200, Acc.wall: 0.8882, Acc.building: 0.9258, Acc.sky: 0.9618, Acc.floor: 0.9156, Acc.tree: 0.8898, Acc.ceiling: 0.9350, Acc.road: 0.9045, Acc.bed : 0.9623, Acc.windowpane: 0.7639, Acc.grass: 0.8512, Acc.cabinet: 0.7467, Acc.sidewalk: 0.8189, Acc.person: 0.9178, Acc.earth: 0.4409, Acc.door: 0.7448, Acc.table: 0.7702, Acc.mountain: 0.7325, Acc.plant: 0.6904, Acc.curtain: 0.8691, Acc.chair: 0.7557, Acc.car: 0.9367, Acc.water: 0.7123, Acc.painting: 0.8874, Acc.sofa: 0.9125, Acc.shelf: 0.7137, Acc.house: 0.4270, Acc.sea: 0.6582, Acc.mirror: 0.7859, Acc.rug: 0.7413, Acc.field: 0.5015, Acc.armchair: 0.5374, Acc.seat: 0.8477, Acc.fence: 0.5732, Acc.desk: 0.7644, Acc.rock: 0.7953, Acc.wardrobe: 0.5666, Acc.lamp: 0.7901, Acc.bathtub: 0.9104, Acc.railing: 0.5459, Acc.cushion: 0.6928, Acc.base: 0.5426, Acc.box: 0.4231, Acc.column: 0.6453, Acc.signboard: 0.4819, Acc.chest of drawers: 0.6424, Acc.counter: 0.6116, Acc.sand: 0.8892, Acc.sink: 0.8421, Acc.skyscraper: 0.7024, Acc.fireplace: 0.8082, Acc.refrigerator: 0.8783, Acc.grandstand: 0.8218, Acc.path: 0.2047, Acc.stairs: 0.4692, Acc.runway: 0.8884, Acc.case: 0.8162, Acc.pool table: 0.9765, Acc.pillow: 0.7672, Acc.screen door: 0.9123, Acc.stairway: 0.3930, Acc.river: 0.5486, Acc.bridge: 0.8126, Acc.bookcase: 0.4895, Acc.blind: 0.4075, Acc.coffee table: 0.8446, Acc.toilet: 0.9174, Acc.flower: 0.5909, Acc.book: 0.7380, Acc.hill: 0.1521, Acc.bench: 0.6114, Acc.countertop: 0.7134, Acc.stove: 0.8378, Acc.palm: 0.7743, Acc.kitchen island: 0.6719, Acc.computer: 0.8880, Acc.swivel chair: 0.6463, Acc.boat: 0.7524, Acc.bar: 0.5030, Acc.arcade machine: 0.9788, Acc.hovel: 0.3920, Acc.bus: 0.9670, Acc.towel: 0.8384, Acc.light: 0.4344, Acc.truck: 0.6130, Acc.tower: 0.4830, Acc.chandelier: 0.7604, Acc.awning: 0.3871, Acc.streetlight: 0.3257, Acc.booth: 0.6207, Acc.television receiver: 0.9025, Acc.airplane: 0.7003, Acc.dirt track: 0.2434, Acc.apparel: 0.7397, Acc.pole: 0.3822, Acc.land: 0.1526, Acc.bannister: 0.1260, Acc.escalator: 0.8214, Acc.ottoman: 0.6933, Acc.bottle: 0.3228, Acc.buffet: 0.7954, Acc.poster: 0.3979, Acc.stage: 0.4217, Acc.van: 0.7792, Acc.ship: 0.1037, Acc.fountain: 0.4473, Acc.conveyer belt: 0.9564, Acc.canopy: 0.4503, Acc.washer: 0.8507, Acc.plaything: 0.4143, Acc.swimming pool: 0.8237, Acc.stool: 0.5680, Acc.barrel: 0.6656, Acc.basket: 0.4927, Acc.waterfall: 0.7276, Acc.tent: 0.9657, Acc.bag: 0.2860, Acc.minibike: 0.8412, Acc.cradle: 0.9688, Acc.oven: 0.7879, Acc.ball: 0.5102, Acc.food: 0.7268, Acc.step: 0.1724, Acc.tank: 0.6676, Acc.trade name: 0.3048, Acc.microwave: 0.9253, Acc.pot: 0.5583, Acc.animal: 0.7758, Acc.bicycle: 0.8167, Acc.lake: 0.6344, Acc.dishwasher: 0.7920, Acc.screen: 0.8664, Acc.blanket: 0.3752, Acc.sculpture: 0.8367, Acc.hood: 0.7126, Acc.sconce: 0.5250, Acc.vase: 0.5861, Acc.traffic light: 0.5813, Acc.tray: 0.2688, Acc.ashcan: 0.6037, Acc.fan: 0.6881, Acc.pier: 0.4660, Acc.crt screen: 0.1927, Acc.plate: 0.6575, Acc.monitor: 0.7701, Acc.bulletin board: 0.5929, Acc.shower: 0.0000, Acc.radiator: 0.7075, Acc.glass: 0.1594, Acc.clock: 0.3502, Acc.flag: 0.8313 2023-11-02 22:31:51,109 - mmseg - INFO - Iter [10050/40000] lr: 2.426e-06, eta: 11:09:31, time: 2.444, data_time: 1.237, memory: 38534, decode.loss_ce: 0.2958, decode.acc_seg: 88.2241, loss: 0.2958 2023-11-02 22:32:51,743 - mmseg - INFO - Iter [10100/40000] lr: 2.422e-06, eta: 11:08:05, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2985, decode.acc_seg: 88.3906, loss: 0.2985 2023-11-02 22:33:54,773 - mmseg - INFO - Iter [10150/40000] lr: 2.418e-06, eta: 11:06:46, time: 1.261, data_time: 0.051, memory: 38534, decode.loss_ce: 0.2892, decode.acc_seg: 88.1233, loss: 0.2892 2023-11-02 22:34:55,513 - mmseg - INFO - Iter [10200/40000] lr: 2.414e-06, eta: 11:05:21, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2838, decode.acc_seg: 89.0957, loss: 0.2838 2023-11-02 22:35:56,141 - mmseg - INFO - Iter [10250/40000] lr: 2.410e-06, eta: 11:03:55, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2742, decode.acc_seg: 89.2157, loss: 0.2742 2023-11-02 22:36:56,858 - mmseg - INFO - Iter [10300/40000] lr: 2.406e-06, eta: 11:02:30, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2847, decode.acc_seg: 89.0451, loss: 0.2847 2023-11-02 22:37:57,541 - mmseg - INFO - Iter [10350/40000] lr: 2.402e-06, eta: 11:01:05, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2832, decode.acc_seg: 88.9410, loss: 0.2832 2023-11-02 22:38:58,266 - mmseg - INFO - Iter [10400/40000] lr: 2.398e-06, eta: 10:59:41, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2801, decode.acc_seg: 89.1898, loss: 0.2801 2023-11-02 22:39:59,037 - mmseg - INFO - Iter [10450/40000] lr: 2.393e-06, eta: 10:58:17, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2869, decode.acc_seg: 88.8941, loss: 0.2869 2023-11-02 22:40:59,781 - mmseg - INFO - Iter [10500/40000] lr: 2.389e-06, eta: 10:56:53, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2633, decode.acc_seg: 89.7158, loss: 0.2633 2023-11-02 22:42:00,563 - mmseg - INFO - Iter [10550/40000] lr: 2.385e-06, eta: 10:55:29, time: 1.216, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2761, decode.acc_seg: 89.1070, loss: 0.2761 2023-11-02 22:43:01,327 - mmseg - INFO - Iter [10600/40000] lr: 2.381e-06, eta: 10:54:06, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2775, decode.acc_seg: 89.1231, loss: 0.2775 2023-11-02 22:44:02,040 - mmseg - INFO - Iter [10650/40000] lr: 2.377e-06, eta: 10:52:43, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2833, decode.acc_seg: 88.8713, loss: 0.2833 2023-11-02 22:45:02,760 - mmseg - INFO - Iter [10700/40000] lr: 2.373e-06, eta: 10:51:19, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2746, decode.acc_seg: 89.0004, loss: 0.2746 2023-11-02 22:46:05,834 - mmseg - INFO - Iter [10750/40000] lr: 2.369e-06, eta: 10:50:03, time: 1.261, data_time: 0.050, memory: 38534, decode.loss_ce: 0.2812, decode.acc_seg: 89.0636, loss: 0.2812 2023-11-02 22:47:06,508 - mmseg - INFO - Iter [10800/40000] lr: 2.365e-06, eta: 10:48:40, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2745, decode.acc_seg: 89.3451, loss: 0.2745 2023-11-02 22:48:07,214 - mmseg - INFO - Iter [10850/40000] lr: 2.361e-06, eta: 10:47:17, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2711, decode.acc_seg: 89.4259, loss: 0.2711 2023-11-02 22:49:07,847 - mmseg - INFO - Iter [10900/40000] lr: 2.357e-06, eta: 10:45:55, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2680, decode.acc_seg: 89.5162, loss: 0.2680 2023-11-02 22:50:08,505 - mmseg - INFO - Iter [10950/40000] lr: 2.353e-06, eta: 10:44:32, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2739, decode.acc_seg: 89.6815, loss: 0.2739 2023-11-02 22:51:09,220 - mmseg - INFO - Saving checkpoint at 11000 iterations 2023-11-02 22:52:12,597 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 22:52:12,597 - mmseg - INFO - Iter [11000/40000] lr: 2.349e-06, eta: 10:45:58, time: 2.482, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2830, decode.acc_seg: 88.8833, loss: 0.2830 2023-11-02 22:53:17,863 - mmseg - INFO - per class results: 2023-11-02 22:53:17,868 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.62 | 89.02 | | building | 82.93 | 93.03 | | sky | 93.49 | 97.7 | | floor | 83.14 | 91.94 | | tree | 73.71 | 83.4 | | ceiling | 84.57 | 91.66 | | road | 84.2 | 88.62 | | bed | 91.44 | 96.24 | | windowpane | 64.28 | 79.09 | | grass | 67.62 | 84.38 | | cabinet | 63.73 | 73.98 | | sidewalk | 67.52 | 86.11 | | person | 82.0 | 92.14 | | earth | 34.43 | 45.74 | | door | 56.08 | 73.28 | | table | 65.9 | 78.82 | | mountain | 60.78 | 76.47 | | plant | 56.9 | 67.16 | | curtain | 75.23 | 87.94 | | chair | 59.14 | 70.51 | | car | 84.39 | 93.69 | | water | 53.39 | 63.08 | | painting | 74.53 | 87.29 | | sofa | 76.75 | 90.35 | | shelf | 43.76 | 61.64 | | house | 48.88 | 62.78 | | sea | 71.05 | 80.62 | | mirror | 71.8 | 79.27 | | rug | 62.66 | 70.38 | | field | 28.81 | 44.69 | | armchair | 55.47 | 71.31 | | seat | 61.32 | 87.38 | | fence | 47.9 | 60.93 | | desk | 49.84 | 66.73 | | rock | 52.5 | 80.03 | | wardrobe | 51.23 | 72.71 | | lamp | 65.08 | 79.59 | | bathtub | 88.58 | 91.47 | | railing | 43.29 | 66.42 | | cushion | 61.59 | 79.2 | | base | 34.69 | 55.46 | | box | 37.54 | 51.56 | | column | 52.29 | 64.51 | | signboard | 37.25 | 49.33 | | chest of drawers | 43.43 | 56.73 | | counter | 49.24 | 58.79 | | sand | 61.65 | 88.65 | | sink | 73.75 | 83.23 | | skyscraper | 46.48 | 64.7 | | fireplace | 71.77 | 83.75 | | refrigerator | 80.42 | 89.23 | | grandstand | 47.79 | 77.09 | | path | 22.44 | 29.63 | | stairs | 27.68 | 34.13 | | runway | 67.11 | 88.64 | | case | 62.67 | 79.79 | | pool table | 92.8 | 97.91 | | pillow | 61.08 | 70.94 | | screen door | 83.34 | 86.53 | | stairway | 30.17 | 44.16 | | river | 16.15 | 57.68 | | bridge | 76.06 | 87.94 | | bookcase | 41.08 | 59.95 | | blind | 45.9 | 52.82 | | coffee table | 64.38 | 87.52 | | toilet | 87.69 | 93.57 | | flower | 41.93 | 50.09 | | book | 49.63 | 68.56 | | hill | 7.82 | 14.77 | | bench | 53.99 | 65.72 | | countertop | 62.49 | 75.07 | | stove | 78.92 | 87.02 | | palm | 46.82 | 81.44 | | kitchen island | 45.25 | 83.89 | | computer | 76.38 | 88.33 | | swivel chair | 42.9 | 60.59 | | boat | 55.61 | 65.53 | | bar | 69.48 | 75.93 | | arcade machine | 84.96 | 90.63 | | hovel | 17.23 | 19.69 | | bus | 91.48 | 94.91 | | towel | 72.6 | 84.84 | | light | 48.1 | 59.13 | | truck | 47.14 | 66.4 | | tower | 25.55 | 50.72 | | chandelier | 66.06 | 84.58 | | awning | 39.66 | 49.17 | | streetlight | 23.76 | 31.97 | | booth | 44.22 | 47.92 | | television receiver | 74.31 | 89.62 | | airplane | 63.11 | 70.63 | | dirt track | 5.53 | 22.03 | | apparel | 45.72 | 61.23 | | pole | 21.34 | 28.35 | | land | 1.85 | 4.06 | | bannister | 10.94 | 15.5 | | escalator | 60.13 | 79.63 | | ottoman | 55.29 | 71.39 | | bottle | 25.15 | 37.01 | | buffet | 52.47 | 76.21 | | poster | 28.15 | 42.55 | | stage | 27.32 | 40.73 | | van | 46.17 | 57.39 | | ship | 12.65 | 16.24 | | fountain | 34.63 | 35.09 | | conveyer belt | 84.28 | 86.03 | | canopy | 30.76 | 36.17 | | washer | 82.65 | 89.15 | | plaything | 30.39 | 43.15 | | swimming pool | 50.29 | 73.11 | | stool | 46.4 | 62.68 | | barrel | 51.29 | 66.5 | | basket | 35.55 | 42.59 | | waterfall | 53.11 | 65.42 | | tent | 83.33 | 97.79 | | bag | 27.03 | 32.1 | | minibike | 69.79 | 87.63 | | cradle | 85.14 | 97.16 | | oven | 65.3 | 74.66 | | ball | 26.56 | 27.91 | | food | 50.57 | 53.67 | | step | 12.22 | 13.66 | | tank | 54.24 | 66.01 | | trade name | 31.54 | 38.82 | | microwave | 87.44 | 93.39 | | pot | 55.48 | 65.29 | | animal | 76.38 | 80.85 | | bicycle | 58.1 | 71.8 | | lake | 58.86 | 63.23 | | dishwasher | 67.68 | 81.03 | | screen | 60.14 | 90.8 | | blanket | 26.78 | 32.51 | | sculpture | 70.26 | 87.31 | | hood | 66.49 | 80.88 | | sconce | 52.1 | 68.55 | | vase | 42.72 | 55.44 | | traffic light | 30.81 | 57.81 | | tray | 16.94 | 21.03 | | ashcan | 47.07 | 61.05 | | fan | 62.15 | 77.13 | | pier | 42.42 | 48.93 | | crt screen | 7.87 | 14.33 | | plate | 55.51 | 79.37 | | monitor | 38.38 | 47.37 | | bulletin board | 50.79 | 59.16 | | shower | 0.0 | 0.0 | | radiator | 66.02 | 76.19 | | glass | 18.37 | 20.06 | | clock | 40.04 | 44.31 | | flag | 65.86 | 73.15 | +---------------------+-------+-------+ 2023-11-02 22:53:17,868 - mmseg - INFO - Summary: 2023-11-02 22:53:17,868 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.32 | 53.51 | 65.87 | +-------+-------+-------+ 2023-11-02 22:53:17,869 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 22:53:17,869 - mmseg - INFO - Iter(val) [250] aAcc: 0.8432, mIoU: 0.5351, mAcc: 0.6587, IoU.wall: 0.7962, IoU.building: 0.8293, IoU.sky: 0.9349, IoU.floor: 0.8314, IoU.tree: 0.7371, IoU.ceiling: 0.8457, IoU.road: 0.8420, IoU.bed : 0.9144, IoU.windowpane: 0.6428, IoU.grass: 0.6762, IoU.cabinet: 0.6373, IoU.sidewalk: 0.6752, IoU.person: 0.8200, IoU.earth: 0.3443, IoU.door: 0.5608, IoU.table: 0.6590, IoU.mountain: 0.6078, IoU.plant: 0.5690, IoU.curtain: 0.7523, IoU.chair: 0.5914, IoU.car: 0.8439, IoU.water: 0.5339, IoU.painting: 0.7453, IoU.sofa: 0.7675, IoU.shelf: 0.4376, IoU.house: 0.4888, IoU.sea: 0.7105, IoU.mirror: 0.7180, IoU.rug: 0.6266, IoU.field: 0.2881, IoU.armchair: 0.5547, IoU.seat: 0.6132, IoU.fence: 0.4790, IoU.desk: 0.4984, IoU.rock: 0.5250, IoU.wardrobe: 0.5123, IoU.lamp: 0.6508, IoU.bathtub: 0.8858, IoU.railing: 0.4329, IoU.cushion: 0.6159, IoU.base: 0.3469, IoU.box: 0.3754, IoU.column: 0.5229, IoU.signboard: 0.3725, IoU.chest of drawers: 0.4343, IoU.counter: 0.4924, IoU.sand: 0.6165, IoU.sink: 0.7375, IoU.skyscraper: 0.4648, IoU.fireplace: 0.7177, IoU.refrigerator: 0.8042, IoU.grandstand: 0.4779, IoU.path: 0.2244, IoU.stairs: 0.2768, IoU.runway: 0.6711, IoU.case: 0.6267, IoU.pool table: 0.9280, IoU.pillow: 0.6108, IoU.screen door: 0.8334, IoU.stairway: 0.3017, IoU.river: 0.1615, IoU.bridge: 0.7606, IoU.bookcase: 0.4108, IoU.blind: 0.4590, IoU.coffee table: 0.6438, IoU.toilet: 0.8769, IoU.flower: 0.4193, IoU.book: 0.4963, IoU.hill: 0.0782, IoU.bench: 0.5399, IoU.countertop: 0.6249, IoU.stove: 0.7892, IoU.palm: 0.4682, IoU.kitchen island: 0.4525, IoU.computer: 0.7638, IoU.swivel chair: 0.4290, IoU.boat: 0.5561, IoU.bar: 0.6948, IoU.arcade machine: 0.8496, IoU.hovel: 0.1723, IoU.bus: 0.9148, IoU.towel: 0.7260, IoU.light: 0.4810, IoU.truck: 0.4714, IoU.tower: 0.2555, IoU.chandelier: 0.6606, IoU.awning: 0.3966, IoU.streetlight: 0.2376, IoU.booth: 0.4422, IoU.television receiver: 0.7431, IoU.airplane: 0.6311, IoU.dirt track: 0.0553, IoU.apparel: 0.4572, IoU.pole: 0.2134, IoU.land: 0.0185, IoU.bannister: 0.1094, IoU.escalator: 0.6013, IoU.ottoman: 0.5529, IoU.bottle: 0.2515, IoU.buffet: 0.5247, IoU.poster: 0.2815, IoU.stage: 0.2732, IoU.van: 0.4617, IoU.ship: 0.1265, IoU.fountain: 0.3463, IoU.conveyer belt: 0.8428, IoU.canopy: 0.3076, IoU.washer: 0.8265, IoU.plaything: 0.3039, IoU.swimming pool: 0.5029, IoU.stool: 0.4640, IoU.barrel: 0.5129, IoU.basket: 0.3555, IoU.waterfall: 0.5311, IoU.tent: 0.8333, IoU.bag: 0.2703, IoU.minibike: 0.6979, IoU.cradle: 0.8514, IoU.oven: 0.6530, IoU.ball: 0.2656, IoU.food: 0.5057, IoU.step: 0.1222, IoU.tank: 0.5424, IoU.trade name: 0.3154, IoU.microwave: 0.8744, IoU.pot: 0.5548, IoU.animal: 0.7638, IoU.bicycle: 0.5810, IoU.lake: 0.5886, IoU.dishwasher: 0.6768, IoU.screen: 0.6014, IoU.blanket: 0.2678, IoU.sculpture: 0.7026, IoU.hood: 0.6649, IoU.sconce: 0.5210, IoU.vase: 0.4272, IoU.traffic light: 0.3081, IoU.tray: 0.1694, IoU.ashcan: 0.4707, IoU.fan: 0.6215, IoU.pier: 0.4242, IoU.crt screen: 0.0787, IoU.plate: 0.5551, IoU.monitor: 0.3838, IoU.bulletin board: 0.5079, IoU.shower: 0.0000, IoU.radiator: 0.6602, IoU.glass: 0.1837, IoU.clock: 0.4004, IoU.flag: 0.6586, Acc.wall: 0.8902, Acc.building: 0.9303, Acc.sky: 0.9770, Acc.floor: 0.9194, Acc.tree: 0.8340, Acc.ceiling: 0.9166, Acc.road: 0.8862, Acc.bed : 0.9624, Acc.windowpane: 0.7909, Acc.grass: 0.8438, Acc.cabinet: 0.7398, Acc.sidewalk: 0.8611, Acc.person: 0.9214, Acc.earth: 0.4574, Acc.door: 0.7328, Acc.table: 0.7882, Acc.mountain: 0.7647, Acc.plant: 0.6716, Acc.curtain: 0.8794, Acc.chair: 0.7051, Acc.car: 0.9369, Acc.water: 0.6308, Acc.painting: 0.8729, Acc.sofa: 0.9035, Acc.shelf: 0.6164, Acc.house: 0.6278, Acc.sea: 0.8062, Acc.mirror: 0.7927, Acc.rug: 0.7038, Acc.field: 0.4469, Acc.armchair: 0.7131, Acc.seat: 0.8738, Acc.fence: 0.6093, Acc.desk: 0.6673, Acc.rock: 0.8003, Acc.wardrobe: 0.7271, Acc.lamp: 0.7959, Acc.bathtub: 0.9147, Acc.railing: 0.6642, Acc.cushion: 0.7920, Acc.base: 0.5546, Acc.box: 0.5156, Acc.column: 0.6451, Acc.signboard: 0.4933, Acc.chest of drawers: 0.5673, Acc.counter: 0.5879, Acc.sand: 0.8865, Acc.sink: 0.8323, Acc.skyscraper: 0.6470, Acc.fireplace: 0.8375, Acc.refrigerator: 0.8923, Acc.grandstand: 0.7709, Acc.path: 0.2963, Acc.stairs: 0.3413, Acc.runway: 0.8864, Acc.case: 0.7979, Acc.pool table: 0.9791, Acc.pillow: 0.7094, Acc.screen door: 0.8653, Acc.stairway: 0.4416, Acc.river: 0.5768, Acc.bridge: 0.8794, Acc.bookcase: 0.5995, Acc.blind: 0.5282, Acc.coffee table: 0.8752, Acc.toilet: 0.9357, Acc.flower: 0.5009, Acc.book: 0.6856, Acc.hill: 0.1477, Acc.bench: 0.6572, Acc.countertop: 0.7507, Acc.stove: 0.8702, Acc.palm: 0.8144, Acc.kitchen island: 0.8389, Acc.computer: 0.8833, Acc.swivel chair: 0.6059, Acc.boat: 0.6553, Acc.bar: 0.7593, Acc.arcade machine: 0.9063, Acc.hovel: 0.1969, Acc.bus: 0.9491, Acc.towel: 0.8484, Acc.light: 0.5913, Acc.truck: 0.6640, Acc.tower: 0.5072, Acc.chandelier: 0.8458, Acc.awning: 0.4917, Acc.streetlight: 0.3197, Acc.booth: 0.4792, Acc.television receiver: 0.8962, Acc.airplane: 0.7063, Acc.dirt track: 0.2203, Acc.apparel: 0.6123, Acc.pole: 0.2835, Acc.land: 0.0406, Acc.bannister: 0.1550, Acc.escalator: 0.7963, Acc.ottoman: 0.7139, Acc.bottle: 0.3701, Acc.buffet: 0.7621, Acc.poster: 0.4255, Acc.stage: 0.4073, Acc.van: 0.5739, Acc.ship: 0.1624, Acc.fountain: 0.3509, Acc.conveyer belt: 0.8603, Acc.canopy: 0.3617, Acc.washer: 0.8915, Acc.plaything: 0.4315, Acc.swimming pool: 0.7311, Acc.stool: 0.6268, Acc.barrel: 0.6650, Acc.basket: 0.4259, Acc.waterfall: 0.6542, Acc.tent: 0.9779, Acc.bag: 0.3210, Acc.minibike: 0.8763, Acc.cradle: 0.9716, Acc.oven: 0.7466, Acc.ball: 0.2791, Acc.food: 0.5367, Acc.step: 0.1366, Acc.tank: 0.6601, Acc.trade name: 0.3882, Acc.microwave: 0.9339, Acc.pot: 0.6529, Acc.animal: 0.8085, Acc.bicycle: 0.7180, Acc.lake: 0.6323, Acc.dishwasher: 0.8103, Acc.screen: 0.9080, Acc.blanket: 0.3251, Acc.sculpture: 0.8731, Acc.hood: 0.8088, Acc.sconce: 0.6855, Acc.vase: 0.5544, Acc.traffic light: 0.5781, Acc.tray: 0.2103, Acc.ashcan: 0.6105, Acc.fan: 0.7713, Acc.pier: 0.4893, Acc.crt screen: 0.1433, Acc.plate: 0.7937, Acc.monitor: 0.4737, Acc.bulletin board: 0.5916, Acc.shower: 0.0000, Acc.radiator: 0.7619, Acc.glass: 0.2006, Acc.clock: 0.4431, Acc.flag: 0.7315 2023-11-02 22:54:18,649 - mmseg - INFO - Iter [11050/40000] lr: 2.345e-06, eta: 10:47:26, time: 2.521, data_time: 1.313, memory: 38534, decode.loss_ce: 0.2666, decode.acc_seg: 89.6265, loss: 0.2666 2023-11-02 22:55:19,336 - mmseg - INFO - Iter [11100/40000] lr: 2.341e-06, eta: 10:46:02, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2767, decode.acc_seg: 89.0795, loss: 0.2767 2023-11-02 22:56:20,020 - mmseg - INFO - Iter [11150/40000] lr: 2.337e-06, eta: 10:44:39, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2779, decode.acc_seg: 89.0197, loss: 0.2779 2023-11-02 22:57:20,715 - mmseg - INFO - Iter [11200/40000] lr: 2.333e-06, eta: 10:43:15, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2764, decode.acc_seg: 89.0496, loss: 0.2764 2023-11-02 22:58:21,444 - mmseg - INFO - Iter [11250/40000] lr: 2.329e-06, eta: 10:41:52, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2778, decode.acc_seg: 88.8456, loss: 0.2778 2023-11-02 22:59:22,143 - mmseg - INFO - Iter [11300/40000] lr: 2.325e-06, eta: 10:40:29, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2799, decode.acc_seg: 89.4222, loss: 0.2799 2023-11-02 23:00:22,848 - mmseg - INFO - Iter [11350/40000] lr: 2.321e-06, eta: 10:39:07, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2712, decode.acc_seg: 88.9220, loss: 0.2712 2023-11-02 23:01:25,914 - mmseg - INFO - Iter [11400/40000] lr: 2.317e-06, eta: 10:37:50, time: 1.261, data_time: 0.054, memory: 38534, decode.loss_ce: 0.2556, decode.acc_seg: 89.7274, loss: 0.2556 2023-11-02 23:02:26,574 - mmseg - INFO - Iter [11450/40000] lr: 2.312e-06, eta: 10:36:27, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2481, decode.acc_seg: 90.0374, loss: 0.2481 2023-11-02 23:03:27,218 - mmseg - INFO - Iter [11500/40000] lr: 2.308e-06, eta: 10:35:05, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2721, decode.acc_seg: 89.0748, loss: 0.2721 2023-11-02 23:04:27,872 - mmseg - INFO - Iter [11550/40000] lr: 2.304e-06, eta: 10:33:43, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2541, decode.acc_seg: 90.1421, loss: 0.2541 2023-11-02 23:05:28,483 - mmseg - INFO - Iter [11600/40000] lr: 2.300e-06, eta: 10:32:21, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2768, decode.acc_seg: 89.4564, loss: 0.2768 2023-11-02 23:06:29,158 - mmseg - INFO - Iter [11650/40000] lr: 2.296e-06, eta: 10:30:59, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2547, decode.acc_seg: 90.0524, loss: 0.2547 2023-11-02 23:07:29,788 - mmseg - INFO - Iter [11700/40000] lr: 2.292e-06, eta: 10:29:38, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2572, decode.acc_seg: 89.6939, loss: 0.2572 2023-11-02 23:08:30,483 - mmseg - INFO - Iter [11750/40000] lr: 2.288e-06, eta: 10:28:16, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2564, decode.acc_seg: 89.6437, loss: 0.2564 2023-11-02 23:09:31,183 - mmseg - INFO - Iter [11800/40000] lr: 2.284e-06, eta: 10:26:55, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2620, decode.acc_seg: 89.5797, loss: 0.2620 2023-11-02 23:10:31,854 - mmseg - INFO - Iter [11850/40000] lr: 2.280e-06, eta: 10:25:34, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2861, decode.acc_seg: 88.9786, loss: 0.2861 2023-11-02 23:11:32,505 - mmseg - INFO - Iter [11900/40000] lr: 2.276e-06, eta: 10:24:13, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2619, decode.acc_seg: 89.5458, loss: 0.2619 2023-11-02 23:12:33,169 - mmseg - INFO - Iter [11950/40000] lr: 2.272e-06, eta: 10:22:53, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2628, decode.acc_seg: 89.3824, loss: 0.2628 2023-11-02 23:13:33,817 - mmseg - INFO - Saving checkpoint at 12000 iterations 2023-11-02 23:14:29,171 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 23:14:29,171 - mmseg - INFO - Iter [12000/40000] lr: 2.268e-06, eta: 10:23:41, time: 2.320, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2722, decode.acc_seg: 89.2010, loss: 0.2722 2023-11-02 23:15:28,755 - mmseg - INFO - per class results: 2023-11-02 23:15:28,760 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.62 | 88.46 | | building | 83.12 | 91.66 | | sky | 94.1 | 96.52 | | floor | 83.63 | 89.95 | | tree | 74.89 | 89.54 | | ceiling | 85.2 | 92.65 | | road | 85.25 | 92.76 | | bed | 90.78 | 96.7 | | windowpane | 64.46 | 81.66 | | grass | 70.2 | 86.46 | | cabinet | 63.8 | 71.65 | | sidewalk | 68.57 | 80.32 | | person | 81.88 | 91.96 | | earth | 37.94 | 49.72 | | door | 54.71 | 72.17 | | table | 66.88 | 79.49 | | mountain | 61.29 | 75.56 | | plant | 55.6 | 66.34 | | curtain | 74.71 | 89.12 | | chair | 59.34 | 71.73 | | car | 84.78 | 92.88 | | water | 59.52 | 78.37 | | painting | 71.55 | 87.44 | | sofa | 77.16 | 90.8 | | shelf | 46.66 | 69.94 | | house | 47.62 | 66.92 | | sea | 51.84 | 57.2 | | mirror | 74.76 | 83.84 | | rug | 67.77 | 86.76 | | field | 33.67 | 47.16 | | armchair | 55.36 | 70.19 | | seat | 61.9 | 87.09 | | fence | 50.52 | 71.64 | | desk | 53.37 | 72.69 | | rock | 51.94 | 74.09 | | wardrobe | 53.8 | 76.13 | | lamp | 63.83 | 74.16 | | bathtub | 88.74 | 91.66 | | railing | 42.29 | 55.2 | | cushion | 59.18 | 68.15 | | base | 30.17 | 48.92 | | box | 35.75 | 47.6 | | column | 50.34 | 60.31 | | signboard | 35.83 | 50.37 | | chest of drawers | 46.96 | 68.96 | | counter | 50.55 | 60.91 | | sand | 61.54 | 86.89 | | sink | 74.37 | 82.32 | | skyscraper | 49.22 | 62.12 | | fireplace | 72.45 | 87.21 | | refrigerator | 80.41 | 88.86 | | grandstand | 50.22 | 81.12 | | path | 25.51 | 34.54 | | stairs | 28.95 | 35.08 | | runway | 70.88 | 92.17 | | case | 60.69 | 86.0 | | pool table | 92.72 | 97.64 | | pillow | 60.0 | 69.93 | | screen door | 60.66 | 65.01 | | stairway | 32.31 | 47.04 | | river | 18.61 | 47.22 | | bridge | 67.02 | 89.08 | | bookcase | 39.55 | 44.06 | | blind | 40.58 | 46.02 | | coffee table | 66.49 | 87.46 | | toilet | 88.37 | 92.18 | | flower | 42.61 | 69.96 | | book | 50.14 | 74.69 | | hill | 6.63 | 10.97 | | bench | 51.6 | 62.41 | | countertop | 66.09 | 78.51 | | stove | 80.87 | 86.9 | | palm | 47.09 | 84.36 | | kitchen island | 41.36 | 72.64 | | computer | 76.35 | 88.47 | | swivel chair | 42.44 | 61.2 | | boat | 39.94 | 62.32 | | bar | 69.19 | 79.24 | | arcade machine | 84.63 | 89.72 | | hovel | 50.61 | 62.1 | | bus | 91.21 | 95.84 | | towel | 71.7 | 83.97 | | light | 45.14 | 51.6 | | truck | 45.37 | 60.6 | | tower | 22.58 | 35.21 | | chandelier | 65.46 | 79.56 | | awning | 48.06 | 65.05 | | streetlight | 24.78 | 34.66 | | booth | 57.91 | 64.6 | | television receiver | 75.32 | 88.34 | | airplane | 70.63 | 83.31 | | dirt track | 27.03 | 41.18 | | apparel | 52.57 | 76.85 | | pole | 18.32 | 22.69 | | land | 2.91 | 4.35 | | bannister | 9.23 | 13.47 | | escalator | 60.41 | 80.08 | | ottoman | 54.16 | 70.42 | | bottle | 16.9 | 18.74 | | buffet | 55.38 | 81.56 | | poster | 30.54 | 35.86 | | stage | 23.0 | 57.2 | | van | 49.6 | 67.75 | | ship | 58.67 | 85.58 | | fountain | 9.78 | 10.07 | | conveyer belt | 79.92 | 94.67 | | canopy | 38.48 | 44.04 | | washer | 87.69 | 93.53 | | plaything | 27.8 | 47.22 | | swimming pool | 55.04 | 74.37 | | stool | 45.1 | 64.86 | | barrel | 29.79 | 77.63 | | basket | 35.66 | 49.38 | | waterfall | 61.56 | 85.93 | | tent | 91.93 | 97.36 | | bag | 28.23 | 35.3 | | minibike | 71.4 | 86.03 | | cradle | 83.6 | 97.79 | | oven | 60.48 | 67.92 | | ball | 48.32 | 53.23 | | food | 49.34 | 52.43 | | step | 12.95 | 16.18 | | tank | 53.36 | 65.31 | | trade name | 29.76 | 35.97 | | microwave | 85.94 | 94.17 | | pot | 47.77 | 52.17 | | animal | 70.28 | 72.43 | | bicycle | 58.51 | 76.05 | | lake | 59.05 | 63.24 | | dishwasher | 67.37 | 80.05 | | screen | 50.3 | 81.18 | | blanket | 26.77 | 33.28 | | sculpture | 72.87 | 84.46 | | hood | 63.84 | 78.49 | | sconce | 51.85 | 66.43 | | vase | 41.95 | 55.05 | | traffic light | 31.05 | 62.81 | | tray | 14.66 | 19.51 | | ashcan | 47.39 | 59.2 | | fan | 60.99 | 73.71 | | pier | 53.4 | 61.77 | | crt screen | 7.76 | 15.68 | | plate | 55.29 | 68.8 | | monitor | 34.79 | 43.68 | | bulletin board | 45.19 | 48.79 | | shower | 0.16 | 1.23 | | radiator | 65.32 | 78.41 | | glass | 17.02 | 18.15 | | clock | 40.72 | 48.78 | | flag | 65.64 | 78.14 | +---------------------+-------+-------+ 2023-11-02 23:15:28,761 - mmseg - INFO - Summary: 2023-11-02 23:15:28,761 - mmseg - INFO - +-------+------+-------+ | aAcc | mIoU | mAcc | +-------+------+-------+ | 84.61 | 53.9 | 67.08 | +-------+------+-------+ 2023-11-02 23:15:28,762 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 23:15:28,762 - mmseg - INFO - Iter(val) [250] aAcc: 0.8461, mIoU: 0.5390, mAcc: 0.6708, IoU.wall: 0.7962, IoU.building: 0.8312, IoU.sky: 0.9410, IoU.floor: 0.8363, IoU.tree: 0.7489, IoU.ceiling: 0.8520, IoU.road: 0.8525, IoU.bed : 0.9078, IoU.windowpane: 0.6446, IoU.grass: 0.7020, IoU.cabinet: 0.6380, IoU.sidewalk: 0.6857, IoU.person: 0.8188, IoU.earth: 0.3794, IoU.door: 0.5471, IoU.table: 0.6688, IoU.mountain: 0.6129, IoU.plant: 0.5560, IoU.curtain: 0.7471, IoU.chair: 0.5934, IoU.car: 0.8478, IoU.water: 0.5952, IoU.painting: 0.7155, IoU.sofa: 0.7716, IoU.shelf: 0.4666, IoU.house: 0.4762, IoU.sea: 0.5184, IoU.mirror: 0.7476, IoU.rug: 0.6777, IoU.field: 0.3367, IoU.armchair: 0.5536, IoU.seat: 0.6190, IoU.fence: 0.5052, IoU.desk: 0.5337, IoU.rock: 0.5194, IoU.wardrobe: 0.5380, IoU.lamp: 0.6383, IoU.bathtub: 0.8874, IoU.railing: 0.4229, IoU.cushion: 0.5918, IoU.base: 0.3017, IoU.box: 0.3575, IoU.column: 0.5034, IoU.signboard: 0.3583, IoU.chest of drawers: 0.4696, IoU.counter: 0.5055, IoU.sand: 0.6154, IoU.sink: 0.7437, IoU.skyscraper: 0.4922, IoU.fireplace: 0.7245, IoU.refrigerator: 0.8041, IoU.grandstand: 0.5022, IoU.path: 0.2551, IoU.stairs: 0.2895, IoU.runway: 0.7088, IoU.case: 0.6069, IoU.pool table: 0.9272, IoU.pillow: 0.6000, IoU.screen door: 0.6066, IoU.stairway: 0.3231, IoU.river: 0.1861, IoU.bridge: 0.6702, IoU.bookcase: 0.3955, IoU.blind: 0.4058, IoU.coffee table: 0.6649, IoU.toilet: 0.8837, IoU.flower: 0.4261, IoU.book: 0.5014, IoU.hill: 0.0663, IoU.bench: 0.5160, IoU.countertop: 0.6609, IoU.stove: 0.8087, IoU.palm: 0.4709, IoU.kitchen island: 0.4136, IoU.computer: 0.7635, IoU.swivel chair: 0.4244, IoU.boat: 0.3994, IoU.bar: 0.6919, IoU.arcade machine: 0.8463, IoU.hovel: 0.5061, IoU.bus: 0.9121, IoU.towel: 0.7170, IoU.light: 0.4514, IoU.truck: 0.4537, IoU.tower: 0.2258, IoU.chandelier: 0.6546, IoU.awning: 0.4806, IoU.streetlight: 0.2478, IoU.booth: 0.5791, IoU.television receiver: 0.7532, IoU.airplane: 0.7063, IoU.dirt track: 0.2703, IoU.apparel: 0.5257, IoU.pole: 0.1832, IoU.land: 0.0291, IoU.bannister: 0.0923, IoU.escalator: 0.6041, IoU.ottoman: 0.5416, IoU.bottle: 0.1690, IoU.buffet: 0.5538, IoU.poster: 0.3054, IoU.stage: 0.2300, IoU.van: 0.4960, IoU.ship: 0.5867, IoU.fountain: 0.0978, IoU.conveyer belt: 0.7992, IoU.canopy: 0.3848, IoU.washer: 0.8769, IoU.plaything: 0.2780, IoU.swimming pool: 0.5504, IoU.stool: 0.4510, IoU.barrel: 0.2979, IoU.basket: 0.3566, IoU.waterfall: 0.6156, IoU.tent: 0.9193, IoU.bag: 0.2823, IoU.minibike: 0.7140, IoU.cradle: 0.8360, IoU.oven: 0.6048, IoU.ball: 0.4832, IoU.food: 0.4934, IoU.step: 0.1295, IoU.tank: 0.5336, IoU.trade name: 0.2976, IoU.microwave: 0.8594, IoU.pot: 0.4777, IoU.animal: 0.7028, IoU.bicycle: 0.5851, IoU.lake: 0.5905, IoU.dishwasher: 0.6737, IoU.screen: 0.5030, IoU.blanket: 0.2677, IoU.sculpture: 0.7287, IoU.hood: 0.6384, IoU.sconce: 0.5185, IoU.vase: 0.4195, IoU.traffic light: 0.3105, IoU.tray: 0.1466, IoU.ashcan: 0.4739, IoU.fan: 0.6099, IoU.pier: 0.5340, IoU.crt screen: 0.0776, IoU.plate: 0.5529, IoU.monitor: 0.3479, IoU.bulletin board: 0.4519, IoU.shower: 0.0016, IoU.radiator: 0.6532, IoU.glass: 0.1702, IoU.clock: 0.4072, IoU.flag: 0.6564, Acc.wall: 0.8846, Acc.building: 0.9166, Acc.sky: 0.9652, Acc.floor: 0.8995, Acc.tree: 0.8954, Acc.ceiling: 0.9265, Acc.road: 0.9276, Acc.bed : 0.9670, Acc.windowpane: 0.8166, Acc.grass: 0.8646, Acc.cabinet: 0.7165, Acc.sidewalk: 0.8032, Acc.person: 0.9196, Acc.earth: 0.4972, Acc.door: 0.7217, Acc.table: 0.7949, Acc.mountain: 0.7556, Acc.plant: 0.6634, Acc.curtain: 0.8912, Acc.chair: 0.7173, Acc.car: 0.9288, Acc.water: 0.7837, Acc.painting: 0.8744, Acc.sofa: 0.9080, Acc.shelf: 0.6994, Acc.house: 0.6692, Acc.sea: 0.5720, Acc.mirror: 0.8384, Acc.rug: 0.8676, Acc.field: 0.4716, Acc.armchair: 0.7019, Acc.seat: 0.8709, Acc.fence: 0.7164, Acc.desk: 0.7269, Acc.rock: 0.7409, Acc.wardrobe: 0.7613, Acc.lamp: 0.7416, Acc.bathtub: 0.9166, Acc.railing: 0.5520, Acc.cushion: 0.6815, Acc.base: 0.4892, Acc.box: 0.4760, Acc.column: 0.6031, Acc.signboard: 0.5037, Acc.chest of drawers: 0.6896, Acc.counter: 0.6091, Acc.sand: 0.8689, Acc.sink: 0.8232, Acc.skyscraper: 0.6212, Acc.fireplace: 0.8721, Acc.refrigerator: 0.8886, Acc.grandstand: 0.8112, Acc.path: 0.3454, Acc.stairs: 0.3508, Acc.runway: 0.9217, Acc.case: 0.8600, Acc.pool table: 0.9764, Acc.pillow: 0.6993, Acc.screen door: 0.6501, Acc.stairway: 0.4704, Acc.river: 0.4722, Acc.bridge: 0.8908, Acc.bookcase: 0.4406, Acc.blind: 0.4602, Acc.coffee table: 0.8746, Acc.toilet: 0.9218, Acc.flower: 0.6996, Acc.book: 0.7469, Acc.hill: 0.1097, Acc.bench: 0.6241, Acc.countertop: 0.7851, Acc.stove: 0.8690, Acc.palm: 0.8436, Acc.kitchen island: 0.7264, Acc.computer: 0.8847, Acc.swivel chair: 0.6120, Acc.boat: 0.6232, Acc.bar: 0.7924, Acc.arcade machine: 0.8972, Acc.hovel: 0.6210, Acc.bus: 0.9584, Acc.towel: 0.8397, Acc.light: 0.5160, Acc.truck: 0.6060, Acc.tower: 0.3521, Acc.chandelier: 0.7956, Acc.awning: 0.6505, Acc.streetlight: 0.3466, Acc.booth: 0.6460, Acc.television receiver: 0.8834, Acc.airplane: 0.8331, Acc.dirt track: 0.4118, Acc.apparel: 0.7685, Acc.pole: 0.2269, Acc.land: 0.0435, Acc.bannister: 0.1347, Acc.escalator: 0.8008, Acc.ottoman: 0.7042, Acc.bottle: 0.1874, Acc.buffet: 0.8156, Acc.poster: 0.3586, Acc.stage: 0.5720, Acc.van: 0.6775, Acc.ship: 0.8558, Acc.fountain: 0.1007, Acc.conveyer belt: 0.9467, Acc.canopy: 0.4404, Acc.washer: 0.9353, Acc.plaything: 0.4722, Acc.swimming pool: 0.7437, Acc.stool: 0.6486, Acc.barrel: 0.7763, Acc.basket: 0.4938, Acc.waterfall: 0.8593, Acc.tent: 0.9736, Acc.bag: 0.3530, Acc.minibike: 0.8603, Acc.cradle: 0.9779, Acc.oven: 0.6792, Acc.ball: 0.5323, Acc.food: 0.5243, Acc.step: 0.1618, Acc.tank: 0.6531, Acc.trade name: 0.3597, Acc.microwave: 0.9417, Acc.pot: 0.5217, Acc.animal: 0.7243, Acc.bicycle: 0.7605, Acc.lake: 0.6324, Acc.dishwasher: 0.8005, Acc.screen: 0.8118, Acc.blanket: 0.3328, Acc.sculpture: 0.8446, Acc.hood: 0.7849, Acc.sconce: 0.6643, Acc.vase: 0.5505, Acc.traffic light: 0.6281, Acc.tray: 0.1951, Acc.ashcan: 0.5920, Acc.fan: 0.7371, Acc.pier: 0.6177, Acc.crt screen: 0.1568, Acc.plate: 0.6880, Acc.monitor: 0.4368, Acc.bulletin board: 0.4879, Acc.shower: 0.0123, Acc.radiator: 0.7841, Acc.glass: 0.1815, Acc.clock: 0.4878, Acc.flag: 0.7814 2023-11-02 23:16:32,005 - mmseg - INFO - Iter [12050/40000] lr: 2.264e-06, eta: 10:24:44, time: 2.457, data_time: 1.246, memory: 38534, decode.loss_ce: 0.2662, decode.acc_seg: 89.3307, loss: 0.2662 2023-11-02 23:17:32,721 - mmseg - INFO - Iter [12100/40000] lr: 2.260e-06, eta: 10:23:23, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2504, decode.acc_seg: 90.1504, loss: 0.2504 2023-11-02 23:18:33,347 - mmseg - INFO - Iter [12150/40000] lr: 2.256e-06, eta: 10:22:01, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2503, decode.acc_seg: 89.5932, loss: 0.2503 2023-11-02 23:19:34,000 - mmseg - INFO - Iter [12200/40000] lr: 2.252e-06, eta: 10:20:39, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2405, decode.acc_seg: 90.5127, loss: 0.2405 2023-11-02 23:20:34,673 - mmseg - INFO - Iter [12250/40000] lr: 2.248e-06, eta: 10:19:18, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2649, decode.acc_seg: 89.7124, loss: 0.2649 2023-11-02 23:21:35,386 - mmseg - INFO - Iter [12300/40000] lr: 2.244e-06, eta: 10:17:57, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2529, decode.acc_seg: 89.8442, loss: 0.2529 2023-11-02 23:22:36,132 - mmseg - INFO - Iter [12350/40000] lr: 2.240e-06, eta: 10:16:36, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2471, decode.acc_seg: 90.2113, loss: 0.2471 2023-11-02 23:23:36,855 - mmseg - INFO - Iter [12400/40000] lr: 2.236e-06, eta: 10:15:16, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2542, decode.acc_seg: 90.1018, loss: 0.2542 2023-11-02 23:24:37,584 - mmseg - INFO - Iter [12450/40000] lr: 2.231e-06, eta: 10:13:55, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2564, decode.acc_seg: 90.0287, loss: 0.2564 2023-11-02 23:25:38,292 - mmseg - INFO - Iter [12500/40000] lr: 2.227e-06, eta: 10:12:35, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2646, decode.acc_seg: 89.2932, loss: 0.2646 2023-11-02 23:26:39,011 - mmseg - INFO - Iter [12550/40000] lr: 2.223e-06, eta: 10:11:15, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2632, decode.acc_seg: 89.8300, loss: 0.2632 2023-11-02 23:27:39,725 - mmseg - INFO - Iter [12600/40000] lr: 2.219e-06, eta: 10:09:55, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2562, decode.acc_seg: 89.8633, loss: 0.2562 2023-11-02 23:28:42,755 - mmseg - INFO - Iter [12650/40000] lr: 2.215e-06, eta: 10:08:40, time: 1.261, data_time: 0.052, memory: 38534, decode.loss_ce: 0.2481, decode.acc_seg: 90.0811, loss: 0.2481 2023-11-02 23:29:43,458 - mmseg - INFO - Iter [12700/40000] lr: 2.211e-06, eta: 10:07:20, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2391, decode.acc_seg: 90.2423, loss: 0.2391 2023-11-02 23:30:44,102 - mmseg - INFO - Iter [12750/40000] lr: 2.207e-06, eta: 10:06:00, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2497, decode.acc_seg: 90.0628, loss: 0.2497 2023-11-02 23:31:44,788 - mmseg - INFO - Iter [12800/40000] lr: 2.203e-06, eta: 10:04:41, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2444, decode.acc_seg: 90.4957, loss: 0.2444 2023-11-02 23:32:45,415 - mmseg - INFO - Iter [12850/40000] lr: 2.199e-06, eta: 10:03:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2517, decode.acc_seg: 89.8052, loss: 0.2517 2023-11-02 23:33:46,072 - mmseg - INFO - Iter [12900/40000] lr: 2.195e-06, eta: 10:02:02, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2462, decode.acc_seg: 90.0752, loss: 0.2462 2023-11-02 23:34:46,746 - mmseg - INFO - Iter [12950/40000] lr: 2.191e-06, eta: 10:00:43, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2507, decode.acc_seg: 89.9879, loss: 0.2507 2023-11-02 23:35:47,476 - mmseg - INFO - Saving checkpoint at 13000 iterations 2023-11-02 23:36:46,643 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 23:36:46,643 - mmseg - INFO - Iter [13000/40000] lr: 2.187e-06, eta: 10:01:27, time: 2.398, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2718, decode.acc_seg: 89.4032, loss: 0.2718 2023-11-02 23:37:44,783 - mmseg - INFO - per class results: 2023-11-02 23:37:44,789 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.08 | 89.3 | | building | 81.58 | 92.21 | | sky | 94.23 | 97.4 | | floor | 82.8 | 89.86 | | tree | 74.03 | 90.69 | | ceiling | 84.88 | 92.92 | | road | 85.32 | 92.24 | | bed | 90.75 | 97.09 | | windowpane | 63.89 | 75.91 | | grass | 68.28 | 87.2 | | cabinet | 64.13 | 74.47 | | sidewalk | 69.38 | 81.02 | | person | 82.02 | 93.1 | | earth | 32.01 | 41.18 | | door | 55.54 | 72.5 | | table | 65.41 | 77.58 | | mountain | 65.89 | 78.08 | | plant | 52.89 | 61.07 | | curtain | 74.93 | 86.92 | | chair | 58.89 | 72.54 | | car | 85.26 | 92.46 | | water | 63.66 | 82.92 | | painting | 77.88 | 86.72 | | sofa | 79.16 | 90.53 | | shelf | 44.7 | 61.71 | | house | 29.77 | 34.59 | | sea | 67.06 | 74.9 | | mirror | 72.23 | 79.57 | | rug | 66.5 | 82.93 | | field | 29.3 | 39.67 | | armchair | 57.47 | 76.59 | | seat | 63.27 | 87.41 | | fence | 51.05 | 69.05 | | desk | 52.79 | 69.4 | | rock | 59.5 | 76.95 | | wardrobe | 49.48 | 74.31 | | lamp | 65.88 | 79.25 | | bathtub | 87.03 | 90.4 | | railing | 43.87 | 65.03 | | cushion | 62.28 | 73.44 | | base | 36.26 | 59.84 | | box | 35.86 | 50.13 | | column | 51.32 | 67.04 | | signboard | 33.96 | 43.09 | | chest of drawers | 41.3 | 58.29 | | counter | 53.35 | 73.79 | | sand | 59.56 | 86.52 | | sink | 74.72 | 83.84 | | skyscraper | 49.21 | 62.41 | | fireplace | 75.45 | 87.14 | | refrigerator | 82.36 | 88.77 | | grandstand | 48.23 | 80.72 | | path | 22.29 | 31.0 | | stairs | 39.7 | 52.54 | | runway | 68.8 | 96.85 | | case | 55.6 | 70.64 | | pool table | 90.93 | 98.23 | | pillow | 59.67 | 68.45 | | screen door | 85.74 | 90.89 | | stairway | 31.49 | 36.46 | | river | 11.73 | 20.95 | | bridge | 73.11 | 85.37 | | bookcase | 42.95 | 56.78 | | blind | 40.82 | 44.95 | | coffee table | 59.39 | 89.74 | | toilet | 87.22 | 89.63 | | flower | 46.23 | 61.26 | | book | 46.69 | 63.88 | | hill | 14.0 | 20.99 | | bench | 53.91 | 62.41 | | countertop | 64.68 | 76.61 | | stove | 79.43 | 85.36 | | palm | 48.46 | 72.76 | | kitchen island | 38.04 | 74.79 | | computer | 77.6 | 89.75 | | swivel chair | 48.96 | 83.08 | | boat | 74.47 | 84.96 | | bar | 69.61 | 76.41 | | arcade machine | 83.01 | 88.79 | | hovel | 26.07 | 28.8 | | bus | 92.39 | 94.57 | | towel | 71.23 | 80.26 | | light | 47.13 | 54.78 | | truck | 48.27 | 59.26 | | tower | 21.08 | 67.15 | | chandelier | 65.99 | 79.97 | | awning | 37.95 | 43.55 | | streetlight | 25.4 | 35.5 | | booth | 54.76 | 67.92 | | television receiver | 72.26 | 88.88 | | airplane | 78.24 | 87.61 | | dirt track | 0.0 | 0.0 | | apparel | 48.29 | 60.76 | | pole | 19.42 | 23.53 | | land | 1.0 | 2.08 | | bannister | 12.32 | 17.76 | | escalator | 61.47 | 77.81 | | ottoman | 49.91 | 67.26 | | bottle | 22.99 | 28.85 | | buffet | 53.78 | 75.92 | | poster | 34.3 | 47.86 | | stage | 23.81 | 41.55 | | van | 44.27 | 58.46 | | ship | 9.54 | 10.1 | | fountain | 17.73 | 18.11 | | conveyer belt | 80.28 | 95.96 | | canopy | 24.73 | 28.45 | | washer | 88.37 | 93.61 | | plaything | 29.57 | 37.14 | | swimming pool | 51.23 | 81.06 | | stool | 43.39 | 65.43 | | barrel | 60.7 | 76.52 | | basket | 38.41 | 49.54 | | waterfall | 47.0 | 53.07 | | tent | 95.45 | 96.5 | | bag | 29.7 | 35.81 | | minibike | 69.57 | 85.12 | | cradle | 85.67 | 96.49 | | oven | 62.52 | 75.48 | | ball | 44.92 | 49.88 | | food | 43.13 | 44.71 | | step | 19.06 | 24.52 | | tank | 53.16 | 65.75 | | trade name | 22.17 | 24.43 | | microwave | 87.0 | 91.86 | | pot | 52.38 | 59.91 | | animal | 76.07 | 83.2 | | bicycle | 56.97 | 75.03 | | lake | 52.23 | 63.59 | | dishwasher | 66.12 | 70.89 | | screen | 61.52 | 88.53 | | blanket | 26.6 | 31.08 | | sculpture | 72.49 | 88.46 | | hood | 63.7 | 74.53 | | sconce | 50.03 | 61.77 | | vase | 42.67 | 56.69 | | traffic light | 30.91 | 57.25 | | tray | 15.47 | 19.15 | | ashcan | 49.09 | 64.81 | | fan | 61.19 | 73.66 | | pier | 52.3 | 60.75 | | crt screen | 5.75 | 9.54 | | plate | 58.11 | 74.88 | | monitor | 39.3 | 50.11 | | bulletin board | 50.39 | 55.37 | | shower | 0.2 | 0.27 | | radiator | 66.32 | 74.3 | | glass | 18.93 | 21.06 | | clock | 37.07 | 41.76 | | flag | 65.39 | 72.28 | +---------------------+-------+-------+ 2023-11-02 23:37:44,789 - mmseg - INFO - Summary: 2023-11-02 23:37:44,789 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.54 | 53.85 | 65.77 | +-------+-------+-------+ 2023-11-02 23:37:44,790 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 23:37:44,790 - mmseg - INFO - Iter(val) [250] aAcc: 0.8454, mIoU: 0.5385, mAcc: 0.6577, IoU.wall: 0.8008, IoU.building: 0.8158, IoU.sky: 0.9423, IoU.floor: 0.8280, IoU.tree: 0.7403, IoU.ceiling: 0.8488, IoU.road: 0.8532, IoU.bed : 0.9075, IoU.windowpane: 0.6389, IoU.grass: 0.6828, IoU.cabinet: 0.6413, IoU.sidewalk: 0.6938, IoU.person: 0.8202, IoU.earth: 0.3201, IoU.door: 0.5554, IoU.table: 0.6541, IoU.mountain: 0.6589, IoU.plant: 0.5289, IoU.curtain: 0.7493, IoU.chair: 0.5889, IoU.car: 0.8526, IoU.water: 0.6366, IoU.painting: 0.7788, IoU.sofa: 0.7916, IoU.shelf: 0.4470, IoU.house: 0.2977, IoU.sea: 0.6706, IoU.mirror: 0.7223, IoU.rug: 0.6650, IoU.field: 0.2930, IoU.armchair: 0.5747, IoU.seat: 0.6327, IoU.fence: 0.5105, IoU.desk: 0.5279, IoU.rock: 0.5950, IoU.wardrobe: 0.4948, IoU.lamp: 0.6588, IoU.bathtub: 0.8703, IoU.railing: 0.4387, IoU.cushion: 0.6228, IoU.base: 0.3626, IoU.box: 0.3586, IoU.column: 0.5132, IoU.signboard: 0.3396, IoU.chest of drawers: 0.4130, IoU.counter: 0.5335, IoU.sand: 0.5956, IoU.sink: 0.7472, IoU.skyscraper: 0.4921, IoU.fireplace: 0.7545, IoU.refrigerator: 0.8236, IoU.grandstand: 0.4823, IoU.path: 0.2229, IoU.stairs: 0.3970, IoU.runway: 0.6880, IoU.case: 0.5560, IoU.pool table: 0.9093, IoU.pillow: 0.5967, IoU.screen door: 0.8574, IoU.stairway: 0.3149, IoU.river: 0.1173, IoU.bridge: 0.7311, IoU.bookcase: 0.4295, IoU.blind: 0.4082, IoU.coffee table: 0.5939, IoU.toilet: 0.8722, IoU.flower: 0.4623, IoU.book: 0.4669, IoU.hill: 0.1400, IoU.bench: 0.5391, IoU.countertop: 0.6468, IoU.stove: 0.7943, IoU.palm: 0.4846, IoU.kitchen island: 0.3804, IoU.computer: 0.7760, IoU.swivel chair: 0.4896, IoU.boat: 0.7447, IoU.bar: 0.6961, IoU.arcade machine: 0.8301, IoU.hovel: 0.2607, IoU.bus: 0.9239, IoU.towel: 0.7123, IoU.light: 0.4713, IoU.truck: 0.4827, IoU.tower: 0.2108, IoU.chandelier: 0.6599, IoU.awning: 0.3795, IoU.streetlight: 0.2540, IoU.booth: 0.5476, IoU.television receiver: 0.7226, IoU.airplane: 0.7824, IoU.dirt track: 0.0000, IoU.apparel: 0.4829, IoU.pole: 0.1942, IoU.land: 0.0100, IoU.bannister: 0.1232, IoU.escalator: 0.6147, IoU.ottoman: 0.4991, IoU.bottle: 0.2299, IoU.buffet: 0.5378, IoU.poster: 0.3430, IoU.stage: 0.2381, IoU.van: 0.4427, IoU.ship: 0.0954, IoU.fountain: 0.1773, IoU.conveyer belt: 0.8028, IoU.canopy: 0.2473, IoU.washer: 0.8837, IoU.plaything: 0.2957, IoU.swimming pool: 0.5123, IoU.stool: 0.4339, IoU.barrel: 0.6070, IoU.basket: 0.3841, IoU.waterfall: 0.4700, IoU.tent: 0.9545, IoU.bag: 0.2970, IoU.minibike: 0.6957, IoU.cradle: 0.8567, IoU.oven: 0.6252, IoU.ball: 0.4492, IoU.food: 0.4313, IoU.step: 0.1906, IoU.tank: 0.5316, IoU.trade name: 0.2217, IoU.microwave: 0.8700, IoU.pot: 0.5238, IoU.animal: 0.7607, IoU.bicycle: 0.5697, IoU.lake: 0.5223, IoU.dishwasher: 0.6612, IoU.screen: 0.6152, IoU.blanket: 0.2660, IoU.sculpture: 0.7249, IoU.hood: 0.6370, IoU.sconce: 0.5003, IoU.vase: 0.4267, IoU.traffic light: 0.3091, IoU.tray: 0.1547, IoU.ashcan: 0.4909, IoU.fan: 0.6119, IoU.pier: 0.5230, IoU.crt screen: 0.0575, IoU.plate: 0.5811, IoU.monitor: 0.3930, IoU.bulletin board: 0.5039, IoU.shower: 0.0020, IoU.radiator: 0.6632, IoU.glass: 0.1893, IoU.clock: 0.3707, IoU.flag: 0.6539, Acc.wall: 0.8930, Acc.building: 0.9221, Acc.sky: 0.9740, Acc.floor: 0.8986, Acc.tree: 0.9069, Acc.ceiling: 0.9292, Acc.road: 0.9224, Acc.bed : 0.9709, Acc.windowpane: 0.7591, Acc.grass: 0.8720, Acc.cabinet: 0.7447, Acc.sidewalk: 0.8102, Acc.person: 0.9310, Acc.earth: 0.4118, Acc.door: 0.7250, Acc.table: 0.7758, Acc.mountain: 0.7808, Acc.plant: 0.6107, Acc.curtain: 0.8692, Acc.chair: 0.7254, Acc.car: 0.9246, Acc.water: 0.8292, Acc.painting: 0.8672, Acc.sofa: 0.9053, Acc.shelf: 0.6171, Acc.house: 0.3459, Acc.sea: 0.7490, Acc.mirror: 0.7957, Acc.rug: 0.8293, Acc.field: 0.3967, Acc.armchair: 0.7659, Acc.seat: 0.8741, Acc.fence: 0.6905, Acc.desk: 0.6940, Acc.rock: 0.7695, Acc.wardrobe: 0.7431, Acc.lamp: 0.7925, Acc.bathtub: 0.9040, Acc.railing: 0.6503, Acc.cushion: 0.7344, Acc.base: 0.5984, Acc.box: 0.5013, Acc.column: 0.6704, Acc.signboard: 0.4309, Acc.chest of drawers: 0.5829, Acc.counter: 0.7379, Acc.sand: 0.8652, Acc.sink: 0.8384, Acc.skyscraper: 0.6241, Acc.fireplace: 0.8714, Acc.refrigerator: 0.8877, Acc.grandstand: 0.8072, Acc.path: 0.3100, Acc.stairs: 0.5254, Acc.runway: 0.9685, Acc.case: 0.7064, Acc.pool table: 0.9823, Acc.pillow: 0.6845, Acc.screen door: 0.9089, Acc.stairway: 0.3646, Acc.river: 0.2095, Acc.bridge: 0.8537, Acc.bookcase: 0.5678, Acc.blind: 0.4495, Acc.coffee table: 0.8974, Acc.toilet: 0.8963, Acc.flower: 0.6126, Acc.book: 0.6388, Acc.hill: 0.2099, Acc.bench: 0.6241, Acc.countertop: 0.7661, Acc.stove: 0.8536, Acc.palm: 0.7276, Acc.kitchen island: 0.7479, Acc.computer: 0.8975, Acc.swivel chair: 0.8308, Acc.boat: 0.8496, Acc.bar: 0.7641, Acc.arcade machine: 0.8879, Acc.hovel: 0.2880, Acc.bus: 0.9457, Acc.towel: 0.8026, Acc.light: 0.5478, Acc.truck: 0.5926, Acc.tower: 0.6715, Acc.chandelier: 0.7997, Acc.awning: 0.4355, Acc.streetlight: 0.3550, Acc.booth: 0.6792, Acc.television receiver: 0.8888, Acc.airplane: 0.8761, Acc.dirt track: 0.0000, Acc.apparel: 0.6076, Acc.pole: 0.2353, Acc.land: 0.0208, Acc.bannister: 0.1776, Acc.escalator: 0.7781, Acc.ottoman: 0.6726, Acc.bottle: 0.2885, Acc.buffet: 0.7592, Acc.poster: 0.4786, Acc.stage: 0.4155, Acc.van: 0.5846, Acc.ship: 0.1010, Acc.fountain: 0.1811, Acc.conveyer belt: 0.9596, Acc.canopy: 0.2845, Acc.washer: 0.9361, Acc.plaything: 0.3714, Acc.swimming pool: 0.8106, Acc.stool: 0.6543, Acc.barrel: 0.7652, Acc.basket: 0.4954, Acc.waterfall: 0.5307, Acc.tent: 0.9650, Acc.bag: 0.3581, Acc.minibike: 0.8512, Acc.cradle: 0.9649, Acc.oven: 0.7548, Acc.ball: 0.4988, Acc.food: 0.4471, Acc.step: 0.2452, Acc.tank: 0.6575, Acc.trade name: 0.2443, Acc.microwave: 0.9186, Acc.pot: 0.5991, Acc.animal: 0.8320, Acc.bicycle: 0.7503, Acc.lake: 0.6359, Acc.dishwasher: 0.7089, Acc.screen: 0.8853, Acc.blanket: 0.3108, Acc.sculpture: 0.8846, Acc.hood: 0.7453, Acc.sconce: 0.6177, Acc.vase: 0.5669, Acc.traffic light: 0.5725, Acc.tray: 0.1915, Acc.ashcan: 0.6481, Acc.fan: 0.7366, Acc.pier: 0.6075, Acc.crt screen: 0.0954, Acc.plate: 0.7488, Acc.monitor: 0.5011, Acc.bulletin board: 0.5537, Acc.shower: 0.0027, Acc.radiator: 0.7430, Acc.glass: 0.2106, Acc.clock: 0.4176, Acc.flag: 0.7228 2023-11-02 23:38:45,558 - mmseg - INFO - Iter [13050/40000] lr: 2.183e-06, eta: 10:02:07, time: 2.378, data_time: 1.171, memory: 38534, decode.loss_ce: 0.2482, decode.acc_seg: 90.0492, loss: 0.2482 2023-11-02 23:39:46,302 - mmseg - INFO - Iter [13100/40000] lr: 2.179e-06, eta: 10:00:48, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2583, decode.acc_seg: 90.0181, loss: 0.2583 2023-11-02 23:40:47,018 - mmseg - INFO - Iter [13150/40000] lr: 2.175e-06, eta: 9:59:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2375, decode.acc_seg: 90.1785, loss: 0.2375 2023-11-02 23:41:47,656 - mmseg - INFO - Iter [13200/40000] lr: 2.171e-06, eta: 9:58:08, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2434, decode.acc_seg: 90.2604, loss: 0.2434 2023-11-02 23:42:48,336 - mmseg - INFO - Iter [13250/40000] lr: 2.167e-06, eta: 9:56:48, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2499, decode.acc_seg: 90.3120, loss: 0.2499 2023-11-02 23:43:51,341 - mmseg - INFO - Iter [13300/40000] lr: 2.163e-06, eta: 9:55:33, time: 1.260, data_time: 0.052, memory: 38534, decode.loss_ce: 0.2520, decode.acc_seg: 89.6975, loss: 0.2520 2023-11-02 23:44:52,023 - mmseg - INFO - Iter [13350/40000] lr: 2.159e-06, eta: 9:54:14, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2590, decode.acc_seg: 89.7500, loss: 0.2590 2023-11-02 23:45:52,737 - mmseg - INFO - Iter [13400/40000] lr: 2.155e-06, eta: 9:52:55, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2596, decode.acc_seg: 89.4795, loss: 0.2596 2023-11-02 23:46:53,445 - mmseg - INFO - Iter [13450/40000] lr: 2.150e-06, eta: 9:51:36, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2470, decode.acc_seg: 90.1239, loss: 0.2470 2023-11-02 23:47:54,118 - mmseg - INFO - Iter [13500/40000] lr: 2.146e-06, eta: 9:50:17, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2338, decode.acc_seg: 90.4833, loss: 0.2338 2023-11-02 23:48:54,752 - mmseg - INFO - Iter [13550/40000] lr: 2.142e-06, eta: 9:48:58, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2345, decode.acc_seg: 90.3991, loss: 0.2345 2023-11-02 23:49:55,415 - mmseg - INFO - Iter [13600/40000] lr: 2.138e-06, eta: 9:47:39, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2521, decode.acc_seg: 89.9619, loss: 0.2521 2023-11-02 23:50:56,066 - mmseg - INFO - Iter [13650/40000] lr: 2.134e-06, eta: 9:46:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2468, decode.acc_seg: 90.2059, loss: 0.2468 2023-11-02 23:51:56,736 - mmseg - INFO - Iter [13700/40000] lr: 2.130e-06, eta: 9:45:02, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2369, decode.acc_seg: 90.3178, loss: 0.2369 2023-11-02 23:52:57,452 - mmseg - INFO - Iter [13750/40000] lr: 2.126e-06, eta: 9:43:44, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2402, decode.acc_seg: 90.1960, loss: 0.2402 2023-11-02 23:53:58,090 - mmseg - INFO - Iter [13800/40000] lr: 2.122e-06, eta: 9:42:26, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2517, decode.acc_seg: 89.7807, loss: 0.2517 2023-11-02 23:54:58,746 - mmseg - INFO - Iter [13850/40000] lr: 2.118e-06, eta: 9:41:08, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2407, decode.acc_seg: 90.4654, loss: 0.2407 2023-11-02 23:55:59,447 - mmseg - INFO - Iter [13900/40000] lr: 2.114e-06, eta: 9:39:50, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2373, decode.acc_seg: 90.5070, loss: 0.2373 2023-11-02 23:57:02,483 - mmseg - INFO - Iter [13950/40000] lr: 2.110e-06, eta: 9:38:36, time: 1.261, data_time: 0.051, memory: 38534, decode.loss_ce: 0.2488, decode.acc_seg: 90.0792, loss: 0.2488 2023-11-02 23:58:03,189 - mmseg - INFO - Saving checkpoint at 14000 iterations 2023-11-02 23:59:01,536 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 23:59:01,536 - mmseg - INFO - Iter [14000/40000] lr: 2.106e-06, eta: 9:39:07, time: 2.381, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2515, decode.acc_seg: 90.0193, loss: 0.2515 2023-11-02 23:59:59,433 - mmseg - INFO - per class results: 2023-11-02 23:59:59,439 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.52 | 88.62 | | building | 83.21 | 94.26 | | sky | 94.09 | 96.99 | | floor | 82.83 | 90.29 | | tree | 74.88 | 87.53 | | ceiling | 84.27 | 92.73 | | road | 85.73 | 90.93 | | bed | 90.47 | 96.75 | | windowpane | 64.33 | 79.85 | | grass | 71.21 | 81.15 | | cabinet | 63.97 | 72.27 | | sidewalk | 69.9 | 83.66 | | person | 81.85 | 92.13 | | earth | 38.31 | 54.75 | | door | 53.81 | 74.46 | | table | 67.72 | 83.08 | | mountain | 61.84 | 76.15 | | plant | 53.87 | 61.81 | | curtain | 71.5 | 90.14 | | chair | 61.21 | 73.31 | | car | 84.65 | 91.59 | | water | 61.28 | 79.3 | | painting | 75.6 | 84.99 | | sofa | 77.08 | 91.21 | | shelf | 47.82 | 67.07 | | house | 40.79 | 44.28 | | sea | 58.92 | 72.75 | | mirror | 73.12 | 81.45 | | rug | 64.19 | 73.95 | | field | 39.71 | 56.81 | | armchair | 55.39 | 70.51 | | seat | 64.44 | 88.18 | | fence | 49.22 | 66.06 | | desk | 52.72 | 79.84 | | rock | 58.38 | 86.55 | | wardrobe | 50.1 | 68.51 | | lamp | 65.31 | 77.6 | | bathtub | 87.52 | 89.51 | | railing | 41.49 | 54.17 | | cushion | 62.67 | 74.75 | | base | 34.48 | 54.81 | | box | 36.73 | 46.72 | | column | 52.25 | 66.63 | | signboard | 34.93 | 47.36 | | chest of drawers | 41.6 | 73.83 | | counter | 50.91 | 62.02 | | sand | 61.05 | 88.99 | | sink | 74.8 | 80.4 | | skyscraper | 45.86 | 49.32 | | fireplace | 72.64 | 92.78 | | refrigerator | 78.66 | 90.52 | | grandstand | 46.94 | 82.41 | | path | 22.11 | 27.91 | | stairs | 39.54 | 51.33 | | runway | 67.22 | 85.9 | | case | 66.59 | 84.32 | | pool table | 91.77 | 97.88 | | pillow | 53.05 | 58.06 | | screen door | 44.5 | 45.55 | | stairway | 37.84 | 46.52 | | river | 24.32 | 42.0 | | bridge | 75.02 | 84.67 | | bookcase | 43.99 | 52.5 | | blind | 41.67 | 46.42 | | coffee table | 69.14 | 84.98 | | toilet | 87.78 | 91.18 | | flower | 44.07 | 64.26 | | book | 49.34 | 71.63 | | hill | 7.0 | 16.43 | | bench | 54.98 | 62.44 | | countertop | 60.01 | 67.75 | | stove | 82.7 | 89.29 | | palm | 47.65 | 78.89 | | kitchen island | 34.53 | 52.99 | | computer | 76.48 | 88.48 | | swivel chair | 47.7 | 72.81 | | boat | 71.2 | 86.98 | | bar | 70.39 | 83.52 | | arcade machine | 78.11 | 81.77 | | hovel | 33.32 | 37.13 | | bus | 91.79 | 97.18 | | towel | 69.19 | 75.61 | | light | 48.92 | 59.57 | | truck | 46.73 | 59.11 | | tower | 24.81 | 43.36 | | chandelier | 65.47 | 81.17 | | awning | 45.44 | 56.29 | | streetlight | 22.78 | 30.51 | | booth | 36.7 | 37.06 | | television receiver | 69.32 | 89.97 | | airplane | 84.37 | 92.72 | | dirt track | 5.04 | 20.64 | | apparel | 44.16 | 59.93 | | pole | 15.98 | 18.34 | | land | 10.86 | 17.5 | | bannister | 13.26 | 16.53 | | escalator | 58.54 | 87.12 | | ottoman | 54.27 | 73.85 | | bottle | 22.74 | 28.99 | | buffet | 56.89 | 70.17 | | poster | 28.55 | 48.67 | | stage | 26.52 | 52.49 | | van | 47.2 | 78.83 | | ship | 15.6 | 16.38 | | fountain | 33.97 | 34.39 | | conveyer belt | 84.41 | 94.66 | | canopy | 10.76 | 13.98 | | washer | 89.0 | 94.32 | | plaything | 29.14 | 42.91 | | swimming pool | 57.6 | 84.34 | | stool | 48.49 | 63.68 | | barrel | 64.78 | 78.28 | | basket | 36.12 | 47.52 | | waterfall | 44.91 | 52.57 | | tent | 95.45 | 96.73 | | bag | 21.36 | 25.61 | | minibike | 71.8 | 83.86 | | cradle | 86.01 | 97.02 | | oven | 62.97 | 76.6 | | ball | 53.04 | 59.24 | | food | 60.0 | 66.98 | | step | 8.27 | 9.36 | | tank | 54.59 | 65.19 | | trade name | 25.98 | 29.89 | | microwave | 85.83 | 89.39 | | pot | 53.51 | 60.11 | | animal | 74.95 | 80.46 | | bicycle | 56.82 | 80.03 | | lake | 57.44 | 63.31 | | dishwasher | 69.2 | 78.13 | | screen | 57.3 | 91.64 | | blanket | 32.42 | 40.61 | | sculpture | 68.96 | 89.61 | | hood | 69.37 | 82.96 | | sconce | 52.12 | 64.2 | | vase | 42.92 | 57.35 | | traffic light | 32.14 | 56.93 | | tray | 19.32 | 25.99 | | ashcan | 53.53 | 65.13 | | fan | 59.95 | 72.07 | | pier | 36.11 | 39.39 | | crt screen | 6.4 | 7.76 | | plate | 56.02 | 69.33 | | monitor | 54.32 | 71.05 | | bulletin board | 48.48 | 58.88 | | shower | 0.8 | 0.94 | | radiator | 63.97 | 76.25 | | glass | 17.58 | 19.04 | | clock | 38.99 | 46.28 | | flag | 63.28 | 66.86 | +---------------------+-------+-------+ 2023-11-02 23:59:59,439 - mmseg - INFO - Summary: 2023-11-02 23:59:59,439 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.67 | 54.09 | 66.21 | +-------+-------+-------+ 2023-11-02 23:59:59,439 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-02 23:59:59,440 - mmseg - INFO - Iter(val) [250] aAcc: 0.8467, mIoU: 0.5409, mAcc: 0.6621, IoU.wall: 0.7952, IoU.building: 0.8321, IoU.sky: 0.9409, IoU.floor: 0.8283, IoU.tree: 0.7488, IoU.ceiling: 0.8427, IoU.road: 0.8573, IoU.bed : 0.9047, IoU.windowpane: 0.6433, IoU.grass: 0.7121, IoU.cabinet: 0.6397, IoU.sidewalk: 0.6990, IoU.person: 0.8185, IoU.earth: 0.3831, IoU.door: 0.5381, IoU.table: 0.6772, IoU.mountain: 0.6184, IoU.plant: 0.5387, IoU.curtain: 0.7150, IoU.chair: 0.6121, IoU.car: 0.8465, IoU.water: 0.6128, IoU.painting: 0.7560, IoU.sofa: 0.7708, IoU.shelf: 0.4782, IoU.house: 0.4079, IoU.sea: 0.5892, IoU.mirror: 0.7312, IoU.rug: 0.6419, IoU.field: 0.3971, IoU.armchair: 0.5539, IoU.seat: 0.6444, IoU.fence: 0.4922, IoU.desk: 0.5272, IoU.rock: 0.5838, IoU.wardrobe: 0.5010, IoU.lamp: 0.6531, IoU.bathtub: 0.8752, IoU.railing: 0.4149, IoU.cushion: 0.6267, IoU.base: 0.3448, IoU.box: 0.3673, IoU.column: 0.5225, IoU.signboard: 0.3493, IoU.chest of drawers: 0.4160, IoU.counter: 0.5091, IoU.sand: 0.6105, IoU.sink: 0.7480, IoU.skyscraper: 0.4586, IoU.fireplace: 0.7264, IoU.refrigerator: 0.7866, IoU.grandstand: 0.4694, IoU.path: 0.2211, IoU.stairs: 0.3954, IoU.runway: 0.6722, IoU.case: 0.6659, IoU.pool table: 0.9177, IoU.pillow: 0.5305, IoU.screen door: 0.4450, IoU.stairway: 0.3784, IoU.river: 0.2432, IoU.bridge: 0.7502, IoU.bookcase: 0.4399, IoU.blind: 0.4167, IoU.coffee table: 0.6914, IoU.toilet: 0.8778, IoU.flower: 0.4407, IoU.book: 0.4934, IoU.hill: 0.0700, IoU.bench: 0.5498, IoU.countertop: 0.6001, IoU.stove: 0.8270, IoU.palm: 0.4765, IoU.kitchen island: 0.3453, IoU.computer: 0.7648, IoU.swivel chair: 0.4770, IoU.boat: 0.7120, IoU.bar: 0.7039, IoU.arcade machine: 0.7811, IoU.hovel: 0.3332, IoU.bus: 0.9179, IoU.towel: 0.6919, IoU.light: 0.4892, IoU.truck: 0.4673, IoU.tower: 0.2481, IoU.chandelier: 0.6547, IoU.awning: 0.4544, IoU.streetlight: 0.2278, IoU.booth: 0.3670, IoU.television receiver: 0.6932, IoU.airplane: 0.8437, IoU.dirt track: 0.0504, IoU.apparel: 0.4416, IoU.pole: 0.1598, IoU.land: 0.1086, IoU.bannister: 0.1326, IoU.escalator: 0.5854, IoU.ottoman: 0.5427, IoU.bottle: 0.2274, IoU.buffet: 0.5689, IoU.poster: 0.2855, IoU.stage: 0.2652, IoU.van: 0.4720, IoU.ship: 0.1560, IoU.fountain: 0.3397, IoU.conveyer belt: 0.8441, IoU.canopy: 0.1076, IoU.washer: 0.8900, IoU.plaything: 0.2914, IoU.swimming pool: 0.5760, IoU.stool: 0.4849, IoU.barrel: 0.6478, IoU.basket: 0.3612, IoU.waterfall: 0.4491, IoU.tent: 0.9545, IoU.bag: 0.2136, IoU.minibike: 0.7180, IoU.cradle: 0.8601, IoU.oven: 0.6297, IoU.ball: 0.5304, IoU.food: 0.6000, IoU.step: 0.0827, IoU.tank: 0.5459, IoU.trade name: 0.2598, IoU.microwave: 0.8583, IoU.pot: 0.5351, IoU.animal: 0.7495, IoU.bicycle: 0.5682, IoU.lake: 0.5744, IoU.dishwasher: 0.6920, IoU.screen: 0.5730, IoU.blanket: 0.3242, IoU.sculpture: 0.6896, IoU.hood: 0.6937, IoU.sconce: 0.5212, IoU.vase: 0.4292, IoU.traffic light: 0.3214, IoU.tray: 0.1932, IoU.ashcan: 0.5353, IoU.fan: 0.5995, IoU.pier: 0.3611, IoU.crt screen: 0.0640, IoU.plate: 0.5602, IoU.monitor: 0.5432, IoU.bulletin board: 0.4848, IoU.shower: 0.0080, IoU.radiator: 0.6397, IoU.glass: 0.1758, IoU.clock: 0.3899, IoU.flag: 0.6328, Acc.wall: 0.8862, Acc.building: 0.9426, Acc.sky: 0.9699, Acc.floor: 0.9029, Acc.tree: 0.8753, Acc.ceiling: 0.9273, Acc.road: 0.9093, Acc.bed : 0.9675, Acc.windowpane: 0.7985, Acc.grass: 0.8115, Acc.cabinet: 0.7227, Acc.sidewalk: 0.8366, Acc.person: 0.9213, Acc.earth: 0.5475, Acc.door: 0.7446, Acc.table: 0.8308, Acc.mountain: 0.7615, Acc.plant: 0.6181, Acc.curtain: 0.9014, Acc.chair: 0.7331, Acc.car: 0.9159, Acc.water: 0.7930, Acc.painting: 0.8499, Acc.sofa: 0.9121, Acc.shelf: 0.6707, Acc.house: 0.4428, Acc.sea: 0.7275, Acc.mirror: 0.8145, Acc.rug: 0.7395, Acc.field: 0.5681, Acc.armchair: 0.7051, Acc.seat: 0.8818, Acc.fence: 0.6606, Acc.desk: 0.7984, Acc.rock: 0.8655, Acc.wardrobe: 0.6851, Acc.lamp: 0.7760, Acc.bathtub: 0.8951, Acc.railing: 0.5417, Acc.cushion: 0.7475, Acc.base: 0.5481, Acc.box: 0.4672, Acc.column: 0.6663, Acc.signboard: 0.4736, Acc.chest of drawers: 0.7383, Acc.counter: 0.6202, Acc.sand: 0.8899, Acc.sink: 0.8040, Acc.skyscraper: 0.4932, Acc.fireplace: 0.9278, Acc.refrigerator: 0.9052, Acc.grandstand: 0.8241, Acc.path: 0.2791, Acc.stairs: 0.5133, Acc.runway: 0.8590, Acc.case: 0.8432, Acc.pool table: 0.9788, Acc.pillow: 0.5806, Acc.screen door: 0.4555, Acc.stairway: 0.4652, Acc.river: 0.4200, Acc.bridge: 0.8467, Acc.bookcase: 0.5250, Acc.blind: 0.4642, Acc.coffee table: 0.8498, Acc.toilet: 0.9118, Acc.flower: 0.6426, Acc.book: 0.7163, Acc.hill: 0.1643, Acc.bench: 0.6244, Acc.countertop: 0.6775, Acc.stove: 0.8929, Acc.palm: 0.7889, Acc.kitchen island: 0.5299, Acc.computer: 0.8848, Acc.swivel chair: 0.7281, Acc.boat: 0.8698, Acc.bar: 0.8352, Acc.arcade machine: 0.8177, Acc.hovel: 0.3713, Acc.bus: 0.9718, Acc.towel: 0.7561, Acc.light: 0.5957, Acc.truck: 0.5911, Acc.tower: 0.4336, Acc.chandelier: 0.8117, Acc.awning: 0.5629, Acc.streetlight: 0.3051, Acc.booth: 0.3706, Acc.television receiver: 0.8997, Acc.airplane: 0.9272, Acc.dirt track: 0.2064, Acc.apparel: 0.5993, Acc.pole: 0.1834, Acc.land: 0.1750, Acc.bannister: 0.1653, Acc.escalator: 0.8712, Acc.ottoman: 0.7385, Acc.bottle: 0.2899, Acc.buffet: 0.7017, Acc.poster: 0.4867, Acc.stage: 0.5249, Acc.van: 0.7883, Acc.ship: 0.1638, Acc.fountain: 0.3439, Acc.conveyer belt: 0.9466, Acc.canopy: 0.1398, Acc.washer: 0.9432, Acc.plaything: 0.4291, Acc.swimming pool: 0.8434, Acc.stool: 0.6368, Acc.barrel: 0.7828, Acc.basket: 0.4752, Acc.waterfall: 0.5257, Acc.tent: 0.9673, Acc.bag: 0.2561, Acc.minibike: 0.8386, Acc.cradle: 0.9702, Acc.oven: 0.7660, Acc.ball: 0.5924, Acc.food: 0.6698, Acc.step: 0.0936, Acc.tank: 0.6519, Acc.trade name: 0.2989, Acc.microwave: 0.8939, Acc.pot: 0.6011, Acc.animal: 0.8046, Acc.bicycle: 0.8003, Acc.lake: 0.6331, Acc.dishwasher: 0.7813, Acc.screen: 0.9164, Acc.blanket: 0.4061, Acc.sculpture: 0.8961, Acc.hood: 0.8296, Acc.sconce: 0.6420, Acc.vase: 0.5735, Acc.traffic light: 0.5693, Acc.tray: 0.2599, Acc.ashcan: 0.6513, Acc.fan: 0.7207, Acc.pier: 0.3939, Acc.crt screen: 0.0776, Acc.plate: 0.6933, Acc.monitor: 0.7105, Acc.bulletin board: 0.5888, Acc.shower: 0.0094, Acc.radiator: 0.7625, Acc.glass: 0.1904, Acc.clock: 0.4628, Acc.flag: 0.6686 2023-11-03 00:01:00,293 - mmseg - INFO - Iter [14050/40000] lr: 2.102e-06, eta: 9:39:36, time: 2.375, data_time: 1.165, memory: 38534, decode.loss_ce: 0.2365, decode.acc_seg: 90.3195, loss: 0.2365 2023-11-03 00:02:00,962 - mmseg - INFO - Iter [14100/40000] lr: 2.098e-06, eta: 9:38:18, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2324, decode.acc_seg: 90.7870, loss: 0.2324 2023-11-03 00:03:01,618 - mmseg - INFO - Iter [14150/40000] lr: 2.094e-06, eta: 9:36:59, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2357, decode.acc_seg: 90.5697, loss: 0.2357 2023-11-03 00:04:02,264 - mmseg - INFO - Iter [14200/40000] lr: 2.090e-06, eta: 9:35:41, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2421, decode.acc_seg: 90.6315, loss: 0.2421 2023-11-03 00:05:02,916 - mmseg - INFO - Iter [14250/40000] lr: 2.086e-06, eta: 9:34:22, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2385, decode.acc_seg: 90.2623, loss: 0.2385 2023-11-03 00:06:03,560 - mmseg - INFO - Iter [14300/40000] lr: 2.082e-06, eta: 9:33:04, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2399, decode.acc_seg: 90.3806, loss: 0.2399 2023-11-03 00:07:04,164 - mmseg - INFO - Iter [14350/40000] lr: 2.078e-06, eta: 9:31:46, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2399, decode.acc_seg: 90.5866, loss: 0.2399 2023-11-03 00:08:04,775 - mmseg - INFO - Iter [14400/40000] lr: 2.074e-06, eta: 9:30:28, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2452, decode.acc_seg: 90.0101, loss: 0.2452 2023-11-03 00:09:05,383 - mmseg - INFO - Iter [14450/40000] lr: 2.069e-06, eta: 9:29:10, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2497, decode.acc_seg: 89.9983, loss: 0.2497 2023-11-03 00:10:06,002 - mmseg - INFO - Iter [14500/40000] lr: 2.065e-06, eta: 9:27:52, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2700, decode.acc_seg: 88.7577, loss: 0.2700 2023-11-03 00:11:09,021 - mmseg - INFO - Iter [14550/40000] lr: 2.061e-06, eta: 9:26:39, time: 1.260, data_time: 0.054, memory: 38534, decode.loss_ce: 0.2515, decode.acc_seg: 89.8489, loss: 0.2515 2023-11-03 00:12:09,645 - mmseg - INFO - Iter [14600/40000] lr: 2.057e-06, eta: 9:25:21, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2677, decode.acc_seg: 89.8456, loss: 0.2677 2023-11-03 00:13:10,323 - mmseg - INFO - Iter [14650/40000] lr: 2.053e-06, eta: 9:24:04, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2370, decode.acc_seg: 90.6419, loss: 0.2370 2023-11-03 00:14:10,968 - mmseg - INFO - Iter [14700/40000] lr: 2.049e-06, eta: 9:22:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2465, decode.acc_seg: 90.1254, loss: 0.2465 2023-11-03 00:15:11,609 - mmseg - INFO - Iter [14750/40000] lr: 2.045e-06, eta: 9:21:29, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2389, decode.acc_seg: 90.7709, loss: 0.2389 2023-11-03 00:16:16,131 - mmseg - INFO - Iter [14800/40000] lr: 2.041e-06, eta: 9:20:19, time: 1.290, data_time: 0.079, memory: 38534, decode.loss_ce: 0.2269, decode.acc_seg: 90.8617, loss: 0.2269 2023-11-03 00:17:16,745 - mmseg - INFO - Iter [14850/40000] lr: 2.037e-06, eta: 9:19:02, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2186, decode.acc_seg: 91.2779, loss: 0.2186 2023-11-03 00:18:17,403 - mmseg - INFO - Iter [14900/40000] lr: 2.033e-06, eta: 9:17:45, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2228, decode.acc_seg: 91.0142, loss: 0.2228 2023-11-03 00:19:18,074 - mmseg - INFO - Iter [14950/40000] lr: 2.029e-06, eta: 9:16:28, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2350, decode.acc_seg: 90.8978, loss: 0.2350 2023-11-03 00:20:18,719 - mmseg - INFO - Saving checkpoint at 15000 iterations 2023-11-03 00:21:16,736 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 00:21:16,736 - mmseg - INFO - Iter [15000/40000] lr: 2.025e-06, eta: 9:16:49, time: 2.373, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2321, decode.acc_seg: 90.4244, loss: 0.2321 2023-11-03 00:22:20,287 - mmseg - INFO - per class results: 2023-11-03 00:22:20,292 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.91 | 89.33 | | building | 82.84 | 93.53 | | sky | 94.13 | 97.35 | | floor | 83.36 | 90.83 | | tree | 74.89 | 86.52 | | ceiling | 84.91 | 92.74 | | road | 84.53 | 91.74 | | bed | 91.33 | 96.34 | | windowpane | 64.71 | 79.34 | | grass | 69.41 | 81.39 | | cabinet | 64.66 | 75.79 | | sidewalk | 66.46 | 80.24 | | person | 82.45 | 92.03 | | earth | 37.71 | 52.39 | | door | 55.47 | 71.02 | | table | 67.29 | 78.79 | | mountain | 60.94 | 76.39 | | plant | 53.74 | 64.29 | | curtain | 76.13 | 86.7 | | chair | 61.06 | 74.94 | | car | 84.5 | 94.01 | | water | 61.66 | 80.54 | | painting | 76.35 | 88.56 | | sofa | 79.52 | 90.24 | | shelf | 46.97 | 63.42 | | house | 45.88 | 56.5 | | sea | 63.06 | 72.14 | | mirror | 73.43 | 83.32 | | rug | 62.35 | 69.64 | | field | 37.14 | 61.99 | | armchair | 56.83 | 72.99 | | seat | 63.71 | 86.48 | | fence | 38.76 | 50.25 | | desk | 53.08 | 66.4 | | rock | 54.2 | 81.3 | | wardrobe | 49.91 | 68.7 | | lamp | 64.79 | 76.13 | | bathtub | 87.8 | 90.06 | | railing | 42.46 | 56.82 | | cushion | 62.39 | 75.84 | | base | 32.81 | 60.49 | | box | 34.39 | 41.66 | | column | 45.83 | 52.49 | | signboard | 37.21 | 49.44 | | chest of drawers | 42.98 | 63.99 | | counter | 44.96 | 55.34 | | sand | 62.26 | 89.17 | | sink | 74.65 | 80.82 | | skyscraper | 46.6 | 64.11 | | fireplace | 75.78 | 83.58 | | refrigerator | 82.03 | 86.12 | | grandstand | 43.93 | 81.39 | | path | 22.84 | 29.5 | | stairs | 27.39 | 31.62 | | runway | 69.24 | 89.46 | | case | 65.11 | 86.18 | | pool table | 91.29 | 97.87 | | pillow | 61.93 | 73.33 | | screen door | 69.02 | 72.06 | | stairway | 30.1 | 34.96 | | river | 16.46 | 31.49 | | bridge | 72.56 | 86.58 | | bookcase | 42.23 | 53.22 | | blind | 38.09 | 40.85 | | coffee table | 66.48 | 88.61 | | toilet | 87.6 | 92.69 | | flower | 32.68 | 68.08 | | book | 50.99 | 69.99 | | hill | 7.89 | 13.49 | | bench | 54.74 | 62.41 | | countertop | 63.19 | 74.91 | | stove | 83.16 | 88.55 | | palm | 45.62 | 76.92 | | kitchen island | 40.66 | 82.29 | | computer | 75.98 | 90.63 | | swivel chair | 43.6 | 62.66 | | boat | 40.82 | 87.59 | | bar | 72.68 | 80.21 | | arcade machine | 73.14 | 75.92 | | hovel | 33.87 | 38.18 | | bus | 92.47 | 95.92 | | towel | 73.04 | 80.82 | | light | 45.92 | 52.72 | | truck | 48.26 | 64.04 | | tower | 23.35 | 36.24 | | chandelier | 64.8 | 87.21 | | awning | 29.35 | 37.42 | | streetlight | 23.19 | 32.12 | | booth | 61.02 | 64.37 | | television receiver | 74.23 | 87.94 | | airplane | 82.79 | 94.24 | | dirt track | 4.54 | 17.6 | | apparel | 51.74 | 82.1 | | pole | 17.73 | 21.02 | | land | 12.02 | 15.33 | | bannister | 11.8 | 14.52 | | escalator | 60.57 | 78.96 | | ottoman | 54.26 | 69.31 | | bottle | 25.09 | 37.76 | | buffet | 53.91 | 71.08 | | poster | 34.02 | 44.48 | | stage | 23.29 | 33.48 | | van | 49.33 | 64.78 | | ship | 7.49 | 7.99 | | fountain | 33.64 | 34.32 | | conveyer belt | 71.1 | 96.58 | | canopy | 37.74 | 39.78 | | washer | 88.12 | 93.89 | | plaything | 31.05 | 45.77 | | swimming pool | 50.13 | 72.2 | | stool | 48.79 | 66.83 | | barrel | 65.01 | 83.72 | | basket | 43.07 | 54.16 | | waterfall | 51.42 | 59.15 | | tent | 92.7 | 98.19 | | bag | 22.1 | 25.55 | | minibike | 71.31 | 88.93 | | cradle | 79.72 | 98.5 | | oven | 54.27 | 77.85 | | ball | 45.72 | 48.18 | | food | 60.15 | 67.95 | | step | 15.65 | 17.26 | | tank | 52.29 | 66.55 | | trade name | 32.92 | 39.78 | | microwave | 86.49 | 91.52 | | pot | 53.18 | 58.94 | | animal | 73.95 | 77.01 | | bicycle | 57.63 | 76.8 | | lake | 55.83 | 63.57 | | dishwasher | 67.75 | 74.9 | | screen | 60.11 | 90.78 | | blanket | 30.51 | 37.47 | | sculpture | 74.33 | 88.03 | | hood | 61.41 | 68.3 | | sconce | 53.37 | 69.16 | | vase | 42.68 | 56.11 | | traffic light | 32.99 | 52.91 | | tray | 18.79 | 25.24 | | ashcan | 51.17 | 67.08 | | fan | 61.37 | 78.16 | | pier | 35.84 | 39.52 | | crt screen | 7.97 | 21.92 | | plate | 55.68 | 76.96 | | monitor | 9.39 | 10.29 | | bulletin board | 45.04 | 51.96 | | shower | 0.3 | 1.06 | | radiator | 64.34 | 73.13 | | glass | 20.34 | 25.32 | | clock | 32.57 | 36.94 | | flag | 65.21 | 74.58 | +---------------------+-------+-------+ 2023-11-03 00:22:20,292 - mmseg - INFO - Summary: 2023-11-03 00:22:20,292 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.63 | 53.55 | 65.87 | +-------+-------+-------+ 2023-11-03 00:22:20,293 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 00:22:20,293 - mmseg - INFO - Iter(val) [250] aAcc: 0.8463, mIoU: 0.5355, mAcc: 0.6587, IoU.wall: 0.7991, IoU.building: 0.8284, IoU.sky: 0.9413, IoU.floor: 0.8336, IoU.tree: 0.7489, IoU.ceiling: 0.8491, IoU.road: 0.8453, IoU.bed : 0.9133, IoU.windowpane: 0.6471, IoU.grass: 0.6941, IoU.cabinet: 0.6466, IoU.sidewalk: 0.6646, IoU.person: 0.8245, IoU.earth: 0.3771, IoU.door: 0.5547, IoU.table: 0.6729, IoU.mountain: 0.6094, IoU.plant: 0.5374, IoU.curtain: 0.7613, IoU.chair: 0.6106, IoU.car: 0.8450, IoU.water: 0.6166, IoU.painting: 0.7635, IoU.sofa: 0.7952, IoU.shelf: 0.4697, IoU.house: 0.4588, IoU.sea: 0.6306, IoU.mirror: 0.7343, IoU.rug: 0.6235, IoU.field: 0.3714, IoU.armchair: 0.5683, IoU.seat: 0.6371, IoU.fence: 0.3876, IoU.desk: 0.5308, IoU.rock: 0.5420, IoU.wardrobe: 0.4991, IoU.lamp: 0.6479, IoU.bathtub: 0.8780, IoU.railing: 0.4246, IoU.cushion: 0.6239, IoU.base: 0.3281, IoU.box: 0.3439, IoU.column: 0.4583, IoU.signboard: 0.3721, IoU.chest of drawers: 0.4298, IoU.counter: 0.4496, IoU.sand: 0.6226, IoU.sink: 0.7465, IoU.skyscraper: 0.4660, IoU.fireplace: 0.7578, IoU.refrigerator: 0.8203, IoU.grandstand: 0.4393, IoU.path: 0.2284, IoU.stairs: 0.2739, IoU.runway: 0.6924, IoU.case: 0.6511, IoU.pool table: 0.9129, IoU.pillow: 0.6193, IoU.screen door: 0.6902, IoU.stairway: 0.3010, IoU.river: 0.1646, IoU.bridge: 0.7256, IoU.bookcase: 0.4223, IoU.blind: 0.3809, IoU.coffee table: 0.6648, IoU.toilet: 0.8760, IoU.flower: 0.3268, IoU.book: 0.5099, IoU.hill: 0.0789, IoU.bench: 0.5474, IoU.countertop: 0.6319, IoU.stove: 0.8316, IoU.palm: 0.4562, IoU.kitchen island: 0.4066, IoU.computer: 0.7598, IoU.swivel chair: 0.4360, IoU.boat: 0.4082, IoU.bar: 0.7268, IoU.arcade machine: 0.7314, IoU.hovel: 0.3387, IoU.bus: 0.9247, IoU.towel: 0.7304, IoU.light: 0.4592, IoU.truck: 0.4826, IoU.tower: 0.2335, IoU.chandelier: 0.6480, IoU.awning: 0.2935, IoU.streetlight: 0.2319, IoU.booth: 0.6102, IoU.television receiver: 0.7423, IoU.airplane: 0.8279, IoU.dirt track: 0.0454, IoU.apparel: 0.5174, IoU.pole: 0.1773, IoU.land: 0.1202, IoU.bannister: 0.1180, IoU.escalator: 0.6057, IoU.ottoman: 0.5426, IoU.bottle: 0.2509, IoU.buffet: 0.5391, IoU.poster: 0.3402, IoU.stage: 0.2329, IoU.van: 0.4933, IoU.ship: 0.0749, IoU.fountain: 0.3364, IoU.conveyer belt: 0.7110, IoU.canopy: 0.3774, IoU.washer: 0.8812, IoU.plaything: 0.3105, IoU.swimming pool: 0.5013, IoU.stool: 0.4879, IoU.barrel: 0.6501, IoU.basket: 0.4307, IoU.waterfall: 0.5142, IoU.tent: 0.9270, IoU.bag: 0.2210, IoU.minibike: 0.7131, IoU.cradle: 0.7972, IoU.oven: 0.5427, IoU.ball: 0.4572, IoU.food: 0.6015, IoU.step: 0.1565, IoU.tank: 0.5229, IoU.trade name: 0.3292, IoU.microwave: 0.8649, IoU.pot: 0.5318, IoU.animal: 0.7395, IoU.bicycle: 0.5763, IoU.lake: 0.5583, IoU.dishwasher: 0.6775, IoU.screen: 0.6011, IoU.blanket: 0.3051, IoU.sculpture: 0.7433, IoU.hood: 0.6141, IoU.sconce: 0.5337, IoU.vase: 0.4268, IoU.traffic light: 0.3299, IoU.tray: 0.1879, IoU.ashcan: 0.5117, IoU.fan: 0.6137, IoU.pier: 0.3584, IoU.crt screen: 0.0797, IoU.plate: 0.5568, IoU.monitor: 0.0939, IoU.bulletin board: 0.4504, IoU.shower: 0.0030, IoU.radiator: 0.6434, IoU.glass: 0.2034, IoU.clock: 0.3257, IoU.flag: 0.6521, Acc.wall: 0.8933, Acc.building: 0.9353, Acc.sky: 0.9735, Acc.floor: 0.9083, Acc.tree: 0.8652, Acc.ceiling: 0.9274, Acc.road: 0.9174, Acc.bed : 0.9634, Acc.windowpane: 0.7934, Acc.grass: 0.8139, Acc.cabinet: 0.7579, Acc.sidewalk: 0.8024, Acc.person: 0.9203, Acc.earth: 0.5239, Acc.door: 0.7102, Acc.table: 0.7879, Acc.mountain: 0.7639, Acc.plant: 0.6429, Acc.curtain: 0.8670, Acc.chair: 0.7494, Acc.car: 0.9401, Acc.water: 0.8054, Acc.painting: 0.8856, Acc.sofa: 0.9024, Acc.shelf: 0.6342, Acc.house: 0.5650, Acc.sea: 0.7214, Acc.mirror: 0.8332, Acc.rug: 0.6964, Acc.field: 0.6199, Acc.armchair: 0.7299, Acc.seat: 0.8648, Acc.fence: 0.5025, Acc.desk: 0.6640, Acc.rock: 0.8130, Acc.wardrobe: 0.6870, Acc.lamp: 0.7613, Acc.bathtub: 0.9006, Acc.railing: 0.5682, Acc.cushion: 0.7584, Acc.base: 0.6049, Acc.box: 0.4166, Acc.column: 0.5249, Acc.signboard: 0.4944, Acc.chest of drawers: 0.6399, Acc.counter: 0.5534, Acc.sand: 0.8917, Acc.sink: 0.8082, Acc.skyscraper: 0.6411, Acc.fireplace: 0.8358, Acc.refrigerator: 0.8612, Acc.grandstand: 0.8139, Acc.path: 0.2950, Acc.stairs: 0.3162, Acc.runway: 0.8946, Acc.case: 0.8618, Acc.pool table: 0.9787, Acc.pillow: 0.7333, Acc.screen door: 0.7206, Acc.stairway: 0.3496, Acc.river: 0.3149, Acc.bridge: 0.8658, Acc.bookcase: 0.5322, Acc.blind: 0.4085, Acc.coffee table: 0.8861, Acc.toilet: 0.9269, Acc.flower: 0.6808, Acc.book: 0.6999, Acc.hill: 0.1349, Acc.bench: 0.6241, Acc.countertop: 0.7491, Acc.stove: 0.8855, Acc.palm: 0.7692, Acc.kitchen island: 0.8229, Acc.computer: 0.9063, Acc.swivel chair: 0.6266, Acc.boat: 0.8759, Acc.bar: 0.8021, Acc.arcade machine: 0.7592, Acc.hovel: 0.3818, Acc.bus: 0.9592, Acc.towel: 0.8082, Acc.light: 0.5272, Acc.truck: 0.6404, Acc.tower: 0.3624, Acc.chandelier: 0.8721, Acc.awning: 0.3742, Acc.streetlight: 0.3212, Acc.booth: 0.6437, Acc.television receiver: 0.8794, Acc.airplane: 0.9424, Acc.dirt track: 0.1760, Acc.apparel: 0.8210, Acc.pole: 0.2102, Acc.land: 0.1533, Acc.bannister: 0.1452, Acc.escalator: 0.7896, Acc.ottoman: 0.6931, Acc.bottle: 0.3776, Acc.buffet: 0.7108, Acc.poster: 0.4448, Acc.stage: 0.3348, Acc.van: 0.6478, Acc.ship: 0.0799, Acc.fountain: 0.3432, Acc.conveyer belt: 0.9658, Acc.canopy: 0.3978, Acc.washer: 0.9389, Acc.plaything: 0.4577, Acc.swimming pool: 0.7220, Acc.stool: 0.6683, Acc.barrel: 0.8372, Acc.basket: 0.5416, Acc.waterfall: 0.5915, Acc.tent: 0.9819, Acc.bag: 0.2555, Acc.minibike: 0.8893, Acc.cradle: 0.9850, Acc.oven: 0.7785, Acc.ball: 0.4818, Acc.food: 0.6795, Acc.step: 0.1726, Acc.tank: 0.6655, Acc.trade name: 0.3978, Acc.microwave: 0.9152, Acc.pot: 0.5894, Acc.animal: 0.7701, Acc.bicycle: 0.7680, Acc.lake: 0.6357, Acc.dishwasher: 0.7490, Acc.screen: 0.9078, Acc.blanket: 0.3747, Acc.sculpture: 0.8803, Acc.hood: 0.6830, Acc.sconce: 0.6916, Acc.vase: 0.5611, Acc.traffic light: 0.5291, Acc.tray: 0.2524, Acc.ashcan: 0.6708, Acc.fan: 0.7816, Acc.pier: 0.3952, Acc.crt screen: 0.2192, Acc.plate: 0.7696, Acc.monitor: 0.1029, Acc.bulletin board: 0.5196, Acc.shower: 0.0106, Acc.radiator: 0.7313, Acc.glass: 0.2532, Acc.clock: 0.3694, Acc.flag: 0.7458 2023-11-03 00:23:21,071 - mmseg - INFO - Iter [15050/40000] lr: 2.021e-06, eta: 9:17:17, time: 2.487, data_time: 1.279, memory: 38534, decode.loss_ce: 0.2448, decode.acc_seg: 90.1657, loss: 0.2448 2023-11-03 00:24:21,762 - mmseg - INFO - Iter [15100/40000] lr: 2.017e-06, eta: 9:16:00, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2391, decode.acc_seg: 90.5565, loss: 0.2391 2023-11-03 00:25:22,427 - mmseg - INFO - Iter [15150/40000] lr: 2.013e-06, eta: 9:14:42, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2382, decode.acc_seg: 90.4629, loss: 0.2382 2023-11-03 00:26:25,551 - mmseg - INFO - Iter [15200/40000] lr: 2.009e-06, eta: 9:13:29, time: 1.262, data_time: 0.053, memory: 38534, decode.loss_ce: 0.2183, decode.acc_seg: 91.0599, loss: 0.2183 2023-11-03 00:27:26,164 - mmseg - INFO - Iter [15250/40000] lr: 2.005e-06, eta: 9:12:12, time: 1.212, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2205, decode.acc_seg: 90.7386, loss: 0.2205 2023-11-03 00:28:26,835 - mmseg - INFO - Iter [15300/40000] lr: 2.001e-06, eta: 9:10:55, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2484, decode.acc_seg: 90.2198, loss: 0.2484 2023-11-03 00:29:27,467 - mmseg - INFO - Iter [15350/40000] lr: 1.997e-06, eta: 9:09:38, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2263, decode.acc_seg: 90.8098, loss: 0.2263 2023-11-03 00:30:28,127 - mmseg - INFO - Iter [15400/40000] lr: 1.993e-06, eta: 9:08:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2350, decode.acc_seg: 90.6026, loss: 0.2350 2023-11-03 00:31:28,766 - mmseg - INFO - Iter [15450/40000] lr: 1.989e-06, eta: 9:07:04, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2318, decode.acc_seg: 90.6965, loss: 0.2318 2023-11-03 00:32:29,429 - mmseg - INFO - Iter [15500/40000] lr: 1.984e-06, eta: 9:05:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2213, decode.acc_seg: 91.0207, loss: 0.2213 2023-11-03 00:33:30,127 - mmseg - INFO - Iter [15550/40000] lr: 1.980e-06, eta: 9:04:31, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2266, decode.acc_seg: 91.0689, loss: 0.2266 2023-11-03 00:34:30,826 - mmseg - INFO - Iter [15600/40000] lr: 1.976e-06, eta: 9:03:15, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2265, decode.acc_seg: 90.9343, loss: 0.2265 2023-11-03 00:35:31,441 - mmseg - INFO - Iter [15650/40000] lr: 1.972e-06, eta: 9:01:58, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2221, decode.acc_seg: 91.1474, loss: 0.2221 2023-11-03 00:36:32,082 - mmseg - INFO - Iter [15700/40000] lr: 1.968e-06, eta: 9:00:42, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2265, decode.acc_seg: 91.1271, loss: 0.2265 2023-11-03 00:37:32,723 - mmseg - INFO - Iter [15750/40000] lr: 1.964e-06, eta: 8:59:26, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2482, decode.acc_seg: 90.2298, loss: 0.2482 2023-11-03 00:38:33,386 - mmseg - INFO - Iter [15800/40000] lr: 1.960e-06, eta: 8:58:10, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2274, decode.acc_seg: 91.1176, loss: 0.2274 2023-11-03 00:39:36,544 - mmseg - INFO - Iter [15850/40000] lr: 1.956e-06, eta: 8:56:58, time: 1.263, data_time: 0.055, memory: 38534, decode.loss_ce: 0.2245, decode.acc_seg: 91.0764, loss: 0.2245 2023-11-03 00:40:37,172 - mmseg - INFO - Iter [15900/40000] lr: 1.952e-06, eta: 8:55:42, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2323, decode.acc_seg: 90.7727, loss: 0.2323 2023-11-03 00:41:37,844 - mmseg - INFO - Iter [15950/40000] lr: 1.948e-06, eta: 8:54:26, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2285, decode.acc_seg: 90.5574, loss: 0.2285 2023-11-03 00:42:38,439 - mmseg - INFO - Saving checkpoint at 16000 iterations 2023-11-03 00:43:33,939 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 00:43:33,939 - mmseg - INFO - Iter [16000/40000] lr: 1.944e-06, eta: 8:54:33, time: 2.322, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2197, decode.acc_seg: 91.2410, loss: 0.2197 2023-11-03 00:44:34,097 - mmseg - INFO - per class results: 2023-11-03 00:44:34,102 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.11 | 88.39 | | building | 82.29 | 94.53 | | sky | 94.09 | 97.46 | | floor | 83.33 | 92.17 | | tree | 75.51 | 85.97 | | ceiling | 85.22 | 93.6 | | road | 86.54 | 91.18 | | bed | 91.21 | 96.07 | | windowpane | 64.04 | 79.36 | | grass | 69.6 | 85.27 | | cabinet | 65.07 | 75.17 | | sidewalk | 68.32 | 84.99 | | person | 82.34 | 91.64 | | earth | 35.36 | 46.14 | | door | 54.72 | 71.05 | | table | 68.3 | 78.66 | | mountain | 64.28 | 77.35 | | plant | 56.56 | 70.08 | | curtain | 75.14 | 88.35 | | chair | 61.58 | 76.09 | | car | 85.55 | 92.6 | | water | 65.17 | 82.5 | | painting | 75.81 | 87.21 | | sofa | 79.78 | 88.85 | | shelf | 46.9 | 75.04 | | house | 35.88 | 43.69 | | sea | 63.6 | 70.87 | | mirror | 73.79 | 86.17 | | rug | 65.62 | 75.4 | | field | 33.7 | 51.85 | | armchair | 58.47 | 71.72 | | seat | 64.01 | 86.19 | | fence | 45.67 | 60.55 | | desk | 55.46 | 71.81 | | rock | 58.56 | 79.83 | | wardrobe | 53.41 | 73.58 | | lamp | 65.84 | 76.66 | | bathtub | 88.46 | 90.85 | | railing | 44.56 | 59.59 | | cushion | 62.39 | 77.28 | | base | 33.16 | 56.51 | | box | 31.78 | 39.83 | | column | 48.78 | 57.95 | | signboard | 38.15 | 53.68 | | chest of drawers | 43.11 | 65.0 | | counter | 45.37 | 57.66 | | sand | 61.45 | 89.27 | | sink | 76.3 | 83.34 | | skyscraper | 41.45 | 47.94 | | fireplace | 73.38 | 90.97 | | refrigerator | 81.81 | 87.61 | | grandstand | 46.06 | 81.38 | | path | 17.95 | 21.04 | | stairs | 28.02 | 34.21 | | runway | 68.73 | 89.49 | | case | 64.38 | 86.86 | | pool table | 92.08 | 97.37 | | pillow | 61.55 | 71.46 | | screen door | 68.51 | 71.63 | | stairway | 35.15 | 40.76 | | river | 19.47 | 39.4 | | bridge | 72.02 | 82.97 | | bookcase | 41.44 | 47.98 | | blind | 40.22 | 43.16 | | coffee table | 68.46 | 86.18 | | toilet | 88.0 | 91.97 | | flower | 40.03 | 63.99 | | book | 51.37 | 75.06 | | hill | 4.37 | 7.57 | | bench | 55.39 | 68.76 | | countertop | 63.4 | 77.07 | | stove | 82.18 | 88.57 | | palm | 46.42 | 79.9 | | kitchen island | 41.04 | 66.22 | | computer | 77.77 | 88.36 | | swivel chair | 40.7 | 57.01 | | boat | 57.72 | 69.91 | | bar | 61.58 | 89.71 | | arcade machine | 84.58 | 89.14 | | hovel | 29.17 | 32.39 | | bus | 92.38 | 95.78 | | towel | 73.29 | 87.66 | | light | 43.49 | 49.49 | | truck | 47.69 | 63.47 | | tower | 14.46 | 20.46 | | chandelier | 66.54 | 83.92 | | awning | 30.4 | 34.85 | | streetlight | 21.24 | 27.49 | | booth | 60.1 | 67.03 | | television receiver | 73.83 | 87.93 | | airplane | 83.96 | 91.92 | | dirt track | 11.54 | 15.62 | | apparel | 48.6 | 60.39 | | pole | 19.37 | 23.73 | | land | 7.53 | 14.82 | | bannister | 8.5 | 9.93 | | escalator | 63.44 | 81.45 | | ottoman | 56.23 | 74.33 | | bottle | 21.49 | 27.19 | | buffet | 49.05 | 68.66 | | poster | 34.16 | 45.47 | | stage | 26.06 | 38.01 | | van | 51.12 | 72.63 | | ship | 8.62 | 10.66 | | fountain | 33.27 | 34.34 | | conveyer belt | 69.43 | 96.36 | | canopy | 47.08 | 54.55 | | washer | 84.11 | 89.32 | | plaything | 34.68 | 47.07 | | swimming pool | 49.0 | 70.76 | | stool | 50.12 | 64.46 | | barrel | 59.48 | 78.94 | | basket | 40.44 | 57.48 | | waterfall | 52.45 | 66.53 | | tent | 94.29 | 96.72 | | bag | 22.03 | 25.87 | | minibike | 71.59 | 80.29 | | cradle | 84.7 | 97.06 | | oven | 62.49 | 74.42 | | ball | 56.83 | 65.75 | | food | 54.45 | 59.51 | | step | 18.62 | 26.31 | | tank | 52.7 | 66.41 | | trade name | 27.74 | 32.14 | | microwave | 87.69 | 93.6 | | pot | 49.6 | 55.8 | | animal | 73.58 | 76.76 | | bicycle | 56.69 | 73.74 | | lake | 61.21 | 63.21 | | dishwasher | 65.82 | 72.4 | | screen | 68.97 | 90.23 | | blanket | 34.1 | 41.46 | | sculpture | 65.65 | 89.46 | | hood | 54.48 | 62.99 | | sconce | 52.81 | 69.27 | | vase | 43.21 | 62.32 | | traffic light | 34.51 | 51.56 | | tray | 18.75 | 25.23 | | ashcan | 51.79 | 65.46 | | fan | 62.38 | 77.17 | | pier | 45.2 | 55.21 | | crt screen | 16.9 | 19.21 | | plate | 55.34 | 76.19 | | monitor | 61.92 | 78.6 | | bulletin board | 49.57 | 61.06 | | shower | 0.36 | 0.96 | | radiator | 65.29 | 77.22 | | glass | 18.48 | 20.42 | | clock | 33.14 | 42.32 | | flag | 66.52 | 74.14 | +---------------------+-------+-------+ 2023-11-03 00:44:34,102 - mmseg - INFO - Summary: 2023-11-03 00:44:34,102 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.87 | 54.41 | 66.32 | +-------+-------+-------+ 2023-11-03 00:44:34,103 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 00:44:34,103 - mmseg - INFO - Iter(val) [250] aAcc: 0.8487, mIoU: 0.5441, mAcc: 0.6632, IoU.wall: 0.8011, IoU.building: 0.8229, IoU.sky: 0.9409, IoU.floor: 0.8333, IoU.tree: 0.7551, IoU.ceiling: 0.8522, IoU.road: 0.8654, IoU.bed : 0.9121, IoU.windowpane: 0.6404, IoU.grass: 0.6960, IoU.cabinet: 0.6507, IoU.sidewalk: 0.6832, IoU.person: 0.8234, IoU.earth: 0.3536, IoU.door: 0.5472, IoU.table: 0.6830, IoU.mountain: 0.6428, IoU.plant: 0.5656, IoU.curtain: 0.7514, IoU.chair: 0.6158, IoU.car: 0.8555, IoU.water: 0.6517, IoU.painting: 0.7581, IoU.sofa: 0.7978, IoU.shelf: 0.4690, IoU.house: 0.3588, IoU.sea: 0.6360, IoU.mirror: 0.7379, IoU.rug: 0.6562, IoU.field: 0.3370, IoU.armchair: 0.5847, IoU.seat: 0.6401, IoU.fence: 0.4567, IoU.desk: 0.5546, IoU.rock: 0.5856, IoU.wardrobe: 0.5341, IoU.lamp: 0.6584, IoU.bathtub: 0.8846, IoU.railing: 0.4456, IoU.cushion: 0.6239, IoU.base: 0.3316, IoU.box: 0.3178, IoU.column: 0.4878, IoU.signboard: 0.3815, IoU.chest of drawers: 0.4311, IoU.counter: 0.4537, IoU.sand: 0.6145, IoU.sink: 0.7630, IoU.skyscraper: 0.4145, IoU.fireplace: 0.7338, IoU.refrigerator: 0.8181, IoU.grandstand: 0.4606, IoU.path: 0.1795, IoU.stairs: 0.2802, IoU.runway: 0.6873, IoU.case: 0.6438, IoU.pool table: 0.9208, IoU.pillow: 0.6155, IoU.screen door: 0.6851, IoU.stairway: 0.3515, IoU.river: 0.1947, IoU.bridge: 0.7202, IoU.bookcase: 0.4144, IoU.blind: 0.4022, IoU.coffee table: 0.6846, IoU.toilet: 0.8800, IoU.flower: 0.4003, IoU.book: 0.5137, IoU.hill: 0.0437, IoU.bench: 0.5539, IoU.countertop: 0.6340, IoU.stove: 0.8218, IoU.palm: 0.4642, IoU.kitchen island: 0.4104, IoU.computer: 0.7777, IoU.swivel chair: 0.4070, IoU.boat: 0.5772, IoU.bar: 0.6158, IoU.arcade machine: 0.8458, IoU.hovel: 0.2917, IoU.bus: 0.9238, IoU.towel: 0.7329, IoU.light: 0.4349, IoU.truck: 0.4769, IoU.tower: 0.1446, IoU.chandelier: 0.6654, IoU.awning: 0.3040, IoU.streetlight: 0.2124, IoU.booth: 0.6010, IoU.television receiver: 0.7383, IoU.airplane: 0.8396, IoU.dirt track: 0.1154, IoU.apparel: 0.4860, IoU.pole: 0.1937, IoU.land: 0.0753, IoU.bannister: 0.0850, IoU.escalator: 0.6344, IoU.ottoman: 0.5623, IoU.bottle: 0.2149, IoU.buffet: 0.4905, IoU.poster: 0.3416, IoU.stage: 0.2606, IoU.van: 0.5112, IoU.ship: 0.0862, IoU.fountain: 0.3327, IoU.conveyer belt: 0.6943, IoU.canopy: 0.4708, IoU.washer: 0.8411, IoU.plaything: 0.3468, IoU.swimming pool: 0.4900, IoU.stool: 0.5012, IoU.barrel: 0.5948, IoU.basket: 0.4044, IoU.waterfall: 0.5245, IoU.tent: 0.9429, IoU.bag: 0.2203, IoU.minibike: 0.7159, IoU.cradle: 0.8470, IoU.oven: 0.6249, IoU.ball: 0.5683, IoU.food: 0.5445, IoU.step: 0.1862, IoU.tank: 0.5270, IoU.trade name: 0.2774, IoU.microwave: 0.8769, IoU.pot: 0.4960, IoU.animal: 0.7358, IoU.bicycle: 0.5669, IoU.lake: 0.6121, IoU.dishwasher: 0.6582, IoU.screen: 0.6897, IoU.blanket: 0.3410, IoU.sculpture: 0.6565, IoU.hood: 0.5448, IoU.sconce: 0.5281, IoU.vase: 0.4321, IoU.traffic light: 0.3451, IoU.tray: 0.1875, IoU.ashcan: 0.5179, IoU.fan: 0.6238, IoU.pier: 0.4520, IoU.crt screen: 0.1690, IoU.plate: 0.5534, IoU.monitor: 0.6192, IoU.bulletin board: 0.4957, IoU.shower: 0.0036, IoU.radiator: 0.6529, IoU.glass: 0.1848, IoU.clock: 0.3314, IoU.flag: 0.6652, Acc.wall: 0.8839, Acc.building: 0.9453, Acc.sky: 0.9746, Acc.floor: 0.9217, Acc.tree: 0.8597, Acc.ceiling: 0.9360, Acc.road: 0.9118, Acc.bed : 0.9607, Acc.windowpane: 0.7936, Acc.grass: 0.8527, Acc.cabinet: 0.7517, Acc.sidewalk: 0.8499, Acc.person: 0.9164, Acc.earth: 0.4614, Acc.door: 0.7105, Acc.table: 0.7866, Acc.mountain: 0.7735, Acc.plant: 0.7008, Acc.curtain: 0.8835, Acc.chair: 0.7609, Acc.car: 0.9260, Acc.water: 0.8250, Acc.painting: 0.8721, Acc.sofa: 0.8885, Acc.shelf: 0.7504, Acc.house: 0.4369, Acc.sea: 0.7087, Acc.mirror: 0.8617, Acc.rug: 0.7540, Acc.field: 0.5185, Acc.armchair: 0.7172, Acc.seat: 0.8619, Acc.fence: 0.6055, Acc.desk: 0.7181, Acc.rock: 0.7983, Acc.wardrobe: 0.7358, Acc.lamp: 0.7666, Acc.bathtub: 0.9085, Acc.railing: 0.5959, Acc.cushion: 0.7728, Acc.base: 0.5651, Acc.box: 0.3983, Acc.column: 0.5795, Acc.signboard: 0.5368, Acc.chest of drawers: 0.6500, Acc.counter: 0.5766, Acc.sand: 0.8927, Acc.sink: 0.8334, Acc.skyscraper: 0.4794, Acc.fireplace: 0.9097, Acc.refrigerator: 0.8761, Acc.grandstand: 0.8138, Acc.path: 0.2104, Acc.stairs: 0.3421, Acc.runway: 0.8949, Acc.case: 0.8686, Acc.pool table: 0.9737, Acc.pillow: 0.7146, Acc.screen door: 0.7163, Acc.stairway: 0.4076, Acc.river: 0.3940, Acc.bridge: 0.8297, Acc.bookcase: 0.4798, Acc.blind: 0.4316, Acc.coffee table: 0.8618, Acc.toilet: 0.9197, Acc.flower: 0.6399, Acc.book: 0.7506, Acc.hill: 0.0757, Acc.bench: 0.6876, Acc.countertop: 0.7707, Acc.stove: 0.8857, Acc.palm: 0.7990, Acc.kitchen island: 0.6622, Acc.computer: 0.8836, Acc.swivel chair: 0.5701, Acc.boat: 0.6991, Acc.bar: 0.8971, Acc.arcade machine: 0.8914, Acc.hovel: 0.3239, Acc.bus: 0.9578, Acc.towel: 0.8766, Acc.light: 0.4949, Acc.truck: 0.6347, Acc.tower: 0.2046, Acc.chandelier: 0.8392, Acc.awning: 0.3485, Acc.streetlight: 0.2749, Acc.booth: 0.6703, Acc.television receiver: 0.8793, Acc.airplane: 0.9192, Acc.dirt track: 0.1562, Acc.apparel: 0.6039, Acc.pole: 0.2373, Acc.land: 0.1482, Acc.bannister: 0.0993, Acc.escalator: 0.8145, Acc.ottoman: 0.7433, Acc.bottle: 0.2719, Acc.buffet: 0.6866, Acc.poster: 0.4547, Acc.stage: 0.3801, Acc.van: 0.7263, Acc.ship: 0.1066, Acc.fountain: 0.3434, Acc.conveyer belt: 0.9636, Acc.canopy: 0.5455, Acc.washer: 0.8932, Acc.plaything: 0.4707, Acc.swimming pool: 0.7076, Acc.stool: 0.6446, Acc.barrel: 0.7894, Acc.basket: 0.5748, Acc.waterfall: 0.6653, Acc.tent: 0.9672, Acc.bag: 0.2587, Acc.minibike: 0.8029, Acc.cradle: 0.9706, Acc.oven: 0.7442, Acc.ball: 0.6575, Acc.food: 0.5951, Acc.step: 0.2631, Acc.tank: 0.6641, Acc.trade name: 0.3214, Acc.microwave: 0.9360, Acc.pot: 0.5580, Acc.animal: 0.7676, Acc.bicycle: 0.7374, Acc.lake: 0.6321, Acc.dishwasher: 0.7240, Acc.screen: 0.9023, Acc.blanket: 0.4146, Acc.sculpture: 0.8946, Acc.hood: 0.6299, Acc.sconce: 0.6927, Acc.vase: 0.6232, Acc.traffic light: 0.5156, Acc.tray: 0.2523, Acc.ashcan: 0.6546, Acc.fan: 0.7717, Acc.pier: 0.5521, Acc.crt screen: 0.1921, Acc.plate: 0.7619, Acc.monitor: 0.7860, Acc.bulletin board: 0.6106, Acc.shower: 0.0096, Acc.radiator: 0.7722, Acc.glass: 0.2042, Acc.clock: 0.4232, Acc.flag: 0.7414 2023-11-03 00:45:34,927 - mmseg - INFO - Iter [16050/40000] lr: 1.940e-06, eta: 8:54:47, time: 2.420, data_time: 1.211, memory: 38534, decode.loss_ce: 0.2196, decode.acc_seg: 91.2452, loss: 0.2196 2023-11-03 00:46:35,622 - mmseg - INFO - Iter [16100/40000] lr: 1.936e-06, eta: 8:53:31, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2306, decode.acc_seg: 90.8279, loss: 0.2306 2023-11-03 00:47:36,318 - mmseg - INFO - Iter [16150/40000] lr: 1.932e-06, eta: 8:52:15, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2347, decode.acc_seg: 90.6772, loss: 0.2347 2023-11-03 00:48:37,056 - mmseg - INFO - Iter [16200/40000] lr: 1.928e-06, eta: 8:50:59, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2211, decode.acc_seg: 90.8382, loss: 0.2211 2023-11-03 00:49:37,794 - mmseg - INFO - Iter [16250/40000] lr: 1.924e-06, eta: 8:49:43, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2251, decode.acc_seg: 90.8958, loss: 0.2251 2023-11-03 00:50:38,571 - mmseg - INFO - Iter [16300/40000] lr: 1.920e-06, eta: 8:48:27, time: 1.216, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2259, decode.acc_seg: 90.7083, loss: 0.2259 2023-11-03 00:51:39,297 - mmseg - INFO - Iter [16350/40000] lr: 1.916e-06, eta: 8:47:11, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2150, decode.acc_seg: 91.3946, loss: 0.2150 2023-11-03 00:52:39,979 - mmseg - INFO - Iter [16400/40000] lr: 1.912e-06, eta: 8:45:55, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2256, decode.acc_seg: 90.6328, loss: 0.2256 2023-11-03 00:53:43,075 - mmseg - INFO - Iter [16450/40000] lr: 1.908e-06, eta: 8:44:43, time: 1.262, data_time: 0.052, memory: 38534, decode.loss_ce: 0.2183, decode.acc_seg: 91.1688, loss: 0.2183 2023-11-03 00:54:43,851 - mmseg - INFO - Iter [16500/40000] lr: 1.903e-06, eta: 8:43:28, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2155, decode.acc_seg: 91.3485, loss: 0.2155 2023-11-03 00:55:44,613 - mmseg - INFO - Iter [16550/40000] lr: 1.899e-06, eta: 8:42:12, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2165, decode.acc_seg: 91.1586, loss: 0.2165 2023-11-03 00:56:45,331 - mmseg - INFO - Iter [16600/40000] lr: 1.895e-06, eta: 8:40:57, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2272, decode.acc_seg: 90.9629, loss: 0.2272 2023-11-03 00:57:46,080 - mmseg - INFO - Iter [16650/40000] lr: 1.891e-06, eta: 8:39:42, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2183, decode.acc_seg: 91.1922, loss: 0.2183 2023-11-03 00:58:46,796 - mmseg - INFO - Iter [16700/40000] lr: 1.887e-06, eta: 8:38:26, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2202, decode.acc_seg: 91.2107, loss: 0.2202 2023-11-03 00:59:47,606 - mmseg - INFO - Iter [16750/40000] lr: 1.883e-06, eta: 8:37:11, time: 1.216, data_time: 0.009, memory: 38534, decode.loss_ce: 0.2013, decode.acc_seg: 91.3891, loss: 0.2013 2023-11-03 01:00:48,358 - mmseg - INFO - Iter [16800/40000] lr: 1.879e-06, eta: 8:35:56, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2182, decode.acc_seg: 91.0696, loss: 0.2182 2023-11-03 01:01:49,053 - mmseg - INFO - Iter [16850/40000] lr: 1.875e-06, eta: 8:34:41, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2070, decode.acc_seg: 91.2218, loss: 0.2070 2023-11-03 01:02:49,783 - mmseg - INFO - Iter [16900/40000] lr: 1.871e-06, eta: 8:33:26, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2159, decode.acc_seg: 91.4065, loss: 0.2159 2023-11-03 01:03:50,584 - mmseg - INFO - Iter [16950/40000] lr: 1.867e-06, eta: 8:32:12, time: 1.216, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2197, decode.acc_seg: 90.9620, loss: 0.2197 2023-11-03 01:04:51,851 - mmseg - INFO - Saving checkpoint at 17000 iterations 2023-11-03 01:05:50,334 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 01:05:50,335 - mmseg - INFO - Iter [17000/40000] lr: 1.863e-06, eta: 8:32:17, time: 2.395, data_time: 0.018, memory: 38534, decode.loss_ce: 0.2097, decode.acc_seg: 91.3926, loss: 0.2097 2023-11-03 01:06:52,593 - mmseg - INFO - per class results: 2023-11-03 01:06:52,599 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.72 | 89.28 | | building | 83.38 | 92.31 | | sky | 94.19 | 97.01 | | floor | 83.63 | 90.17 | | tree | 75.66 | 89.46 | | ceiling | 84.96 | 93.6 | | road | 85.85 | 92.62 | | bed | 91.02 | 97.14 | | windowpane | 64.51 | 76.13 | | grass | 68.8 | 84.29 | | cabinet | 64.8 | 75.13 | | sidewalk | 67.67 | 80.03 | | person | 82.3 | 91.84 | | earth | 34.18 | 45.78 | | door | 56.0 | 72.4 | | table | 68.01 | 81.23 | | mountain | 59.84 | 73.73 | | plant | 55.35 | 64.91 | | curtain | 74.48 | 89.13 | | chair | 59.6 | 71.32 | | car | 84.69 | 93.47 | | water | 63.17 | 82.54 | | painting | 78.82 | 86.53 | | sofa | 79.05 | 91.01 | | shelf | 48.18 | 70.12 | | house | 54.33 | 71.82 | | sea | 60.56 | 67.72 | | mirror | 73.87 | 82.93 | | rug | 69.05 | 81.76 | | field | 33.25 | 57.85 | | armchair | 59.27 | 75.82 | | seat | 65.67 | 86.71 | | fence | 45.49 | 58.42 | | desk | 54.74 | 71.32 | | rock | 53.25 | 84.44 | | wardrobe | 51.84 | 72.56 | | lamp | 67.19 | 78.87 | | bathtub | 87.92 | 89.97 | | railing | 44.94 | 58.81 | | cushion | 58.66 | 66.8 | | base | 31.91 | 50.43 | | box | 37.51 | 48.62 | | column | 51.77 | 64.95 | | signboard | 36.87 | 50.41 | | chest of drawers | 41.76 | 65.73 | | counter | 48.91 | 59.35 | | sand | 61.25 | 89.05 | | sink | 75.42 | 80.41 | | skyscraper | 45.0 | 58.57 | | fireplace | 72.27 | 92.43 | | refrigerator | 79.39 | 86.93 | | grandstand | 41.71 | 79.35 | | path | 21.29 | 26.76 | | stairs | 27.64 | 34.48 | | runway | 67.38 | 90.08 | | case | 61.51 | 83.01 | | pool table | 90.04 | 98.29 | | pillow | 62.12 | 75.57 | | screen door | 73.17 | 74.78 | | stairway | 34.53 | 41.84 | | river | 21.32 | 41.97 | | bridge | 69.92 | 83.84 | | bookcase | 43.13 | 53.88 | | blind | 40.44 | 48.15 | | coffee table | 67.22 | 88.07 | | toilet | 89.55 | 93.15 | | flower | 43.29 | 60.54 | | book | 50.03 | 70.83 | | hill | 2.41 | 4.34 | | bench | 63.47 | 76.94 | | countertop | 63.48 | 75.62 | | stove | 82.07 | 85.67 | | palm | 48.09 | 78.66 | | kitchen island | 46.63 | 73.97 | | computer | 76.41 | 89.37 | | swivel chair | 43.16 | 77.15 | | boat | 67.01 | 84.42 | | bar | 70.87 | 80.67 | | arcade machine | 85.6 | 91.54 | | hovel | 31.06 | 33.81 | | bus | 92.34 | 95.62 | | towel | 73.43 | 83.3 | | light | 46.25 | 51.81 | | truck | 33.6 | 43.43 | | tower | 22.54 | 43.77 | | chandelier | 66.92 | 82.61 | | awning | 40.33 | 50.05 | | streetlight | 25.16 | 37.28 | | booth | 48.05 | 50.7 | | television receiver | 76.96 | 86.64 | | airplane | 83.34 | 93.11 | | dirt track | 7.46 | 7.76 | | apparel | 34.19 | 44.09 | | pole | 20.77 | 27.86 | | land | 7.72 | 15.65 | | bannister | 7.56 | 9.83 | | escalator | 58.78 | 80.06 | | ottoman | 54.06 | 64.38 | | bottle | 24.02 | 31.85 | | buffet | 54.87 | 71.78 | | poster | 36.67 | 44.97 | | stage | 30.16 | 51.6 | | van | 47.03 | 64.91 | | ship | 40.51 | 42.98 | | fountain | 33.48 | 34.25 | | conveyer belt | 83.64 | 95.2 | | canopy | 41.77 | 44.06 | | washer | 85.23 | 90.09 | | plaything | 33.51 | 39.94 | | swimming pool | 54.57 | 78.7 | | stool | 50.38 | 65.38 | | barrel | 63.91 | 79.14 | | basket | 37.87 | 53.0 | | waterfall | 36.4 | 40.86 | | tent | 96.55 | 97.53 | | bag | 21.39 | 25.63 | | minibike | 73.05 | 83.06 | | cradle | 84.6 | 98.28 | | oven | 57.38 | 72.07 | | ball | 29.75 | 30.41 | | food | 49.88 | 51.9 | | step | 14.53 | 17.06 | | tank | 54.94 | 65.87 | | trade name | 30.91 | 36.81 | | microwave | 88.38 | 93.38 | | pot | 49.88 | 54.52 | | animal | 72.31 | 75.1 | | bicycle | 53.14 | 64.17 | | lake | 62.02 | 63.39 | | dishwasher | 64.23 | 80.96 | | screen | 64.78 | 92.18 | | blanket | 26.66 | 31.8 | | sculpture | 77.17 | 85.95 | | hood | 60.76 | 69.06 | | sconce | 52.7 | 68.33 | | vase | 43.41 | 58.73 | | traffic light | 35.47 | 58.8 | | tray | 17.89 | 28.82 | | ashcan | 51.5 | 65.11 | | fan | 57.96 | 64.98 | | pier | 42.06 | 52.9 | | crt screen | 17.58 | 18.15 | | plate | 55.14 | 71.65 | | monitor | 62.25 | 81.67 | | bulletin board | 52.22 | 59.31 | | shower | 1.1 | 1.53 | | radiator | 65.08 | 77.72 | | glass | 18.56 | 20.1 | | clock | 32.78 | 36.24 | | flag | 66.91 | 71.23 | +---------------------+-------+-------+ 2023-11-03 01:06:52,599 - mmseg - INFO - Summary: 2023-11-03 01:06:52,599 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.86 | 54.62 | 66.28 | +-------+-------+-------+ 2023-11-03 01:06:52,600 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 01:06:52,600 - mmseg - INFO - Iter(val) [250] aAcc: 0.8486, mIoU: 0.5462, mAcc: 0.6628, IoU.wall: 0.7972, IoU.building: 0.8338, IoU.sky: 0.9419, IoU.floor: 0.8363, IoU.tree: 0.7566, IoU.ceiling: 0.8496, IoU.road: 0.8585, IoU.bed : 0.9102, IoU.windowpane: 0.6451, IoU.grass: 0.6880, IoU.cabinet: 0.6480, IoU.sidewalk: 0.6767, IoU.person: 0.8230, IoU.earth: 0.3418, IoU.door: 0.5600, IoU.table: 0.6801, IoU.mountain: 0.5984, IoU.plant: 0.5535, IoU.curtain: 0.7448, IoU.chair: 0.5960, IoU.car: 0.8469, IoU.water: 0.6317, IoU.painting: 0.7882, IoU.sofa: 0.7905, IoU.shelf: 0.4818, IoU.house: 0.5433, IoU.sea: 0.6056, IoU.mirror: 0.7387, IoU.rug: 0.6905, IoU.field: 0.3325, IoU.armchair: 0.5927, IoU.seat: 0.6567, IoU.fence: 0.4549, IoU.desk: 0.5474, IoU.rock: 0.5325, IoU.wardrobe: 0.5184, IoU.lamp: 0.6719, IoU.bathtub: 0.8792, IoU.railing: 0.4494, IoU.cushion: 0.5866, IoU.base: 0.3191, IoU.box: 0.3751, IoU.column: 0.5177, IoU.signboard: 0.3687, IoU.chest of drawers: 0.4176, IoU.counter: 0.4891, IoU.sand: 0.6125, IoU.sink: 0.7542, IoU.skyscraper: 0.4500, IoU.fireplace: 0.7227, IoU.refrigerator: 0.7939, IoU.grandstand: 0.4171, IoU.path: 0.2129, IoU.stairs: 0.2764, IoU.runway: 0.6738, IoU.case: 0.6151, IoU.pool table: 0.9004, IoU.pillow: 0.6212, IoU.screen door: 0.7317, IoU.stairway: 0.3453, IoU.river: 0.2132, IoU.bridge: 0.6992, IoU.bookcase: 0.4313, IoU.blind: 0.4044, IoU.coffee table: 0.6722, IoU.toilet: 0.8955, IoU.flower: 0.4329, IoU.book: 0.5003, IoU.hill: 0.0241, IoU.bench: 0.6347, IoU.countertop: 0.6348, IoU.stove: 0.8207, IoU.palm: 0.4809, IoU.kitchen island: 0.4663, IoU.computer: 0.7641, IoU.swivel chair: 0.4316, IoU.boat: 0.6701, IoU.bar: 0.7087, IoU.arcade machine: 0.8560, IoU.hovel: 0.3106, IoU.bus: 0.9234, IoU.towel: 0.7343, IoU.light: 0.4625, IoU.truck: 0.3360, IoU.tower: 0.2254, IoU.chandelier: 0.6692, IoU.awning: 0.4033, IoU.streetlight: 0.2516, IoU.booth: 0.4805, IoU.television receiver: 0.7696, IoU.airplane: 0.8334, IoU.dirt track: 0.0746, IoU.apparel: 0.3419, IoU.pole: 0.2077, IoU.land: 0.0772, IoU.bannister: 0.0756, IoU.escalator: 0.5878, IoU.ottoman: 0.5406, IoU.bottle: 0.2402, IoU.buffet: 0.5487, IoU.poster: 0.3667, IoU.stage: 0.3016, IoU.van: 0.4703, IoU.ship: 0.4051, IoU.fountain: 0.3348, IoU.conveyer belt: 0.8364, IoU.canopy: 0.4177, IoU.washer: 0.8523, IoU.plaything: 0.3351, IoU.swimming pool: 0.5457, IoU.stool: 0.5038, IoU.barrel: 0.6391, IoU.basket: 0.3787, IoU.waterfall: 0.3640, IoU.tent: 0.9655, IoU.bag: 0.2139, IoU.minibike: 0.7305, IoU.cradle: 0.8460, IoU.oven: 0.5738, IoU.ball: 0.2975, IoU.food: 0.4988, IoU.step: 0.1453, IoU.tank: 0.5494, IoU.trade name: 0.3091, IoU.microwave: 0.8838, IoU.pot: 0.4988, IoU.animal: 0.7231, IoU.bicycle: 0.5314, IoU.lake: 0.6202, IoU.dishwasher: 0.6423, IoU.screen: 0.6478, IoU.blanket: 0.2666, IoU.sculpture: 0.7717, IoU.hood: 0.6076, IoU.sconce: 0.5270, IoU.vase: 0.4341, IoU.traffic light: 0.3547, IoU.tray: 0.1789, IoU.ashcan: 0.5150, IoU.fan: 0.5796, IoU.pier: 0.4206, IoU.crt screen: 0.1758, IoU.plate: 0.5514, IoU.monitor: 0.6225, IoU.bulletin board: 0.5222, IoU.shower: 0.0110, IoU.radiator: 0.6508, IoU.glass: 0.1856, IoU.clock: 0.3278, IoU.flag: 0.6691, Acc.wall: 0.8928, Acc.building: 0.9231, Acc.sky: 0.9701, Acc.floor: 0.9017, Acc.tree: 0.8946, Acc.ceiling: 0.9360, Acc.road: 0.9262, Acc.bed : 0.9714, Acc.windowpane: 0.7613, Acc.grass: 0.8429, Acc.cabinet: 0.7513, Acc.sidewalk: 0.8003, Acc.person: 0.9184, Acc.earth: 0.4578, Acc.door: 0.7240, Acc.table: 0.8123, Acc.mountain: 0.7373, Acc.plant: 0.6491, Acc.curtain: 0.8913, Acc.chair: 0.7132, Acc.car: 0.9347, Acc.water: 0.8254, Acc.painting: 0.8653, Acc.sofa: 0.9101, Acc.shelf: 0.7012, Acc.house: 0.7182, Acc.sea: 0.6772, Acc.mirror: 0.8293, Acc.rug: 0.8176, Acc.field: 0.5785, Acc.armchair: 0.7582, Acc.seat: 0.8671, Acc.fence: 0.5842, Acc.desk: 0.7132, Acc.rock: 0.8444, Acc.wardrobe: 0.7256, Acc.lamp: 0.7887, Acc.bathtub: 0.8997, Acc.railing: 0.5881, Acc.cushion: 0.6680, Acc.base: 0.5043, Acc.box: 0.4862, Acc.column: 0.6495, Acc.signboard: 0.5041, Acc.chest of drawers: 0.6573, Acc.counter: 0.5935, Acc.sand: 0.8905, Acc.sink: 0.8041, Acc.skyscraper: 0.5857, Acc.fireplace: 0.9243, Acc.refrigerator: 0.8693, Acc.grandstand: 0.7935, Acc.path: 0.2676, Acc.stairs: 0.3448, Acc.runway: 0.9008, Acc.case: 0.8301, Acc.pool table: 0.9829, Acc.pillow: 0.7557, Acc.screen door: 0.7478, Acc.stairway: 0.4184, Acc.river: 0.4197, Acc.bridge: 0.8384, Acc.bookcase: 0.5388, Acc.blind: 0.4815, Acc.coffee table: 0.8807, Acc.toilet: 0.9315, Acc.flower: 0.6054, Acc.book: 0.7083, Acc.hill: 0.0434, Acc.bench: 0.7694, Acc.countertop: 0.7562, Acc.stove: 0.8567, Acc.palm: 0.7866, Acc.kitchen island: 0.7397, Acc.computer: 0.8937, Acc.swivel chair: 0.7715, Acc.boat: 0.8442, Acc.bar: 0.8067, Acc.arcade machine: 0.9154, Acc.hovel: 0.3381, Acc.bus: 0.9562, Acc.towel: 0.8330, Acc.light: 0.5181, Acc.truck: 0.4343, Acc.tower: 0.4377, Acc.chandelier: 0.8261, Acc.awning: 0.5005, Acc.streetlight: 0.3728, Acc.booth: 0.5070, Acc.television receiver: 0.8664, Acc.airplane: 0.9311, Acc.dirt track: 0.0776, Acc.apparel: 0.4409, Acc.pole: 0.2786, Acc.land: 0.1565, Acc.bannister: 0.0983, Acc.escalator: 0.8006, Acc.ottoman: 0.6438, Acc.bottle: 0.3185, Acc.buffet: 0.7178, Acc.poster: 0.4497, Acc.stage: 0.5160, Acc.van: 0.6491, Acc.ship: 0.4298, Acc.fountain: 0.3425, Acc.conveyer belt: 0.9520, Acc.canopy: 0.4406, Acc.washer: 0.9009, Acc.plaything: 0.3994, Acc.swimming pool: 0.7870, Acc.stool: 0.6538, Acc.barrel: 0.7914, Acc.basket: 0.5300, Acc.waterfall: 0.4086, Acc.tent: 0.9753, Acc.bag: 0.2563, Acc.minibike: 0.8306, Acc.cradle: 0.9828, Acc.oven: 0.7207, Acc.ball: 0.3041, Acc.food: 0.5190, Acc.step: 0.1706, Acc.tank: 0.6587, Acc.trade name: 0.3681, Acc.microwave: 0.9338, Acc.pot: 0.5452, Acc.animal: 0.7510, Acc.bicycle: 0.6417, Acc.lake: 0.6339, Acc.dishwasher: 0.8096, Acc.screen: 0.9218, Acc.blanket: 0.3180, Acc.sculpture: 0.8595, Acc.hood: 0.6906, Acc.sconce: 0.6833, Acc.vase: 0.5873, Acc.traffic light: 0.5880, Acc.tray: 0.2882, Acc.ashcan: 0.6511, Acc.fan: 0.6498, Acc.pier: 0.5290, Acc.crt screen: 0.1815, Acc.plate: 0.7165, Acc.monitor: 0.8167, Acc.bulletin board: 0.5931, Acc.shower: 0.0153, Acc.radiator: 0.7772, Acc.glass: 0.2010, Acc.clock: 0.3624, Acc.flag: 0.7123 2023-11-03 01:07:53,398 - mmseg - INFO - Iter [17050/40000] lr: 1.859e-06, eta: 8:32:26, time: 2.461, data_time: 1.253, memory: 38534, decode.loss_ce: 0.2190, decode.acc_seg: 91.2974, loss: 0.2190 2023-11-03 01:08:56,549 - mmseg - INFO - Iter [17100/40000] lr: 1.855e-06, eta: 8:31:14, time: 1.263, data_time: 0.053, memory: 38534, decode.loss_ce: 0.2232, decode.acc_seg: 91.0108, loss: 0.2232 2023-11-03 01:09:57,301 - mmseg - INFO - Iter [17150/40000] lr: 1.851e-06, eta: 8:29:58, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2089, decode.acc_seg: 91.7185, loss: 0.2089 2023-11-03 01:10:58,045 - mmseg - INFO - Iter [17200/40000] lr: 1.847e-06, eta: 8:28:43, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2318, decode.acc_seg: 90.8241, loss: 0.2318 2023-11-03 01:11:58,739 - mmseg - INFO - Iter [17250/40000] lr: 1.843e-06, eta: 8:27:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2109, decode.acc_seg: 91.4853, loss: 0.2109 2023-11-03 01:12:59,415 - mmseg - INFO - Iter [17300/40000] lr: 1.839e-06, eta: 8:26:13, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2067, decode.acc_seg: 91.3278, loss: 0.2067 2023-11-03 01:14:00,063 - mmseg - INFO - Iter [17350/40000] lr: 1.835e-06, eta: 8:24:58, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2081, decode.acc_seg: 91.3822, loss: 0.2081 2023-11-03 01:15:00,780 - mmseg - INFO - Iter [17400/40000] lr: 1.831e-06, eta: 8:23:43, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2242, decode.acc_seg: 90.9131, loss: 0.2242 2023-11-03 01:16:01,522 - mmseg - INFO - Iter [17450/40000] lr: 1.827e-06, eta: 8:22:28, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2188, decode.acc_seg: 90.9726, loss: 0.2188 2023-11-03 01:17:02,250 - mmseg - INFO - Iter [17500/40000] lr: 1.822e-06, eta: 8:21:14, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2138, decode.acc_seg: 91.3235, loss: 0.2138 2023-11-03 01:18:02,942 - mmseg - INFO - Iter [17550/40000] lr: 1.818e-06, eta: 8:19:59, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2171, decode.acc_seg: 91.2762, loss: 0.2171 2023-11-03 01:19:03,610 - mmseg - INFO - Iter [17600/40000] lr: 1.814e-06, eta: 8:18:44, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2157, decode.acc_seg: 91.2975, loss: 0.2157 2023-11-03 01:20:04,294 - mmseg - INFO - Iter [17650/40000] lr: 1.810e-06, eta: 8:17:30, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2030, decode.acc_seg: 91.7060, loss: 0.2030 2023-11-03 01:21:07,350 - mmseg - INFO - Iter [17700/40000] lr: 1.806e-06, eta: 8:16:18, time: 1.261, data_time: 0.052, memory: 38534, decode.loss_ce: 0.2172, decode.acc_seg: 91.2994, loss: 0.2172 2023-11-03 01:22:08,048 - mmseg - INFO - Iter [17750/40000] lr: 1.802e-06, eta: 8:15:04, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2148, decode.acc_seg: 91.2340, loss: 0.2148 2023-11-03 01:23:08,793 - mmseg - INFO - Iter [17800/40000] lr: 1.798e-06, eta: 8:13:50, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2194, decode.acc_seg: 91.0451, loss: 0.2194 2023-11-03 01:24:09,460 - mmseg - INFO - Iter [17850/40000] lr: 1.794e-06, eta: 8:12:35, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2020, decode.acc_seg: 91.7730, loss: 0.2020 2023-11-03 01:25:10,217 - mmseg - INFO - Iter [17900/40000] lr: 1.790e-06, eta: 8:11:21, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2102, decode.acc_seg: 91.5935, loss: 0.2102 2023-11-03 01:26:10,987 - mmseg - INFO - Iter [17950/40000] lr: 1.786e-06, eta: 8:10:07, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2038, decode.acc_seg: 91.5710, loss: 0.2038 2023-11-03 01:27:11,726 - mmseg - INFO - Saving checkpoint at 18000 iterations 2023-11-03 01:28:10,771 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 01:28:10,771 - mmseg - INFO - Iter [18000/40000] lr: 1.782e-06, eta: 8:10:05, time: 2.396, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2183, decode.acc_seg: 91.1264, loss: 0.2183 2023-11-03 01:29:11,267 - mmseg - INFO - per class results: 2023-11-03 01:29:11,273 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.77 | 89.53 | | building | 83.52 | 92.83 | | sky | 94.05 | 96.9 | | floor | 83.16 | 91.85 | | tree | 75.79 | 89.0 | | ceiling | 84.83 | 92.73 | | road | 86.23 | 91.47 | | bed | 91.48 | 96.55 | | windowpane | 64.84 | 80.97 | | grass | 69.18 | 84.15 | | cabinet | 65.24 | 75.39 | | sidewalk | 68.74 | 85.56 | | person | 82.42 | 90.4 | | earth | 37.23 | 51.01 | | door | 54.87 | 73.23 | | table | 68.4 | 81.22 | | mountain | 64.28 | 74.74 | | plant | 53.69 | 62.39 | | curtain | 75.49 | 86.45 | | chair | 58.99 | 69.34 | | car | 84.81 | 92.77 | | water | 63.52 | 80.1 | | painting | 75.84 | 87.64 | | sofa | 78.16 | 89.48 | | shelf | 44.06 | 57.83 | | house | 49.3 | 61.17 | | sea | 62.4 | 70.15 | | mirror | 74.52 | 83.72 | | rug | 63.42 | 71.3 | | field | 32.73 | 51.81 | | armchair | 56.65 | 77.8 | | seat | 62.21 | 87.18 | | fence | 41.47 | 55.59 | | desk | 52.14 | 70.43 | | rock | 61.76 | 83.49 | | wardrobe | 51.11 | 71.06 | | lamp | 66.15 | 77.73 | | bathtub | 89.48 | 93.62 | | railing | 44.57 | 55.27 | | cushion | 60.14 | 68.61 | | base | 31.06 | 53.28 | | box | 36.11 | 50.1 | | column | 48.45 | 56.21 | | signboard | 36.27 | 50.94 | | chest of drawers | 45.33 | 68.25 | | counter | 32.5 | 39.2 | | sand | 61.66 | 85.18 | | sink | 76.49 | 84.71 | | skyscraper | 46.97 | 59.72 | | fireplace | 76.54 | 85.82 | | refrigerator | 84.53 | 91.65 | | grandstand | 41.25 | 82.07 | | path | 20.58 | 25.64 | | stairs | 28.28 | 38.47 | | runway | 69.55 | 88.92 | | case | 57.93 | 84.91 | | pool table | 93.62 | 97.13 | | pillow | 62.6 | 76.14 | | screen door | 77.9 | 79.32 | | stairway | 41.03 | 53.99 | | river | 20.87 | 46.45 | | bridge | 77.58 | 89.26 | | bookcase | 42.87 | 57.81 | | blind | 41.3 | 44.78 | | coffee table | 67.45 | 84.28 | | toilet | 89.47 | 93.08 | | flower | 42.39 | 63.52 | | book | 48.38 | 71.62 | | hill | 3.08 | 5.67 | | bench | 54.83 | 61.57 | | countertop | 63.53 | 75.92 | | stove | 81.65 | 90.77 | | palm | 47.13 | 84.87 | | kitchen island | 44.7 | 63.48 | | computer | 77.09 | 89.19 | | swivel chair | 39.02 | 53.64 | | boat | 55.98 | 71.27 | | bar | 66.42 | 88.23 | | arcade machine | 75.42 | 78.52 | | hovel | 31.24 | 33.24 | | bus | 91.92 | 95.44 | | towel | 74.4 | 83.79 | | light | 47.44 | 55.99 | | truck | 43.68 | 57.2 | | tower | 23.48 | 37.56 | | chandelier | 66.93 | 82.91 | | awning | 44.05 | 59.76 | | streetlight | 23.36 | 30.09 | | booth | 43.7 | 50.32 | | television receiver | 74.3 | 88.61 | | airplane | 84.32 | 94.09 | | dirt track | 10.97 | 11.9 | | apparel | 52.01 | 74.19 | | pole | 25.9 | 34.62 | | land | 7.47 | 17.7 | | bannister | 12.15 | 15.37 | | escalator | 63.12 | 85.63 | | ottoman | 55.43 | 67.6 | | bottle | 22.64 | 29.77 | | buffet | 58.78 | 69.78 | | poster | 36.44 | 44.59 | | stage | 24.41 | 41.46 | | van | 47.89 | 64.58 | | ship | 18.75 | 23.3 | | fountain | 36.33 | 37.15 | | conveyer belt | 87.47 | 94.1 | | canopy | 47.28 | 52.21 | | washer | 89.58 | 96.52 | | plaything | 32.93 | 42.67 | | swimming pool | 48.7 | 69.93 | | stool | 47.03 | 67.64 | | barrel | 50.64 | 66.99 | | basket | 42.89 | 54.23 | | waterfall | 50.88 | 67.32 | | tent | 90.79 | 97.63 | | bag | 26.42 | 32.1 | | minibike | 72.34 | 85.66 | | cradle | 86.74 | 96.86 | | oven | 62.99 | 75.55 | | ball | 23.77 | 24.59 | | food | 50.5 | 53.65 | | step | 15.76 | 17.83 | | tank | 53.2 | 65.7 | | trade name | 29.49 | 36.27 | | microwave | 87.98 | 94.82 | | pot | 55.44 | 63.22 | | animal | 75.17 | 79.21 | | bicycle | 57.69 | 71.71 | | lake | 56.63 | 63.4 | | dishwasher | 63.69 | 72.26 | | screen | 59.44 | 83.43 | | blanket | 29.89 | 35.63 | | sculpture | 70.04 | 88.9 | | hood | 74.74 | 80.68 | | sconce | 51.76 | 64.82 | | vase | 43.02 | 56.01 | | traffic light | 34.46 | 58.35 | | tray | 19.56 | 26.13 | | ashcan | 50.66 | 65.19 | | fan | 63.17 | 77.37 | | pier | 35.49 | 39.42 | | crt screen | 11.44 | 17.54 | | plate | 55.95 | 71.28 | | monitor | 52.53 | 63.84 | | bulletin board | 51.84 | 61.62 | | shower | 2.12 | 2.24 | | radiator | 65.74 | 81.42 | | glass | 17.86 | 19.02 | | clock | 35.82 | 43.79 | | flag | 65.77 | 80.02 | +---------------------+-------+-------+ 2023-11-03 01:29:11,273 - mmseg - INFO - Summary: 2023-11-03 01:29:11,273 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.87 | 54.55 | 66.36 | +-------+-------+-------+ 2023-11-03 01:29:11,273 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 01:29:11,274 - mmseg - INFO - Iter(val) [250] aAcc: 0.8487, mIoU: 0.5455, mAcc: 0.6636, IoU.wall: 0.7977, IoU.building: 0.8352, IoU.sky: 0.9405, IoU.floor: 0.8316, IoU.tree: 0.7579, IoU.ceiling: 0.8483, IoU.road: 0.8623, IoU.bed : 0.9148, IoU.windowpane: 0.6484, IoU.grass: 0.6918, IoU.cabinet: 0.6524, IoU.sidewalk: 0.6874, IoU.person: 0.8242, IoU.earth: 0.3723, IoU.door: 0.5487, IoU.table: 0.6840, IoU.mountain: 0.6428, IoU.plant: 0.5369, IoU.curtain: 0.7549, IoU.chair: 0.5899, IoU.car: 0.8481, IoU.water: 0.6352, IoU.painting: 0.7584, IoU.sofa: 0.7816, IoU.shelf: 0.4406, IoU.house: 0.4930, IoU.sea: 0.6240, IoU.mirror: 0.7452, IoU.rug: 0.6342, IoU.field: 0.3273, IoU.armchair: 0.5665, IoU.seat: 0.6221, IoU.fence: 0.4147, IoU.desk: 0.5214, IoU.rock: 0.6176, IoU.wardrobe: 0.5111, IoU.lamp: 0.6615, IoU.bathtub: 0.8948, IoU.railing: 0.4457, IoU.cushion: 0.6014, IoU.base: 0.3106, IoU.box: 0.3611, IoU.column: 0.4845, IoU.signboard: 0.3627, IoU.chest of drawers: 0.4533, IoU.counter: 0.3250, IoU.sand: 0.6166, IoU.sink: 0.7649, IoU.skyscraper: 0.4697, IoU.fireplace: 0.7654, IoU.refrigerator: 0.8453, IoU.grandstand: 0.4125, IoU.path: 0.2058, IoU.stairs: 0.2828, IoU.runway: 0.6955, IoU.case: 0.5793, IoU.pool table: 0.9362, IoU.pillow: 0.6260, IoU.screen door: 0.7790, IoU.stairway: 0.4103, IoU.river: 0.2087, IoU.bridge: 0.7758, IoU.bookcase: 0.4287, IoU.blind: 0.4130, IoU.coffee table: 0.6745, IoU.toilet: 0.8947, IoU.flower: 0.4239, IoU.book: 0.4838, IoU.hill: 0.0308, IoU.bench: 0.5483, IoU.countertop: 0.6353, IoU.stove: 0.8165, IoU.palm: 0.4713, IoU.kitchen island: 0.4470, IoU.computer: 0.7709, IoU.swivel chair: 0.3902, IoU.boat: 0.5598, IoU.bar: 0.6642, IoU.arcade machine: 0.7542, IoU.hovel: 0.3124, IoU.bus: 0.9192, IoU.towel: 0.7440, IoU.light: 0.4744, IoU.truck: 0.4368, IoU.tower: 0.2348, IoU.chandelier: 0.6693, IoU.awning: 0.4405, IoU.streetlight: 0.2336, IoU.booth: 0.4370, IoU.television receiver: 0.7430, IoU.airplane: 0.8432, IoU.dirt track: 0.1097, IoU.apparel: 0.5201, IoU.pole: 0.2590, IoU.land: 0.0747, IoU.bannister: 0.1215, IoU.escalator: 0.6312, IoU.ottoman: 0.5543, IoU.bottle: 0.2264, IoU.buffet: 0.5878, IoU.poster: 0.3644, IoU.stage: 0.2441, IoU.van: 0.4789, IoU.ship: 0.1875, IoU.fountain: 0.3633, IoU.conveyer belt: 0.8747, IoU.canopy: 0.4728, IoU.washer: 0.8958, IoU.plaything: 0.3293, IoU.swimming pool: 0.4870, IoU.stool: 0.4703, IoU.barrel: 0.5064, IoU.basket: 0.4289, IoU.waterfall: 0.5088, IoU.tent: 0.9079, IoU.bag: 0.2642, IoU.minibike: 0.7234, IoU.cradle: 0.8674, IoU.oven: 0.6299, IoU.ball: 0.2377, IoU.food: 0.5050, IoU.step: 0.1576, IoU.tank: 0.5320, IoU.trade name: 0.2949, IoU.microwave: 0.8798, IoU.pot: 0.5544, IoU.animal: 0.7517, IoU.bicycle: 0.5769, IoU.lake: 0.5663, IoU.dishwasher: 0.6369, IoU.screen: 0.5944, IoU.blanket: 0.2989, IoU.sculpture: 0.7004, IoU.hood: 0.7474, IoU.sconce: 0.5176, IoU.vase: 0.4302, IoU.traffic light: 0.3446, IoU.tray: 0.1956, IoU.ashcan: 0.5066, IoU.fan: 0.6317, IoU.pier: 0.3549, IoU.crt screen: 0.1144, IoU.plate: 0.5595, IoU.monitor: 0.5253, IoU.bulletin board: 0.5184, IoU.shower: 0.0212, IoU.radiator: 0.6574, IoU.glass: 0.1786, IoU.clock: 0.3582, IoU.flag: 0.6577, Acc.wall: 0.8953, Acc.building: 0.9283, Acc.sky: 0.9690, Acc.floor: 0.9185, Acc.tree: 0.8900, Acc.ceiling: 0.9273, Acc.road: 0.9147, Acc.bed : 0.9655, Acc.windowpane: 0.8097, Acc.grass: 0.8415, Acc.cabinet: 0.7539, Acc.sidewalk: 0.8556, Acc.person: 0.9040, Acc.earth: 0.5101, Acc.door: 0.7323, Acc.table: 0.8122, Acc.mountain: 0.7474, Acc.plant: 0.6239, Acc.curtain: 0.8645, Acc.chair: 0.6934, Acc.car: 0.9277, Acc.water: 0.8010, Acc.painting: 0.8764, Acc.sofa: 0.8948, Acc.shelf: 0.5783, Acc.house: 0.6117, Acc.sea: 0.7015, Acc.mirror: 0.8372, Acc.rug: 0.7130, Acc.field: 0.5181, Acc.armchair: 0.7780, Acc.seat: 0.8718, Acc.fence: 0.5559, Acc.desk: 0.7043, Acc.rock: 0.8349, Acc.wardrobe: 0.7106, Acc.lamp: 0.7773, Acc.bathtub: 0.9362, Acc.railing: 0.5527, Acc.cushion: 0.6861, Acc.base: 0.5328, Acc.box: 0.5010, Acc.column: 0.5621, Acc.signboard: 0.5094, Acc.chest of drawers: 0.6825, Acc.counter: 0.3920, Acc.sand: 0.8518, Acc.sink: 0.8471, Acc.skyscraper: 0.5972, Acc.fireplace: 0.8582, Acc.refrigerator: 0.9165, Acc.grandstand: 0.8207, Acc.path: 0.2564, Acc.stairs: 0.3847, Acc.runway: 0.8892, Acc.case: 0.8491, Acc.pool table: 0.9713, Acc.pillow: 0.7614, Acc.screen door: 0.7932, Acc.stairway: 0.5399, Acc.river: 0.4645, Acc.bridge: 0.8926, Acc.bookcase: 0.5781, Acc.blind: 0.4478, Acc.coffee table: 0.8428, Acc.toilet: 0.9308, Acc.flower: 0.6352, Acc.book: 0.7162, Acc.hill: 0.0567, Acc.bench: 0.6157, Acc.countertop: 0.7592, Acc.stove: 0.9077, Acc.palm: 0.8487, Acc.kitchen island: 0.6348, Acc.computer: 0.8919, Acc.swivel chair: 0.5364, Acc.boat: 0.7127, Acc.bar: 0.8823, Acc.arcade machine: 0.7852, Acc.hovel: 0.3324, Acc.bus: 0.9544, Acc.towel: 0.8379, Acc.light: 0.5599, Acc.truck: 0.5720, Acc.tower: 0.3756, Acc.chandelier: 0.8291, Acc.awning: 0.5976, Acc.streetlight: 0.3009, Acc.booth: 0.5032, Acc.television receiver: 0.8861, Acc.airplane: 0.9409, Acc.dirt track: 0.1190, Acc.apparel: 0.7419, Acc.pole: 0.3462, Acc.land: 0.1770, Acc.bannister: 0.1537, Acc.escalator: 0.8563, Acc.ottoman: 0.6760, Acc.bottle: 0.2977, Acc.buffet: 0.6978, Acc.poster: 0.4459, Acc.stage: 0.4146, Acc.van: 0.6458, Acc.ship: 0.2330, Acc.fountain: 0.3715, Acc.conveyer belt: 0.9410, Acc.canopy: 0.5221, Acc.washer: 0.9652, Acc.plaything: 0.4267, Acc.swimming pool: 0.6993, Acc.stool: 0.6764, Acc.barrel: 0.6699, Acc.basket: 0.5423, Acc.waterfall: 0.6732, Acc.tent: 0.9763, Acc.bag: 0.3210, Acc.minibike: 0.8566, Acc.cradle: 0.9686, Acc.oven: 0.7555, Acc.ball: 0.2459, Acc.food: 0.5365, Acc.step: 0.1783, Acc.tank: 0.6570, Acc.trade name: 0.3627, Acc.microwave: 0.9482, Acc.pot: 0.6322, Acc.animal: 0.7921, Acc.bicycle: 0.7171, Acc.lake: 0.6340, Acc.dishwasher: 0.7226, Acc.screen: 0.8343, Acc.blanket: 0.3563, Acc.sculpture: 0.8890, Acc.hood: 0.8068, Acc.sconce: 0.6482, Acc.vase: 0.5601, Acc.traffic light: 0.5835, Acc.tray: 0.2613, Acc.ashcan: 0.6519, Acc.fan: 0.7737, Acc.pier: 0.3942, Acc.crt screen: 0.1754, Acc.plate: 0.7128, Acc.monitor: 0.6384, Acc.bulletin board: 0.6162, Acc.shower: 0.0224, Acc.radiator: 0.8142, Acc.glass: 0.1902, Acc.clock: 0.4379, Acc.flag: 0.8002 2023-11-03 01:30:12,093 - mmseg - INFO - Iter [18050/40000] lr: 1.778e-06, eta: 8:10:05, time: 2.426, data_time: 1.218, memory: 38534, decode.loss_ce: 0.2037, decode.acc_seg: 91.7828, loss: 0.2037 2023-11-03 01:31:12,832 - mmseg - INFO - Iter [18100/40000] lr: 1.774e-06, eta: 8:08:50, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2086, decode.acc_seg: 91.8583, loss: 0.2086 2023-11-03 01:32:13,541 - mmseg - INFO - Iter [18150/40000] lr: 1.770e-06, eta: 8:07:36, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.1968, decode.acc_seg: 91.8959, loss: 0.1968 2023-11-03 01:33:14,200 - mmseg - INFO - Iter [18200/40000] lr: 1.766e-06, eta: 8:06:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2161, decode.acc_seg: 91.2994, loss: 0.2161 2023-11-03 01:34:14,864 - mmseg - INFO - Iter [18250/40000] lr: 1.762e-06, eta: 8:05:07, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2073, decode.acc_seg: 91.5682, loss: 0.2073 2023-11-03 01:35:15,612 - mmseg - INFO - Iter [18300/40000] lr: 1.758e-06, eta: 8:03:53, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2156, decode.acc_seg: 91.3193, loss: 0.2156 2023-11-03 01:36:19,052 - mmseg - INFO - Iter [18350/40000] lr: 1.754e-06, eta: 8:02:42, time: 1.269, data_time: 0.058, memory: 38534, decode.loss_ce: 0.2087, decode.acc_seg: 91.5251, loss: 0.2087 2023-11-03 01:37:19,748 - mmseg - INFO - Iter [18400/40000] lr: 1.750e-06, eta: 8:01:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1973, decode.acc_seg: 92.0163, loss: 0.1973 2023-11-03 01:38:20,491 - mmseg - INFO - Iter [18450/40000] lr: 1.746e-06, eta: 8:00:14, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2031, decode.acc_seg: 91.7243, loss: 0.2031 2023-11-03 01:39:21,236 - mmseg - INFO - Iter [18500/40000] lr: 1.741e-06, eta: 7:59:00, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2111, decode.acc_seg: 91.2054, loss: 0.2111 2023-11-03 01:40:22,001 - mmseg - INFO - Iter [18550/40000] lr: 1.737e-06, eta: 7:57:46, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2055, decode.acc_seg: 91.8035, loss: 0.2055 2023-11-03 01:41:22,776 - mmseg - INFO - Iter [18600/40000] lr: 1.733e-06, eta: 7:56:32, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2080, decode.acc_seg: 91.5198, loss: 0.2080 2023-11-03 01:42:23,510 - mmseg - INFO - Iter [18650/40000] lr: 1.729e-06, eta: 7:55:18, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2024, decode.acc_seg: 91.6317, loss: 0.2024 2023-11-03 01:43:24,249 - mmseg - INFO - Iter [18700/40000] lr: 1.725e-06, eta: 7:54:05, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2093, decode.acc_seg: 91.6064, loss: 0.2093 2023-11-03 01:44:24,952 - mmseg - INFO - Iter [18750/40000] lr: 1.721e-06, eta: 7:52:51, time: 1.214, data_time: 0.008, memory: 38534, decode.loss_ce: 0.1939, decode.acc_seg: 92.0115, loss: 0.1939 2023-11-03 01:45:25,655 - mmseg - INFO - Iter [18800/40000] lr: 1.717e-06, eta: 7:51:37, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2109, decode.acc_seg: 91.4605, loss: 0.2109 2023-11-03 01:46:26,392 - mmseg - INFO - Iter [18850/40000] lr: 1.713e-06, eta: 7:50:24, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2126, decode.acc_seg: 91.4164, loss: 0.2126 2023-11-03 01:47:27,159 - mmseg - INFO - Iter [18900/40000] lr: 1.709e-06, eta: 7:49:10, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2042, decode.acc_seg: 91.6334, loss: 0.2042 2023-11-03 01:48:27,924 - mmseg - INFO - Iter [18950/40000] lr: 1.705e-06, eta: 7:47:57, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2180, decode.acc_seg: 91.0754, loss: 0.2180 2023-11-03 01:49:30,981 - mmseg - INFO - Saving checkpoint at 19000 iterations 2023-11-03 01:50:28,087 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 01:50:28,088 - mmseg - INFO - Iter [19000/40000] lr: 1.701e-06, eta: 7:47:50, time: 2.403, data_time: 0.054, memory: 38534, decode.loss_ce: 0.1994, decode.acc_seg: 91.6160, loss: 0.1994 2023-11-03 01:51:27,516 - mmseg - INFO - per class results: 2023-11-03 01:51:27,521 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.17 | 88.98 | | building | 82.69 | 92.58 | | sky | 94.11 | 97.27 | | floor | 82.35 | 90.73 | | tree | 75.6 | 87.53 | | ceiling | 85.09 | 92.58 | | road | 85.34 | 92.1 | | bed | 91.24 | 96.97 | | windowpane | 63.94 | 80.28 | | grass | 69.37 | 83.52 | | cabinet | 65.36 | 77.85 | | sidewalk | 68.72 | 83.67 | | person | 82.49 | 91.13 | | earth | 34.82 | 48.57 | | door | 54.6 | 74.29 | | table | 67.34 | 82.38 | | mountain | 62.44 | 77.33 | | plant | 54.8 | 66.28 | | curtain | 75.15 | 87.99 | | chair | 61.24 | 73.82 | | car | 85.77 | 91.81 | | water | 63.51 | 78.17 | | painting | 77.07 | 87.91 | | sofa | 79.22 | 90.22 | | shelf | 45.59 | 63.97 | | house | 36.84 | 46.09 | | sea | 69.07 | 81.84 | | mirror | 74.84 | 85.02 | | rug | 64.27 | 73.59 | | field | 31.02 | 47.68 | | armchair | 57.88 | 73.22 | | seat | 63.61 | 86.02 | | fence | 42.8 | 55.97 | | desk | 56.94 | 72.7 | | rock | 59.69 | 78.72 | | wardrobe | 50.42 | 66.98 | | lamp | 66.17 | 77.27 | | bathtub | 89.14 | 91.74 | | railing | 41.89 | 55.24 | | cushion | 63.23 | 74.42 | | base | 30.0 | 52.44 | | box | 31.03 | 37.84 | | column | 48.68 | 58.6 | | signboard | 38.53 | 51.37 | | chest of drawers | 45.6 | 61.38 | | counter | 45.65 | 55.66 | | sand | 61.66 | 86.08 | | sink | 74.44 | 78.4 | | skyscraper | 50.08 | 58.45 | | fireplace | 75.08 | 86.14 | | refrigerator | 81.66 | 88.77 | | grandstand | 42.65 | 81.17 | | path | 20.69 | 25.5 | | stairs | 30.9 | 43.37 | | runway | 69.11 | 89.35 | | case | 66.67 | 91.21 | | pool table | 93.36 | 97.57 | | pillow | 58.36 | 65.6 | | screen door | 72.68 | 76.65 | | stairway | 34.76 | 43.28 | | river | 21.23 | 47.64 | | bridge | 78.5 | 89.81 | | bookcase | 41.32 | 47.4 | | blind | 37.81 | 42.47 | | coffee table | 67.41 | 86.09 | | toilet | 88.36 | 92.91 | | flower | 43.1 | 64.36 | | book | 50.53 | 68.3 | | hill | 9.27 | 17.35 | | bench | 55.61 | 61.69 | | countertop | 63.24 | 77.13 | | stove | 82.95 | 91.18 | | palm | 45.74 | 85.35 | | kitchen island | 46.28 | 69.39 | | computer | 78.14 | 87.93 | | swivel chair | 44.08 | 64.66 | | boat | 55.24 | 72.5 | | bar | 69.8 | 76.9 | | arcade machine | 77.26 | 79.82 | | hovel | 46.44 | 50.2 | | bus | 91.02 | 96.18 | | towel | 73.28 | 82.51 | | light | 47.38 | 56.3 | | truck | 44.82 | 58.46 | | tower | 4.34 | 5.49 | | chandelier | 66.61 | 83.52 | | awning | 34.67 | 42.2 | | streetlight | 25.83 | 35.28 | | booth | 45.32 | 50.11 | | television receiver | 71.78 | 87.79 | | airplane | 84.01 | 94.25 | | dirt track | 6.9 | 18.63 | | apparel | 33.31 | 44.23 | | pole | 22.4 | 29.65 | | land | 4.59 | 7.36 | | bannister | 12.89 | 17.4 | | escalator | 64.49 | 80.92 | | ottoman | 54.52 | 70.7 | | bottle | 22.22 | 28.23 | | buffet | 56.69 | 68.31 | | poster | 34.66 | 39.21 | | stage | 23.33 | 47.89 | | van | 47.82 | 82.76 | | ship | 30.68 | 37.16 | | fountain | 37.0 | 37.52 | | conveyer belt | 84.62 | 93.43 | | canopy | 48.66 | 53.57 | | washer | 89.13 | 94.87 | | plaything | 31.7 | 39.72 | | swimming pool | 49.18 | 70.03 | | stool | 47.94 | 60.55 | | barrel | 64.93 | 79.19 | | basket | 35.55 | 46.72 | | waterfall | 48.92 | 64.13 | | tent | 87.93 | 97.69 | | bag | 20.28 | 23.9 | | minibike | 72.91 | 85.55 | | cradle | 83.91 | 97.36 | | oven | 62.42 | 67.65 | | ball | 43.36 | 46.52 | | food | 52.14 | 54.81 | | step | 17.46 | 19.87 | | tank | 55.06 | 63.86 | | trade name | 28.28 | 34.07 | | microwave | 87.84 | 93.5 | | pot | 54.62 | 62.62 | | animal | 75.63 | 79.84 | | bicycle | 59.37 | 82.6 | | lake | 55.71 | 62.7 | | dishwasher | 63.77 | 77.93 | | screen | 66.8 | 84.34 | | blanket | 24.88 | 27.87 | | sculpture | 77.41 | 85.84 | | hood | 64.45 | 74.14 | | sconce | 52.79 | 70.92 | | vase | 43.87 | 57.41 | | traffic light | 32.62 | 62.47 | | tray | 18.14 | 23.24 | | ashcan | 50.66 | 64.02 | | fan | 63.3 | 79.39 | | pier | 39.3 | 44.24 | | crt screen | 15.3 | 20.16 | | plate | 52.58 | 72.85 | | monitor | 59.71 | 72.9 | | bulletin board | 47.83 | 57.11 | | shower | 3.61 | 3.81 | | radiator | 66.97 | 81.53 | | glass | 18.67 | 20.3 | | clock | 38.79 | 42.84 | | flag | 65.34 | 73.91 | +---------------------+-------+-------+ 2023-11-03 01:51:27,521 - mmseg - INFO - Summary: 2023-11-03 01:51:27,521 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.67 | 54.62 | 66.14 | +-------+-------+-------+ 2023-11-03 01:51:27,522 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 01:51:27,522 - mmseg - INFO - Iter(val) [250] aAcc: 0.8467, mIoU: 0.5462, mAcc: 0.6614, IoU.wall: 0.7917, IoU.building: 0.8269, IoU.sky: 0.9411, IoU.floor: 0.8235, IoU.tree: 0.7560, IoU.ceiling: 0.8509, IoU.road: 0.8534, IoU.bed : 0.9124, IoU.windowpane: 0.6394, IoU.grass: 0.6937, IoU.cabinet: 0.6536, IoU.sidewalk: 0.6872, IoU.person: 0.8249, IoU.earth: 0.3482, IoU.door: 0.5460, IoU.table: 0.6734, IoU.mountain: 0.6244, IoU.plant: 0.5480, IoU.curtain: 0.7515, IoU.chair: 0.6124, IoU.car: 0.8577, IoU.water: 0.6351, IoU.painting: 0.7707, IoU.sofa: 0.7922, IoU.shelf: 0.4559, IoU.house: 0.3684, IoU.sea: 0.6907, IoU.mirror: 0.7484, IoU.rug: 0.6427, IoU.field: 0.3102, IoU.armchair: 0.5788, IoU.seat: 0.6361, IoU.fence: 0.4280, IoU.desk: 0.5694, IoU.rock: 0.5969, IoU.wardrobe: 0.5042, IoU.lamp: 0.6617, IoU.bathtub: 0.8914, IoU.railing: 0.4189, IoU.cushion: 0.6323, IoU.base: 0.3000, IoU.box: 0.3103, IoU.column: 0.4868, IoU.signboard: 0.3853, IoU.chest of drawers: 0.4560, IoU.counter: 0.4565, IoU.sand: 0.6166, IoU.sink: 0.7444, IoU.skyscraper: 0.5008, IoU.fireplace: 0.7508, IoU.refrigerator: 0.8166, IoU.grandstand: 0.4265, IoU.path: 0.2069, IoU.stairs: 0.3090, IoU.runway: 0.6911, IoU.case: 0.6667, IoU.pool table: 0.9336, IoU.pillow: 0.5836, IoU.screen door: 0.7268, IoU.stairway: 0.3476, IoU.river: 0.2123, IoU.bridge: 0.7850, IoU.bookcase: 0.4132, IoU.blind: 0.3781, IoU.coffee table: 0.6741, IoU.toilet: 0.8836, IoU.flower: 0.4310, IoU.book: 0.5053, IoU.hill: 0.0927, IoU.bench: 0.5561, IoU.countertop: 0.6324, IoU.stove: 0.8295, IoU.palm: 0.4574, IoU.kitchen island: 0.4628, IoU.computer: 0.7814, IoU.swivel chair: 0.4408, IoU.boat: 0.5524, IoU.bar: 0.6980, IoU.arcade machine: 0.7726, IoU.hovel: 0.4644, IoU.bus: 0.9102, IoU.towel: 0.7328, IoU.light: 0.4738, IoU.truck: 0.4482, IoU.tower: 0.0434, IoU.chandelier: 0.6661, IoU.awning: 0.3467, IoU.streetlight: 0.2583, IoU.booth: 0.4532, IoU.television receiver: 0.7178, IoU.airplane: 0.8401, IoU.dirt track: 0.0690, IoU.apparel: 0.3331, IoU.pole: 0.2240, IoU.land: 0.0459, IoU.bannister: 0.1289, IoU.escalator: 0.6449, IoU.ottoman: 0.5452, IoU.bottle: 0.2222, IoU.buffet: 0.5669, IoU.poster: 0.3466, IoU.stage: 0.2333, IoU.van: 0.4782, IoU.ship: 0.3068, IoU.fountain: 0.3700, IoU.conveyer belt: 0.8462, IoU.canopy: 0.4866, IoU.washer: 0.8913, IoU.plaything: 0.3170, IoU.swimming pool: 0.4918, IoU.stool: 0.4794, IoU.barrel: 0.6493, IoU.basket: 0.3555, IoU.waterfall: 0.4892, IoU.tent: 0.8793, IoU.bag: 0.2028, IoU.minibike: 0.7291, IoU.cradle: 0.8391, IoU.oven: 0.6242, IoU.ball: 0.4336, IoU.food: 0.5214, IoU.step: 0.1746, IoU.tank: 0.5506, IoU.trade name: 0.2828, IoU.microwave: 0.8784, IoU.pot: 0.5462, IoU.animal: 0.7563, IoU.bicycle: 0.5937, IoU.lake: 0.5571, IoU.dishwasher: 0.6377, IoU.screen: 0.6680, IoU.blanket: 0.2488, IoU.sculpture: 0.7741, IoU.hood: 0.6445, IoU.sconce: 0.5279, IoU.vase: 0.4387, IoU.traffic light: 0.3262, IoU.tray: 0.1814, IoU.ashcan: 0.5066, IoU.fan: 0.6330, IoU.pier: 0.3930, IoU.crt screen: 0.1530, IoU.plate: 0.5258, IoU.monitor: 0.5971, IoU.bulletin board: 0.4783, IoU.shower: 0.0361, IoU.radiator: 0.6697, IoU.glass: 0.1867, IoU.clock: 0.3879, IoU.flag: 0.6534, Acc.wall: 0.8898, Acc.building: 0.9258, Acc.sky: 0.9727, Acc.floor: 0.9073, Acc.tree: 0.8753, Acc.ceiling: 0.9258, Acc.road: 0.9210, Acc.bed : 0.9697, Acc.windowpane: 0.8028, Acc.grass: 0.8352, Acc.cabinet: 0.7785, Acc.sidewalk: 0.8367, Acc.person: 0.9113, Acc.earth: 0.4857, Acc.door: 0.7429, Acc.table: 0.8238, Acc.mountain: 0.7733, Acc.plant: 0.6628, Acc.curtain: 0.8799, Acc.chair: 0.7382, Acc.car: 0.9181, Acc.water: 0.7817, Acc.painting: 0.8791, Acc.sofa: 0.9022, Acc.shelf: 0.6397, Acc.house: 0.4609, Acc.sea: 0.8184, Acc.mirror: 0.8502, Acc.rug: 0.7359, Acc.field: 0.4768, Acc.armchair: 0.7322, Acc.seat: 0.8602, Acc.fence: 0.5597, Acc.desk: 0.7270, Acc.rock: 0.7872, Acc.wardrobe: 0.6698, Acc.lamp: 0.7727, Acc.bathtub: 0.9174, Acc.railing: 0.5524, Acc.cushion: 0.7442, Acc.base: 0.5244, Acc.box: 0.3784, Acc.column: 0.5860, Acc.signboard: 0.5137, Acc.chest of drawers: 0.6138, Acc.counter: 0.5566, Acc.sand: 0.8608, Acc.sink: 0.7840, Acc.skyscraper: 0.5845, Acc.fireplace: 0.8614, Acc.refrigerator: 0.8877, Acc.grandstand: 0.8117, Acc.path: 0.2550, Acc.stairs: 0.4337, Acc.runway: 0.8935, Acc.case: 0.9121, Acc.pool table: 0.9757, Acc.pillow: 0.6560, Acc.screen door: 0.7665, Acc.stairway: 0.4328, Acc.river: 0.4764, Acc.bridge: 0.8981, Acc.bookcase: 0.4740, Acc.blind: 0.4247, Acc.coffee table: 0.8609, Acc.toilet: 0.9291, Acc.flower: 0.6436, Acc.book: 0.6830, Acc.hill: 0.1735, Acc.bench: 0.6169, Acc.countertop: 0.7713, Acc.stove: 0.9118, Acc.palm: 0.8535, Acc.kitchen island: 0.6939, Acc.computer: 0.8793, Acc.swivel chair: 0.6466, Acc.boat: 0.7250, Acc.bar: 0.7690, Acc.arcade machine: 0.7982, Acc.hovel: 0.5020, Acc.bus: 0.9618, Acc.towel: 0.8251, Acc.light: 0.5630, Acc.truck: 0.5846, Acc.tower: 0.0549, Acc.chandelier: 0.8352, Acc.awning: 0.4220, Acc.streetlight: 0.3528, Acc.booth: 0.5011, Acc.television receiver: 0.8779, Acc.airplane: 0.9425, Acc.dirt track: 0.1863, Acc.apparel: 0.4423, Acc.pole: 0.2965, Acc.land: 0.0736, Acc.bannister: 0.1740, Acc.escalator: 0.8092, Acc.ottoman: 0.7070, Acc.bottle: 0.2823, Acc.buffet: 0.6831, Acc.poster: 0.3921, Acc.stage: 0.4789, Acc.van: 0.8276, Acc.ship: 0.3716, Acc.fountain: 0.3752, Acc.conveyer belt: 0.9343, Acc.canopy: 0.5357, Acc.washer: 0.9487, Acc.plaything: 0.3972, Acc.swimming pool: 0.7003, Acc.stool: 0.6055, Acc.barrel: 0.7919, Acc.basket: 0.4672, Acc.waterfall: 0.6413, Acc.tent: 0.9769, Acc.bag: 0.2390, Acc.minibike: 0.8555, Acc.cradle: 0.9736, Acc.oven: 0.6765, Acc.ball: 0.4652, Acc.food: 0.5481, Acc.step: 0.1987, Acc.tank: 0.6386, Acc.trade name: 0.3407, Acc.microwave: 0.9350, Acc.pot: 0.6262, Acc.animal: 0.7984, Acc.bicycle: 0.8260, Acc.lake: 0.6270, Acc.dishwasher: 0.7793, Acc.screen: 0.8434, Acc.blanket: 0.2787, Acc.sculpture: 0.8584, Acc.hood: 0.7414, Acc.sconce: 0.7092, Acc.vase: 0.5741, Acc.traffic light: 0.6247, Acc.tray: 0.2324, Acc.ashcan: 0.6402, Acc.fan: 0.7939, Acc.pier: 0.4424, Acc.crt screen: 0.2016, Acc.plate: 0.7285, Acc.monitor: 0.7290, Acc.bulletin board: 0.5711, Acc.shower: 0.0381, Acc.radiator: 0.8153, Acc.glass: 0.2030, Acc.clock: 0.4284, Acc.flag: 0.7391 2023-11-03 01:52:28,339 - mmseg - INFO - Iter [19050/40000] lr: 1.697e-06, eta: 7:47:41, time: 2.405, data_time: 1.196, memory: 38534, decode.loss_ce: 0.2008, decode.acc_seg: 91.8407, loss: 0.2008 2023-11-03 01:53:29,067 - mmseg - INFO - Iter [19100/40000] lr: 1.693e-06, eta: 7:46:28, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2009, decode.acc_seg: 91.8474, loss: 0.2009 2023-11-03 01:54:29,792 - mmseg - INFO - Iter [19150/40000] lr: 1.689e-06, eta: 7:45:14, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1955, decode.acc_seg: 92.1591, loss: 0.1955 2023-11-03 01:55:30,499 - mmseg - INFO - Iter [19200/40000] lr: 1.685e-06, eta: 7:44:00, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1909, decode.acc_seg: 92.1178, loss: 0.1909 2023-11-03 01:56:31,260 - mmseg - INFO - Iter [19250/40000] lr: 1.681e-06, eta: 7:42:47, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2030, decode.acc_seg: 91.7375, loss: 0.2030 2023-11-03 01:57:31,942 - mmseg - INFO - Iter [19300/40000] lr: 1.677e-06, eta: 7:41:33, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1976, decode.acc_seg: 91.7954, loss: 0.1976 2023-11-03 01:58:32,578 - mmseg - INFO - Iter [19350/40000] lr: 1.673e-06, eta: 7:40:19, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2163, decode.acc_seg: 91.2138, loss: 0.2163 2023-11-03 01:59:33,271 - mmseg - INFO - Iter [19400/40000] lr: 1.669e-06, eta: 7:39:06, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2030, decode.acc_seg: 91.5952, loss: 0.2030 2023-11-03 02:00:33,965 - mmseg - INFO - Iter [19450/40000] lr: 1.665e-06, eta: 7:37:53, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2194, decode.acc_seg: 91.3801, loss: 0.2194 2023-11-03 02:01:34,593 - mmseg - INFO - Iter [19500/40000] lr: 1.660e-06, eta: 7:36:39, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2041, decode.acc_seg: 91.5518, loss: 0.2041 2023-11-03 02:02:35,224 - mmseg - INFO - Iter [19550/40000] lr: 1.656e-06, eta: 7:35:26, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1975, decode.acc_seg: 91.7904, loss: 0.1975 2023-11-03 02:03:38,193 - mmseg - INFO - Iter [19600/40000] lr: 1.652e-06, eta: 7:34:15, time: 1.259, data_time: 0.052, memory: 38534, decode.loss_ce: 0.1987, decode.acc_seg: 92.0307, loss: 0.1987 2023-11-03 02:04:38,902 - mmseg - INFO - Iter [19650/40000] lr: 1.648e-06, eta: 7:33:02, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1873, decode.acc_seg: 92.3335, loss: 0.1873 2023-11-03 02:05:39,586 - mmseg - INFO - Iter [19700/40000] lr: 1.644e-06, eta: 7:31:49, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1904, decode.acc_seg: 92.2760, loss: 0.1904 2023-11-03 02:06:40,252 - mmseg - INFO - Iter [19750/40000] lr: 1.640e-06, eta: 7:30:36, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1973, decode.acc_seg: 92.0310, loss: 0.1973 2023-11-03 02:07:40,923 - mmseg - INFO - Iter [19800/40000] lr: 1.636e-06, eta: 7:29:23, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1930, decode.acc_seg: 92.0869, loss: 0.1930 2023-11-03 02:08:41,636 - mmseg - INFO - Iter [19850/40000] lr: 1.632e-06, eta: 7:28:10, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1853, decode.acc_seg: 92.2748, loss: 0.1853 2023-11-03 02:09:42,382 - mmseg - INFO - Iter [19900/40000] lr: 1.628e-06, eta: 7:26:57, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2002, decode.acc_seg: 91.7701, loss: 0.2002 2023-11-03 02:10:43,071 - mmseg - INFO - Iter [19950/40000] lr: 1.624e-06, eta: 7:25:45, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2013, decode.acc_seg: 91.8220, loss: 0.2013 2023-11-03 02:11:43,780 - mmseg - INFO - Saving checkpoint at 20000 iterations 2023-11-03 02:12:42,306 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 02:12:42,306 - mmseg - INFO - Iter [20000/40000] lr: 1.620e-06, eta: 7:25:30, time: 2.385, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2066, decode.acc_seg: 91.3630, loss: 0.2066 2023-11-03 02:13:40,792 - mmseg - INFO - per class results: 2023-11-03 02:13:40,797 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.32 | 88.86 | | building | 83.43 | 93.28 | | sky | 94.18 | 96.99 | | floor | 83.12 | 90.54 | | tree | 75.21 | 87.5 | | ceiling | 85.2 | 92.57 | | road | 84.47 | 92.25 | | bed | 90.14 | 96.65 | | windowpane | 65.04 | 80.36 | | grass | 70.12 | 85.64 | | cabinet | 64.59 | 77.59 | | sidewalk | 67.21 | 82.58 | | person | 82.66 | 91.58 | | earth | 34.33 | 43.42 | | door | 56.72 | 70.56 | | table | 64.65 | 74.53 | | mountain | 63.49 | 77.36 | | plant | 54.6 | 64.2 | | curtain | 76.77 | 87.69 | | chair | 60.38 | 74.72 | | car | 85.24 | 92.44 | | water | 60.19 | 77.01 | | painting | 76.87 | 89.34 | | sofa | 78.85 | 88.57 | | shelf | 44.87 | 65.33 | | house | 48.23 | 65.53 | | sea | 63.3 | 71.3 | | mirror | 72.47 | 81.27 | | rug | 63.94 | 72.93 | | field | 33.29 | 62.27 | | armchair | 56.92 | 74.22 | | seat | 64.96 | 87.21 | | fence | 47.53 | 60.91 | | desk | 48.68 | 66.92 | | rock | 58.34 | 80.4 | | wardrobe | 52.39 | 71.93 | | lamp | 67.08 | 80.12 | | bathtub | 89.4 | 92.13 | | railing | 46.6 | 63.23 | | cushion | 61.08 | 77.87 | | base | 31.42 | 45.1 | | box | 36.66 | 47.13 | | column | 50.24 | 61.22 | | signboard | 36.57 | 50.15 | | chest of drawers | 43.49 | 64.17 | | counter | 44.64 | 60.01 | | sand | 60.69 | 88.99 | | sink | 76.83 | 83.28 | | skyscraper | 45.03 | 63.85 | | fireplace | 72.69 | 95.99 | | refrigerator | 81.44 | 89.48 | | grandstand | 46.29 | 83.28 | | path | 21.75 | 29.02 | | stairs | 31.57 | 40.26 | | runway | 69.73 | 88.84 | | case | 61.84 | 90.91 | | pool table | 93.73 | 97.22 | | pillow | 48.0 | 52.09 | | screen door | 81.73 | 85.38 | | stairway | 39.78 | 58.0 | | river | 17.64 | 43.04 | | bridge | 77.21 | 87.63 | | bookcase | 45.08 | 62.01 | | blind | 43.48 | 49.6 | | coffee table | 67.6 | 87.05 | | toilet | 88.89 | 93.08 | | flower | 45.68 | 63.08 | | book | 48.53 | 74.13 | | hill | 7.31 | 14.79 | | bench | 60.3 | 72.75 | | countertop | 63.55 | 79.33 | | stove | 80.85 | 85.66 | | palm | 45.65 | 78.52 | | kitchen island | 40.2 | 73.57 | | computer | 77.84 | 89.95 | | swivel chair | 36.94 | 48.28 | | boat | 68.1 | 87.29 | | bar | 69.97 | 82.37 | | arcade machine | 72.95 | 77.59 | | hovel | 27.79 | 29.69 | | bus | 92.71 | 95.43 | | towel | 72.68 | 85.75 | | light | 50.2 | 63.41 | | truck | 46.16 | 59.61 | | tower | 22.36 | 35.24 | | chandelier | 66.39 | 81.24 | | awning | 30.48 | 38.48 | | streetlight | 24.06 | 32.83 | | booth | 39.9 | 43.74 | | television receiver | 74.06 | 87.67 | | airplane | 83.01 | 93.0 | | dirt track | 9.51 | 15.3 | | apparel | 46.68 | 64.99 | | pole | 25.03 | 32.13 | | land | 2.07 | 2.67 | | bannister | 13.33 | 16.42 | | escalator | 62.06 | 82.11 | | ottoman | 54.69 | 64.4 | | bottle | 28.72 | 40.46 | | buffet | 59.84 | 72.78 | | poster | 33.94 | 51.94 | | stage | 19.09 | 39.14 | | van | 50.0 | 73.58 | | ship | 64.92 | 66.12 | | fountain | 31.78 | 33.04 | | conveyer belt | 80.1 | 95.48 | | canopy | 44.13 | 55.79 | | washer | 88.64 | 95.05 | | plaything | 33.02 | 41.66 | | swimming pool | 51.35 | 74.76 | | stool | 44.31 | 55.41 | | barrel | 57.21 | 86.28 | | basket | 39.96 | 52.25 | | waterfall | 48.17 | 62.1 | | tent | 91.95 | 97.4 | | bag | 26.14 | 30.23 | | minibike | 73.59 | 85.54 | | cradle | 84.23 | 96.65 | | oven | 59.79 | 76.58 | | ball | 58.55 | 70.98 | | food | 55.07 | 59.83 | | step | 15.39 | 17.16 | | tank | 50.97 | 66.22 | | trade name | 28.77 | 33.84 | | microwave | 88.32 | 93.92 | | pot | 57.06 | 66.33 | | animal | 74.81 | 77.98 | | bicycle | 57.65 | 72.87 | | lake | 55.94 | 63.5 | | dishwasher | 69.79 | 80.88 | | screen | 64.22 | 79.58 | | blanket | 26.32 | 31.08 | | sculpture | 78.04 | 86.61 | | hood | 63.26 | 74.84 | | sconce | 52.42 | 65.45 | | vase | 41.72 | 60.82 | | traffic light | 36.41 | 53.82 | | tray | 16.34 | 23.3 | | ashcan | 50.98 | 64.22 | | fan | 61.22 | 75.92 | | pier | 40.52 | 46.56 | | crt screen | 13.76 | 20.93 | | plate | 54.47 | 76.26 | | monitor | 59.34 | 73.41 | | bulletin board | 53.94 | 63.73 | | shower | 3.77 | 4.8 | | radiator | 68.02 | 79.48 | | glass | 19.49 | 21.45 | | clock | 39.64 | 46.09 | | flag | 65.28 | 71.69 | +---------------------+-------+-------+ 2023-11-03 02:13:40,797 - mmseg - INFO - Summary: 2023-11-03 02:13:40,797 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.84 | 55.09 | 67.49 | +-------+-------+-------+ 2023-11-03 02:13:40,798 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 02:13:40,799 - mmseg - INFO - Iter(val) [250] aAcc: 0.8484, mIoU: 0.5509, mAcc: 0.6749, IoU.wall: 0.8032, IoU.building: 0.8343, IoU.sky: 0.9418, IoU.floor: 0.8312, IoU.tree: 0.7521, IoU.ceiling: 0.8520, IoU.road: 0.8447, IoU.bed : 0.9014, IoU.windowpane: 0.6504, IoU.grass: 0.7012, IoU.cabinet: 0.6459, IoU.sidewalk: 0.6721, IoU.person: 0.8266, IoU.earth: 0.3433, IoU.door: 0.5672, IoU.table: 0.6465, IoU.mountain: 0.6349, IoU.plant: 0.5460, IoU.curtain: 0.7677, IoU.chair: 0.6038, IoU.car: 0.8524, IoU.water: 0.6019, IoU.painting: 0.7687, IoU.sofa: 0.7885, IoU.shelf: 0.4487, IoU.house: 0.4823, IoU.sea: 0.6330, IoU.mirror: 0.7247, IoU.rug: 0.6394, IoU.field: 0.3329, IoU.armchair: 0.5692, IoU.seat: 0.6496, IoU.fence: 0.4753, IoU.desk: 0.4868, IoU.rock: 0.5834, IoU.wardrobe: 0.5239, IoU.lamp: 0.6708, IoU.bathtub: 0.8940, IoU.railing: 0.4660, IoU.cushion: 0.6108, IoU.base: 0.3142, IoU.box: 0.3666, IoU.column: 0.5024, IoU.signboard: 0.3657, IoU.chest of drawers: 0.4349, IoU.counter: 0.4464, IoU.sand: 0.6069, IoU.sink: 0.7683, IoU.skyscraper: 0.4503, IoU.fireplace: 0.7269, IoU.refrigerator: 0.8144, IoU.grandstand: 0.4629, IoU.path: 0.2175, IoU.stairs: 0.3157, IoU.runway: 0.6973, IoU.case: 0.6184, IoU.pool table: 0.9373, IoU.pillow: 0.4800, IoU.screen door: 0.8173, IoU.stairway: 0.3978, IoU.river: 0.1764, IoU.bridge: 0.7721, IoU.bookcase: 0.4508, IoU.blind: 0.4348, IoU.coffee table: 0.6760, IoU.toilet: 0.8889, IoU.flower: 0.4568, IoU.book: 0.4853, IoU.hill: 0.0731, IoU.bench: 0.6030, IoU.countertop: 0.6355, IoU.stove: 0.8085, IoU.palm: 0.4565, IoU.kitchen island: 0.4020, IoU.computer: 0.7784, IoU.swivel chair: 0.3694, IoU.boat: 0.6810, IoU.bar: 0.6997, IoU.arcade machine: 0.7295, IoU.hovel: 0.2779, IoU.bus: 0.9271, IoU.towel: 0.7268, IoU.light: 0.5020, IoU.truck: 0.4616, IoU.tower: 0.2236, IoU.chandelier: 0.6639, IoU.awning: 0.3048, IoU.streetlight: 0.2406, IoU.booth: 0.3990, IoU.television receiver: 0.7406, IoU.airplane: 0.8301, IoU.dirt track: 0.0951, IoU.apparel: 0.4668, IoU.pole: 0.2503, IoU.land: 0.0207, IoU.bannister: 0.1333, IoU.escalator: 0.6206, IoU.ottoman: 0.5469, IoU.bottle: 0.2872, IoU.buffet: 0.5984, IoU.poster: 0.3394, IoU.stage: 0.1909, IoU.van: 0.5000, IoU.ship: 0.6492, IoU.fountain: 0.3178, IoU.conveyer belt: 0.8010, IoU.canopy: 0.4413, IoU.washer: 0.8864, IoU.plaything: 0.3302, IoU.swimming pool: 0.5135, IoU.stool: 0.4431, IoU.barrel: 0.5721, IoU.basket: 0.3996, IoU.waterfall: 0.4817, IoU.tent: 0.9195, IoU.bag: 0.2614, IoU.minibike: 0.7359, IoU.cradle: 0.8423, IoU.oven: 0.5979, IoU.ball: 0.5855, IoU.food: 0.5507, IoU.step: 0.1539, IoU.tank: 0.5097, IoU.trade name: 0.2877, IoU.microwave: 0.8832, IoU.pot: 0.5706, IoU.animal: 0.7481, IoU.bicycle: 0.5765, IoU.lake: 0.5594, IoU.dishwasher: 0.6979, IoU.screen: 0.6422, IoU.blanket: 0.2632, IoU.sculpture: 0.7804, IoU.hood: 0.6326, IoU.sconce: 0.5242, IoU.vase: 0.4172, IoU.traffic light: 0.3641, IoU.tray: 0.1634, IoU.ashcan: 0.5098, IoU.fan: 0.6122, IoU.pier: 0.4052, IoU.crt screen: 0.1376, IoU.plate: 0.5447, IoU.monitor: 0.5934, IoU.bulletin board: 0.5394, IoU.shower: 0.0377, IoU.radiator: 0.6802, IoU.glass: 0.1949, IoU.clock: 0.3964, IoU.flag: 0.6528, Acc.wall: 0.8886, Acc.building: 0.9328, Acc.sky: 0.9699, Acc.floor: 0.9054, Acc.tree: 0.8750, Acc.ceiling: 0.9257, Acc.road: 0.9225, Acc.bed : 0.9665, Acc.windowpane: 0.8036, Acc.grass: 0.8564, Acc.cabinet: 0.7759, Acc.sidewalk: 0.8258, Acc.person: 0.9158, Acc.earth: 0.4342, Acc.door: 0.7056, Acc.table: 0.7453, Acc.mountain: 0.7736, Acc.plant: 0.6420, Acc.curtain: 0.8769, Acc.chair: 0.7472, Acc.car: 0.9244, Acc.water: 0.7701, Acc.painting: 0.8934, Acc.sofa: 0.8857, Acc.shelf: 0.6533, Acc.house: 0.6553, Acc.sea: 0.7130, Acc.mirror: 0.8127, Acc.rug: 0.7293, Acc.field: 0.6227, Acc.armchair: 0.7422, Acc.seat: 0.8721, Acc.fence: 0.6091, Acc.desk: 0.6692, Acc.rock: 0.8040, Acc.wardrobe: 0.7193, Acc.lamp: 0.8012, Acc.bathtub: 0.9213, Acc.railing: 0.6323, Acc.cushion: 0.7787, Acc.base: 0.4510, Acc.box: 0.4713, Acc.column: 0.6122, Acc.signboard: 0.5015, Acc.chest of drawers: 0.6417, Acc.counter: 0.6001, Acc.sand: 0.8899, Acc.sink: 0.8328, Acc.skyscraper: 0.6385, Acc.fireplace: 0.9599, Acc.refrigerator: 0.8948, Acc.grandstand: 0.8328, Acc.path: 0.2902, Acc.stairs: 0.4026, Acc.runway: 0.8884, Acc.case: 0.9091, Acc.pool table: 0.9722, Acc.pillow: 0.5209, Acc.screen door: 0.8538, Acc.stairway: 0.5800, Acc.river: 0.4304, Acc.bridge: 0.8763, Acc.bookcase: 0.6201, Acc.blind: 0.4960, Acc.coffee table: 0.8705, Acc.toilet: 0.9308, Acc.flower: 0.6308, Acc.book: 0.7413, Acc.hill: 0.1479, Acc.bench: 0.7275, Acc.countertop: 0.7933, Acc.stove: 0.8566, Acc.palm: 0.7852, Acc.kitchen island: 0.7357, Acc.computer: 0.8995, Acc.swivel chair: 0.4828, Acc.boat: 0.8729, Acc.bar: 0.8237, Acc.arcade machine: 0.7759, Acc.hovel: 0.2969, Acc.bus: 0.9543, Acc.towel: 0.8575, Acc.light: 0.6341, Acc.truck: 0.5961, Acc.tower: 0.3524, Acc.chandelier: 0.8124, Acc.awning: 0.3848, Acc.streetlight: 0.3283, Acc.booth: 0.4374, Acc.television receiver: 0.8767, Acc.airplane: 0.9300, Acc.dirt track: 0.1530, Acc.apparel: 0.6499, Acc.pole: 0.3213, Acc.land: 0.0267, Acc.bannister: 0.1642, Acc.escalator: 0.8211, Acc.ottoman: 0.6440, Acc.bottle: 0.4046, Acc.buffet: 0.7278, Acc.poster: 0.5194, Acc.stage: 0.3914, Acc.van: 0.7358, Acc.ship: 0.6612, Acc.fountain: 0.3304, Acc.conveyer belt: 0.9548, Acc.canopy: 0.5579, Acc.washer: 0.9505, Acc.plaything: 0.4166, Acc.swimming pool: 0.7476, Acc.stool: 0.5541, Acc.barrel: 0.8628, Acc.basket: 0.5225, Acc.waterfall: 0.6210, Acc.tent: 0.9740, Acc.bag: 0.3023, Acc.minibike: 0.8554, Acc.cradle: 0.9665, Acc.oven: 0.7658, Acc.ball: 0.7098, Acc.food: 0.5983, Acc.step: 0.1716, Acc.tank: 0.6622, Acc.trade name: 0.3384, Acc.microwave: 0.9392, Acc.pot: 0.6633, Acc.animal: 0.7798, Acc.bicycle: 0.7287, Acc.lake: 0.6350, Acc.dishwasher: 0.8088, Acc.screen: 0.7958, Acc.blanket: 0.3108, Acc.sculpture: 0.8661, Acc.hood: 0.7484, Acc.sconce: 0.6545, Acc.vase: 0.6082, Acc.traffic light: 0.5382, Acc.tray: 0.2330, Acc.ashcan: 0.6422, Acc.fan: 0.7592, Acc.pier: 0.4656, Acc.crt screen: 0.2093, Acc.plate: 0.7626, Acc.monitor: 0.7341, Acc.bulletin board: 0.6373, Acc.shower: 0.0480, Acc.radiator: 0.7948, Acc.glass: 0.2145, Acc.clock: 0.4609, Acc.flag: 0.7169 2023-11-03 02:14:41,524 - mmseg - INFO - Iter [20050/40000] lr: 1.616e-06, eta: 7:25:16, time: 2.384, data_time: 1.177, memory: 38534, decode.loss_ce: 0.2099, decode.acc_seg: 91.5366, loss: 0.2099 2023-11-03 02:15:42,206 - mmseg - INFO - Iter [20100/40000] lr: 1.612e-06, eta: 7:24:03, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2037, decode.acc_seg: 91.7197, loss: 0.2037 2023-11-03 02:16:42,917 - mmseg - INFO - Iter [20150/40000] lr: 1.608e-06, eta: 7:22:49, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1977, decode.acc_seg: 91.7331, loss: 0.1977 2023-11-03 02:17:43,585 - mmseg - INFO - Iter [20200/40000] lr: 1.604e-06, eta: 7:21:36, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.2001, decode.acc_seg: 91.6892, loss: 0.2001 2023-11-03 02:18:46,619 - mmseg - INFO - Iter [20250/40000] lr: 1.600e-06, eta: 7:20:26, time: 1.261, data_time: 0.053, memory: 38534, decode.loss_ce: 0.2000, decode.acc_seg: 91.6066, loss: 0.2000 2023-11-03 02:19:47,377 - mmseg - INFO - Iter [20300/40000] lr: 1.596e-06, eta: 7:19:13, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1850, decode.acc_seg: 92.3377, loss: 0.1850 2023-11-03 02:20:48,147 - mmseg - INFO - Iter [20350/40000] lr: 1.592e-06, eta: 7:18:00, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1979, decode.acc_seg: 92.0848, loss: 0.1979 2023-11-03 02:21:48,868 - mmseg - INFO - Iter [20400/40000] lr: 1.588e-06, eta: 7:16:47, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2001, decode.acc_seg: 91.9150, loss: 0.2001 2023-11-03 02:22:49,549 - mmseg - INFO - Iter [20450/40000] lr: 1.584e-06, eta: 7:15:35, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1870, decode.acc_seg: 92.1823, loss: 0.1870 2023-11-03 02:23:50,291 - mmseg - INFO - Iter [20500/40000] lr: 1.579e-06, eta: 7:14:22, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1925, decode.acc_seg: 92.0489, loss: 0.1925 2023-11-03 02:24:50,963 - mmseg - INFO - Iter [20550/40000] lr: 1.575e-06, eta: 7:13:09, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2152, decode.acc_seg: 91.5210, loss: 0.2152 2023-11-03 02:25:51,680 - mmseg - INFO - Iter [20600/40000] lr: 1.571e-06, eta: 7:11:57, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1865, decode.acc_seg: 92.3461, loss: 0.1865 2023-11-03 02:26:52,424 - mmseg - INFO - Iter [20650/40000] lr: 1.567e-06, eta: 7:10:44, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1949, decode.acc_seg: 91.9738, loss: 0.1949 2023-11-03 02:27:53,154 - mmseg - INFO - Iter [20700/40000] lr: 1.563e-06, eta: 7:09:32, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.2005, decode.acc_seg: 92.0098, loss: 0.2005 2023-11-03 02:28:53,885 - mmseg - INFO - Iter [20750/40000] lr: 1.559e-06, eta: 7:08:19, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1934, decode.acc_seg: 91.9513, loss: 0.1934 2023-11-03 02:29:54,586 - mmseg - INFO - Iter [20800/40000] lr: 1.555e-06, eta: 7:07:07, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1848, decode.acc_seg: 92.2541, loss: 0.1848 2023-11-03 02:30:55,239 - mmseg - INFO - Iter [20850/40000] lr: 1.551e-06, eta: 7:05:55, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1976, decode.acc_seg: 91.9168, loss: 0.1976 2023-11-03 02:31:58,313 - mmseg - INFO - Iter [20900/40000] lr: 1.547e-06, eta: 7:04:45, time: 1.261, data_time: 0.050, memory: 38534, decode.loss_ce: 0.1897, decode.acc_seg: 92.1542, loss: 0.1897 2023-11-03 02:32:59,063 - mmseg - INFO - Iter [20950/40000] lr: 1.543e-06, eta: 7:03:33, time: 1.215, data_time: 0.008, memory: 38534, decode.loss_ce: 0.1905, decode.acc_seg: 92.1222, loss: 0.1905 2023-11-03 02:33:59,785 - mmseg - INFO - Saving checkpoint at 21000 iterations 2023-11-03 02:34:56,553 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 02:34:56,553 - mmseg - INFO - Iter [21000/40000] lr: 1.539e-06, eta: 7:03:12, time: 2.350, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1985, decode.acc_seg: 91.8829, loss: 0.1985 2023-11-03 02:35:55,605 - mmseg - INFO - per class results: 2023-11-03 02:35:55,610 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.23 | 89.13 | | building | 82.87 | 93.72 | | sky | 94.27 | 97.8 | | floor | 82.98 | 90.34 | | tree | 75.56 | 87.6 | | ceiling | 84.94 | 92.62 | | road | 84.51 | 92.0 | | bed | 91.07 | 96.79 | | windowpane | 64.53 | 80.35 | | grass | 68.69 | 86.46 | | cabinet | 65.28 | 78.36 | | sidewalk | 68.72 | 81.97 | | person | 82.29 | 92.65 | | earth | 32.08 | 42.36 | | door | 57.31 | 73.49 | | table | 69.04 | 80.25 | | mountain | 61.86 | 75.06 | | plant | 54.87 | 66.41 | | curtain | 75.19 | 85.79 | | chair | 62.2 | 78.05 | | car | 85.68 | 93.2 | | water | 65.9 | 79.32 | | painting | 76.39 | 88.37 | | sofa | 77.19 | 92.93 | | shelf | 47.16 | 71.27 | | house | 39.64 | 48.22 | | sea | 72.17 | 85.7 | | mirror | 74.59 | 84.11 | | rug | 64.1 | 73.87 | | field | 30.5 | 46.22 | | armchair | 56.13 | 67.84 | | seat | 64.69 | 88.19 | | fence | 48.67 | 61.95 | | desk | 54.1 | 72.49 | | rock | 57.24 | 80.34 | | wardrobe | 49.89 | 66.08 | | lamp | 66.19 | 76.38 | | bathtub | 88.28 | 91.53 | | railing | 44.16 | 57.66 | | cushion | 62.48 | 73.32 | | base | 31.24 | 50.41 | | box | 36.03 | 46.62 | | column | 53.4 | 67.15 | | signboard | 35.68 | 50.16 | | chest of drawers | 36.66 | 48.29 | | counter | 50.79 | 64.36 | | sand | 63.22 | 87.76 | | sink | 76.63 | 83.94 | | skyscraper | 47.99 | 61.65 | | fireplace | 75.21 | 93.93 | | refrigerator | 81.5 | 90.36 | | grandstand | 46.17 | 81.38 | | path | 20.42 | 27.99 | | stairs | 27.38 | 34.67 | | runway | 69.61 | 88.79 | | case | 63.38 | 87.17 | | pool table | 93.31 | 97.88 | | pillow | 57.52 | 65.52 | | screen door | 76.27 | 78.49 | | stairway | 38.76 | 55.8 | | river | 20.51 | 41.91 | | bridge | 60.41 | 67.46 | | bookcase | 44.2 | 52.44 | | blind | 39.07 | 43.63 | | coffee table | 61.79 | 90.64 | | toilet | 88.64 | 92.23 | | flower | 44.54 | 61.39 | | book | 48.56 | 77.18 | | hill | 4.72 | 9.48 | | bench | 56.06 | 62.48 | | countertop | 62.8 | 76.09 | | stove | 82.92 | 88.17 | | palm | 47.74 | 72.72 | | kitchen island | 45.16 | 65.86 | | computer | 77.59 | 87.51 | | swivel chair | 39.84 | 54.9 | | boat | 66.93 | 76.38 | | bar | 78.13 | 84.58 | | arcade machine | 69.65 | 72.28 | | hovel | 38.41 | 44.16 | | bus | 92.84 | 96.42 | | towel | 74.57 | 81.82 | | light | 48.49 | 59.15 | | truck | 47.34 | 60.18 | | tower | 24.12 | 33.44 | | chandelier | 65.53 | 78.2 | | awning | 34.61 | 42.52 | | streetlight | 22.88 | 29.88 | | booth | 44.52 | 48.62 | | television receiver | 75.82 | 87.12 | | airplane | 84.06 | 93.33 | | dirt track | 6.55 | 19.72 | | apparel | 48.32 | 64.22 | | pole | 20.5 | 25.64 | | land | 8.44 | 11.46 | | bannister | 11.87 | 15.45 | | escalator | 60.05 | 78.71 | | ottoman | 51.4 | 70.24 | | bottle | 31.66 | 41.08 | | buffet | 51.18 | 54.43 | | poster | 34.35 | 44.72 | | stage | 19.2 | 25.13 | | van | 51.23 | 75.95 | | ship | 26.45 | 30.1 | | fountain | 32.58 | 33.73 | | conveyer belt | 86.63 | 92.57 | | canopy | 39.5 | 48.18 | | washer | 89.7 | 95.68 | | plaything | 32.05 | 44.35 | | swimming pool | 47.84 | 68.15 | | stool | 47.8 | 63.77 | | barrel | 59.94 | 78.46 | | basket | 36.11 | 45.58 | | waterfall | 45.4 | 60.77 | | tent | 90.53 | 97.16 | | bag | 25.75 | 29.94 | | minibike | 74.38 | 84.81 | | cradle | 83.18 | 97.96 | | oven | 65.57 | 76.5 | | ball | 32.17 | 33.25 | | food | 49.46 | 51.58 | | step | 9.61 | 10.69 | | tank | 55.23 | 66.11 | | trade name | 24.13 | 27.73 | | microwave | 88.42 | 93.95 | | pot | 52.27 | 57.45 | | animal | 74.9 | 79.05 | | bicycle | 58.45 | 79.97 | | lake | 59.68 | 63.39 | | dishwasher | 69.84 | 80.09 | | screen | 62.57 | 80.12 | | blanket | 24.68 | 29.61 | | sculpture | 74.76 | 86.81 | | hood | 68.64 | 80.11 | | sconce | 52.76 | 66.35 | | vase | 43.77 | 60.09 | | traffic light | 35.01 | 56.37 | | tray | 15.95 | 18.82 | | ashcan | 49.96 | 61.81 | | fan | 61.25 | 71.7 | | pier | 38.0 | 40.9 | | crt screen | 7.2 | 13.29 | | plate | 56.61 | 75.3 | | monitor | 49.05 | 58.99 | | bulletin board | 47.05 | 54.56 | | shower | 3.0 | 5.41 | | radiator | 67.06 | 78.17 | | glass | 19.11 | 20.74 | | clock | 33.31 | 37.65 | | flag | 63.79 | 67.56 | +---------------------+-------+-------+ 2023-11-03 02:35:55,610 - mmseg - INFO - Summary: 2023-11-03 02:35:55,610 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.87 | 54.42 | 65.58 | +-------+-------+-------+ 2023-11-03 02:35:55,611 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 02:35:55,611 - mmseg - INFO - Iter(val) [250] aAcc: 0.8487, mIoU: 0.5442, mAcc: 0.6558, IoU.wall: 0.8023, IoU.building: 0.8287, IoU.sky: 0.9427, IoU.floor: 0.8298, IoU.tree: 0.7556, IoU.ceiling: 0.8494, IoU.road: 0.8451, IoU.bed : 0.9107, IoU.windowpane: 0.6453, IoU.grass: 0.6869, IoU.cabinet: 0.6528, IoU.sidewalk: 0.6872, IoU.person: 0.8229, IoU.earth: 0.3208, IoU.door: 0.5731, IoU.table: 0.6904, IoU.mountain: 0.6186, IoU.plant: 0.5487, IoU.curtain: 0.7519, IoU.chair: 0.6220, IoU.car: 0.8568, IoU.water: 0.6590, IoU.painting: 0.7639, IoU.sofa: 0.7719, IoU.shelf: 0.4716, IoU.house: 0.3964, IoU.sea: 0.7217, IoU.mirror: 0.7459, IoU.rug: 0.6410, IoU.field: 0.3050, IoU.armchair: 0.5613, IoU.seat: 0.6469, IoU.fence: 0.4867, IoU.desk: 0.5410, IoU.rock: 0.5724, IoU.wardrobe: 0.4989, IoU.lamp: 0.6619, IoU.bathtub: 0.8828, IoU.railing: 0.4416, IoU.cushion: 0.6248, IoU.base: 0.3124, IoU.box: 0.3603, IoU.column: 0.5340, IoU.signboard: 0.3568, IoU.chest of drawers: 0.3666, IoU.counter: 0.5079, IoU.sand: 0.6322, IoU.sink: 0.7663, IoU.skyscraper: 0.4799, IoU.fireplace: 0.7521, IoU.refrigerator: 0.8150, IoU.grandstand: 0.4617, IoU.path: 0.2042, IoU.stairs: 0.2738, IoU.runway: 0.6961, IoU.case: 0.6338, IoU.pool table: 0.9331, IoU.pillow: 0.5752, IoU.screen door: 0.7627, IoU.stairway: 0.3876, IoU.river: 0.2051, IoU.bridge: 0.6041, IoU.bookcase: 0.4420, IoU.blind: 0.3907, IoU.coffee table: 0.6179, IoU.toilet: 0.8864, IoU.flower: 0.4454, IoU.book: 0.4856, IoU.hill: 0.0472, IoU.bench: 0.5606, IoU.countertop: 0.6280, IoU.stove: 0.8292, IoU.palm: 0.4774, IoU.kitchen island: 0.4516, IoU.computer: 0.7759, IoU.swivel chair: 0.3984, IoU.boat: 0.6693, IoU.bar: 0.7813, IoU.arcade machine: 0.6965, IoU.hovel: 0.3841, IoU.bus: 0.9284, IoU.towel: 0.7457, IoU.light: 0.4849, IoU.truck: 0.4734, IoU.tower: 0.2412, IoU.chandelier: 0.6553, IoU.awning: 0.3461, IoU.streetlight: 0.2288, IoU.booth: 0.4452, IoU.television receiver: 0.7582, IoU.airplane: 0.8406, IoU.dirt track: 0.0655, IoU.apparel: 0.4832, IoU.pole: 0.2050, IoU.land: 0.0844, IoU.bannister: 0.1187, IoU.escalator: 0.6005, IoU.ottoman: 0.5140, IoU.bottle: 0.3166, IoU.buffet: 0.5118, IoU.poster: 0.3435, IoU.stage: 0.1920, IoU.van: 0.5123, IoU.ship: 0.2645, IoU.fountain: 0.3258, IoU.conveyer belt: 0.8663, IoU.canopy: 0.3950, IoU.washer: 0.8970, IoU.plaything: 0.3205, IoU.swimming pool: 0.4784, IoU.stool: 0.4780, IoU.barrel: 0.5994, IoU.basket: 0.3611, IoU.waterfall: 0.4540, IoU.tent: 0.9053, IoU.bag: 0.2575, IoU.minibike: 0.7438, IoU.cradle: 0.8318, IoU.oven: 0.6557, IoU.ball: 0.3217, IoU.food: 0.4946, IoU.step: 0.0961, IoU.tank: 0.5523, IoU.trade name: 0.2413, IoU.microwave: 0.8842, IoU.pot: 0.5227, IoU.animal: 0.7490, IoU.bicycle: 0.5845, IoU.lake: 0.5968, IoU.dishwasher: 0.6984, IoU.screen: 0.6257, IoU.blanket: 0.2468, IoU.sculpture: 0.7476, IoU.hood: 0.6864, IoU.sconce: 0.5276, IoU.vase: 0.4377, IoU.traffic light: 0.3501, IoU.tray: 0.1595, IoU.ashcan: 0.4996, IoU.fan: 0.6125, IoU.pier: 0.3800, IoU.crt screen: 0.0720, IoU.plate: 0.5661, IoU.monitor: 0.4905, IoU.bulletin board: 0.4705, IoU.shower: 0.0300, IoU.radiator: 0.6706, IoU.glass: 0.1911, IoU.clock: 0.3331, IoU.flag: 0.6379, Acc.wall: 0.8913, Acc.building: 0.9372, Acc.sky: 0.9780, Acc.floor: 0.9034, Acc.tree: 0.8760, Acc.ceiling: 0.9262, Acc.road: 0.9200, Acc.bed : 0.9679, Acc.windowpane: 0.8035, Acc.grass: 0.8646, Acc.cabinet: 0.7836, Acc.sidewalk: 0.8197, Acc.person: 0.9265, Acc.earth: 0.4236, Acc.door: 0.7349, Acc.table: 0.8025, Acc.mountain: 0.7506, Acc.plant: 0.6641, Acc.curtain: 0.8579, Acc.chair: 0.7805, Acc.car: 0.9320, Acc.water: 0.7932, Acc.painting: 0.8837, Acc.sofa: 0.9293, Acc.shelf: 0.7127, Acc.house: 0.4822, Acc.sea: 0.8570, Acc.mirror: 0.8411, Acc.rug: 0.7387, Acc.field: 0.4622, Acc.armchair: 0.6784, Acc.seat: 0.8819, Acc.fence: 0.6195, Acc.desk: 0.7249, Acc.rock: 0.8034, Acc.wardrobe: 0.6608, Acc.lamp: 0.7638, Acc.bathtub: 0.9153, Acc.railing: 0.5766, Acc.cushion: 0.7332, Acc.base: 0.5041, Acc.box: 0.4662, Acc.column: 0.6715, Acc.signboard: 0.5016, Acc.chest of drawers: 0.4829, Acc.counter: 0.6436, Acc.sand: 0.8776, Acc.sink: 0.8394, Acc.skyscraper: 0.6165, Acc.fireplace: 0.9393, Acc.refrigerator: 0.9036, Acc.grandstand: 0.8138, Acc.path: 0.2799, Acc.stairs: 0.3467, Acc.runway: 0.8879, Acc.case: 0.8717, Acc.pool table: 0.9788, Acc.pillow: 0.6552, Acc.screen door: 0.7849, Acc.stairway: 0.5580, Acc.river: 0.4191, Acc.bridge: 0.6746, Acc.bookcase: 0.5244, Acc.blind: 0.4363, Acc.coffee table: 0.9064, Acc.toilet: 0.9223, Acc.flower: 0.6139, Acc.book: 0.7718, Acc.hill: 0.0948, Acc.bench: 0.6248, Acc.countertop: 0.7609, Acc.stove: 0.8817, Acc.palm: 0.7272, Acc.kitchen island: 0.6586, Acc.computer: 0.8751, Acc.swivel chair: 0.5490, Acc.boat: 0.7638, Acc.bar: 0.8458, Acc.arcade machine: 0.7228, Acc.hovel: 0.4416, Acc.bus: 0.9642, Acc.towel: 0.8182, Acc.light: 0.5915, Acc.truck: 0.6018, Acc.tower: 0.3344, Acc.chandelier: 0.7820, Acc.awning: 0.4252, Acc.streetlight: 0.2988, Acc.booth: 0.4862, Acc.television receiver: 0.8712, Acc.airplane: 0.9333, Acc.dirt track: 0.1972, Acc.apparel: 0.6422, Acc.pole: 0.2564, Acc.land: 0.1146, Acc.bannister: 0.1545, Acc.escalator: 0.7871, Acc.ottoman: 0.7024, Acc.bottle: 0.4108, Acc.buffet: 0.5443, Acc.poster: 0.4472, Acc.stage: 0.2513, Acc.van: 0.7595, Acc.ship: 0.3010, Acc.fountain: 0.3373, Acc.conveyer belt: 0.9257, Acc.canopy: 0.4818, Acc.washer: 0.9568, Acc.plaything: 0.4435, Acc.swimming pool: 0.6815, Acc.stool: 0.6377, Acc.barrel: 0.7846, Acc.basket: 0.4558, Acc.waterfall: 0.6077, Acc.tent: 0.9716, Acc.bag: 0.2994, Acc.minibike: 0.8481, Acc.cradle: 0.9796, Acc.oven: 0.7650, Acc.ball: 0.3325, Acc.food: 0.5158, Acc.step: 0.1069, Acc.tank: 0.6611, Acc.trade name: 0.2773, Acc.microwave: 0.9395, Acc.pot: 0.5745, Acc.animal: 0.7905, Acc.bicycle: 0.7997, Acc.lake: 0.6339, Acc.dishwasher: 0.8009, Acc.screen: 0.8012, Acc.blanket: 0.2961, Acc.sculpture: 0.8681, Acc.hood: 0.8011, Acc.sconce: 0.6635, Acc.vase: 0.6009, Acc.traffic light: 0.5637, Acc.tray: 0.1882, Acc.ashcan: 0.6181, Acc.fan: 0.7170, Acc.pier: 0.4090, Acc.crt screen: 0.1329, Acc.plate: 0.7530, Acc.monitor: 0.5899, Acc.bulletin board: 0.5456, Acc.shower: 0.0541, Acc.radiator: 0.7817, Acc.glass: 0.2074, Acc.clock: 0.3765, Acc.flag: 0.6756 2023-11-03 02:36:56,814 - mmseg - INFO - Iter [21050/40000] lr: 1.535e-06, eta: 7:02:53, time: 2.405, data_time: 1.189, memory: 38534, decode.loss_ce: 0.1964, decode.acc_seg: 91.8410, loss: 0.1964 2023-11-03 02:37:57,512 - mmseg - INFO - Iter [21100/40000] lr: 1.531e-06, eta: 7:01:41, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1952, decode.acc_seg: 92.1306, loss: 0.1952 2023-11-03 02:38:58,232 - mmseg - INFO - Iter [21150/40000] lr: 1.527e-06, eta: 7:00:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1961, decode.acc_seg: 91.9230, loss: 0.1961 2023-11-03 02:39:58,919 - mmseg - INFO - Iter [21200/40000] lr: 1.523e-06, eta: 6:59:16, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1909, decode.acc_seg: 91.8868, loss: 0.1909 2023-11-03 02:40:59,603 - mmseg - INFO - Iter [21250/40000] lr: 1.519e-06, eta: 6:58:03, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1967, decode.acc_seg: 91.9698, loss: 0.1967 2023-11-03 02:42:00,339 - mmseg - INFO - Iter [21300/40000] lr: 1.515e-06, eta: 6:56:51, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1945, decode.acc_seg: 92.0090, loss: 0.1945 2023-11-03 02:43:01,052 - mmseg - INFO - Iter [21350/40000] lr: 1.511e-06, eta: 6:55:39, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1932, decode.acc_seg: 92.0683, loss: 0.1932 2023-11-03 02:44:01,701 - mmseg - INFO - Iter [21400/40000] lr: 1.507e-06, eta: 6:54:26, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1883, decode.acc_seg: 92.2710, loss: 0.1883 2023-11-03 02:45:02,337 - mmseg - INFO - Iter [21450/40000] lr: 1.503e-06, eta: 6:53:14, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1893, decode.acc_seg: 92.1704, loss: 0.1893 2023-11-03 02:46:05,361 - mmseg - INFO - Iter [21500/40000] lr: 1.498e-06, eta: 6:52:04, time: 1.260, data_time: 0.052, memory: 38534, decode.loss_ce: 0.2018, decode.acc_seg: 91.8184, loss: 0.2018 2023-11-03 02:47:06,036 - mmseg - INFO - Iter [21550/40000] lr: 1.494e-06, eta: 6:50:52, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1820, decode.acc_seg: 92.4952, loss: 0.1820 2023-11-03 02:48:06,748 - mmseg - INFO - Iter [21600/40000] lr: 1.490e-06, eta: 6:49:40, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1888, decode.acc_seg: 92.3067, loss: 0.1888 2023-11-03 02:49:07,491 - mmseg - INFO - Iter [21650/40000] lr: 1.486e-06, eta: 6:48:28, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1798, decode.acc_seg: 92.3842, loss: 0.1798 2023-11-03 02:50:08,149 - mmseg - INFO - Iter [21700/40000] lr: 1.482e-06, eta: 6:47:16, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1850, decode.acc_seg: 92.3784, loss: 0.1850 2023-11-03 02:51:08,823 - mmseg - INFO - Iter [21750/40000] lr: 1.478e-06, eta: 6:46:04, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1943, decode.acc_seg: 91.8553, loss: 0.1943 2023-11-03 02:52:09,523 - mmseg - INFO - Iter [21800/40000] lr: 1.474e-06, eta: 6:44:52, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1873, decode.acc_seg: 92.3846, loss: 0.1873 2023-11-03 02:53:10,152 - mmseg - INFO - Iter [21850/40000] lr: 1.470e-06, eta: 6:43:41, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1922, decode.acc_seg: 92.1060, loss: 0.1922 2023-11-03 02:54:10,812 - mmseg - INFO - Iter [21900/40000] lr: 1.466e-06, eta: 6:42:29, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1870, decode.acc_seg: 92.3916, loss: 0.1870 2023-11-03 02:55:11,524 - mmseg - INFO - Iter [21950/40000] lr: 1.462e-06, eta: 6:41:17, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1840, decode.acc_seg: 92.2737, loss: 0.1840 2023-11-03 02:56:12,229 - mmseg - INFO - Saving checkpoint at 22000 iterations 2023-11-03 02:57:08,911 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 02:57:08,911 - mmseg - INFO - Iter [22000/40000] lr: 1.458e-06, eta: 6:40:52, time: 2.348, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1895, decode.acc_seg: 92.1596, loss: 0.1895 2023-11-03 02:58:09,948 - mmseg - INFO - per class results: 2023-11-03 02:58:09,953 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 79.78 | 88.17 | | building | 82.97 | 93.4 | | sky | 94.25 | 97.76 | | floor | 82.99 | 90.48 | | tree | 75.72 | 87.18 | | ceiling | 85.28 | 92.44 | | road | 84.9 | 92.36 | | bed | 91.71 | 96.7 | | windowpane | 65.8 | 81.16 | | grass | 66.66 | 83.2 | | cabinet | 64.48 | 74.33 | | sidewalk | 69.23 | 83.43 | | person | 82.69 | 93.16 | | earth | 36.79 | 48.91 | | door | 57.23 | 77.61 | | table | 68.97 | 81.87 | | mountain | 62.89 | 73.16 | | plant | 56.98 | 68.27 | | curtain | 76.97 | 86.84 | | chair | 59.25 | 70.64 | | car | 85.63 | 93.85 | | water | 66.41 | 82.1 | | painting | 76.48 | 89.8 | | sofa | 80.25 | 87.1 | | shelf | 48.49 | 71.07 | | house | 38.82 | 46.0 | | sea | 71.39 | 81.13 | | mirror | 73.17 | 80.96 | | rug | 66.22 | 81.9 | | field | 32.96 | 48.73 | | armchair | 55.94 | 81.96 | | seat | 65.44 | 86.54 | | fence | 48.23 | 64.74 | | desk | 55.67 | 73.38 | | rock | 56.59 | 86.91 | | wardrobe | 55.56 | 80.03 | | lamp | 66.69 | 79.42 | | bathtub | 88.45 | 92.51 | | railing | 43.18 | 57.21 | | cushion | 62.57 | 77.37 | | base | 29.2 | 43.28 | | box | 37.35 | 50.93 | | column | 52.42 | 63.39 | | signboard | 36.68 | 53.84 | | chest of drawers | 41.07 | 61.97 | | counter | 47.66 | 60.72 | | sand | 59.76 | 88.14 | | sink | 77.63 | 83.66 | | skyscraper | 47.96 | 60.28 | | fireplace | 77.22 | 93.89 | | refrigerator | 81.48 | 90.32 | | grandstand | 48.39 | 74.88 | | path | 23.07 | 27.33 | | stairs | 31.19 | 43.07 | | runway | 68.96 | 88.87 | | case | 62.9 | 83.23 | | pool table | 92.48 | 98.5 | | pillow | 61.02 | 71.6 | | screen door | 74.68 | 79.36 | | stairway | 34.31 | 46.29 | | river | 18.44 | 34.18 | | bridge | 60.9 | 67.89 | | bookcase | 45.12 | 54.72 | | blind | 39.47 | 43.34 | | coffee table | 67.86 | 84.09 | | toilet | 89.04 | 94.45 | | flower | 42.48 | 61.09 | | book | 47.62 | 73.9 | | hill | 4.35 | 7.83 | | bench | 55.57 | 64.54 | | countertop | 63.94 | 76.84 | | stove | 82.22 | 88.02 | | palm | 46.13 | 84.84 | | kitchen island | 39.54 | 67.75 | | computer | 78.46 | 88.92 | | swivel chair | 45.37 | 68.04 | | boat | 62.68 | 83.95 | | bar | 76.25 | 84.42 | | arcade machine | 77.73 | 81.04 | | hovel | 23.89 | 25.77 | | bus | 92.22 | 96.54 | | towel | 77.02 | 86.43 | | light | 48.38 | 58.25 | | truck | 48.92 | 66.16 | | tower | 23.03 | 33.13 | | chandelier | 66.9 | 81.73 | | awning | 37.38 | 48.51 | | streetlight | 25.22 | 33.69 | | booth | 37.3 | 41.29 | | television receiver | 71.05 | 89.59 | | airplane | 85.39 | 92.1 | | dirt track | 15.37 | 21.13 | | apparel | 53.56 | 64.92 | | pole | 22.66 | 30.88 | | land | 9.73 | 14.31 | | bannister | 12.95 | 19.98 | | escalator | 60.87 | 78.05 | | ottoman | 54.2 | 73.5 | | bottle | 29.9 | 38.49 | | buffet | 58.66 | 74.08 | | poster | 34.77 | 43.63 | | stage | 18.42 | 45.81 | | van | 50.01 | 68.88 | | ship | 18.69 | 20.18 | | fountain | 32.06 | 32.94 | | conveyer belt | 84.52 | 96.28 | | canopy | 34.4 | 45.6 | | washer | 80.53 | 91.65 | | plaything | 32.59 | 46.62 | | swimming pool | 48.07 | 71.12 | | stool | 48.22 | 62.51 | | barrel | 62.44 | 82.59 | | basket | 35.35 | 44.26 | | waterfall | 50.98 | 69.17 | | tent | 95.99 | 97.43 | | bag | 26.77 | 32.52 | | minibike | 75.74 | 85.67 | | cradle | 86.01 | 97.89 | | oven | 65.87 | 74.93 | | ball | 48.19 | 73.19 | | food | 52.1 | 55.85 | | step | 14.47 | 17.86 | | tank | 60.0 | 65.43 | | trade name | 27.64 | 33.89 | | microwave | 88.48 | 94.4 | | pot | 56.51 | 66.72 | | animal | 76.4 | 80.57 | | bicycle | 57.51 | 73.62 | | lake | 55.82 | 63.56 | | dishwasher | 69.87 | 76.46 | | screen | 58.6 | 80.3 | | blanket | 28.77 | 33.81 | | sculpture | 71.75 | 89.13 | | hood | 62.87 | 72.1 | | sconce | 54.28 | 70.7 | | vase | 43.81 | 61.79 | | traffic light | 35.06 | 61.94 | | tray | 17.93 | 25.48 | | ashcan | 47.28 | 64.45 | | fan | 62.96 | 75.81 | | pier | 42.21 | 47.02 | | crt screen | 13.99 | 21.98 | | plate | 56.78 | 76.01 | | monitor | 54.64 | 67.16 | | bulletin board | 47.15 | 55.41 | | shower | 4.49 | 5.24 | | radiator | 66.89 | 82.03 | | glass | 19.02 | 21.09 | | clock | 33.45 | 38.41 | | flag | 67.47 | 76.66 | +---------------------+-------+-------+ 2023-11-03 02:58:09,953 - mmseg - INFO - Summary: 2023-11-03 02:58:09,953 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 84.95 | 54.96 | 67.22 | +-------+-------+-------+ 2023-11-03 02:58:09,954 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 02:58:09,954 - mmseg - INFO - Iter(val) [250] aAcc: 0.8495, mIoU: 0.5496, mAcc: 0.6722, IoU.wall: 0.7978, IoU.building: 0.8297, IoU.sky: 0.9425, IoU.floor: 0.8299, IoU.tree: 0.7572, IoU.ceiling: 0.8528, IoU.road: 0.8490, IoU.bed : 0.9171, IoU.windowpane: 0.6580, IoU.grass: 0.6666, IoU.cabinet: 0.6448, IoU.sidewalk: 0.6923, IoU.person: 0.8269, IoU.earth: 0.3679, IoU.door: 0.5723, IoU.table: 0.6897, IoU.mountain: 0.6289, IoU.plant: 0.5698, IoU.curtain: 0.7697, IoU.chair: 0.5925, IoU.car: 0.8563, IoU.water: 0.6641, IoU.painting: 0.7648, IoU.sofa: 0.8025, IoU.shelf: 0.4849, IoU.house: 0.3882, IoU.sea: 0.7139, IoU.mirror: 0.7317, IoU.rug: 0.6622, IoU.field: 0.3296, IoU.armchair: 0.5594, IoU.seat: 0.6544, IoU.fence: 0.4823, IoU.desk: 0.5567, IoU.rock: 0.5659, IoU.wardrobe: 0.5556, IoU.lamp: 0.6669, IoU.bathtub: 0.8845, IoU.railing: 0.4318, IoU.cushion: 0.6257, IoU.base: 0.2920, IoU.box: 0.3735, IoU.column: 0.5242, IoU.signboard: 0.3668, IoU.chest of drawers: 0.4107, IoU.counter: 0.4766, IoU.sand: 0.5976, IoU.sink: 0.7763, IoU.skyscraper: 0.4796, IoU.fireplace: 0.7722, IoU.refrigerator: 0.8148, IoU.grandstand: 0.4839, IoU.path: 0.2307, IoU.stairs: 0.3119, IoU.runway: 0.6896, IoU.case: 0.6290, IoU.pool table: 0.9248, IoU.pillow: 0.6102, IoU.screen door: 0.7468, IoU.stairway: 0.3431, IoU.river: 0.1844, IoU.bridge: 0.6090, IoU.bookcase: 0.4512, IoU.blind: 0.3947, IoU.coffee table: 0.6786, IoU.toilet: 0.8904, IoU.flower: 0.4248, IoU.book: 0.4762, IoU.hill: 0.0435, IoU.bench: 0.5557, IoU.countertop: 0.6394, IoU.stove: 0.8222, IoU.palm: 0.4613, IoU.kitchen island: 0.3954, IoU.computer: 0.7846, IoU.swivel chair: 0.4537, IoU.boat: 0.6268, IoU.bar: 0.7625, IoU.arcade machine: 0.7773, IoU.hovel: 0.2389, IoU.bus: 0.9222, IoU.towel: 0.7702, IoU.light: 0.4838, IoU.truck: 0.4892, IoU.tower: 0.2303, IoU.chandelier: 0.6690, IoU.awning: 0.3738, IoU.streetlight: 0.2522, IoU.booth: 0.3730, IoU.television receiver: 0.7105, IoU.airplane: 0.8539, IoU.dirt track: 0.1537, IoU.apparel: 0.5356, IoU.pole: 0.2266, IoU.land: 0.0973, IoU.bannister: 0.1295, IoU.escalator: 0.6087, IoU.ottoman: 0.5420, IoU.bottle: 0.2990, IoU.buffet: 0.5866, IoU.poster: 0.3477, IoU.stage: 0.1842, IoU.van: 0.5001, IoU.ship: 0.1869, IoU.fountain: 0.3206, IoU.conveyer belt: 0.8452, IoU.canopy: 0.3440, IoU.washer: 0.8053, IoU.plaything: 0.3259, IoU.swimming pool: 0.4807, IoU.stool: 0.4822, IoU.barrel: 0.6244, IoU.basket: 0.3535, IoU.waterfall: 0.5098, IoU.tent: 0.9599, IoU.bag: 0.2677, IoU.minibike: 0.7574, IoU.cradle: 0.8601, IoU.oven: 0.6587, IoU.ball: 0.4819, IoU.food: 0.5210, IoU.step: 0.1447, IoU.tank: 0.6000, IoU.trade name: 0.2764, IoU.microwave: 0.8848, IoU.pot: 0.5651, IoU.animal: 0.7640, IoU.bicycle: 0.5751, IoU.lake: 0.5582, IoU.dishwasher: 0.6987, IoU.screen: 0.5860, IoU.blanket: 0.2877, IoU.sculpture: 0.7175, IoU.hood: 0.6287, IoU.sconce: 0.5428, IoU.vase: 0.4381, IoU.traffic light: 0.3506, IoU.tray: 0.1793, IoU.ashcan: 0.4728, IoU.fan: 0.6296, IoU.pier: 0.4221, IoU.crt screen: 0.1399, IoU.plate: 0.5678, IoU.monitor: 0.5464, IoU.bulletin board: 0.4715, IoU.shower: 0.0449, IoU.radiator: 0.6689, IoU.glass: 0.1902, IoU.clock: 0.3345, IoU.flag: 0.6747, Acc.wall: 0.8817, Acc.building: 0.9340, Acc.sky: 0.9776, Acc.floor: 0.9048, Acc.tree: 0.8718, Acc.ceiling: 0.9244, Acc.road: 0.9236, Acc.bed : 0.9670, Acc.windowpane: 0.8116, Acc.grass: 0.8320, Acc.cabinet: 0.7433, Acc.sidewalk: 0.8343, Acc.person: 0.9316, Acc.earth: 0.4891, Acc.door: 0.7761, Acc.table: 0.8187, Acc.mountain: 0.7316, Acc.plant: 0.6827, Acc.curtain: 0.8684, Acc.chair: 0.7064, Acc.car: 0.9385, Acc.water: 0.8210, Acc.painting: 0.8980, Acc.sofa: 0.8710, Acc.shelf: 0.7107, Acc.house: 0.4600, Acc.sea: 0.8113, Acc.mirror: 0.8096, Acc.rug: 0.8190, Acc.field: 0.4873, Acc.armchair: 0.8196, Acc.seat: 0.8654, Acc.fence: 0.6474, Acc.desk: 0.7338, Acc.rock: 0.8691, Acc.wardrobe: 0.8003, Acc.lamp: 0.7942, Acc.bathtub: 0.9251, Acc.railing: 0.5721, Acc.cushion: 0.7737, Acc.base: 0.4328, Acc.box: 0.5093, Acc.column: 0.6339, Acc.signboard: 0.5384, Acc.chest of drawers: 0.6197, Acc.counter: 0.6072, Acc.sand: 0.8814, Acc.sink: 0.8366, Acc.skyscraper: 0.6028, Acc.fireplace: 0.9389, Acc.refrigerator: 0.9032, Acc.grandstand: 0.7488, Acc.path: 0.2733, Acc.stairs: 0.4307, Acc.runway: 0.8887, Acc.case: 0.8323, Acc.pool table: 0.9850, Acc.pillow: 0.7160, Acc.screen door: 0.7936, Acc.stairway: 0.4629, Acc.river: 0.3418, Acc.bridge: 0.6789, Acc.bookcase: 0.5472, Acc.blind: 0.4334, Acc.coffee table: 0.8409, Acc.toilet: 0.9445, Acc.flower: 0.6109, Acc.book: 0.7390, Acc.hill: 0.0783, Acc.bench: 0.6454, Acc.countertop: 0.7684, Acc.stove: 0.8802, Acc.palm: 0.8484, Acc.kitchen island: 0.6775, Acc.computer: 0.8892, Acc.swivel chair: 0.6804, Acc.boat: 0.8395, Acc.bar: 0.8442, Acc.arcade machine: 0.8104, Acc.hovel: 0.2577, Acc.bus: 0.9654, Acc.towel: 0.8643, Acc.light: 0.5825, Acc.truck: 0.6616, Acc.tower: 0.3313, Acc.chandelier: 0.8173, Acc.awning: 0.4851, Acc.streetlight: 0.3369, Acc.booth: 0.4129, Acc.television receiver: 0.8959, Acc.airplane: 0.9210, Acc.dirt track: 0.2113, Acc.apparel: 0.6492, Acc.pole: 0.3088, Acc.land: 0.1431, Acc.bannister: 0.1998, Acc.escalator: 0.7805, Acc.ottoman: 0.7350, Acc.bottle: 0.3849, Acc.buffet: 0.7408, Acc.poster: 0.4363, Acc.stage: 0.4581, Acc.van: 0.6888, Acc.ship: 0.2018, Acc.fountain: 0.3294, Acc.conveyer belt: 0.9628, Acc.canopy: 0.4560, Acc.washer: 0.9165, Acc.plaything: 0.4662, Acc.swimming pool: 0.7112, Acc.stool: 0.6251, Acc.barrel: 0.8259, Acc.basket: 0.4426, Acc.waterfall: 0.6917, Acc.tent: 0.9743, Acc.bag: 0.3252, Acc.minibike: 0.8567, Acc.cradle: 0.9789, Acc.oven: 0.7493, Acc.ball: 0.7319, Acc.food: 0.5585, Acc.step: 0.1786, Acc.tank: 0.6543, Acc.trade name: 0.3389, Acc.microwave: 0.9440, Acc.pot: 0.6672, Acc.animal: 0.8057, Acc.bicycle: 0.7362, Acc.lake: 0.6356, Acc.dishwasher: 0.7646, Acc.screen: 0.8030, Acc.blanket: 0.3381, Acc.sculpture: 0.8913, Acc.hood: 0.7210, Acc.sconce: 0.7070, Acc.vase: 0.6179, Acc.traffic light: 0.6194, Acc.tray: 0.2548, Acc.ashcan: 0.6445, Acc.fan: 0.7581, Acc.pier: 0.4702, Acc.crt screen: 0.2198, Acc.plate: 0.7601, Acc.monitor: 0.6716, Acc.bulletin board: 0.5541, Acc.shower: 0.0524, Acc.radiator: 0.8203, Acc.glass: 0.2109, Acc.clock: 0.3841, Acc.flag: 0.7666 2023-11-03 02:59:10,732 - mmseg - INFO - Iter [22050/40000] lr: 1.454e-06, eta: 6:40:30, time: 2.436, data_time: 1.228, memory: 38534, decode.loss_ce: 0.1971, decode.acc_seg: 92.0548, loss: 0.1971 2023-11-03 03:00:11,430 - mmseg - INFO - Iter [22100/40000] lr: 1.450e-06, eta: 6:39:18, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1921, decode.acc_seg: 92.0970, loss: 0.1921 2023-11-03 03:01:14,476 - mmseg - INFO - Iter [22150/40000] lr: 1.446e-06, eta: 6:38:08, time: 1.261, data_time: 0.051, memory: 38534, decode.loss_ce: 0.1816, decode.acc_seg: 92.3394, loss: 0.1816 2023-11-03 03:02:15,127 - mmseg - INFO - Iter [22200/40000] lr: 1.442e-06, eta: 6:36:56, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1803, decode.acc_seg: 92.6498, loss: 0.1803 2023-11-03 03:03:15,750 - mmseg - INFO - Iter [22250/40000] lr: 1.438e-06, eta: 6:35:44, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1842, decode.acc_seg: 92.3991, loss: 0.1842 2023-11-03 03:04:16,434 - mmseg - INFO - Iter [22300/40000] lr: 1.434e-06, eta: 6:34:32, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1895, decode.acc_seg: 92.2363, loss: 0.1895 2023-11-03 03:05:17,079 - mmseg - INFO - Iter [22350/40000] lr: 1.430e-06, eta: 6:33:20, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1740, decode.acc_seg: 92.6613, loss: 0.1740 2023-11-03 03:06:17,742 - mmseg - INFO - Iter [22400/40000] lr: 1.426e-06, eta: 6:32:09, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1818, decode.acc_seg: 92.4102, loss: 0.1818 2023-11-03 03:07:18,419 - mmseg - INFO - Iter [22450/40000] lr: 1.422e-06, eta: 6:30:57, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1858, decode.acc_seg: 92.3546, loss: 0.1858 2023-11-03 03:08:19,060 - mmseg - INFO - Iter [22500/40000] lr: 1.417e-06, eta: 6:29:45, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1862, decode.acc_seg: 92.4046, loss: 0.1862 2023-11-03 03:09:19,696 - mmseg - INFO - Iter [22550/40000] lr: 1.413e-06, eta: 6:28:34, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1815, decode.acc_seg: 92.2470, loss: 0.1815 2023-11-03 03:10:20,350 - mmseg - INFO - Iter [22600/40000] lr: 1.409e-06, eta: 6:27:22, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1806, decode.acc_seg: 92.5162, loss: 0.1806 2023-11-03 03:11:20,998 - mmseg - INFO - Iter [22650/40000] lr: 1.405e-06, eta: 6:26:11, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1980, decode.acc_seg: 91.9018, loss: 0.1980 2023-11-03 03:12:21,693 - mmseg - INFO - Iter [22700/40000] lr: 1.401e-06, eta: 6:24:59, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1803, decode.acc_seg: 92.6268, loss: 0.1803 2023-11-03 03:13:22,296 - mmseg - INFO - Iter [22750/40000] lr: 1.397e-06, eta: 6:23:48, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1801, decode.acc_seg: 92.4458, loss: 0.1801 2023-11-03 03:14:25,404 - mmseg - INFO - Iter [22800/40000] lr: 1.393e-06, eta: 6:22:38, time: 1.262, data_time: 0.055, memory: 38534, decode.loss_ce: 0.1836, decode.acc_seg: 92.6245, loss: 0.1836 2023-11-03 03:15:26,122 - mmseg - INFO - Iter [22850/40000] lr: 1.389e-06, eta: 6:21:27, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1687, decode.acc_seg: 92.9923, loss: 0.1687 2023-11-03 03:16:26,873 - mmseg - INFO - Iter [22900/40000] lr: 1.385e-06, eta: 6:20:16, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1913, decode.acc_seg: 92.1518, loss: 0.1913 2023-11-03 03:17:27,604 - mmseg - INFO - Iter [22950/40000] lr: 1.381e-06, eta: 6:19:05, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1783, decode.acc_seg: 92.4695, loss: 0.1783 2023-11-03 03:18:28,275 - mmseg - INFO - Saving checkpoint at 23000 iterations 2023-11-03 03:19:32,137 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 03:19:32,137 - mmseg - INFO - Iter [23000/40000] lr: 1.377e-06, eta: 6:18:41, time: 2.491, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1808, decode.acc_seg: 92.4437, loss: 0.1808 2023-11-03 03:20:29,184 - mmseg - INFO - per class results: 2023-11-03 03:20:29,189 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.43 | 90.03 | | building | 83.47 | 92.47 | | sky | 94.32 | 97.07 | | floor | 83.04 | 92.32 | | tree | 76.06 | 88.62 | | ceiling | 85.23 | 92.94 | | road | 85.85 | 92.33 | | bed | 91.2 | 95.86 | | windowpane | 64.9 | 78.46 | | grass | 68.98 | 86.47 | | cabinet | 64.3 | 74.37 | | sidewalk | 68.86 | 83.23 | | person | 82.52 | 92.16 | | earth | 31.83 | 41.18 | | door | 57.44 | 71.41 | | table | 69.02 | 81.59 | | mountain | 61.0 | 75.08 | | plant | 55.47 | 66.34 | | curtain | 75.33 | 88.45 | | chair | 60.82 | 73.53 | | car | 85.52 | 93.1 | | water | 65.92 | 80.42 | | painting | 77.88 | 86.91 | | sofa | 79.38 | 88.86 | | shelf | 47.98 | 67.11 | | house | 48.14 | 63.7 | | sea | 70.97 | 80.34 | | mirror | 74.78 | 82.94 | | rug | 61.65 | 68.33 | | field | 37.64 | 62.45 | | armchair | 58.34 | 78.83 | | seat | 64.18 | 87.0 | | fence | 44.27 | 55.44 | | desk | 54.21 | 66.1 | | rock | 55.88 | 75.95 | | wardrobe | 52.79 | 75.73 | | lamp | 66.18 | 77.74 | | bathtub | 88.67 | 90.9 | | railing | 44.08 | 58.39 | | cushion | 62.66 | 74.28 | | base | 32.64 | 53.64 | | box | 36.91 | 44.75 | | column | 55.5 | 73.99 | | signboard | 36.65 | 49.73 | | chest of drawers | 44.24 | 66.65 | | counter | 44.78 | 62.67 | | sand | 68.32 | 88.94 | | sink | 78.2 | 83.87 | | skyscraper | 47.6 | 63.08 | | fireplace | 76.56 | 95.73 | | refrigerator | 81.65 | 87.7 | | grandstand | 46.23 | 79.68 | | path | 21.99 | 30.57 | | stairs | 29.35 | 38.14 | | runway | 69.4 | 89.57 | | case | 59.29 | 84.94 | | pool table | 93.29 | 97.91 | | pillow | 56.71 | 63.78 | | screen door | 81.04 | 84.41 | | stairway | 34.15 | 44.97 | | river | 19.54 | 42.06 | | bridge | 75.6 | 83.59 | | bookcase | 47.5 | 57.04 | | blind | 42.0 | 48.13 | | coffee table | 66.78 | 86.29 | | toilet | 88.33 | 92.59 | | flower | 40.36 | 64.55 | | book | 51.15 | 72.13 | | hill | 7.69 | 12.78 | | bench | 54.12 | 60.0 | | countertop | 64.96 | 77.37 | | stove | 82.3 | 89.25 | | palm | 47.49 | 85.13 | | kitchen island | 43.12 | 71.0 | | computer | 77.86 | 87.05 | | swivel chair | 43.01 | 58.54 | | boat | 70.7 | 86.98 | | bar | 71.92 | 82.37 | | arcade machine | 72.06 | 74.34 | | hovel | 34.58 | 39.48 | | bus | 93.03 | 96.33 | | towel | 75.61 | 86.23 | | light | 49.08 | 60.14 | | truck | 48.87 | 62.08 | | tower | 22.27 | 38.49 | | chandelier | 67.28 | 82.09 | | awning | 35.06 | 43.5 | | streetlight | 25.11 | 34.79 | | booth | 38.29 | 40.14 | | television receiver | 76.91 | 87.0 | | airplane | 67.47 | 72.49 | | dirt track | 15.62 | 21.87 | | apparel | 55.83 | 71.59 | | pole | 22.27 | 29.05 | | land | 8.41 | 13.62 | | bannister | 13.21 | 17.85 | | escalator | 59.63 | 72.99 | | ottoman | 51.46 | 71.23 | | bottle | 28.79 | 41.13 | | buffet | 57.24 | 75.02 | | poster | 34.52 | 42.34 | | stage | 28.72 | 50.64 | | van | 49.97 | 68.44 | | ship | 33.41 | 34.75 | | fountain | 36.5 | 37.38 | | conveyer belt | 85.28 | 90.71 | | canopy | 42.5 | 49.82 | | washer | 86.29 | 91.25 | | plaything | 31.81 | 44.41 | | swimming pool | 49.83 | 72.31 | | stool | 49.71 | 61.03 | | barrel | 67.27 | 86.74 | | basket | 37.47 | 47.49 | | waterfall | 47.55 | 62.49 | | tent | 93.83 | 97.62 | | bag | 28.54 | 34.9 | | minibike | 73.56 | 85.38 | | cradle | 85.34 | 96.35 | | oven | 64.43 | 73.24 | | ball | 57.86 | 66.25 | | food | 45.43 | 47.73 | | step | 12.43 | 13.9 | | tank | 54.74 | 64.94 | | trade name | 26.03 | 30.94 | | microwave | 88.63 | 93.21 | | pot | 55.42 | 62.87 | | animal | 73.77 | 76.25 | | bicycle | 59.88 | 80.32 | | lake | 59.02 | 63.4 | | dishwasher | 70.99 | 77.78 | | screen | 68.5 | 90.58 | | blanket | 29.37 | 32.4 | | sculpture | 73.74 | 88.79 | | hood | 72.19 | 86.75 | | sconce | 53.33 | 65.63 | | vase | 43.21 | 59.14 | | traffic light | 34.85 | 62.67 | | tray | 17.55 | 23.32 | | ashcan | 47.54 | 65.62 | | fan | 63.17 | 77.28 | | pier | 40.04 | 46.44 | | crt screen | 13.46 | 21.14 | | plate | 56.55 | 73.73 | | monitor | 50.03 | 61.18 | | bulletin board | 47.21 | 60.76 | | shower | 2.74 | 2.91 | | radiator | 66.45 | 84.74 | | glass | 18.27 | 20.1 | | clock | 32.88 | 38.05 | | flag | 66.29 | 71.35 | +---------------------+-------+-------+ 2023-11-03 03:20:29,189 - mmseg - INFO - Summary: 2023-11-03 03:20:29,189 - mmseg - INFO - +------+-------+-------+ | aAcc | mIoU | mAcc | +------+-------+-------+ | 85.1 | 55.44 | 67.19 | +------+-------+-------+ 2023-11-03 03:20:29,190 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 03:20:29,190 - mmseg - INFO - Iter(val) [250] aAcc: 0.8510, mIoU: 0.5544, mAcc: 0.6719, IoU.wall: 0.8043, IoU.building: 0.8347, IoU.sky: 0.9432, IoU.floor: 0.8304, IoU.tree: 0.7606, IoU.ceiling: 0.8523, IoU.road: 0.8585, IoU.bed : 0.9120, IoU.windowpane: 0.6490, IoU.grass: 0.6898, IoU.cabinet: 0.6430, IoU.sidewalk: 0.6886, IoU.person: 0.8252, IoU.earth: 0.3183, IoU.door: 0.5744, IoU.table: 0.6902, IoU.mountain: 0.6100, IoU.plant: 0.5547, IoU.curtain: 0.7533, IoU.chair: 0.6082, IoU.car: 0.8552, IoU.water: 0.6592, IoU.painting: 0.7788, IoU.sofa: 0.7938, IoU.shelf: 0.4798, IoU.house: 0.4814, IoU.sea: 0.7097, IoU.mirror: 0.7478, IoU.rug: 0.6165, IoU.field: 0.3764, IoU.armchair: 0.5834, IoU.seat: 0.6418, IoU.fence: 0.4427, IoU.desk: 0.5421, IoU.rock: 0.5588, IoU.wardrobe: 0.5279, IoU.lamp: 0.6618, IoU.bathtub: 0.8867, IoU.railing: 0.4408, IoU.cushion: 0.6266, IoU.base: 0.3264, IoU.box: 0.3691, IoU.column: 0.5550, IoU.signboard: 0.3665, IoU.chest of drawers: 0.4424, IoU.counter: 0.4478, IoU.sand: 0.6832, IoU.sink: 0.7820, IoU.skyscraper: 0.4760, IoU.fireplace: 0.7656, IoU.refrigerator: 0.8165, IoU.grandstand: 0.4623, IoU.path: 0.2199, IoU.stairs: 0.2935, IoU.runway: 0.6940, IoU.case: 0.5929, IoU.pool table: 0.9329, IoU.pillow: 0.5671, IoU.screen door: 0.8104, IoU.stairway: 0.3415, IoU.river: 0.1954, IoU.bridge: 0.7560, IoU.bookcase: 0.4750, IoU.blind: 0.4200, IoU.coffee table: 0.6678, IoU.toilet: 0.8833, IoU.flower: 0.4036, IoU.book: 0.5115, IoU.hill: 0.0769, IoU.bench: 0.5412, IoU.countertop: 0.6496, IoU.stove: 0.8230, IoU.palm: 0.4749, IoU.kitchen island: 0.4312, IoU.computer: 0.7786, IoU.swivel chair: 0.4301, IoU.boat: 0.7070, IoU.bar: 0.7192, IoU.arcade machine: 0.7206, IoU.hovel: 0.3458, IoU.bus: 0.9303, IoU.towel: 0.7561, IoU.light: 0.4908, IoU.truck: 0.4887, IoU.tower: 0.2227, IoU.chandelier: 0.6728, IoU.awning: 0.3506, IoU.streetlight: 0.2511, IoU.booth: 0.3829, IoU.television receiver: 0.7691, IoU.airplane: 0.6747, IoU.dirt track: 0.1562, IoU.apparel: 0.5583, IoU.pole: 0.2227, IoU.land: 0.0841, IoU.bannister: 0.1321, IoU.escalator: 0.5963, IoU.ottoman: 0.5146, IoU.bottle: 0.2879, IoU.buffet: 0.5724, IoU.poster: 0.3452, IoU.stage: 0.2872, IoU.van: 0.4997, IoU.ship: 0.3341, IoU.fountain: 0.3650, IoU.conveyer belt: 0.8528, IoU.canopy: 0.4250, IoU.washer: 0.8629, IoU.plaything: 0.3181, IoU.swimming pool: 0.4983, IoU.stool: 0.4971, IoU.barrel: 0.6727, IoU.basket: 0.3747, IoU.waterfall: 0.4755, IoU.tent: 0.9383, IoU.bag: 0.2854, IoU.minibike: 0.7356, IoU.cradle: 0.8534, IoU.oven: 0.6443, IoU.ball: 0.5786, IoU.food: 0.4543, IoU.step: 0.1243, IoU.tank: 0.5474, IoU.trade name: 0.2603, IoU.microwave: 0.8863, IoU.pot: 0.5542, IoU.animal: 0.7377, IoU.bicycle: 0.5988, IoU.lake: 0.5902, IoU.dishwasher: 0.7099, IoU.screen: 0.6850, IoU.blanket: 0.2937, IoU.sculpture: 0.7374, IoU.hood: 0.7219, IoU.sconce: 0.5333, IoU.vase: 0.4321, IoU.traffic light: 0.3485, IoU.tray: 0.1755, IoU.ashcan: 0.4754, IoU.fan: 0.6317, IoU.pier: 0.4004, IoU.crt screen: 0.1346, IoU.plate: 0.5655, IoU.monitor: 0.5003, IoU.bulletin board: 0.4721, IoU.shower: 0.0274, IoU.radiator: 0.6645, IoU.glass: 0.1827, IoU.clock: 0.3288, IoU.flag: 0.6629, Acc.wall: 0.9003, Acc.building: 0.9247, Acc.sky: 0.9707, Acc.floor: 0.9232, Acc.tree: 0.8862, Acc.ceiling: 0.9294, Acc.road: 0.9233, Acc.bed : 0.9586, Acc.windowpane: 0.7846, Acc.grass: 0.8647, Acc.cabinet: 0.7437, Acc.sidewalk: 0.8323, Acc.person: 0.9216, Acc.earth: 0.4118, Acc.door: 0.7141, Acc.table: 0.8159, Acc.mountain: 0.7508, Acc.plant: 0.6634, Acc.curtain: 0.8845, Acc.chair: 0.7353, Acc.car: 0.9310, Acc.water: 0.8042, Acc.painting: 0.8691, Acc.sofa: 0.8886, Acc.shelf: 0.6711, Acc.house: 0.6370, Acc.sea: 0.8034, Acc.mirror: 0.8294, Acc.rug: 0.6833, Acc.field: 0.6245, Acc.armchair: 0.7883, Acc.seat: 0.8700, Acc.fence: 0.5544, Acc.desk: 0.6610, Acc.rock: 0.7595, Acc.wardrobe: 0.7573, Acc.lamp: 0.7774, Acc.bathtub: 0.9090, Acc.railing: 0.5839, Acc.cushion: 0.7428, Acc.base: 0.5364, Acc.box: 0.4475, Acc.column: 0.7399, Acc.signboard: 0.4973, Acc.chest of drawers: 0.6665, Acc.counter: 0.6267, Acc.sand: 0.8894, Acc.sink: 0.8387, Acc.skyscraper: 0.6308, Acc.fireplace: 0.9573, Acc.refrigerator: 0.8770, Acc.grandstand: 0.7968, Acc.path: 0.3057, Acc.stairs: 0.3814, Acc.runway: 0.8957, Acc.case: 0.8494, Acc.pool table: 0.9791, Acc.pillow: 0.6378, Acc.screen door: 0.8441, Acc.stairway: 0.4497, Acc.river: 0.4206, Acc.bridge: 0.8359, Acc.bookcase: 0.5704, Acc.blind: 0.4813, Acc.coffee table: 0.8629, Acc.toilet: 0.9259, Acc.flower: 0.6455, Acc.book: 0.7213, Acc.hill: 0.1278, Acc.bench: 0.6000, Acc.countertop: 0.7737, Acc.stove: 0.8925, Acc.palm: 0.8513, Acc.kitchen island: 0.7100, Acc.computer: 0.8705, Acc.swivel chair: 0.5854, Acc.boat: 0.8698, Acc.bar: 0.8237, Acc.arcade machine: 0.7434, Acc.hovel: 0.3948, Acc.bus: 0.9633, Acc.towel: 0.8623, Acc.light: 0.6014, Acc.truck: 0.6208, Acc.tower: 0.3849, Acc.chandelier: 0.8209, Acc.awning: 0.4350, Acc.streetlight: 0.3479, Acc.booth: 0.4014, Acc.television receiver: 0.8700, Acc.airplane: 0.7249, Acc.dirt track: 0.2187, Acc.apparel: 0.7159, Acc.pole: 0.2905, Acc.land: 0.1362, Acc.bannister: 0.1785, Acc.escalator: 0.7299, Acc.ottoman: 0.7123, Acc.bottle: 0.4113, Acc.buffet: 0.7502, Acc.poster: 0.4234, Acc.stage: 0.5064, Acc.van: 0.6844, Acc.ship: 0.3475, Acc.fountain: 0.3738, Acc.conveyer belt: 0.9071, Acc.canopy: 0.4982, Acc.washer: 0.9125, Acc.plaything: 0.4441, Acc.swimming pool: 0.7231, Acc.stool: 0.6103, Acc.barrel: 0.8674, Acc.basket: 0.4749, Acc.waterfall: 0.6249, Acc.tent: 0.9762, Acc.bag: 0.3490, Acc.minibike: 0.8538, Acc.cradle: 0.9635, Acc.oven: 0.7324, Acc.ball: 0.6625, Acc.food: 0.4773, Acc.step: 0.1390, Acc.tank: 0.6494, Acc.trade name: 0.3094, Acc.microwave: 0.9321, Acc.pot: 0.6287, Acc.animal: 0.7625, Acc.bicycle: 0.8032, Acc.lake: 0.6340, Acc.dishwasher: 0.7778, Acc.screen: 0.9058, Acc.blanket: 0.3240, Acc.sculpture: 0.8879, Acc.hood: 0.8675, Acc.sconce: 0.6563, Acc.vase: 0.5914, Acc.traffic light: 0.6267, Acc.tray: 0.2332, Acc.ashcan: 0.6562, Acc.fan: 0.7728, Acc.pier: 0.4644, Acc.crt screen: 0.2114, Acc.plate: 0.7373, Acc.monitor: 0.6118, Acc.bulletin board: 0.6076, Acc.shower: 0.0291, Acc.radiator: 0.8474, Acc.glass: 0.2010, Acc.clock: 0.3805, Acc.flag: 0.7135 2023-11-03 03:21:29,955 - mmseg - INFO - Iter [23050/40000] lr: 1.373e-06, eta: 6:18:12, time: 2.356, data_time: 1.149, memory: 38534, decode.loss_ce: 0.1905, decode.acc_seg: 92.0275, loss: 0.1905 2023-11-03 03:22:30,641 - mmseg - INFO - Iter [23100/40000] lr: 1.369e-06, eta: 6:17:00, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1747, decode.acc_seg: 92.6791, loss: 0.1747 2023-11-03 03:23:31,265 - mmseg - INFO - Iter [23150/40000] lr: 1.365e-06, eta: 6:15:49, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1783, decode.acc_seg: 92.5019, loss: 0.1783 2023-11-03 03:24:31,983 - mmseg - INFO - Iter [23200/40000] lr: 1.361e-06, eta: 6:14:37, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1752, decode.acc_seg: 92.7047, loss: 0.1752 2023-11-03 03:25:32,642 - mmseg - INFO - Iter [23250/40000] lr: 1.357e-06, eta: 6:13:26, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1922, decode.acc_seg: 92.1376, loss: 0.1922 2023-11-03 03:26:33,278 - mmseg - INFO - Iter [23300/40000] lr: 1.353e-06, eta: 6:12:14, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1721, decode.acc_seg: 92.5194, loss: 0.1721 2023-11-03 03:27:33,960 - mmseg - INFO - Iter [23350/40000] lr: 1.349e-06, eta: 6:11:03, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1712, decode.acc_seg: 92.8994, loss: 0.1712 2023-11-03 03:28:37,045 - mmseg - INFO - Iter [23400/40000] lr: 1.345e-06, eta: 6:09:54, time: 1.262, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1807, decode.acc_seg: 92.5135, loss: 0.1807 2023-11-03 03:29:37,691 - mmseg - INFO - Iter [23450/40000] lr: 1.341e-06, eta: 6:08:42, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1745, decode.acc_seg: 92.7780, loss: 0.1745 2023-11-03 03:30:38,316 - mmseg - INFO - Iter [23500/40000] lr: 1.336e-06, eta: 6:07:31, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1842, decode.acc_seg: 92.2710, loss: 0.1842 2023-11-03 03:31:38,958 - mmseg - INFO - Iter [23550/40000] lr: 1.332e-06, eta: 6:06:20, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1707, decode.acc_seg: 92.9028, loss: 0.1707 2023-11-03 03:32:39,584 - mmseg - INFO - Iter [23600/40000] lr: 1.328e-06, eta: 6:05:09, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1803, decode.acc_seg: 92.5036, loss: 0.1803 2023-11-03 03:33:40,167 - mmseg - INFO - Iter [23650/40000] lr: 1.324e-06, eta: 6:03:58, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1796, decode.acc_seg: 92.6738, loss: 0.1796 2023-11-03 03:34:40,799 - mmseg - INFO - Iter [23700/40000] lr: 1.320e-06, eta: 6:02:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1778, decode.acc_seg: 92.5077, loss: 0.1778 2023-11-03 03:35:41,434 - mmseg - INFO - Iter [23750/40000] lr: 1.316e-06, eta: 6:01:36, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1741, decode.acc_seg: 92.7557, loss: 0.1741 2023-11-03 03:36:42,072 - mmseg - INFO - Iter [23800/40000] lr: 1.312e-06, eta: 6:00:25, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1766, decode.acc_seg: 92.6949, loss: 0.1766 2023-11-03 03:37:42,692 - mmseg - INFO - Iter [23850/40000] lr: 1.308e-06, eta: 5:59:14, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1785, decode.acc_seg: 92.6222, loss: 0.1785 2023-11-03 03:38:43,289 - mmseg - INFO - Iter [23900/40000] lr: 1.304e-06, eta: 5:58:03, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1910, decode.acc_seg: 92.1555, loss: 0.1910 2023-11-03 03:39:43,894 - mmseg - INFO - Iter [23950/40000] lr: 1.300e-06, eta: 5:56:52, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1771, decode.acc_seg: 92.8130, loss: 0.1771 2023-11-03 03:40:44,537 - mmseg - INFO - Saving checkpoint at 24000 iterations 2023-11-03 03:41:40,697 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 03:41:40,697 - mmseg - INFO - Iter [24000/40000] lr: 1.296e-06, eta: 5:56:19, time: 2.336, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1752, decode.acc_seg: 92.6655, loss: 0.1752 2023-11-03 03:42:40,038 - mmseg - INFO - per class results: 2023-11-03 03:42:40,043 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.39 | 89.56 | | building | 83.42 | 93.98 | | sky | 94.25 | 97.09 | | floor | 83.51 | 92.3 | | tree | 76.08 | 87.41 | | ceiling | 85.33 | 93.25 | | road | 86.17 | 91.78 | | bed | 91.35 | 96.33 | | windowpane | 64.9 | 78.28 | | grass | 70.13 | 85.55 | | cabinet | 64.33 | 74.66 | | sidewalk | 69.95 | 84.33 | | person | 82.59 | 92.13 | | earth | 34.6 | 45.52 | | door | 58.33 | 76.42 | | table | 69.04 | 80.94 | | mountain | 60.58 | 73.84 | | plant | 57.0 | 68.47 | | curtain | 75.21 | 87.55 | | chair | 60.98 | 72.5 | | car | 86.26 | 92.66 | | water | 65.62 | 81.16 | | painting | 78.54 | 87.77 | | sofa | 80.56 | 90.4 | | shelf | 47.42 | 65.73 | | house | 47.54 | 60.24 | | sea | 70.78 | 80.95 | | mirror | 74.81 | 83.41 | | rug | 61.88 | 66.96 | | field | 36.21 | 55.73 | | armchair | 58.43 | 78.34 | | seat | 64.1 | 87.97 | | fence | 43.43 | 54.37 | | desk | 51.76 | 75.7 | | rock | 57.97 | 85.89 | | wardrobe | 50.96 | 69.99 | | lamp | 65.63 | 76.49 | | bathtub | 87.17 | 90.08 | | railing | 42.88 | 55.2 | | cushion | 63.29 | 72.95 | | base | 34.07 | 53.93 | | box | 36.9 | 46.17 | | column | 53.95 | 67.42 | | signboard | 35.17 | 46.38 | | chest of drawers | 44.01 | 65.1 | | counter | 45.38 | 58.48 | | sand | 61.01 | 90.23 | | sink | 77.77 | 82.82 | | skyscraper | 49.57 | 61.73 | | fireplace | 76.24 | 90.73 | | refrigerator | 82.9 | 91.66 | | grandstand | 49.92 | 80.43 | | path | 22.53 | 29.02 | | stairs | 29.73 | 35.69 | | runway | 69.36 | 89.85 | | case | 58.84 | 91.96 | | pool table | 93.62 | 97.6 | | pillow | 60.97 | 71.37 | | screen door | 77.84 | 80.59 | | stairway | 36.96 | 47.66 | | river | 18.27 | 34.33 | | bridge | 75.85 | 84.52 | | bookcase | 44.96 | 56.52 | | blind | 38.73 | 45.08 | | coffee table | 68.42 | 86.54 | | toilet | 89.13 | 93.39 | | flower | 41.88 | 66.42 | | book | 49.65 | 72.58 | | hill | 8.79 | 17.9 | | bench | 53.58 | 62.04 | | countertop | 65.46 | 80.68 | | stove | 84.02 | 89.93 | | palm | 47.09 | 83.43 | | kitchen island | 46.1 | 65.5 | | computer | 76.23 | 87.77 | | swivel chair | 46.43 | 66.17 | | boat | 72.02 | 87.7 | | bar | 72.42 | 85.45 | | arcade machine | 70.21 | 73.48 | | hovel | 25.28 | 27.36 | | bus | 92.95 | 96.49 | | towel | 73.54 | 84.15 | | light | 48.64 | 58.17 | | truck | 50.63 | 64.31 | | tower | 32.19 | 47.44 | | chandelier | 67.61 | 83.68 | | awning | 29.46 | 34.25 | | streetlight | 23.48 | 31.08 | | booth | 54.4 | 57.4 | | television receiver | 76.26 | 87.47 | | airplane | 72.76 | 79.74 | | dirt track | 7.05 | 21.3 | | apparel | 44.52 | 54.65 | | pole | 22.02 | 27.31 | | land | 10.27 | 15.28 | | bannister | 13.58 | 18.4 | | escalator | 61.83 | 78.99 | | ottoman | 53.54 | 70.51 | | bottle | 27.42 | 37.88 | | buffet | 52.61 | 75.61 | | poster | 38.17 | 43.68 | | stage | 24.74 | 41.99 | | van | 52.84 | 79.34 | | ship | 53.14 | 55.21 | | fountain | 33.11 | 34.33 | | conveyer belt | 86.22 | 92.45 | | canopy | 37.35 | 41.84 | | washer | 86.57 | 92.34 | | plaything | 32.0 | 40.37 | | swimming pool | 49.57 | 71.8 | | stool | 48.03 | 58.33 | | barrel | 62.78 | 75.87 | | basket | 42.87 | 57.42 | | waterfall | 46.55 | 61.17 | | tent | 94.41 | 97.72 | | bag | 23.83 | 27.08 | | minibike | 73.69 | 85.35 | | cradle | 86.1 | 97.07 | | oven | 64.87 | 73.89 | | ball | 35.52 | 36.32 | | food | 49.89 | 53.18 | | step | 14.18 | 16.0 | | tank | 55.09 | 65.29 | | trade name | 22.67 | 25.88 | | microwave | 89.05 | 94.75 | | pot | 54.89 | 62.64 | | animal | 72.91 | 76.01 | | bicycle | 58.67 | 73.58 | | lake | 57.0 | 63.52 | | dishwasher | 70.56 | 77.12 | | screen | 67.64 | 92.2 | | blanket | 32.26 | 39.17 | | sculpture | 69.31 | 88.0 | | hood | 75.79 | 80.19 | | sconce | 53.05 | 68.49 | | vase | 44.29 | 57.75 | | traffic light | 33.1 | 57.73 | | tray | 18.52 | 24.31 | | ashcan | 50.11 | 61.71 | | fan | 64.25 | 78.21 | | pier | 36.65 | 39.37 | | crt screen | 13.16 | 21.05 | | plate | 57.03 | 73.66 | | monitor | 51.9 | 62.84 | | bulletin board | 47.04 | 58.01 | | shower | 2.86 | 9.82 | | radiator | 66.86 | 79.71 | | glass | 17.95 | 19.58 | | clock | 33.38 | 38.64 | | flag | 65.99 | 71.21 | +---------------------+-------+-------+ 2023-11-03 03:42:40,044 - mmseg - INFO - Summary: 2023-11-03 03:42:40,044 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.22 | 55.39 | 66.91 | +-------+-------+-------+ 2023-11-03 03:42:40,044 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 03:42:40,045 - mmseg - INFO - Iter(val) [250] aAcc: 0.8522, mIoU: 0.5539, mAcc: 0.6691, IoU.wall: 0.8039, IoU.building: 0.8342, IoU.sky: 0.9425, IoU.floor: 0.8351, IoU.tree: 0.7608, IoU.ceiling: 0.8533, IoU.road: 0.8617, IoU.bed : 0.9135, IoU.windowpane: 0.6490, IoU.grass: 0.7013, IoU.cabinet: 0.6433, IoU.sidewalk: 0.6995, IoU.person: 0.8259, IoU.earth: 0.3460, IoU.door: 0.5833, IoU.table: 0.6904, IoU.mountain: 0.6058, IoU.plant: 0.5700, IoU.curtain: 0.7521, IoU.chair: 0.6098, IoU.car: 0.8626, IoU.water: 0.6562, IoU.painting: 0.7854, IoU.sofa: 0.8056, IoU.shelf: 0.4742, IoU.house: 0.4754, IoU.sea: 0.7078, IoU.mirror: 0.7481, IoU.rug: 0.6188, IoU.field: 0.3621, IoU.armchair: 0.5843, IoU.seat: 0.6410, IoU.fence: 0.4343, IoU.desk: 0.5176, IoU.rock: 0.5797, IoU.wardrobe: 0.5096, IoU.lamp: 0.6563, IoU.bathtub: 0.8717, IoU.railing: 0.4288, IoU.cushion: 0.6329, IoU.base: 0.3407, IoU.box: 0.3690, IoU.column: 0.5395, IoU.signboard: 0.3517, IoU.chest of drawers: 0.4401, IoU.counter: 0.4538, IoU.sand: 0.6101, IoU.sink: 0.7777, IoU.skyscraper: 0.4957, IoU.fireplace: 0.7624, IoU.refrigerator: 0.8290, IoU.grandstand: 0.4992, IoU.path: 0.2253, IoU.stairs: 0.2973, IoU.runway: 0.6936, IoU.case: 0.5884, IoU.pool table: 0.9362, IoU.pillow: 0.6097, IoU.screen door: 0.7784, IoU.stairway: 0.3696, IoU.river: 0.1827, IoU.bridge: 0.7585, IoU.bookcase: 0.4496, IoU.blind: 0.3873, IoU.coffee table: 0.6842, IoU.toilet: 0.8913, IoU.flower: 0.4188, IoU.book: 0.4965, IoU.hill: 0.0879, IoU.bench: 0.5358, IoU.countertop: 0.6546, IoU.stove: 0.8402, IoU.palm: 0.4709, IoU.kitchen island: 0.4610, IoU.computer: 0.7623, IoU.swivel chair: 0.4643, IoU.boat: 0.7202, IoU.bar: 0.7242, IoU.arcade machine: 0.7021, IoU.hovel: 0.2528, IoU.bus: 0.9295, IoU.towel: 0.7354, IoU.light: 0.4864, IoU.truck: 0.5063, IoU.tower: 0.3219, IoU.chandelier: 0.6761, IoU.awning: 0.2946, IoU.streetlight: 0.2348, IoU.booth: 0.5440, IoU.television receiver: 0.7626, IoU.airplane: 0.7276, IoU.dirt track: 0.0705, IoU.apparel: 0.4452, IoU.pole: 0.2202, IoU.land: 0.1027, IoU.bannister: 0.1358, IoU.escalator: 0.6183, IoU.ottoman: 0.5354, IoU.bottle: 0.2742, IoU.buffet: 0.5261, IoU.poster: 0.3817, IoU.stage: 0.2474, IoU.van: 0.5284, IoU.ship: 0.5314, IoU.fountain: 0.3311, IoU.conveyer belt: 0.8622, IoU.canopy: 0.3735, IoU.washer: 0.8657, IoU.plaything: 0.3200, IoU.swimming pool: 0.4957, IoU.stool: 0.4803, IoU.barrel: 0.6278, IoU.basket: 0.4287, IoU.waterfall: 0.4655, IoU.tent: 0.9441, IoU.bag: 0.2383, IoU.minibike: 0.7369, IoU.cradle: 0.8610, IoU.oven: 0.6487, IoU.ball: 0.3552, IoU.food: 0.4989, IoU.step: 0.1418, IoU.tank: 0.5509, IoU.trade name: 0.2267, IoU.microwave: 0.8905, IoU.pot: 0.5489, IoU.animal: 0.7291, IoU.bicycle: 0.5867, IoU.lake: 0.5700, IoU.dishwasher: 0.7056, IoU.screen: 0.6764, IoU.blanket: 0.3226, IoU.sculpture: 0.6931, IoU.hood: 0.7579, IoU.sconce: 0.5305, IoU.vase: 0.4429, IoU.traffic light: 0.3310, IoU.tray: 0.1852, IoU.ashcan: 0.5011, IoU.fan: 0.6425, IoU.pier: 0.3665, IoU.crt screen: 0.1316, IoU.plate: 0.5703, IoU.monitor: 0.5190, IoU.bulletin board: 0.4704, IoU.shower: 0.0286, IoU.radiator: 0.6686, IoU.glass: 0.1795, IoU.clock: 0.3338, IoU.flag: 0.6599, Acc.wall: 0.8956, Acc.building: 0.9398, Acc.sky: 0.9709, Acc.floor: 0.9230, Acc.tree: 0.8741, Acc.ceiling: 0.9325, Acc.road: 0.9178, Acc.bed : 0.9633, Acc.windowpane: 0.7828, Acc.grass: 0.8555, Acc.cabinet: 0.7466, Acc.sidewalk: 0.8433, Acc.person: 0.9213, Acc.earth: 0.4552, Acc.door: 0.7642, Acc.table: 0.8094, Acc.mountain: 0.7384, Acc.plant: 0.6847, Acc.curtain: 0.8755, Acc.chair: 0.7250, Acc.car: 0.9266, Acc.water: 0.8116, Acc.painting: 0.8777, Acc.sofa: 0.9040, Acc.shelf: 0.6573, Acc.house: 0.6024, Acc.sea: 0.8095, Acc.mirror: 0.8341, Acc.rug: 0.6696, Acc.field: 0.5573, Acc.armchair: 0.7834, Acc.seat: 0.8797, Acc.fence: 0.5437, Acc.desk: 0.7570, Acc.rock: 0.8589, Acc.wardrobe: 0.6999, Acc.lamp: 0.7649, Acc.bathtub: 0.9008, Acc.railing: 0.5520, Acc.cushion: 0.7295, Acc.base: 0.5393, Acc.box: 0.4617, Acc.column: 0.6742, Acc.signboard: 0.4638, Acc.chest of drawers: 0.6510, Acc.counter: 0.5848, Acc.sand: 0.9023, Acc.sink: 0.8282, Acc.skyscraper: 0.6173, Acc.fireplace: 0.9073, Acc.refrigerator: 0.9166, Acc.grandstand: 0.8043, Acc.path: 0.2902, Acc.stairs: 0.3569, Acc.runway: 0.8985, Acc.case: 0.9196, Acc.pool table: 0.9760, Acc.pillow: 0.7137, Acc.screen door: 0.8059, Acc.stairway: 0.4766, Acc.river: 0.3433, Acc.bridge: 0.8452, Acc.bookcase: 0.5652, Acc.blind: 0.4508, Acc.coffee table: 0.8654, Acc.toilet: 0.9339, Acc.flower: 0.6642, Acc.book: 0.7258, Acc.hill: 0.1790, Acc.bench: 0.6204, Acc.countertop: 0.8068, Acc.stove: 0.8993, Acc.palm: 0.8343, Acc.kitchen island: 0.6550, Acc.computer: 0.8777, Acc.swivel chair: 0.6617, Acc.boat: 0.8770, Acc.bar: 0.8545, Acc.arcade machine: 0.7348, Acc.hovel: 0.2736, Acc.bus: 0.9649, Acc.towel: 0.8415, Acc.light: 0.5817, Acc.truck: 0.6431, Acc.tower: 0.4744, Acc.chandelier: 0.8368, Acc.awning: 0.3425, Acc.streetlight: 0.3108, Acc.booth: 0.5740, Acc.television receiver: 0.8747, Acc.airplane: 0.7974, Acc.dirt track: 0.2130, Acc.apparel: 0.5465, Acc.pole: 0.2731, Acc.land: 0.1528, Acc.bannister: 0.1840, Acc.escalator: 0.7899, Acc.ottoman: 0.7051, Acc.bottle: 0.3788, Acc.buffet: 0.7561, Acc.poster: 0.4368, Acc.stage: 0.4199, Acc.van: 0.7934, Acc.ship: 0.5521, Acc.fountain: 0.3433, Acc.conveyer belt: 0.9245, Acc.canopy: 0.4184, Acc.washer: 0.9234, Acc.plaything: 0.4037, Acc.swimming pool: 0.7180, Acc.stool: 0.5833, Acc.barrel: 0.7587, Acc.basket: 0.5742, Acc.waterfall: 0.6117, Acc.tent: 0.9772, Acc.bag: 0.2708, Acc.minibike: 0.8535, Acc.cradle: 0.9707, Acc.oven: 0.7389, Acc.ball: 0.3632, Acc.food: 0.5318, Acc.step: 0.1600, Acc.tank: 0.6529, Acc.trade name: 0.2588, Acc.microwave: 0.9475, Acc.pot: 0.6264, Acc.animal: 0.7601, Acc.bicycle: 0.7358, Acc.lake: 0.6352, Acc.dishwasher: 0.7712, Acc.screen: 0.9220, Acc.blanket: 0.3917, Acc.sculpture: 0.8800, Acc.hood: 0.8019, Acc.sconce: 0.6849, Acc.vase: 0.5775, Acc.traffic light: 0.5773, Acc.tray: 0.2431, Acc.ashcan: 0.6171, Acc.fan: 0.7821, Acc.pier: 0.3937, Acc.crt screen: 0.2105, Acc.plate: 0.7366, Acc.monitor: 0.6284, Acc.bulletin board: 0.5801, Acc.shower: 0.0982, Acc.radiator: 0.7971, Acc.glass: 0.1958, Acc.clock: 0.3864, Acc.flag: 0.7121 2023-11-03 03:43:43,027 - mmseg - INFO - Iter [24050/40000] lr: 1.292e-06, eta: 5:55:49, time: 2.447, data_time: 1.239, memory: 38534, decode.loss_ce: 0.1767, decode.acc_seg: 92.6728, loss: 0.1767 2023-11-03 03:44:43,644 - mmseg - INFO - Iter [24100/40000] lr: 1.288e-06, eta: 5:54:38, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1724, decode.acc_seg: 92.6330, loss: 0.1724 2023-11-03 03:45:44,339 - mmseg - INFO - Iter [24150/40000] lr: 1.284e-06, eta: 5:53:27, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1723, decode.acc_seg: 92.7366, loss: 0.1723 2023-11-03 03:46:45,014 - mmseg - INFO - Iter [24200/40000] lr: 1.280e-06, eta: 5:52:16, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1645, decode.acc_seg: 93.0534, loss: 0.1645 2023-11-03 03:47:45,638 - mmseg - INFO - Iter [24250/40000] lr: 1.276e-06, eta: 5:51:05, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1686, decode.acc_seg: 92.9316, loss: 0.1686 2023-11-03 03:48:46,306 - mmseg - INFO - Iter [24300/40000] lr: 1.272e-06, eta: 5:49:54, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1752, decode.acc_seg: 92.5578, loss: 0.1752 2023-11-03 03:49:46,942 - mmseg - INFO - Iter [24350/40000] lr: 1.268e-06, eta: 5:48:43, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1766, decode.acc_seg: 92.5944, loss: 0.1766 2023-11-03 03:50:47,594 - mmseg - INFO - Iter [24400/40000] lr: 1.264e-06, eta: 5:47:32, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1581, decode.acc_seg: 93.3341, loss: 0.1581 2023-11-03 03:51:48,238 - mmseg - INFO - Iter [24450/40000] lr: 1.260e-06, eta: 5:46:22, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1773, decode.acc_seg: 92.7563, loss: 0.1773 2023-11-03 03:52:48,827 - mmseg - INFO - Iter [24500/40000] lr: 1.255e-06, eta: 5:45:11, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1829, decode.acc_seg: 92.4960, loss: 0.1829 2023-11-03 03:53:49,519 - mmseg - INFO - Iter [24550/40000] lr: 1.251e-06, eta: 5:44:00, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1932, decode.acc_seg: 91.8343, loss: 0.1932 2023-11-03 03:54:50,224 - mmseg - INFO - Iter [24600/40000] lr: 1.247e-06, eta: 5:42:50, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1695, decode.acc_seg: 92.8203, loss: 0.1695 2023-11-03 03:55:53,334 - mmseg - INFO - Iter [24650/40000] lr: 1.243e-06, eta: 5:41:41, time: 1.262, data_time: 0.051, memory: 38534, decode.loss_ce: 0.1746, decode.acc_seg: 92.7643, loss: 0.1746 2023-11-03 03:56:53,996 - mmseg - INFO - Iter [24700/40000] lr: 1.239e-06, eta: 5:40:30, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1746, decode.acc_seg: 92.6691, loss: 0.1746 2023-11-03 03:57:54,628 - mmseg - INFO - Iter [24750/40000] lr: 1.235e-06, eta: 5:39:19, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1697, decode.acc_seg: 92.9287, loss: 0.1697 2023-11-03 03:58:55,286 - mmseg - INFO - Iter [24800/40000] lr: 1.231e-06, eta: 5:38:09, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1695, decode.acc_seg: 92.9571, loss: 0.1695 2023-11-03 03:59:55,921 - mmseg - INFO - Iter [24850/40000] lr: 1.227e-06, eta: 5:36:58, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1764, decode.acc_seg: 92.7994, loss: 0.1764 2023-11-03 04:00:56,530 - mmseg - INFO - Iter [24900/40000] lr: 1.223e-06, eta: 5:35:48, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1729, decode.acc_seg: 92.7182, loss: 0.1729 2023-11-03 04:01:57,202 - mmseg - INFO - Iter [24950/40000] lr: 1.219e-06, eta: 5:34:38, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1666, decode.acc_seg: 93.0047, loss: 0.1666 2023-11-03 04:02:57,853 - mmseg - INFO - Saving checkpoint at 25000 iterations 2023-11-03 04:03:54,043 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 04:03:54,044 - mmseg - INFO - Iter [25000/40000] lr: 1.215e-06, eta: 5:34:01, time: 2.337, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1851, decode.acc_seg: 92.5959, loss: 0.1851 2023-11-03 04:04:52,614 - mmseg - INFO - per class results: 2023-11-03 04:04:52,620 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.66 | 89.4 | | building | 83.38 | 94.51 | | sky | 94.41 | 97.28 | | floor | 83.71 | 91.42 | | tree | 75.75 | 87.45 | | ceiling | 85.2 | 94.27 | | road | 86.47 | 91.78 | | bed | 91.4 | 96.56 | | windowpane | 64.91 | 77.11 | | grass | 66.79 | 81.0 | | cabinet | 64.67 | 73.75 | | sidewalk | 69.62 | 83.62 | | person | 82.89 | 92.55 | | earth | 35.8 | 50.43 | | door | 57.77 | 73.99 | | table | 69.29 | 83.67 | | mountain | 60.74 | 74.41 | | plant | 57.75 | 68.18 | | curtain | 75.43 | 86.95 | | chair | 60.96 | 74.35 | | car | 85.46 | 92.77 | | water | 66.26 | 80.13 | | painting | 77.79 | 85.67 | | sofa | 80.13 | 90.05 | | shelf | 48.48 | 65.69 | | house | 40.43 | 45.78 | | sea | 73.03 | 85.6 | | mirror | 74.95 | 84.7 | | rug | 64.36 | 72.17 | | field | 37.26 | 54.22 | | armchair | 57.44 | 75.98 | | seat | 64.92 | 88.3 | | fence | 47.8 | 62.8 | | desk | 56.93 | 74.91 | | rock | 57.13 | 84.28 | | wardrobe | 49.67 | 71.05 | | lamp | 67.87 | 78.66 | | bathtub | 88.92 | 93.23 | | railing | 45.32 | 60.29 | | cushion | 63.29 | 73.45 | | base | 34.65 | 56.06 | | box | 38.02 | 50.32 | | column | 55.01 | 69.55 | | signboard | 36.69 | 51.12 | | chest of drawers | 46.02 | 66.11 | | counter | 46.51 | 57.91 | | sand | 62.29 | 88.62 | | sink | 76.4 | 83.25 | | skyscraper | 49.94 | 59.76 | | fireplace | 75.82 | 91.13 | | refrigerator | 83.58 | 92.08 | | grandstand | 47.67 | 83.44 | | path | 21.17 | 26.07 | | stairs | 28.71 | 37.59 | | runway | 69.28 | 90.04 | | case | 58.66 | 88.29 | | pool table | 93.38 | 97.81 | | pillow | 59.66 | 68.98 | | screen door | 82.01 | 84.44 | | stairway | 34.83 | 45.25 | | river | 24.19 | 45.46 | | bridge | 72.86 | 81.12 | | bookcase | 44.42 | 53.42 | | blind | 42.26 | 51.41 | | coffee table | 68.02 | 83.46 | | toilet | 89.07 | 93.15 | | flower | 37.2 | 61.32 | | book | 49.44 | 71.5 | | hill | 3.56 | 5.89 | | bench | 51.91 | 61.15 | | countertop | 64.36 | 82.31 | | stove | 83.03 | 89.69 | | palm | 47.15 | 83.17 | | kitchen island | 43.87 | 75.33 | | computer | 76.73 | 88.36 | | swivel chair | 39.53 | 52.37 | | boat | 71.89 | 86.05 | | bar | 76.42 | 85.01 | | arcade machine | 70.76 | 74.11 | | hovel | 33.73 | 37.26 | | bus | 92.1 | 96.88 | | towel | 75.32 | 85.05 | | light | 47.7 | 55.31 | | truck | 48.41 | 60.36 | | tower | 28.32 | 42.92 | | chandelier | 66.12 | 79.3 | | awning | 33.47 | 40.27 | | streetlight | 24.33 | 34.12 | | booth | 60.69 | 61.64 | | television receiver | 75.19 | 88.71 | | airplane | 76.87 | 85.31 | | dirt track | 5.74 | 20.5 | | apparel | 42.92 | 53.63 | | pole | 23.65 | 30.76 | | land | 9.85 | 14.35 | | bannister | 14.37 | 21.67 | | escalator | 61.58 | 81.23 | | ottoman | 55.62 | 67.97 | | bottle | 25.66 | 32.8 | | buffet | 58.48 | 76.78 | | poster | 33.98 | 47.35 | | stage | 21.09 | 35.67 | | van | 49.78 | 74.26 | | ship | 56.54 | 59.9 | | fountain | 31.89 | 33.16 | | conveyer belt | 85.97 | 94.8 | | canopy | 39.05 | 43.86 | | washer | 87.33 | 93.79 | | plaything | 30.76 | 46.65 | | swimming pool | 49.16 | 70.8 | | stool | 49.5 | 67.81 | | barrel | 64.14 | 78.23 | | basket | 35.62 | 46.91 | | waterfall | 47.47 | 61.27 | | tent | 96.05 | 96.8 | | bag | 23.43 | 27.58 | | minibike | 72.36 | 87.27 | | cradle | 84.48 | 98.16 | | oven | 68.13 | 78.8 | | ball | 46.62 | 48.58 | | food | 52.74 | 57.03 | | step | 15.61 | 18.08 | | tank | 54.32 | 65.58 | | trade name | 20.99 | 23.05 | | microwave | 88.95 | 94.5 | | pot | 56.22 | 64.21 | | animal | 71.87 | 74.67 | | bicycle | 58.73 | 78.05 | | lake | 55.92 | 63.64 | | dishwasher | 72.85 | 82.18 | | screen | 69.57 | 92.29 | | blanket | 29.85 | 34.43 | | sculpture | 73.26 | 86.92 | | hood | 74.22 | 77.39 | | sconce | 53.15 | 68.86 | | vase | 44.26 | 57.09 | | traffic light | 37.03 | 54.71 | | tray | 21.57 | 29.86 | | ashcan | 47.41 | 67.11 | | fan | 62.7 | 73.31 | | pier | 36.8 | 40.02 | | crt screen | 17.35 | 23.11 | | plate | 57.57 | 71.46 | | monitor | 61.22 | 76.18 | | bulletin board | 50.11 | 62.3 | | shower | 4.28 | 5.96 | | radiator | 67.57 | 79.36 | | glass | 18.66 | 20.31 | | clock | 39.02 | 46.85 | | flag | 66.01 | 69.88 | +---------------------+-------+-------+ 2023-11-03 04:04:52,620 - mmseg - INFO - Summary: 2023-11-03 04:04:52,620 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.27 | 55.77 | 67.41 | +-------+-------+-------+ 2023-11-03 04:04:52,621 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 04:04:52,621 - mmseg - INFO - Iter(val) [250] aAcc: 0.8527, mIoU: 0.5577, mAcc: 0.6741, IoU.wall: 0.8066, IoU.building: 0.8338, IoU.sky: 0.9441, IoU.floor: 0.8371, IoU.tree: 0.7575, IoU.ceiling: 0.8520, IoU.road: 0.8647, IoU.bed : 0.9140, IoU.windowpane: 0.6491, IoU.grass: 0.6679, IoU.cabinet: 0.6467, IoU.sidewalk: 0.6962, IoU.person: 0.8289, IoU.earth: 0.3580, IoU.door: 0.5777, IoU.table: 0.6929, IoU.mountain: 0.6074, IoU.plant: 0.5775, IoU.curtain: 0.7543, IoU.chair: 0.6096, IoU.car: 0.8546, IoU.water: 0.6626, IoU.painting: 0.7779, IoU.sofa: 0.8013, IoU.shelf: 0.4848, IoU.house: 0.4043, IoU.sea: 0.7303, IoU.mirror: 0.7495, IoU.rug: 0.6436, IoU.field: 0.3726, IoU.armchair: 0.5744, IoU.seat: 0.6492, IoU.fence: 0.4780, IoU.desk: 0.5693, IoU.rock: 0.5713, IoU.wardrobe: 0.4967, IoU.lamp: 0.6787, IoU.bathtub: 0.8892, IoU.railing: 0.4532, IoU.cushion: 0.6329, IoU.base: 0.3465, IoU.box: 0.3802, IoU.column: 0.5501, IoU.signboard: 0.3669, IoU.chest of drawers: 0.4602, IoU.counter: 0.4651, IoU.sand: 0.6229, IoU.sink: 0.7640, IoU.skyscraper: 0.4994, IoU.fireplace: 0.7582, IoU.refrigerator: 0.8358, IoU.grandstand: 0.4767, IoU.path: 0.2117, IoU.stairs: 0.2871, IoU.runway: 0.6928, IoU.case: 0.5866, IoU.pool table: 0.9338, IoU.pillow: 0.5966, IoU.screen door: 0.8201, IoU.stairway: 0.3483, IoU.river: 0.2419, IoU.bridge: 0.7286, IoU.bookcase: 0.4442, IoU.blind: 0.4226, IoU.coffee table: 0.6802, IoU.toilet: 0.8907, IoU.flower: 0.3720, IoU.book: 0.4944, IoU.hill: 0.0356, IoU.bench: 0.5191, IoU.countertop: 0.6436, IoU.stove: 0.8303, IoU.palm: 0.4715, IoU.kitchen island: 0.4387, IoU.computer: 0.7673, IoU.swivel chair: 0.3953, IoU.boat: 0.7189, IoU.bar: 0.7642, IoU.arcade machine: 0.7076, IoU.hovel: 0.3373, IoU.bus: 0.9210, IoU.towel: 0.7532, IoU.light: 0.4770, IoU.truck: 0.4841, IoU.tower: 0.2832, IoU.chandelier: 0.6612, IoU.awning: 0.3347, IoU.streetlight: 0.2433, IoU.booth: 0.6069, IoU.television receiver: 0.7519, IoU.airplane: 0.7687, IoU.dirt track: 0.0574, IoU.apparel: 0.4292, IoU.pole: 0.2365, IoU.land: 0.0985, IoU.bannister: 0.1437, IoU.escalator: 0.6158, IoU.ottoman: 0.5562, IoU.bottle: 0.2566, IoU.buffet: 0.5848, IoU.poster: 0.3398, IoU.stage: 0.2109, IoU.van: 0.4978, IoU.ship: 0.5654, IoU.fountain: 0.3189, IoU.conveyer belt: 0.8597, IoU.canopy: 0.3905, IoU.washer: 0.8733, IoU.plaything: 0.3076, IoU.swimming pool: 0.4916, IoU.stool: 0.4950, IoU.barrel: 0.6414, IoU.basket: 0.3562, IoU.waterfall: 0.4747, IoU.tent: 0.9605, IoU.bag: 0.2343, IoU.minibike: 0.7236, IoU.cradle: 0.8448, IoU.oven: 0.6813, IoU.ball: 0.4662, IoU.food: 0.5274, IoU.step: 0.1561, IoU.tank: 0.5432, IoU.trade name: 0.2099, IoU.microwave: 0.8895, IoU.pot: 0.5622, IoU.animal: 0.7187, IoU.bicycle: 0.5873, IoU.lake: 0.5592, IoU.dishwasher: 0.7285, IoU.screen: 0.6957, IoU.blanket: 0.2985, IoU.sculpture: 0.7326, IoU.hood: 0.7422, IoU.sconce: 0.5315, IoU.vase: 0.4426, IoU.traffic light: 0.3703, IoU.tray: 0.2157, IoU.ashcan: 0.4741, IoU.fan: 0.6270, IoU.pier: 0.3680, IoU.crt screen: 0.1735, IoU.plate: 0.5757, IoU.monitor: 0.6122, IoU.bulletin board: 0.5011, IoU.shower: 0.0428, IoU.radiator: 0.6757, IoU.glass: 0.1866, IoU.clock: 0.3902, IoU.flag: 0.6601, Acc.wall: 0.8940, Acc.building: 0.9451, Acc.sky: 0.9728, Acc.floor: 0.9142, Acc.tree: 0.8745, Acc.ceiling: 0.9427, Acc.road: 0.9178, Acc.bed : 0.9656, Acc.windowpane: 0.7711, Acc.grass: 0.8100, Acc.cabinet: 0.7375, Acc.sidewalk: 0.8362, Acc.person: 0.9255, Acc.earth: 0.5043, Acc.door: 0.7399, Acc.table: 0.8367, Acc.mountain: 0.7441, Acc.plant: 0.6818, Acc.curtain: 0.8695, Acc.chair: 0.7435, Acc.car: 0.9277, Acc.water: 0.8013, Acc.painting: 0.8567, Acc.sofa: 0.9005, Acc.shelf: 0.6569, Acc.house: 0.4578, Acc.sea: 0.8560, Acc.mirror: 0.8470, Acc.rug: 0.7217, Acc.field: 0.5422, Acc.armchair: 0.7598, Acc.seat: 0.8830, Acc.fence: 0.6280, Acc.desk: 0.7491, Acc.rock: 0.8428, Acc.wardrobe: 0.7105, Acc.lamp: 0.7866, Acc.bathtub: 0.9323, Acc.railing: 0.6029, Acc.cushion: 0.7345, Acc.base: 0.5606, Acc.box: 0.5032, Acc.column: 0.6955, Acc.signboard: 0.5112, Acc.chest of drawers: 0.6611, Acc.counter: 0.5791, Acc.sand: 0.8862, Acc.sink: 0.8325, Acc.skyscraper: 0.5976, Acc.fireplace: 0.9113, Acc.refrigerator: 0.9208, Acc.grandstand: 0.8344, Acc.path: 0.2607, Acc.stairs: 0.3759, Acc.runway: 0.9004, Acc.case: 0.8829, Acc.pool table: 0.9781, Acc.pillow: 0.6898, Acc.screen door: 0.8444, Acc.stairway: 0.4525, Acc.river: 0.4546, Acc.bridge: 0.8112, Acc.bookcase: 0.5342, Acc.blind: 0.5141, Acc.coffee table: 0.8346, Acc.toilet: 0.9315, Acc.flower: 0.6132, Acc.book: 0.7150, Acc.hill: 0.0589, Acc.bench: 0.6115, Acc.countertop: 0.8231, Acc.stove: 0.8969, Acc.palm: 0.8317, Acc.kitchen island: 0.7533, Acc.computer: 0.8836, Acc.swivel chair: 0.5237, Acc.boat: 0.8605, Acc.bar: 0.8501, Acc.arcade machine: 0.7411, Acc.hovel: 0.3726, Acc.bus: 0.9688, Acc.towel: 0.8505, Acc.light: 0.5531, Acc.truck: 0.6036, Acc.tower: 0.4292, Acc.chandelier: 0.7930, Acc.awning: 0.4027, Acc.streetlight: 0.3412, Acc.booth: 0.6164, Acc.television receiver: 0.8871, Acc.airplane: 0.8531, Acc.dirt track: 0.2050, Acc.apparel: 0.5363, Acc.pole: 0.3076, Acc.land: 0.1435, Acc.bannister: 0.2167, Acc.escalator: 0.8123, Acc.ottoman: 0.6797, Acc.bottle: 0.3280, Acc.buffet: 0.7678, Acc.poster: 0.4735, Acc.stage: 0.3567, Acc.van: 0.7426, Acc.ship: 0.5990, Acc.fountain: 0.3316, Acc.conveyer belt: 0.9480, Acc.canopy: 0.4386, Acc.washer: 0.9379, Acc.plaything: 0.4665, Acc.swimming pool: 0.7080, Acc.stool: 0.6781, Acc.barrel: 0.7823, Acc.basket: 0.4691, Acc.waterfall: 0.6127, Acc.tent: 0.9680, Acc.bag: 0.2758, Acc.minibike: 0.8727, Acc.cradle: 0.9816, Acc.oven: 0.7880, Acc.ball: 0.4858, Acc.food: 0.5703, Acc.step: 0.1808, Acc.tank: 0.6558, Acc.trade name: 0.2305, Acc.microwave: 0.9450, Acc.pot: 0.6421, Acc.animal: 0.7467, Acc.bicycle: 0.7805, Acc.lake: 0.6364, Acc.dishwasher: 0.8218, Acc.screen: 0.9229, Acc.blanket: 0.3443, Acc.sculpture: 0.8692, Acc.hood: 0.7739, Acc.sconce: 0.6886, Acc.vase: 0.5709, Acc.traffic light: 0.5471, Acc.tray: 0.2986, Acc.ashcan: 0.6711, Acc.fan: 0.7331, Acc.pier: 0.4002, Acc.crt screen: 0.2311, Acc.plate: 0.7146, Acc.monitor: 0.7618, Acc.bulletin board: 0.6230, Acc.shower: 0.0596, Acc.radiator: 0.7936, Acc.glass: 0.2031, Acc.clock: 0.4685, Acc.flag: 0.6988 2023-11-03 04:05:53,358 - mmseg - INFO - Iter [25050/40000] lr: 1.211e-06, eta: 5:33:26, time: 2.386, data_time: 1.179, memory: 38534, decode.loss_ce: 0.1877, decode.acc_seg: 92.4671, loss: 0.1877 2023-11-03 04:06:53,982 - mmseg - INFO - Iter [25100/40000] lr: 1.207e-06, eta: 5:32:15, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1713, decode.acc_seg: 92.7361, loss: 0.1713 2023-11-03 04:07:54,655 - mmseg - INFO - Iter [25150/40000] lr: 1.203e-06, eta: 5:31:04, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1689, decode.acc_seg: 93.0307, loss: 0.1689 2023-11-03 04:08:55,280 - mmseg - INFO - Iter [25200/40000] lr: 1.199e-06, eta: 5:29:54, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1690, decode.acc_seg: 92.9903, loss: 0.1690 2023-11-03 04:09:55,891 - mmseg - INFO - Iter [25250/40000] lr: 1.195e-06, eta: 5:28:43, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1771, decode.acc_seg: 92.5925, loss: 0.1771 2023-11-03 04:11:01,358 - mmseg - INFO - Iter [25300/40000] lr: 1.191e-06, eta: 5:27:36, time: 1.309, data_time: 0.103, memory: 38534, decode.loss_ce: 0.1682, decode.acc_seg: 93.0923, loss: 0.1682 2023-11-03 04:12:02,003 - mmseg - INFO - Iter [25350/40000] lr: 1.187e-06, eta: 5:26:25, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1750, decode.acc_seg: 92.5960, loss: 0.1750 2023-11-03 04:13:02,650 - mmseg - INFO - Iter [25400/40000] lr: 1.183e-06, eta: 5:25:15, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1727, decode.acc_seg: 92.6917, loss: 0.1727 2023-11-03 04:14:03,315 - mmseg - INFO - Iter [25450/40000] lr: 1.179e-06, eta: 5:24:04, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1650, decode.acc_seg: 93.0923, loss: 0.1650 2023-11-03 04:15:03,977 - mmseg - INFO - Iter [25500/40000] lr: 1.175e-06, eta: 5:22:54, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1705, decode.acc_seg: 92.7843, loss: 0.1705 2023-11-03 04:16:04,659 - mmseg - INFO - Iter [25550/40000] lr: 1.170e-06, eta: 5:21:44, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1663, decode.acc_seg: 93.0841, loss: 0.1663 2023-11-03 04:17:05,271 - mmseg - INFO - Iter [25600/40000] lr: 1.166e-06, eta: 5:20:34, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1680, decode.acc_seg: 92.8265, loss: 0.1680 2023-11-03 04:18:05,868 - mmseg - INFO - Iter [25650/40000] lr: 1.162e-06, eta: 5:19:23, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1640, decode.acc_seg: 93.2034, loss: 0.1640 2023-11-03 04:19:06,477 - mmseg - INFO - Iter [25700/40000] lr: 1.158e-06, eta: 5:18:13, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1666, decode.acc_seg: 92.8827, loss: 0.1666 2023-11-03 04:20:07,083 - mmseg - INFO - Iter [25750/40000] lr: 1.154e-06, eta: 5:17:03, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1704, decode.acc_seg: 92.9046, loss: 0.1704 2023-11-03 04:21:07,715 - mmseg - INFO - Iter [25800/40000] lr: 1.150e-06, eta: 5:15:53, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.1750, decode.acc_seg: 92.4989, loss: 0.1750 2023-11-03 04:22:08,319 - mmseg - INFO - Iter [25850/40000] lr: 1.146e-06, eta: 5:14:43, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1682, decode.acc_seg: 93.0328, loss: 0.1682 2023-11-03 04:23:08,928 - mmseg - INFO - Iter [25900/40000] lr: 1.142e-06, eta: 5:13:33, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1760, decode.acc_seg: 92.8103, loss: 0.1760 2023-11-03 04:24:12,450 - mmseg - INFO - Iter [25950/40000] lr: 1.138e-06, eta: 5:12:24, time: 1.270, data_time: 0.061, memory: 38534, decode.loss_ce: 0.1762, decode.acc_seg: 92.8095, loss: 0.1762 2023-11-03 04:25:13,057 - mmseg - INFO - Saving checkpoint at 26000 iterations 2023-11-03 04:26:11,848 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 04:26:11,848 - mmseg - INFO - Iter [26000/40000] lr: 1.134e-06, eta: 5:11:46, time: 2.388, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1726, decode.acc_seg: 92.5923, loss: 0.1726 2023-11-03 04:27:09,374 - mmseg - INFO - per class results: 2023-11-03 04:27:09,380 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.09 | 87.65 | | building | 82.41 | 94.61 | | sky | 94.24 | 97.43 | | floor | 82.97 | 90.4 | | tree | 76.04 | 87.1 | | ceiling | 85.18 | 93.81 | | road | 85.91 | 91.8 | | bed | 90.65 | 96.79 | | windowpane | 65.48 | 80.31 | | grass | 67.56 | 78.31 | | cabinet | 65.03 | 76.87 | | sidewalk | 68.84 | 83.6 | | person | 82.47 | 92.85 | | earth | 37.99 | 52.5 | | door | 56.63 | 73.31 | | table | 69.27 | 83.91 | | mountain | 61.46 | 77.94 | | plant | 56.16 | 67.71 | | curtain | 77.47 | 88.49 | | chair | 61.84 | 77.96 | | car | 85.22 | 93.63 | | water | 63.12 | 77.1 | | painting | 78.53 | 87.84 | | sofa | 80.31 | 90.76 | | shelf | 48.18 | 68.22 | | house | 41.04 | 49.88 | | sea | 74.78 | 87.08 | | mirror | 73.25 | 81.45 | | rug | 65.6 | 77.15 | | field | 38.55 | 60.75 | | armchair | 58.18 | 73.7 | | seat | 62.82 | 88.9 | | fence | 46.93 | 62.63 | | desk | 55.9 | 72.87 | | rock | 56.42 | 75.97 | | wardrobe | 51.3 | 71.84 | | lamp | 67.5 | 81.48 | | bathtub | 88.75 | 93.07 | | railing | 45.56 | 59.83 | | cushion | 63.92 | 72.83 | | base | 31.83 | 56.5 | | box | 38.28 | 47.93 | | column | 52.06 | 66.96 | | signboard | 36.23 | 48.64 | | chest of drawers | 43.67 | 64.74 | | counter | 47.83 | 59.97 | | sand | 63.04 | 89.2 | | sink | 76.17 | 81.41 | | skyscraper | 44.84 | 50.02 | | fireplace | 76.42 | 91.62 | | refrigerator | 82.95 | 87.75 | | grandstand | 46.87 | 82.63 | | path | 22.57 | 29.99 | | stairs | 29.9 | 40.57 | | runway | 69.35 | 89.33 | | case | 59.63 | 80.79 | | pool table | 93.72 | 97.3 | | pillow | 59.82 | 68.48 | | screen door | 70.17 | 73.38 | | stairway | 32.3 | 43.89 | | river | 19.52 | 40.41 | | bridge | 78.22 | 89.64 | | bookcase | 44.71 | 53.57 | | blind | 41.96 | 48.81 | | coffee table | 68.3 | 82.28 | | toilet | 88.61 | 92.91 | | flower | 41.32 | 63.84 | | book | 49.82 | 74.87 | | hill | 9.42 | 17.85 | | bench | 53.92 | 63.52 | | countertop | 63.5 | 77.72 | | stove | 82.52 | 86.89 | | palm | 46.4 | 78.24 | | kitchen island | 46.25 | 66.75 | | computer | 76.49 | 88.26 | | swivel chair | 41.67 | 55.59 | | boat | 69.94 | 90.43 | | bar | 75.11 | 83.51 | | arcade machine | 69.77 | 73.24 | | hovel | 24.75 | 27.55 | | bus | 93.21 | 95.91 | | towel | 74.49 | 84.21 | | light | 47.2 | 54.46 | | truck | 52.07 | 63.29 | | tower | 23.3 | 34.14 | | chandelier | 66.76 | 77.87 | | awning | 34.63 | 40.83 | | streetlight | 23.26 | 30.64 | | booth | 62.91 | 66.16 | | television receiver | 76.18 | 88.79 | | airplane | 77.66 | 87.3 | | dirt track | 13.1 | 19.74 | | apparel | 48.6 | 60.31 | | pole | 23.55 | 29.1 | | land | 9.66 | 15.99 | | bannister | 14.95 | 21.98 | | escalator | 61.43 | 81.01 | | ottoman | 58.25 | 68.58 | | bottle | 26.25 | 33.23 | | buffet | 62.57 | 79.01 | | poster | 34.46 | 48.27 | | stage | 21.19 | 46.66 | | van | 47.28 | 64.64 | | ship | 31.03 | 32.14 | | fountain | 25.78 | 26.69 | | conveyer belt | 84.6 | 92.04 | | canopy | 50.1 | 61.16 | | washer | 85.27 | 93.35 | | plaything | 30.95 | 45.68 | | swimming pool | 46.36 | 66.23 | | stool | 52.28 | 64.32 | | barrel | 67.9 | 83.42 | | basket | 37.56 | 50.22 | | waterfall | 44.05 | 56.34 | | tent | 91.51 | 97.82 | | bag | 20.45 | 22.27 | | minibike | 73.11 | 84.16 | | cradle | 84.25 | 98.38 | | oven | 63.82 | 76.38 | | ball | 58.39 | 66.15 | | food | 48.44 | 50.97 | | step | 15.28 | 18.36 | | tank | 55.59 | 64.97 | | trade name | 29.15 | 34.39 | | microwave | 89.14 | 94.41 | | pot | 54.74 | 61.93 | | animal | 75.02 | 79.14 | | bicycle | 59.42 | 76.69 | | lake | 55.96 | 63.63 | | dishwasher | 72.07 | 77.35 | | screen | 65.39 | 88.96 | | blanket | 30.8 | 36.43 | | sculpture | 70.7 | 88.17 | | hood | 75.33 | 78.8 | | sconce | 53.47 | 69.23 | | vase | 44.27 | 60.44 | | traffic light | 36.06 | 52.26 | | tray | 21.98 | 35.44 | | ashcan | 48.71 | 63.02 | | fan | 62.96 | 75.02 | | pier | 36.92 | 39.83 | | crt screen | 15.82 | 22.6 | | plate | 56.18 | 68.73 | | monitor | 59.11 | 74.77 | | bulletin board | 49.14 | 64.78 | | shower | 4.6 | 6.07 | | radiator | 66.23 | 82.84 | | glass | 19.52 | 22.06 | | clock | 35.7 | 40.99 | | flag | 66.18 | 73.77 | +---------------------+-------+-------+ 2023-11-03 04:27:09,380 - mmseg - INFO - Summary: 2023-11-03 04:27:09,380 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.04 | 55.58 | 67.21 | +-------+-------+-------+ 2023-11-03 04:27:09,381 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 04:27:09,381 - mmseg - INFO - Iter(val) [250] aAcc: 0.8504, mIoU: 0.5558, mAcc: 0.6721, IoU.wall: 0.8009, IoU.building: 0.8241, IoU.sky: 0.9424, IoU.floor: 0.8297, IoU.tree: 0.7604, IoU.ceiling: 0.8518, IoU.road: 0.8591, IoU.bed : 0.9065, IoU.windowpane: 0.6548, IoU.grass: 0.6756, IoU.cabinet: 0.6503, IoU.sidewalk: 0.6884, IoU.person: 0.8247, IoU.earth: 0.3799, IoU.door: 0.5663, IoU.table: 0.6927, IoU.mountain: 0.6146, IoU.plant: 0.5616, IoU.curtain: 0.7747, IoU.chair: 0.6184, IoU.car: 0.8522, IoU.water: 0.6312, IoU.painting: 0.7853, IoU.sofa: 0.8031, IoU.shelf: 0.4818, IoU.house: 0.4104, IoU.sea: 0.7478, IoU.mirror: 0.7325, IoU.rug: 0.6560, IoU.field: 0.3855, IoU.armchair: 0.5818, IoU.seat: 0.6282, IoU.fence: 0.4693, IoU.desk: 0.5590, IoU.rock: 0.5642, IoU.wardrobe: 0.5130, IoU.lamp: 0.6750, IoU.bathtub: 0.8875, IoU.railing: 0.4556, IoU.cushion: 0.6392, IoU.base: 0.3183, IoU.box: 0.3828, IoU.column: 0.5206, IoU.signboard: 0.3623, IoU.chest of drawers: 0.4367, IoU.counter: 0.4783, IoU.sand: 0.6304, IoU.sink: 0.7617, IoU.skyscraper: 0.4484, IoU.fireplace: 0.7642, IoU.refrigerator: 0.8295, IoU.grandstand: 0.4687, IoU.path: 0.2257, IoU.stairs: 0.2990, IoU.runway: 0.6935, IoU.case: 0.5963, IoU.pool table: 0.9372, IoU.pillow: 0.5982, IoU.screen door: 0.7017, IoU.stairway: 0.3230, IoU.river: 0.1952, IoU.bridge: 0.7822, IoU.bookcase: 0.4471, IoU.blind: 0.4196, IoU.coffee table: 0.6830, IoU.toilet: 0.8861, IoU.flower: 0.4132, IoU.book: 0.4982, IoU.hill: 0.0942, IoU.bench: 0.5392, IoU.countertop: 0.6350, IoU.stove: 0.8252, IoU.palm: 0.4640, IoU.kitchen island: 0.4625, IoU.computer: 0.7649, IoU.swivel chair: 0.4167, IoU.boat: 0.6994, IoU.bar: 0.7511, IoU.arcade machine: 0.6977, IoU.hovel: 0.2475, IoU.bus: 0.9321, IoU.towel: 0.7449, IoU.light: 0.4720, IoU.truck: 0.5207, IoU.tower: 0.2330, IoU.chandelier: 0.6676, IoU.awning: 0.3463, IoU.streetlight: 0.2326, IoU.booth: 0.6291, IoU.television receiver: 0.7618, IoU.airplane: 0.7766, IoU.dirt track: 0.1310, IoU.apparel: 0.4860, IoU.pole: 0.2355, IoU.land: 0.0966, IoU.bannister: 0.1495, IoU.escalator: 0.6143, IoU.ottoman: 0.5825, IoU.bottle: 0.2625, IoU.buffet: 0.6257, IoU.poster: 0.3446, IoU.stage: 0.2119, IoU.van: 0.4728, IoU.ship: 0.3103, IoU.fountain: 0.2578, IoU.conveyer belt: 0.8460, IoU.canopy: 0.5010, IoU.washer: 0.8527, IoU.plaything: 0.3095, IoU.swimming pool: 0.4636, IoU.stool: 0.5228, IoU.barrel: 0.6790, IoU.basket: 0.3756, IoU.waterfall: 0.4405, IoU.tent: 0.9151, IoU.bag: 0.2045, IoU.minibike: 0.7311, IoU.cradle: 0.8425, IoU.oven: 0.6382, IoU.ball: 0.5839, IoU.food: 0.4844, IoU.step: 0.1528, IoU.tank: 0.5559, IoU.trade name: 0.2915, IoU.microwave: 0.8914, IoU.pot: 0.5474, IoU.animal: 0.7502, IoU.bicycle: 0.5942, IoU.lake: 0.5596, IoU.dishwasher: 0.7207, IoU.screen: 0.6539, IoU.blanket: 0.3080, IoU.sculpture: 0.7070, IoU.hood: 0.7533, IoU.sconce: 0.5347, IoU.vase: 0.4427, IoU.traffic light: 0.3606, IoU.tray: 0.2198, IoU.ashcan: 0.4871, IoU.fan: 0.6296, IoU.pier: 0.3692, IoU.crt screen: 0.1582, IoU.plate: 0.5618, IoU.monitor: 0.5911, IoU.bulletin board: 0.4914, IoU.shower: 0.0460, IoU.radiator: 0.6623, IoU.glass: 0.1952, IoU.clock: 0.3570, IoU.flag: 0.6618, Acc.wall: 0.8765, Acc.building: 0.9461, Acc.sky: 0.9743, Acc.floor: 0.9040, Acc.tree: 0.8710, Acc.ceiling: 0.9381, Acc.road: 0.9180, Acc.bed : 0.9679, Acc.windowpane: 0.8031, Acc.grass: 0.7831, Acc.cabinet: 0.7687, Acc.sidewalk: 0.8360, Acc.person: 0.9285, Acc.earth: 0.5250, Acc.door: 0.7331, Acc.table: 0.8391, Acc.mountain: 0.7794, Acc.plant: 0.6771, Acc.curtain: 0.8849, Acc.chair: 0.7796, Acc.car: 0.9363, Acc.water: 0.7710, Acc.painting: 0.8784, Acc.sofa: 0.9076, Acc.shelf: 0.6822, Acc.house: 0.4988, Acc.sea: 0.8708, Acc.mirror: 0.8145, Acc.rug: 0.7715, Acc.field: 0.6075, Acc.armchair: 0.7370, Acc.seat: 0.8890, Acc.fence: 0.6263, Acc.desk: 0.7287, Acc.rock: 0.7597, Acc.wardrobe: 0.7184, Acc.lamp: 0.8148, Acc.bathtub: 0.9307, Acc.railing: 0.5983, Acc.cushion: 0.7283, Acc.base: 0.5650, Acc.box: 0.4793, Acc.column: 0.6696, Acc.signboard: 0.4864, Acc.chest of drawers: 0.6474, Acc.counter: 0.5997, Acc.sand: 0.8920, Acc.sink: 0.8141, Acc.skyscraper: 0.5002, Acc.fireplace: 0.9162, Acc.refrigerator: 0.8775, Acc.grandstand: 0.8263, Acc.path: 0.2999, Acc.stairs: 0.4057, Acc.runway: 0.8933, Acc.case: 0.8079, Acc.pool table: 0.9730, Acc.pillow: 0.6848, Acc.screen door: 0.7338, Acc.stairway: 0.4389, Acc.river: 0.4041, Acc.bridge: 0.8964, Acc.bookcase: 0.5357, Acc.blind: 0.4881, Acc.coffee table: 0.8228, Acc.toilet: 0.9291, Acc.flower: 0.6384, Acc.book: 0.7487, Acc.hill: 0.1785, Acc.bench: 0.6352, Acc.countertop: 0.7772, Acc.stove: 0.8689, Acc.palm: 0.7824, Acc.kitchen island: 0.6675, Acc.computer: 0.8826, Acc.swivel chair: 0.5559, Acc.boat: 0.9043, Acc.bar: 0.8351, Acc.arcade machine: 0.7324, Acc.hovel: 0.2755, Acc.bus: 0.9591, Acc.towel: 0.8421, Acc.light: 0.5446, Acc.truck: 0.6329, Acc.tower: 0.3414, Acc.chandelier: 0.7787, Acc.awning: 0.4083, Acc.streetlight: 0.3064, Acc.booth: 0.6616, Acc.television receiver: 0.8879, Acc.airplane: 0.8730, Acc.dirt track: 0.1974, Acc.apparel: 0.6031, Acc.pole: 0.2910, Acc.land: 0.1599, Acc.bannister: 0.2198, Acc.escalator: 0.8101, Acc.ottoman: 0.6858, Acc.bottle: 0.3323, Acc.buffet: 0.7901, Acc.poster: 0.4827, Acc.stage: 0.4666, Acc.van: 0.6464, Acc.ship: 0.3214, Acc.fountain: 0.2669, Acc.conveyer belt: 0.9204, Acc.canopy: 0.6116, Acc.washer: 0.9335, Acc.plaything: 0.4568, Acc.swimming pool: 0.6623, Acc.stool: 0.6432, Acc.barrel: 0.8342, Acc.basket: 0.5022, Acc.waterfall: 0.5634, Acc.tent: 0.9782, Acc.bag: 0.2227, Acc.minibike: 0.8416, Acc.cradle: 0.9838, Acc.oven: 0.7638, Acc.ball: 0.6615, Acc.food: 0.5097, Acc.step: 0.1836, Acc.tank: 0.6497, Acc.trade name: 0.3439, Acc.microwave: 0.9441, Acc.pot: 0.6193, Acc.animal: 0.7914, Acc.bicycle: 0.7669, Acc.lake: 0.6363, Acc.dishwasher: 0.7735, Acc.screen: 0.8896, Acc.blanket: 0.3643, Acc.sculpture: 0.8817, Acc.hood: 0.7880, Acc.sconce: 0.6923, Acc.vase: 0.6044, Acc.traffic light: 0.5226, Acc.tray: 0.3544, Acc.ashcan: 0.6302, Acc.fan: 0.7502, Acc.pier: 0.3983, Acc.crt screen: 0.2260, Acc.plate: 0.6873, Acc.monitor: 0.7477, Acc.bulletin board: 0.6478, Acc.shower: 0.0607, Acc.radiator: 0.8284, Acc.glass: 0.2206, Acc.clock: 0.4099, Acc.flag: 0.7377 2023-11-03 04:28:10,079 - mmseg - INFO - Iter [26050/40000] lr: 1.130e-06, eta: 5:11:07, time: 2.365, data_time: 1.158, memory: 38534, decode.loss_ce: 0.1670, decode.acc_seg: 92.8997, loss: 0.1670 2023-11-03 04:29:10,673 - mmseg - INFO - Iter [26100/40000] lr: 1.126e-06, eta: 5:09:56, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1635, decode.acc_seg: 93.0689, loss: 0.1635 2023-11-03 04:30:11,291 - mmseg - INFO - Iter [26150/40000] lr: 1.122e-06, eta: 5:08:46, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1691, decode.acc_seg: 92.9847, loss: 0.1691 2023-11-03 04:31:11,884 - mmseg - INFO - Iter [26200/40000] lr: 1.118e-06, eta: 5:07:36, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1740, decode.acc_seg: 92.7611, loss: 0.1740 2023-11-03 04:32:12,510 - mmseg - INFO - Iter [26250/40000] lr: 1.114e-06, eta: 5:06:26, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1701, decode.acc_seg: 92.8460, loss: 0.1701 2023-11-03 04:33:13,137 - mmseg - INFO - Iter [26300/40000] lr: 1.110e-06, eta: 5:05:16, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1597, decode.acc_seg: 93.1706, loss: 0.1597 2023-11-03 04:34:13,742 - mmseg - INFO - Iter [26350/40000] lr: 1.106e-06, eta: 5:04:06, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1712, decode.acc_seg: 92.8668, loss: 0.1712 2023-11-03 04:35:14,363 - mmseg - INFO - Iter [26400/40000] lr: 1.102e-06, eta: 5:02:56, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1777, decode.acc_seg: 92.6289, loss: 0.1777 2023-11-03 04:36:15,031 - mmseg - INFO - Iter [26450/40000] lr: 1.098e-06, eta: 5:01:46, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1767, decode.acc_seg: 92.8825, loss: 0.1767 2023-11-03 04:37:15,703 - mmseg - INFO - Iter [26500/40000] lr: 1.094e-06, eta: 5:00:36, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1732, decode.acc_seg: 92.7562, loss: 0.1732 2023-11-03 04:38:18,780 - mmseg - INFO - Iter [26550/40000] lr: 1.089e-06, eta: 4:59:27, time: 1.262, data_time: 0.052, memory: 38534, decode.loss_ce: 0.1653, decode.acc_seg: 92.8444, loss: 0.1653 2023-11-03 04:39:19,426 - mmseg - INFO - Iter [26600/40000] lr: 1.085e-06, eta: 4:58:17, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1595, decode.acc_seg: 93.1762, loss: 0.1595 2023-11-03 04:40:20,101 - mmseg - INFO - Iter [26650/40000] lr: 1.081e-06, eta: 4:57:07, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1737, decode.acc_seg: 92.6962, loss: 0.1737 2023-11-03 04:41:20,771 - mmseg - INFO - Iter [26700/40000] lr: 1.077e-06, eta: 4:55:57, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1615, decode.acc_seg: 93.0806, loss: 0.1615 2023-11-03 04:42:21,459 - mmseg - INFO - Iter [26750/40000] lr: 1.073e-06, eta: 4:54:48, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1734, decode.acc_seg: 92.9116, loss: 0.1734 2023-11-03 04:43:22,082 - mmseg - INFO - Iter [26800/40000] lr: 1.069e-06, eta: 4:53:38, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1706, decode.acc_seg: 92.9809, loss: 0.1706 2023-11-03 04:44:22,766 - mmseg - INFO - Iter [26850/40000] lr: 1.065e-06, eta: 4:52:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1640, decode.acc_seg: 92.9461, loss: 0.1640 2023-11-03 04:45:23,380 - mmseg - INFO - Iter [26900/40000] lr: 1.061e-06, eta: 4:51:18, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1717, decode.acc_seg: 92.8629, loss: 0.1717 2023-11-03 04:46:24,124 - mmseg - INFO - Iter [26950/40000] lr: 1.057e-06, eta: 4:50:09, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1718, decode.acc_seg: 92.8878, loss: 0.1718 2023-11-03 04:47:24,833 - mmseg - INFO - Saving checkpoint at 27000 iterations 2023-11-03 04:48:22,212 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 04:48:22,213 - mmseg - INFO - Iter [27000/40000] lr: 1.053e-06, eta: 4:49:27, time: 2.362, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1675, decode.acc_seg: 92.8292, loss: 0.1675 2023-11-03 04:49:20,062 - mmseg - INFO - per class results: 2023-11-03 04:49:20,068 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.51 | 90.0 | | building | 83.61 | 93.0 | | sky | 94.3 | 97.81 | | floor | 83.35 | 90.89 | | tree | 76.1 | 87.73 | | ceiling | 85.4 | 93.24 | | road | 85.44 | 92.21 | | bed | 91.47 | 96.56 | | windowpane | 65.12 | 77.24 | | grass | 68.07 | 86.32 | | cabinet | 65.31 | 74.67 | | sidewalk | 70.43 | 82.87 | | person | 82.59 | 93.4 | | earth | 35.7 | 46.6 | | door | 57.76 | 77.13 | | table | 68.9 | 81.39 | | mountain | 61.55 | 74.71 | | plant | 54.75 | 65.45 | | curtain | 75.8 | 90.22 | | chair | 60.9 | 73.21 | | car | 86.08 | 93.25 | | water | 65.9 | 78.02 | | painting | 77.78 | 87.83 | | sofa | 79.71 | 90.34 | | shelf | 48.18 | 65.3 | | house | 49.76 | 64.93 | | sea | 73.21 | 85.34 | | mirror | 74.5 | 83.32 | | rug | 65.13 | 76.59 | | field | 32.38 | 50.63 | | armchair | 56.59 | 72.47 | | seat | 63.56 | 88.98 | | fence | 44.98 | 54.6 | | desk | 56.66 | 75.42 | | rock | 56.64 | 84.9 | | wardrobe | 50.51 | 68.57 | | lamp | 67.47 | 78.47 | | bathtub | 88.95 | 92.24 | | railing | 44.73 | 58.7 | | cushion | 63.88 | 75.87 | | base | 32.76 | 51.57 | | box | 37.67 | 46.44 | | column | 53.08 | 65.72 | | signboard | 37.17 | 48.69 | | chest of drawers | 45.43 | 69.96 | | counter | 54.2 | 69.82 | | sand | 62.54 | 88.24 | | sink | 77.19 | 83.93 | | skyscraper | 48.53 | 61.89 | | fireplace | 73.82 | 86.6 | | refrigerator | 83.13 | 90.91 | | grandstand | 45.66 | 81.4 | | path | 28.59 | 37.84 | | stairs | 28.88 | 37.02 | | runway | 68.47 | 89.42 | | case | 60.67 | 82.91 | | pool table | 93.63 | 97.58 | | pillow | 61.91 | 72.16 | | screen door | 84.22 | 87.78 | | stairway | 36.8 | 48.56 | | river | 26.75 | 56.15 | | bridge | 78.22 | 84.82 | | bookcase | 44.45 | 53.35 | | blind | 41.48 | 46.59 | | coffee table | 66.29 | 87.2 | | toilet | 89.3 | 94.37 | | flower | 42.55 | 64.04 | | book | 48.57 | 73.1 | | hill | 3.89 | 6.95 | | bench | 52.93 | 60.72 | | countertop | 63.42 | 77.35 | | stove | 82.67 | 87.68 | | palm | 47.82 | 80.53 | | kitchen island | 43.75 | 74.68 | | computer | 76.94 | 88.53 | | swivel chair | 45.9 | 64.58 | | boat | 75.16 | 88.19 | | bar | 75.04 | 84.78 | | arcade machine | 71.26 | 74.3 | | hovel | 23.83 | 26.54 | | bus | 92.99 | 95.91 | | towel | 71.28 | 79.53 | | light | 49.51 | 60.37 | | truck | 51.82 | 65.61 | | tower | 7.87 | 10.02 | | chandelier | 66.88 | 81.94 | | awning | 35.2 | 41.84 | | streetlight | 25.15 | 35.1 | | booth | 56.48 | 59.21 | | television receiver | 75.13 | 87.45 | | airplane | 76.9 | 83.72 | | dirt track | 10.53 | 20.44 | | apparel | 44.78 | 60.75 | | pole | 24.48 | 31.58 | | land | 10.39 | 14.09 | | bannister | 14.25 | 19.04 | | escalator | 61.95 | 80.6 | | ottoman | 55.58 | 69.27 | | bottle | 23.88 | 30.8 | | buffet | 63.91 | 72.6 | | poster | 32.19 | 37.25 | | stage | 24.86 | 39.34 | | van | 50.06 | 67.24 | | ship | 33.73 | 35.28 | | fountain | 32.15 | 32.43 | | conveyer belt | 86.4 | 91.54 | | canopy | 35.01 | 38.77 | | washer | 84.85 | 90.28 | | plaything | 30.6 | 39.79 | | swimming pool | 47.61 | 68.15 | | stool | 47.82 | 61.84 | | barrel | 58.53 | 84.32 | | basket | 39.87 | 54.01 | | waterfall | 45.62 | 60.49 | | tent | 92.6 | 97.36 | | bag | 22.89 | 26.98 | | minibike | 73.71 | 85.79 | | cradle | 84.78 | 96.84 | | oven | 64.36 | 79.16 | | ball | 57.68 | 68.82 | | food | 55.97 | 60.4 | | step | 18.92 | 21.32 | | tank | 55.71 | 63.24 | | trade name | 31.57 | 39.44 | | microwave | 88.74 | 94.21 | | pot | 52.94 | 59.22 | | animal | 75.53 | 79.55 | | bicycle | 59.44 | 75.38 | | lake | 60.54 | 63.61 | | dishwasher | 72.11 | 79.91 | | screen | 66.09 | 88.2 | | blanket | 28.17 | 32.97 | | sculpture | 74.53 | 86.15 | | hood | 73.99 | 86.0 | | sconce | 53.21 | 65.44 | | vase | 44.02 | 60.05 | | traffic light | 38.41 | 55.58 | | tray | 20.16 | 25.68 | | ashcan | 49.25 | 64.72 | | fan | 60.03 | 67.97 | | pier | 39.44 | 42.8 | | crt screen | 14.14 | 21.18 | | plate | 54.8 | 72.57 | | monitor | 52.11 | 63.23 | | bulletin board | 48.49 | 58.07 | | shower | 3.49 | 3.58 | | radiator | 68.28 | 81.39 | | glass | 18.52 | 20.54 | | clock | 32.45 | 38.36 | | flag | 64.6 | 72.92 | +---------------------+-------+-------+ 2023-11-03 04:49:20,068 - mmseg - INFO - Summary: 2023-11-03 04:49:20,068 - mmseg - INFO - +------+-------+-------+ | aAcc | mIoU | mAcc | +------+-------+-------+ | 85.3 | 55.65 | 67.08 | +------+-------+-------+ 2023-11-03 04:49:20,069 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 04:49:20,069 - mmseg - INFO - Iter(val) [250] aAcc: 0.8530, mIoU: 0.5565, mAcc: 0.6708, IoU.wall: 0.8051, IoU.building: 0.8361, IoU.sky: 0.9430, IoU.floor: 0.8335, IoU.tree: 0.7610, IoU.ceiling: 0.8540, IoU.road: 0.8544, IoU.bed : 0.9147, IoU.windowpane: 0.6512, IoU.grass: 0.6807, IoU.cabinet: 0.6531, IoU.sidewalk: 0.7043, IoU.person: 0.8259, IoU.earth: 0.3570, IoU.door: 0.5776, IoU.table: 0.6890, IoU.mountain: 0.6155, IoU.plant: 0.5475, IoU.curtain: 0.7580, IoU.chair: 0.6090, IoU.car: 0.8608, IoU.water: 0.6590, IoU.painting: 0.7778, IoU.sofa: 0.7971, IoU.shelf: 0.4818, IoU.house: 0.4976, IoU.sea: 0.7321, IoU.mirror: 0.7450, IoU.rug: 0.6513, IoU.field: 0.3238, IoU.armchair: 0.5659, IoU.seat: 0.6356, IoU.fence: 0.4498, IoU.desk: 0.5666, IoU.rock: 0.5664, IoU.wardrobe: 0.5051, IoU.lamp: 0.6747, IoU.bathtub: 0.8895, IoU.railing: 0.4473, IoU.cushion: 0.6388, IoU.base: 0.3276, IoU.box: 0.3767, IoU.column: 0.5308, IoU.signboard: 0.3717, IoU.chest of drawers: 0.4543, IoU.counter: 0.5420, IoU.sand: 0.6254, IoU.sink: 0.7719, IoU.skyscraper: 0.4853, IoU.fireplace: 0.7382, IoU.refrigerator: 0.8313, IoU.grandstand: 0.4566, IoU.path: 0.2859, IoU.stairs: 0.2888, IoU.runway: 0.6847, IoU.case: 0.6067, IoU.pool table: 0.9363, IoU.pillow: 0.6191, IoU.screen door: 0.8422, IoU.stairway: 0.3680, IoU.river: 0.2675, IoU.bridge: 0.7822, IoU.bookcase: 0.4445, IoU.blind: 0.4148, IoU.coffee table: 0.6629, IoU.toilet: 0.8930, IoU.flower: 0.4255, IoU.book: 0.4857, IoU.hill: 0.0389, IoU.bench: 0.5293, IoU.countertop: 0.6342, IoU.stove: 0.8267, IoU.palm: 0.4782, IoU.kitchen island: 0.4375, IoU.computer: 0.7694, IoU.swivel chair: 0.4590, IoU.boat: 0.7516, IoU.bar: 0.7504, IoU.arcade machine: 0.7126, IoU.hovel: 0.2383, IoU.bus: 0.9299, IoU.towel: 0.7128, IoU.light: 0.4951, IoU.truck: 0.5182, IoU.tower: 0.0787, IoU.chandelier: 0.6688, IoU.awning: 0.3520, IoU.streetlight: 0.2515, IoU.booth: 0.5648, IoU.television receiver: 0.7513, IoU.airplane: 0.7690, IoU.dirt track: 0.1053, IoU.apparel: 0.4478, IoU.pole: 0.2448, IoU.land: 0.1039, IoU.bannister: 0.1425, IoU.escalator: 0.6195, IoU.ottoman: 0.5558, IoU.bottle: 0.2388, IoU.buffet: 0.6391, IoU.poster: 0.3219, IoU.stage: 0.2486, IoU.van: 0.5006, IoU.ship: 0.3373, IoU.fountain: 0.3215, IoU.conveyer belt: 0.8640, IoU.canopy: 0.3501, IoU.washer: 0.8485, IoU.plaything: 0.3060, IoU.swimming pool: 0.4761, IoU.stool: 0.4782, IoU.barrel: 0.5853, IoU.basket: 0.3987, IoU.waterfall: 0.4562, IoU.tent: 0.9260, IoU.bag: 0.2289, IoU.minibike: 0.7371, IoU.cradle: 0.8478, IoU.oven: 0.6436, IoU.ball: 0.5768, IoU.food: 0.5597, IoU.step: 0.1892, IoU.tank: 0.5571, IoU.trade name: 0.3157, IoU.microwave: 0.8874, IoU.pot: 0.5294, IoU.animal: 0.7553, IoU.bicycle: 0.5944, IoU.lake: 0.6054, IoU.dishwasher: 0.7211, IoU.screen: 0.6609, IoU.blanket: 0.2817, IoU.sculpture: 0.7453, IoU.hood: 0.7399, IoU.sconce: 0.5321, IoU.vase: 0.4402, IoU.traffic light: 0.3841, IoU.tray: 0.2016, IoU.ashcan: 0.4925, IoU.fan: 0.6003, IoU.pier: 0.3944, IoU.crt screen: 0.1414, IoU.plate: 0.5480, IoU.monitor: 0.5211, IoU.bulletin board: 0.4849, IoU.shower: 0.0349, IoU.radiator: 0.6828, IoU.glass: 0.1852, IoU.clock: 0.3245, IoU.flag: 0.6460, Acc.wall: 0.9000, Acc.building: 0.9300, Acc.sky: 0.9781, Acc.floor: 0.9089, Acc.tree: 0.8773, Acc.ceiling: 0.9324, Acc.road: 0.9221, Acc.bed : 0.9656, Acc.windowpane: 0.7724, Acc.grass: 0.8632, Acc.cabinet: 0.7467, Acc.sidewalk: 0.8287, Acc.person: 0.9340, Acc.earth: 0.4660, Acc.door: 0.7713, Acc.table: 0.8139, Acc.mountain: 0.7471, Acc.plant: 0.6545, Acc.curtain: 0.9022, Acc.chair: 0.7321, Acc.car: 0.9325, Acc.water: 0.7802, Acc.painting: 0.8783, Acc.sofa: 0.9034, Acc.shelf: 0.6530, Acc.house: 0.6493, Acc.sea: 0.8534, Acc.mirror: 0.8332, Acc.rug: 0.7659, Acc.field: 0.5063, Acc.armchair: 0.7247, Acc.seat: 0.8898, Acc.fence: 0.5460, Acc.desk: 0.7542, Acc.rock: 0.8490, Acc.wardrobe: 0.6857, Acc.lamp: 0.7847, Acc.bathtub: 0.9224, Acc.railing: 0.5870, Acc.cushion: 0.7587, Acc.base: 0.5157, Acc.box: 0.4644, Acc.column: 0.6572, Acc.signboard: 0.4869, Acc.chest of drawers: 0.6996, Acc.counter: 0.6982, Acc.sand: 0.8824, Acc.sink: 0.8393, Acc.skyscraper: 0.6189, Acc.fireplace: 0.8660, Acc.refrigerator: 0.9091, Acc.grandstand: 0.8140, Acc.path: 0.3784, Acc.stairs: 0.3702, Acc.runway: 0.8942, Acc.case: 0.8291, Acc.pool table: 0.9758, Acc.pillow: 0.7216, Acc.screen door: 0.8778, Acc.stairway: 0.4856, Acc.river: 0.5615, Acc.bridge: 0.8482, Acc.bookcase: 0.5335, Acc.blind: 0.4659, Acc.coffee table: 0.8720, Acc.toilet: 0.9437, Acc.flower: 0.6404, Acc.book: 0.7310, Acc.hill: 0.0695, Acc.bench: 0.6072, Acc.countertop: 0.7735, Acc.stove: 0.8768, Acc.palm: 0.8053, Acc.kitchen island: 0.7468, Acc.computer: 0.8853, Acc.swivel chair: 0.6458, Acc.boat: 0.8819, Acc.bar: 0.8478, Acc.arcade machine: 0.7430, Acc.hovel: 0.2654, Acc.bus: 0.9591, Acc.towel: 0.7953, Acc.light: 0.6037, Acc.truck: 0.6561, Acc.tower: 0.1002, Acc.chandelier: 0.8194, Acc.awning: 0.4184, Acc.streetlight: 0.3510, Acc.booth: 0.5921, Acc.television receiver: 0.8745, Acc.airplane: 0.8372, Acc.dirt track: 0.2044, Acc.apparel: 0.6075, Acc.pole: 0.3158, Acc.land: 0.1409, Acc.bannister: 0.1904, Acc.escalator: 0.8060, Acc.ottoman: 0.6927, Acc.bottle: 0.3080, Acc.buffet: 0.7260, Acc.poster: 0.3725, Acc.stage: 0.3934, Acc.van: 0.6724, Acc.ship: 0.3528, Acc.fountain: 0.3243, Acc.conveyer belt: 0.9154, Acc.canopy: 0.3877, Acc.washer: 0.9028, Acc.plaything: 0.3979, Acc.swimming pool: 0.6815, Acc.stool: 0.6184, Acc.barrel: 0.8432, Acc.basket: 0.5401, Acc.waterfall: 0.6049, Acc.tent: 0.9736, Acc.bag: 0.2698, Acc.minibike: 0.8579, Acc.cradle: 0.9684, Acc.oven: 0.7916, Acc.ball: 0.6882, Acc.food: 0.6040, Acc.step: 0.2132, Acc.tank: 0.6324, Acc.trade name: 0.3944, Acc.microwave: 0.9421, Acc.pot: 0.5922, Acc.animal: 0.7955, Acc.bicycle: 0.7538, Acc.lake: 0.6361, Acc.dishwasher: 0.7991, Acc.screen: 0.8820, Acc.blanket: 0.3297, Acc.sculpture: 0.8615, Acc.hood: 0.8600, Acc.sconce: 0.6544, Acc.vase: 0.6005, Acc.traffic light: 0.5558, Acc.tray: 0.2568, Acc.ashcan: 0.6472, Acc.fan: 0.6797, Acc.pier: 0.4280, Acc.crt screen: 0.2118, Acc.plate: 0.7257, Acc.monitor: 0.6323, Acc.bulletin board: 0.5807, Acc.shower: 0.0358, Acc.radiator: 0.8139, Acc.glass: 0.2054, Acc.clock: 0.3836, Acc.flag: 0.7292 2023-11-03 04:50:20,817 - mmseg - INFO - Iter [27050/40000] lr: 1.049e-06, eta: 4:48:45, time: 2.372, data_time: 1.165, memory: 38534, decode.loss_ce: 0.1693, decode.acc_seg: 93.1406, loss: 0.1693 2023-11-03 04:51:21,463 - mmseg - INFO - Iter [27100/40000] lr: 1.045e-06, eta: 4:47:35, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1639, decode.acc_seg: 93.1731, loss: 0.1639 2023-11-03 04:52:22,085 - mmseg - INFO - Iter [27150/40000] lr: 1.041e-06, eta: 4:46:25, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1712, decode.acc_seg: 92.9062, loss: 0.1712 2023-11-03 04:53:25,136 - mmseg - INFO - Iter [27200/40000] lr: 1.037e-06, eta: 4:45:17, time: 1.261, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1625, decode.acc_seg: 93.1738, loss: 0.1625 2023-11-03 04:54:25,793 - mmseg - INFO - Iter [27250/40000] lr: 1.033e-06, eta: 4:44:07, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1740, decode.acc_seg: 92.6441, loss: 0.1740 2023-11-03 04:55:26,482 - mmseg - INFO - Iter [27300/40000] lr: 1.029e-06, eta: 4:42:57, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1722, decode.acc_seg: 92.8970, loss: 0.1722 2023-11-03 04:56:27,140 - mmseg - INFO - Iter [27350/40000] lr: 1.025e-06, eta: 4:41:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1640, decode.acc_seg: 93.2015, loss: 0.1640 2023-11-03 04:57:27,821 - mmseg - INFO - Iter [27400/40000] lr: 1.021e-06, eta: 4:40:38, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1545, decode.acc_seg: 93.3463, loss: 0.1545 2023-11-03 04:58:28,527 - mmseg - INFO - Iter [27450/40000] lr: 1.017e-06, eta: 4:39:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1702, decode.acc_seg: 92.9113, loss: 0.1702 2023-11-03 04:59:29,236 - mmseg - INFO - Iter [27500/40000] lr: 1.013e-06, eta: 4:38:18, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1600, decode.acc_seg: 93.2769, loss: 0.1600 2023-11-03 05:00:29,908 - mmseg - INFO - Iter [27550/40000] lr: 1.008e-06, eta: 4:37:09, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1526, decode.acc_seg: 93.3188, loss: 0.1526 2023-11-03 05:01:30,539 - mmseg - INFO - Iter [27600/40000] lr: 1.004e-06, eta: 4:35:59, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1590, decode.acc_seg: 93.2867, loss: 0.1590 2023-11-03 05:02:31,200 - mmseg - INFO - Iter [27650/40000] lr: 1.000e-06, eta: 4:34:50, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1607, decode.acc_seg: 93.1975, loss: 0.1607 2023-11-03 05:03:31,868 - mmseg - INFO - Iter [27700/40000] lr: 9.963e-07, eta: 4:33:40, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1668, decode.acc_seg: 93.1157, loss: 0.1668 2023-11-03 05:04:32,568 - mmseg - INFO - Iter [27750/40000] lr: 9.923e-07, eta: 4:32:31, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1621, decode.acc_seg: 93.1419, loss: 0.1621 2023-11-03 05:05:33,297 - mmseg - INFO - Iter [27800/40000] lr: 9.882e-07, eta: 4:31:22, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1663, decode.acc_seg: 93.1728, loss: 0.1663 2023-11-03 05:06:36,341 - mmseg - INFO - Iter [27850/40000] lr: 9.842e-07, eta: 4:30:13, time: 1.261, data_time: 0.054, memory: 38534, decode.loss_ce: 0.1558, decode.acc_seg: 93.5002, loss: 0.1558 2023-11-03 05:07:37,035 - mmseg - INFO - Iter [27900/40000] lr: 9.801e-07, eta: 4:29:04, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1574, decode.acc_seg: 93.1813, loss: 0.1574 2023-11-03 05:08:37,665 - mmseg - INFO - Iter [27950/40000] lr: 9.761e-07, eta: 4:27:55, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1642, decode.acc_seg: 93.3085, loss: 0.1642 2023-11-03 05:09:38,346 - mmseg - INFO - Saving checkpoint at 28000 iterations 2023-11-03 05:10:35,780 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 05:10:35,780 - mmseg - INFO - Iter [28000/40000] lr: 9.720e-07, eta: 4:27:10, time: 2.362, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1595, decode.acc_seg: 93.3008, loss: 0.1595 2023-11-03 05:11:33,724 - mmseg - INFO - per class results: 2023-11-03 05:11:33,729 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 81.01 | 89.49 | | building | 83.82 | 92.58 | | sky | 94.17 | 97.02 | | floor | 83.33 | 90.71 | | tree | 76.08 | 87.64 | | ceiling | 85.23 | 93.52 | | road | 85.0 | 92.5 | | bed | 91.22 | 97.23 | | windowpane | 65.01 | 78.17 | | grass | 70.16 | 87.34 | | cabinet | 65.97 | 76.54 | | sidewalk | 68.86 | 82.19 | | person | 82.78 | 92.35 | | earth | 37.92 | 50.39 | | door | 56.53 | 72.4 | | table | 69.27 | 81.25 | | mountain | 61.2 | 76.67 | | plant | 56.76 | 69.01 | | curtain | 77.15 | 89.71 | | chair | 61.05 | 74.19 | | car | 85.96 | 93.05 | | water | 64.33 | 79.86 | | painting | 78.44 | 88.35 | | sofa | 81.4 | 90.12 | | shelf | 49.54 | 67.91 | | house | 50.59 | 67.49 | | sea | 68.99 | 87.9 | | mirror | 74.55 | 85.69 | | rug | 65.94 | 80.32 | | field | 34.31 | 47.43 | | armchair | 58.31 | 79.82 | | seat | 64.22 | 88.08 | | fence | 50.04 | 64.15 | | desk | 56.32 | 74.17 | | rock | 56.35 | 80.87 | | wardrobe | 52.42 | 74.3 | | lamp | 69.08 | 80.94 | | bathtub | 87.35 | 90.76 | | railing | 45.16 | 60.71 | | cushion | 63.5 | 76.45 | | base | 34.03 | 56.49 | | box | 37.82 | 48.87 | | column | 54.13 | 67.42 | | signboard | 37.38 | 52.3 | | chest of drawers | 45.32 | 65.5 | | counter | 48.71 | 58.7 | | sand | 62.44 | 89.2 | | sink | 77.81 | 84.15 | | skyscraper | 47.14 | 62.14 | | fireplace | 75.01 | 92.23 | | refrigerator | 83.6 | 91.28 | | grandstand | 44.48 | 81.03 | | path | 23.77 | 32.16 | | stairs | 29.83 | 36.61 | | runway | 66.97 | 89.87 | | case | 62.58 | 87.67 | | pool table | 93.24 | 98.11 | | pillow | 55.67 | 62.3 | | screen door | 71.76 | 74.96 | | stairway | 36.35 | 47.56 | | river | 30.51 | 42.88 | | bridge | 77.11 | 91.27 | | bookcase | 43.49 | 50.74 | | blind | 42.66 | 48.24 | | coffee table | 66.85 | 86.52 | | toilet | 89.02 | 92.67 | | flower | 41.49 | 58.54 | | book | 50.33 | 75.43 | | hill | 5.35 | 8.52 | | bench | 53.61 | 62.55 | | countertop | 63.58 | 77.07 | | stove | 83.96 | 89.03 | | palm | 47.75 | 82.29 | | kitchen island | 44.33 | 68.51 | | computer | 77.61 | 87.91 | | swivel chair | 45.3 | 63.69 | | boat | 68.96 | 79.91 | | bar | 73.64 | 85.95 | | arcade machine | 80.36 | 84.22 | | hovel | 23.86 | 26.61 | | bus | 92.4 | 96.59 | | towel | 71.74 | 81.15 | | light | 46.02 | 52.17 | | truck | 50.11 | 60.48 | | tower | 15.43 | 20.16 | | chandelier | 68.47 | 83.65 | | awning | 37.42 | 45.34 | | streetlight | 28.06 | 42.16 | | booth | 58.27 | 62.05 | | television receiver | 76.93 | 91.02 | | airplane | 82.7 | 91.19 | | dirt track | 13.85 | 21.88 | | apparel | 38.11 | 48.24 | | pole | 21.16 | 26.24 | | land | 9.41 | 16.11 | | bannister | 14.29 | 19.41 | | escalator | 62.31 | 80.89 | | ottoman | 52.14 | 72.02 | | bottle | 30.14 | 42.09 | | buffet | 65.17 | 79.07 | | poster | 34.92 | 44.84 | | stage | 22.13 | 45.92 | | van | 46.4 | 66.75 | | ship | 29.64 | 33.24 | | fountain | 25.26 | 25.79 | | conveyer belt | 85.1 | 91.17 | | canopy | 42.74 | 57.14 | | washer | 86.13 | 92.38 | | plaything | 31.0 | 42.45 | | swimming pool | 50.03 | 72.68 | | stool | 48.59 | 60.56 | | barrel | 67.23 | 85.28 | | basket | 35.24 | 47.57 | | waterfall | 44.43 | 59.65 | | tent | 91.45 | 97.97 | | bag | 23.11 | 27.84 | | minibike | 74.18 | 86.87 | | cradle | 82.87 | 97.96 | | oven | 63.32 | 77.02 | | ball | 57.02 | 64.6 | | food | 49.48 | 51.48 | | step | 12.95 | 14.34 | | tank | 55.08 | 63.84 | | trade name | 26.53 | 31.64 | | microwave | 88.32 | 92.16 | | pot | 54.53 | 61.01 | | animal | 73.86 | 76.74 | | bicycle | 61.18 | 77.67 | | lake | 55.73 | 63.65 | | dishwasher | 70.01 | 75.78 | | screen | 61.02 | 80.21 | | blanket | 29.01 | 33.91 | | sculpture | 76.22 | 86.59 | | hood | 74.09 | 82.31 | | sconce | 54.65 | 69.19 | | vase | 44.26 | 58.99 | | traffic light | 37.28 | 61.27 | | tray | 20.74 | 27.59 | | ashcan | 49.63 | 68.75 | | fan | 62.49 | 74.17 | | pier | 36.11 | 40.64 | | crt screen | 15.46 | 26.4 | | plate | 55.92 | 77.62 | | monitor | 52.67 | 66.53 | | bulletin board | 49.2 | 61.57 | | shower | 4.11 | 4.9 | | radiator | 67.59 | 81.66 | | glass | 19.02 | 21.28 | | clock | 33.85 | 40.08 | | flag | 65.44 | 72.36 | +---------------------+-------+-------+ 2023-11-03 05:11:33,729 - mmseg - INFO - Summary: 2023-11-03 05:11:33,729 - mmseg - INFO - +-------+-------+------+ | aAcc | mIoU | mAcc | +-------+-------+------+ | 85.38 | 55.59 | 67.5 | +-------+-------+------+ 2023-11-03 05:11:33,730 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 05:11:33,730 - mmseg - INFO - Iter(val) [250] aAcc: 0.8538, mIoU: 0.5559, mAcc: 0.6750, IoU.wall: 0.8101, IoU.building: 0.8382, IoU.sky: 0.9417, IoU.floor: 0.8333, IoU.tree: 0.7608, IoU.ceiling: 0.8523, IoU.road: 0.8500, IoU.bed : 0.9122, IoU.windowpane: 0.6501, IoU.grass: 0.7016, IoU.cabinet: 0.6597, IoU.sidewalk: 0.6886, IoU.person: 0.8278, IoU.earth: 0.3792, IoU.door: 0.5653, IoU.table: 0.6927, IoU.mountain: 0.6120, IoU.plant: 0.5676, IoU.curtain: 0.7715, IoU.chair: 0.6105, IoU.car: 0.8596, IoU.water: 0.6433, IoU.painting: 0.7844, IoU.sofa: 0.8140, IoU.shelf: 0.4954, IoU.house: 0.5059, IoU.sea: 0.6899, IoU.mirror: 0.7455, IoU.rug: 0.6594, IoU.field: 0.3431, IoU.armchair: 0.5831, IoU.seat: 0.6422, IoU.fence: 0.5004, IoU.desk: 0.5632, IoU.rock: 0.5635, IoU.wardrobe: 0.5242, IoU.lamp: 0.6908, IoU.bathtub: 0.8735, IoU.railing: 0.4516, IoU.cushion: 0.6350, IoU.base: 0.3403, IoU.box: 0.3782, IoU.column: 0.5413, IoU.signboard: 0.3738, IoU.chest of drawers: 0.4532, IoU.counter: 0.4871, IoU.sand: 0.6244, IoU.sink: 0.7781, IoU.skyscraper: 0.4714, IoU.fireplace: 0.7501, IoU.refrigerator: 0.8360, IoU.grandstand: 0.4448, IoU.path: 0.2377, IoU.stairs: 0.2983, IoU.runway: 0.6697, IoU.case: 0.6258, IoU.pool table: 0.9324, IoU.pillow: 0.5567, IoU.screen door: 0.7176, IoU.stairway: 0.3635, IoU.river: 0.3051, IoU.bridge: 0.7711, IoU.bookcase: 0.4349, IoU.blind: 0.4266, IoU.coffee table: 0.6685, IoU.toilet: 0.8902, IoU.flower: 0.4149, IoU.book: 0.5033, IoU.hill: 0.0535, IoU.bench: 0.5361, IoU.countertop: 0.6358, IoU.stove: 0.8396, IoU.palm: 0.4775, IoU.kitchen island: 0.4433, IoU.computer: 0.7761, IoU.swivel chair: 0.4530, IoU.boat: 0.6896, IoU.bar: 0.7364, IoU.arcade machine: 0.8036, IoU.hovel: 0.2386, IoU.bus: 0.9240, IoU.towel: 0.7174, IoU.light: 0.4602, IoU.truck: 0.5011, IoU.tower: 0.1543, IoU.chandelier: 0.6847, IoU.awning: 0.3742, IoU.streetlight: 0.2806, IoU.booth: 0.5827, IoU.television receiver: 0.7693, IoU.airplane: 0.8270, IoU.dirt track: 0.1385, IoU.apparel: 0.3811, IoU.pole: 0.2116, IoU.land: 0.0941, IoU.bannister: 0.1429, IoU.escalator: 0.6231, IoU.ottoman: 0.5214, IoU.bottle: 0.3014, IoU.buffet: 0.6517, IoU.poster: 0.3492, IoU.stage: 0.2213, IoU.van: 0.4640, IoU.ship: 0.2964, IoU.fountain: 0.2526, IoU.conveyer belt: 0.8510, IoU.canopy: 0.4274, IoU.washer: 0.8613, IoU.plaything: 0.3100, IoU.swimming pool: 0.5003, IoU.stool: 0.4859, IoU.barrel: 0.6723, IoU.basket: 0.3524, IoU.waterfall: 0.4443, IoU.tent: 0.9145, IoU.bag: 0.2311, IoU.minibike: 0.7418, IoU.cradle: 0.8287, IoU.oven: 0.6332, IoU.ball: 0.5702, IoU.food: 0.4948, IoU.step: 0.1295, IoU.tank: 0.5508, IoU.trade name: 0.2653, IoU.microwave: 0.8832, IoU.pot: 0.5453, IoU.animal: 0.7386, IoU.bicycle: 0.6118, IoU.lake: 0.5573, IoU.dishwasher: 0.7001, IoU.screen: 0.6102, IoU.blanket: 0.2901, IoU.sculpture: 0.7622, IoU.hood: 0.7409, IoU.sconce: 0.5465, IoU.vase: 0.4426, IoU.traffic light: 0.3728, IoU.tray: 0.2074, IoU.ashcan: 0.4963, IoU.fan: 0.6249, IoU.pier: 0.3611, IoU.crt screen: 0.1546, IoU.plate: 0.5592, IoU.monitor: 0.5267, IoU.bulletin board: 0.4920, IoU.shower: 0.0411, IoU.radiator: 0.6759, IoU.glass: 0.1902, IoU.clock: 0.3385, IoU.flag: 0.6544, Acc.wall: 0.8949, Acc.building: 0.9258, Acc.sky: 0.9702, Acc.floor: 0.9071, Acc.tree: 0.8764, Acc.ceiling: 0.9352, Acc.road: 0.9250, Acc.bed : 0.9723, Acc.windowpane: 0.7817, Acc.grass: 0.8734, Acc.cabinet: 0.7654, Acc.sidewalk: 0.8219, Acc.person: 0.9235, Acc.earth: 0.5039, Acc.door: 0.7240, Acc.table: 0.8125, Acc.mountain: 0.7667, Acc.plant: 0.6901, Acc.curtain: 0.8971, Acc.chair: 0.7419, Acc.car: 0.9305, Acc.water: 0.7986, Acc.painting: 0.8835, Acc.sofa: 0.9012, Acc.shelf: 0.6791, Acc.house: 0.6749, Acc.sea: 0.8790, Acc.mirror: 0.8569, Acc.rug: 0.8032, Acc.field: 0.4743, Acc.armchair: 0.7982, Acc.seat: 0.8808, Acc.fence: 0.6415, Acc.desk: 0.7417, Acc.rock: 0.8087, Acc.wardrobe: 0.7430, Acc.lamp: 0.8094, Acc.bathtub: 0.9076, Acc.railing: 0.6071, Acc.cushion: 0.7645, Acc.base: 0.5649, Acc.box: 0.4887, Acc.column: 0.6742, Acc.signboard: 0.5230, Acc.chest of drawers: 0.6550, Acc.counter: 0.5870, Acc.sand: 0.8920, Acc.sink: 0.8415, Acc.skyscraper: 0.6214, Acc.fireplace: 0.9223, Acc.refrigerator: 0.9128, Acc.grandstand: 0.8103, Acc.path: 0.3216, Acc.stairs: 0.3661, Acc.runway: 0.8987, Acc.case: 0.8767, Acc.pool table: 0.9811, Acc.pillow: 0.6230, Acc.screen door: 0.7496, Acc.stairway: 0.4756, Acc.river: 0.4288, Acc.bridge: 0.9127, Acc.bookcase: 0.5074, Acc.blind: 0.4824, Acc.coffee table: 0.8652, Acc.toilet: 0.9267, Acc.flower: 0.5854, Acc.book: 0.7543, Acc.hill: 0.0852, Acc.bench: 0.6255, Acc.countertop: 0.7707, Acc.stove: 0.8903, Acc.palm: 0.8229, Acc.kitchen island: 0.6851, Acc.computer: 0.8791, Acc.swivel chair: 0.6369, Acc.boat: 0.7991, Acc.bar: 0.8595, Acc.arcade machine: 0.8422, Acc.hovel: 0.2661, Acc.bus: 0.9659, Acc.towel: 0.8115, Acc.light: 0.5217, Acc.truck: 0.6048, Acc.tower: 0.2016, Acc.chandelier: 0.8365, Acc.awning: 0.4534, Acc.streetlight: 0.4216, Acc.booth: 0.6205, Acc.television receiver: 0.9102, Acc.airplane: 0.9119, Acc.dirt track: 0.2188, Acc.apparel: 0.4824, Acc.pole: 0.2624, Acc.land: 0.1611, Acc.bannister: 0.1941, Acc.escalator: 0.8089, Acc.ottoman: 0.7202, Acc.bottle: 0.4209, Acc.buffet: 0.7907, Acc.poster: 0.4484, Acc.stage: 0.4592, Acc.van: 0.6675, Acc.ship: 0.3324, Acc.fountain: 0.2579, Acc.conveyer belt: 0.9117, Acc.canopy: 0.5714, Acc.washer: 0.9238, Acc.plaything: 0.4245, Acc.swimming pool: 0.7268, Acc.stool: 0.6056, Acc.barrel: 0.8528, Acc.basket: 0.4757, Acc.waterfall: 0.5965, Acc.tent: 0.9797, Acc.bag: 0.2784, Acc.minibike: 0.8687, Acc.cradle: 0.9796, Acc.oven: 0.7702, Acc.ball: 0.6460, Acc.food: 0.5148, Acc.step: 0.1434, Acc.tank: 0.6384, Acc.trade name: 0.3164, Acc.microwave: 0.9216, Acc.pot: 0.6101, Acc.animal: 0.7674, Acc.bicycle: 0.7767, Acc.lake: 0.6365, Acc.dishwasher: 0.7578, Acc.screen: 0.8021, Acc.blanket: 0.3391, Acc.sculpture: 0.8659, Acc.hood: 0.8231, Acc.sconce: 0.6919, Acc.vase: 0.5899, Acc.traffic light: 0.6127, Acc.tray: 0.2759, Acc.ashcan: 0.6875, Acc.fan: 0.7417, Acc.pier: 0.4064, Acc.crt screen: 0.2640, Acc.plate: 0.7762, Acc.monitor: 0.6653, Acc.bulletin board: 0.6157, Acc.shower: 0.0490, Acc.radiator: 0.8166, Acc.glass: 0.2128, Acc.clock: 0.4008, Acc.flag: 0.7236 2023-11-03 05:12:34,555 - mmseg - INFO - Iter [28050/40000] lr: 9.680e-07, eta: 4:26:25, time: 2.375, data_time: 1.166, memory: 38534, decode.loss_ce: 0.1635, decode.acc_seg: 92.9336, loss: 0.1635 2023-11-03 05:13:35,266 - mmseg - INFO - Iter [28100/40000] lr: 9.639e-07, eta: 4:25:16, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1538, decode.acc_seg: 93.3523, loss: 0.1538 2023-11-03 05:14:35,918 - mmseg - INFO - Iter [28150/40000] lr: 9.599e-07, eta: 4:24:06, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1632, decode.acc_seg: 93.1465, loss: 0.1632 2023-11-03 05:15:36,626 - mmseg - INFO - Iter [28200/40000] lr: 9.558e-07, eta: 4:22:57, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1499, decode.acc_seg: 93.6060, loss: 0.1499 2023-11-03 05:16:37,380 - mmseg - INFO - Iter [28250/40000] lr: 9.518e-07, eta: 4:21:47, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1625, decode.acc_seg: 93.2618, loss: 0.1625 2023-11-03 05:17:38,080 - mmseg - INFO - Iter [28300/40000] lr: 9.477e-07, eta: 4:20:38, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1625, decode.acc_seg: 93.0941, loss: 0.1625 2023-11-03 05:18:38,703 - mmseg - INFO - Iter [28350/40000] lr: 9.437e-07, eta: 4:19:29, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1605, decode.acc_seg: 93.3944, loss: 0.1605 2023-11-03 05:19:39,432 - mmseg - INFO - Iter [28400/40000] lr: 9.396e-07, eta: 4:18:19, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1551, decode.acc_seg: 93.3764, loss: 0.1551 2023-11-03 05:20:42,457 - mmseg - INFO - Iter [28450/40000] lr: 9.356e-07, eta: 4:17:11, time: 1.260, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1530, decode.acc_seg: 93.5523, loss: 0.1530 2023-11-03 05:21:43,161 - mmseg - INFO - Iter [28500/40000] lr: 9.315e-07, eta: 4:16:02, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1611, decode.acc_seg: 93.3056, loss: 0.1611 2023-11-03 05:22:43,834 - mmseg - INFO - Iter [28550/40000] lr: 9.275e-07, eta: 4:14:53, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1550, decode.acc_seg: 93.5374, loss: 0.1550 2023-11-03 05:23:44,547 - mmseg - INFO - Iter [28600/40000] lr: 9.234e-07, eta: 4:13:43, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1572, decode.acc_seg: 93.2094, loss: 0.1572 2023-11-03 05:24:45,191 - mmseg - INFO - Iter [28650/40000] lr: 9.194e-07, eta: 4:12:34, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1667, decode.acc_seg: 93.0916, loss: 0.1667 2023-11-03 05:25:45,816 - mmseg - INFO - Iter [28700/40000] lr: 9.153e-07, eta: 4:11:25, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1660, decode.acc_seg: 93.0617, loss: 0.1660 2023-11-03 05:26:46,536 - mmseg - INFO - Iter [28750/40000] lr: 9.113e-07, eta: 4:10:16, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1597, decode.acc_seg: 93.1640, loss: 0.1597 2023-11-03 05:27:47,208 - mmseg - INFO - Iter [28800/40000] lr: 9.072e-07, eta: 4:09:07, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1575, decode.acc_seg: 93.3520, loss: 0.1575 2023-11-03 05:28:47,837 - mmseg - INFO - Iter [28850/40000] lr: 9.032e-07, eta: 4:07:58, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1575, decode.acc_seg: 93.5292, loss: 0.1575 2023-11-03 05:29:48,491 - mmseg - INFO - Iter [28900/40000] lr: 8.991e-07, eta: 4:06:49, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1595, decode.acc_seg: 93.3241, loss: 0.1595 2023-11-03 05:30:49,176 - mmseg - INFO - Iter [28950/40000] lr: 8.951e-07, eta: 4:05:40, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1616, decode.acc_seg: 93.3863, loss: 0.1616 2023-11-03 05:31:49,863 - mmseg - INFO - Saving checkpoint at 29000 iterations 2023-11-03 05:32:46,991 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 05:32:46,991 - mmseg - INFO - Iter [29000/40000] lr: 8.910e-07, eta: 4:04:52, time: 2.356, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1624, decode.acc_seg: 93.0074, loss: 0.1624 2023-11-03 05:33:46,769 - mmseg - INFO - per class results: 2023-11-03 05:33:46,774 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.5 | 89.95 | | building | 83.78 | 92.45 | | sky | 94.45 | 97.33 | | floor | 83.62 | 91.89 | | tree | 76.64 | 88.67 | | ceiling | 84.98 | 91.94 | | road | 87.22 | 92.04 | | bed | 91.14 | 96.44 | | windowpane | 64.78 | 79.26 | | grass | 68.49 | 84.23 | | cabinet | 64.89 | 74.04 | | sidewalk | 70.84 | 84.23 | | person | 82.64 | 93.0 | | earth | 34.84 | 45.15 | | door | 56.92 | 73.57 | | table | 69.57 | 83.49 | | mountain | 61.9 | 74.38 | | plant | 56.14 | 66.58 | | curtain | 77.75 | 88.65 | | chair | 61.39 | 74.44 | | car | 86.08 | 92.7 | | water | 64.75 | 80.98 | | painting | 78.4 | 88.86 | | sofa | 81.37 | 90.71 | | shelf | 49.14 | 73.93 | | house | 48.4 | 61.26 | | sea | 70.36 | 81.35 | | mirror | 72.36 | 79.51 | | rug | 65.27 | 77.51 | | field | 35.76 | 60.37 | | armchair | 58.32 | 77.33 | | seat | 64.18 | 86.94 | | fence | 48.64 | 63.78 | | desk | 56.08 | 76.71 | | rock | 56.38 | 81.41 | | wardrobe | 48.27 | 62.04 | | lamp | 68.18 | 80.72 | | bathtub | 89.28 | 93.18 | | railing | 42.29 | 55.8 | | cushion | 63.23 | 76.63 | | base | 33.14 | 51.67 | | box | 36.23 | 46.34 | | column | 52.19 | 64.52 | | signboard | 37.02 | 50.17 | | chest of drawers | 39.52 | 73.66 | | counter | 48.85 | 63.99 | | sand | 61.26 | 89.75 | | sink | 77.9 | 85.8 | | skyscraper | 45.99 | 56.11 | | fireplace | 75.75 | 89.54 | | refrigerator | 83.55 | 91.49 | | grandstand | 44.82 | 82.53 | | path | 27.85 | 39.7 | | stairs | 28.72 | 37.82 | | runway | 70.34 | 96.48 | | case | 64.3 | 85.24 | | pool table | 93.45 | 98.07 | | pillow | 58.97 | 67.59 | | screen door | 81.9 | 84.67 | | stairway | 35.16 | 47.4 | | river | 17.05 | 32.71 | | bridge | 76.44 | 85.0 | | bookcase | 45.13 | 53.39 | | blind | 41.8 | 47.92 | | coffee table | 68.02 | 87.0 | | toilet | 89.76 | 93.88 | | flower | 42.97 | 61.7 | | book | 48.77 | 74.09 | | hill | 10.94 | 20.45 | | bench | 55.1 | 62.63 | | countertop | 63.1 | 77.72 | | stove | 83.93 | 88.46 | | palm | 46.73 | 83.65 | | kitchen island | 43.69 | 67.69 | | computer | 76.5 | 88.72 | | swivel chair | 45.02 | 63.29 | | boat | 70.59 | 84.85 | | bar | 73.75 | 79.66 | | arcade machine | 67.45 | 69.52 | | hovel | 24.55 | 27.14 | | bus | 92.86 | 96.9 | | towel | 72.44 | 82.55 | | light | 47.91 | 57.56 | | truck | 47.51 | 54.51 | | tower | 20.7 | 27.93 | | chandelier | 66.76 | 81.11 | | awning | 43.44 | 56.08 | | streetlight | 25.03 | 34.58 | | booth | 58.31 | 59.3 | | television receiver | 74.6 | 88.45 | | airplane | 82.58 | 94.32 | | dirt track | 5.77 | 8.02 | | apparel | 51.33 | 71.19 | | pole | 25.43 | 34.09 | | land | 9.97 | 16.26 | | bannister | 14.45 | 19.42 | | escalator | 61.11 | 78.01 | | ottoman | 52.93 | 70.42 | | bottle | 24.34 | 29.9 | | buffet | 66.88 | 79.72 | | poster | 35.32 | 45.41 | | stage | 22.31 | 44.46 | | van | 46.09 | 75.63 | | ship | 27.59 | 30.26 | | fountain | 31.91 | 33.35 | | conveyer belt | 85.05 | 89.39 | | canopy | 36.21 | 40.44 | | washer | 82.68 | 87.98 | | plaything | 31.65 | 41.0 | | swimming pool | 47.58 | 67.77 | | stool | 49.2 | 60.65 | | barrel | 69.13 | 82.65 | | basket | 35.27 | 46.71 | | waterfall | 45.15 | 59.67 | | tent | 94.67 | 98.0 | | bag | 23.52 | 27.58 | | minibike | 73.79 | 87.29 | | cradle | 85.87 | 96.56 | | oven | 64.14 | 76.78 | | ball | 45.19 | 48.35 | | food | 53.79 | 57.59 | | step | 17.88 | 21.75 | | tank | 55.04 | 63.67 | | trade name | 30.93 | 39.25 | | microwave | 88.85 | 94.11 | | pot | 56.41 | 65.08 | | animal | 74.55 | 78.6 | | bicycle | 59.43 | 76.88 | | lake | 55.84 | 63.62 | | dishwasher | 72.95 | 80.33 | | screen | 60.2 | 79.83 | | blanket | 30.91 | 35.15 | | sculpture | 74.27 | 87.3 | | hood | 71.01 | 81.97 | | sconce | 53.99 | 65.99 | | vase | 44.03 | 59.5 | | traffic light | 36.85 | 63.03 | | tray | 22.74 | 28.99 | | ashcan | 48.42 | 68.53 | | fan | 63.62 | 77.04 | | pier | 37.43 | 42.84 | | crt screen | 13.78 | 24.51 | | plate | 56.54 | 73.16 | | monitor | 50.09 | 61.4 | | bulletin board | 49.53 | 59.05 | | shower | 8.03 | 8.57 | | radiator | 67.43 | 82.89 | | glass | 19.36 | 21.99 | | clock | 32.07 | 37.98 | | flag | 65.93 | 74.45 | +---------------------+-------+-------+ 2023-11-03 05:33:46,774 - mmseg - INFO - Summary: 2023-11-03 05:33:46,774 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.29 | 55.57 | 67.41 | +-------+-------+-------+ 2023-11-03 05:33:46,775 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 05:33:46,775 - mmseg - INFO - Iter(val) [250] aAcc: 0.8529, mIoU: 0.5557, mAcc: 0.6741, IoU.wall: 0.8050, IoU.building: 0.8378, IoU.sky: 0.9445, IoU.floor: 0.8362, IoU.tree: 0.7664, IoU.ceiling: 0.8498, IoU.road: 0.8722, IoU.bed : 0.9114, IoU.windowpane: 0.6478, IoU.grass: 0.6849, IoU.cabinet: 0.6489, IoU.sidewalk: 0.7084, IoU.person: 0.8264, IoU.earth: 0.3484, IoU.door: 0.5692, IoU.table: 0.6957, IoU.mountain: 0.6190, IoU.plant: 0.5614, IoU.curtain: 0.7775, IoU.chair: 0.6139, IoU.car: 0.8608, IoU.water: 0.6475, IoU.painting: 0.7840, IoU.sofa: 0.8137, IoU.shelf: 0.4914, IoU.house: 0.4840, IoU.sea: 0.7036, IoU.mirror: 0.7236, IoU.rug: 0.6527, IoU.field: 0.3576, IoU.armchair: 0.5832, IoU.seat: 0.6418, IoU.fence: 0.4864, IoU.desk: 0.5608, IoU.rock: 0.5638, IoU.wardrobe: 0.4827, IoU.lamp: 0.6818, IoU.bathtub: 0.8928, IoU.railing: 0.4229, IoU.cushion: 0.6323, IoU.base: 0.3314, IoU.box: 0.3623, IoU.column: 0.5219, IoU.signboard: 0.3702, IoU.chest of drawers: 0.3952, IoU.counter: 0.4885, IoU.sand: 0.6126, IoU.sink: 0.7790, IoU.skyscraper: 0.4599, IoU.fireplace: 0.7575, IoU.refrigerator: 0.8355, IoU.grandstand: 0.4482, IoU.path: 0.2785, IoU.stairs: 0.2872, IoU.runway: 0.7034, IoU.case: 0.6430, IoU.pool table: 0.9345, IoU.pillow: 0.5897, IoU.screen door: 0.8190, IoU.stairway: 0.3516, IoU.river: 0.1705, IoU.bridge: 0.7644, IoU.bookcase: 0.4513, IoU.blind: 0.4180, IoU.coffee table: 0.6802, IoU.toilet: 0.8976, IoU.flower: 0.4297, IoU.book: 0.4877, IoU.hill: 0.1094, IoU.bench: 0.5510, IoU.countertop: 0.6310, IoU.stove: 0.8393, IoU.palm: 0.4673, IoU.kitchen island: 0.4369, IoU.computer: 0.7650, IoU.swivel chair: 0.4502, IoU.boat: 0.7059, IoU.bar: 0.7375, IoU.arcade machine: 0.6745, IoU.hovel: 0.2455, IoU.bus: 0.9286, IoU.towel: 0.7244, IoU.light: 0.4791, IoU.truck: 0.4751, IoU.tower: 0.2070, IoU.chandelier: 0.6676, IoU.awning: 0.4344, IoU.streetlight: 0.2503, IoU.booth: 0.5831, IoU.television receiver: 0.7460, IoU.airplane: 0.8258, IoU.dirt track: 0.0577, IoU.apparel: 0.5133, IoU.pole: 0.2543, IoU.land: 0.0997, IoU.bannister: 0.1445, IoU.escalator: 0.6111, IoU.ottoman: 0.5293, IoU.bottle: 0.2434, IoU.buffet: 0.6688, IoU.poster: 0.3532, IoU.stage: 0.2231, IoU.van: 0.4609, IoU.ship: 0.2759, IoU.fountain: 0.3191, IoU.conveyer belt: 0.8505, IoU.canopy: 0.3621, IoU.washer: 0.8268, IoU.plaything: 0.3165, IoU.swimming pool: 0.4758, IoU.stool: 0.4920, IoU.barrel: 0.6913, IoU.basket: 0.3527, IoU.waterfall: 0.4515, IoU.tent: 0.9467, IoU.bag: 0.2352, IoU.minibike: 0.7379, IoU.cradle: 0.8587, IoU.oven: 0.6414, IoU.ball: 0.4519, IoU.food: 0.5379, IoU.step: 0.1788, IoU.tank: 0.5504, IoU.trade name: 0.3093, IoU.microwave: 0.8885, IoU.pot: 0.5641, IoU.animal: 0.7455, IoU.bicycle: 0.5943, IoU.lake: 0.5584, IoU.dishwasher: 0.7295, IoU.screen: 0.6020, IoU.blanket: 0.3091, IoU.sculpture: 0.7427, IoU.hood: 0.7101, IoU.sconce: 0.5399, IoU.vase: 0.4403, IoU.traffic light: 0.3685, IoU.tray: 0.2274, IoU.ashcan: 0.4842, IoU.fan: 0.6362, IoU.pier: 0.3743, IoU.crt screen: 0.1378, IoU.plate: 0.5654, IoU.monitor: 0.5009, IoU.bulletin board: 0.4953, IoU.shower: 0.0803, IoU.radiator: 0.6743, IoU.glass: 0.1936, IoU.clock: 0.3207, IoU.flag: 0.6593, Acc.wall: 0.8995, Acc.building: 0.9245, Acc.sky: 0.9733, Acc.floor: 0.9189, Acc.tree: 0.8867, Acc.ceiling: 0.9194, Acc.road: 0.9204, Acc.bed : 0.9644, Acc.windowpane: 0.7926, Acc.grass: 0.8423, Acc.cabinet: 0.7404, Acc.sidewalk: 0.8423, Acc.person: 0.9300, Acc.earth: 0.4515, Acc.door: 0.7357, Acc.table: 0.8349, Acc.mountain: 0.7438, Acc.plant: 0.6658, Acc.curtain: 0.8865, Acc.chair: 0.7444, Acc.car: 0.9270, Acc.water: 0.8098, Acc.painting: 0.8886, Acc.sofa: 0.9071, Acc.shelf: 0.7393, Acc.house: 0.6126, Acc.sea: 0.8135, Acc.mirror: 0.7951, Acc.rug: 0.7751, Acc.field: 0.6037, Acc.armchair: 0.7733, Acc.seat: 0.8694, Acc.fence: 0.6378, Acc.desk: 0.7671, Acc.rock: 0.8141, Acc.wardrobe: 0.6204, Acc.lamp: 0.8072, Acc.bathtub: 0.9318, Acc.railing: 0.5580, Acc.cushion: 0.7663, Acc.base: 0.5167, Acc.box: 0.4634, Acc.column: 0.6452, Acc.signboard: 0.5017, Acc.chest of drawers: 0.7366, Acc.counter: 0.6399, Acc.sand: 0.8975, Acc.sink: 0.8580, Acc.skyscraper: 0.5611, Acc.fireplace: 0.8954, Acc.refrigerator: 0.9149, Acc.grandstand: 0.8253, Acc.path: 0.3970, Acc.stairs: 0.3782, Acc.runway: 0.9648, Acc.case: 0.8524, Acc.pool table: 0.9807, Acc.pillow: 0.6759, Acc.screen door: 0.8467, Acc.stairway: 0.4740, Acc.river: 0.3271, Acc.bridge: 0.8500, Acc.bookcase: 0.5339, Acc.blind: 0.4792, Acc.coffee table: 0.8700, Acc.toilet: 0.9388, Acc.flower: 0.6170, Acc.book: 0.7409, Acc.hill: 0.2045, Acc.bench: 0.6263, Acc.countertop: 0.7772, Acc.stove: 0.8846, Acc.palm: 0.8365, Acc.kitchen island: 0.6769, Acc.computer: 0.8872, Acc.swivel chair: 0.6329, Acc.boat: 0.8485, Acc.bar: 0.7966, Acc.arcade machine: 0.6952, Acc.hovel: 0.2714, Acc.bus: 0.9690, Acc.towel: 0.8255, Acc.light: 0.5756, Acc.truck: 0.5451, Acc.tower: 0.2793, Acc.chandelier: 0.8111, Acc.awning: 0.5608, Acc.streetlight: 0.3458, Acc.booth: 0.5930, Acc.television receiver: 0.8845, Acc.airplane: 0.9432, Acc.dirt track: 0.0802, Acc.apparel: 0.7119, Acc.pole: 0.3409, Acc.land: 0.1626, Acc.bannister: 0.1942, Acc.escalator: 0.7801, Acc.ottoman: 0.7042, Acc.bottle: 0.2990, Acc.buffet: 0.7972, Acc.poster: 0.4541, Acc.stage: 0.4446, Acc.van: 0.7563, Acc.ship: 0.3026, Acc.fountain: 0.3335, Acc.conveyer belt: 0.8939, Acc.canopy: 0.4044, Acc.washer: 0.8798, Acc.plaything: 0.4100, Acc.swimming pool: 0.6777, Acc.stool: 0.6065, Acc.barrel: 0.8265, Acc.basket: 0.4671, Acc.waterfall: 0.5967, Acc.tent: 0.9800, Acc.bag: 0.2758, Acc.minibike: 0.8729, Acc.cradle: 0.9656, Acc.oven: 0.7678, Acc.ball: 0.4835, Acc.food: 0.5759, Acc.step: 0.2175, Acc.tank: 0.6367, Acc.trade name: 0.3925, Acc.microwave: 0.9411, Acc.pot: 0.6508, Acc.animal: 0.7860, Acc.bicycle: 0.7688, Acc.lake: 0.6362, Acc.dishwasher: 0.8033, Acc.screen: 0.7983, Acc.blanket: 0.3515, Acc.sculpture: 0.8730, Acc.hood: 0.8197, Acc.sconce: 0.6599, Acc.vase: 0.5950, Acc.traffic light: 0.6303, Acc.tray: 0.2899, Acc.ashcan: 0.6853, Acc.fan: 0.7704, Acc.pier: 0.4284, Acc.crt screen: 0.2451, Acc.plate: 0.7316, Acc.monitor: 0.6140, Acc.bulletin board: 0.5905, Acc.shower: 0.0857, Acc.radiator: 0.8289, Acc.glass: 0.2199, Acc.clock: 0.3798, Acc.flag: 0.7445 2023-11-03 05:34:47,535 - mmseg - INFO - Iter [29050/40000] lr: 8.870e-07, eta: 4:04:06, time: 2.411, data_time: 1.203, memory: 38534, decode.loss_ce: 0.1546, decode.acc_seg: 93.4118, loss: 0.1546 2023-11-03 05:35:50,595 - mmseg - INFO - Iter [29100/40000] lr: 8.829e-07, eta: 4:02:57, time: 1.261, data_time: 0.051, memory: 38534, decode.loss_ce: 0.1577, decode.acc_seg: 93.3547, loss: 0.1577 2023-11-03 05:36:51,305 - mmseg - INFO - Iter [29150/40000] lr: 8.789e-07, eta: 4:01:48, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1566, decode.acc_seg: 93.3797, loss: 0.1566 2023-11-03 05:37:51,906 - mmseg - INFO - Iter [29200/40000] lr: 8.748e-07, eta: 4:00:39, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1637, decode.acc_seg: 92.9365, loss: 0.1637 2023-11-03 05:38:52,605 - mmseg - INFO - Iter [29250/40000] lr: 8.708e-07, eta: 3:59:30, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1580, decode.acc_seg: 93.3609, loss: 0.1580 2023-11-03 05:39:53,275 - mmseg - INFO - Iter [29300/40000] lr: 8.667e-07, eta: 3:58:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1551, decode.acc_seg: 93.2932, loss: 0.1551 2023-11-03 05:40:53,951 - mmseg - INFO - Iter [29350/40000] lr: 8.627e-07, eta: 3:57:12, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1581, decode.acc_seg: 93.3972, loss: 0.1581 2023-11-03 05:41:54,657 - mmseg - INFO - Iter [29400/40000] lr: 8.586e-07, eta: 3:56:03, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1663, decode.acc_seg: 93.1326, loss: 0.1663 2023-11-03 05:42:55,334 - mmseg - INFO - Iter [29450/40000] lr: 8.546e-07, eta: 3:54:54, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1623, decode.acc_seg: 92.9878, loss: 0.1623 2023-11-03 05:43:55,981 - mmseg - INFO - Iter [29500/40000] lr: 8.505e-07, eta: 3:53:45, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1595, decode.acc_seg: 93.3517, loss: 0.1595 2023-11-03 05:44:56,703 - mmseg - INFO - Iter [29550/40000] lr: 8.465e-07, eta: 3:52:36, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1623, decode.acc_seg: 93.2051, loss: 0.1623 2023-11-03 05:45:57,405 - mmseg - INFO - Iter [29600/40000] lr: 8.424e-07, eta: 3:51:27, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1518, decode.acc_seg: 93.4714, loss: 0.1518 2023-11-03 05:46:58,136 - mmseg - INFO - Iter [29650/40000] lr: 8.384e-07, eta: 3:50:18, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1702, decode.acc_seg: 92.7704, loss: 0.1702 2023-11-03 05:47:58,836 - mmseg - INFO - Iter [29700/40000] lr: 8.343e-07, eta: 3:49:09, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1632, decode.acc_seg: 93.1504, loss: 0.1632 2023-11-03 05:49:01,834 - mmseg - INFO - Iter [29750/40000] lr: 8.303e-07, eta: 3:48:01, time: 1.260, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1556, decode.acc_seg: 93.4072, loss: 0.1556 2023-11-03 05:50:02,581 - mmseg - INFO - Iter [29800/40000] lr: 8.262e-07, eta: 3:46:52, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1524, decode.acc_seg: 93.4717, loss: 0.1524 2023-11-03 05:51:03,324 - mmseg - INFO - Iter [29850/40000] lr: 8.222e-07, eta: 3:45:44, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1511, decode.acc_seg: 93.6096, loss: 0.1511 2023-11-03 05:52:04,051 - mmseg - INFO - Iter [29900/40000] lr: 8.181e-07, eta: 3:44:35, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1662, decode.acc_seg: 92.7894, loss: 0.1662 2023-11-03 05:53:04,726 - mmseg - INFO - Iter [29950/40000] lr: 8.141e-07, eta: 3:43:26, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1516, decode.acc_seg: 93.4127, loss: 0.1516 2023-11-03 05:54:05,372 - mmseg - INFO - Saving checkpoint at 30000 iterations 2023-11-03 05:54:59,328 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 05:54:59,328 - mmseg - INFO - Iter [30000/40000] lr: 8.100e-07, eta: 3:42:35, time: 2.292, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1507, decode.acc_seg: 93.4884, loss: 0.1507 2023-11-03 05:56:00,885 - mmseg - INFO - per class results: 2023-11-03 05:56:00,890 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.86 | 88.98 | | building | 83.53 | 92.76 | | sky | 94.49 | 97.23 | | floor | 83.63 | 91.1 | | tree | 76.59 | 90.03 | | ceiling | 85.1 | 94.17 | | road | 86.04 | 92.34 | | bed | 91.23 | 97.07 | | windowpane | 63.98 | 78.22 | | grass | 68.97 | 82.21 | | cabinet | 65.89 | 76.97 | | sidewalk | 70.27 | 83.35 | | person | 82.46 | 92.99 | | earth | 34.11 | 44.85 | | door | 56.47 | 71.95 | | table | 69.33 | 83.4 | | mountain | 61.9 | 78.34 | | plant | 55.25 | 66.54 | | curtain | 77.19 | 90.66 | | chair | 61.47 | 73.75 | | car | 86.07 | 93.54 | | water | 65.88 | 80.48 | | painting | 79.29 | 89.09 | | sofa | 81.69 | 89.64 | | shelf | 48.29 | 68.24 | | house | 44.7 | 57.09 | | sea | 70.28 | 80.89 | | mirror | 72.88 | 81.73 | | rug | 65.69 | 78.12 | | field | 37.35 | 67.36 | | armchair | 58.16 | 78.32 | | seat | 63.61 | 88.38 | | fence | 48.19 | 66.02 | | desk | 57.35 | 71.95 | | rock | 56.35 | 76.58 | | wardrobe | 52.03 | 73.79 | | lamp | 67.88 | 79.63 | | bathtub | 89.36 | 92.77 | | railing | 42.94 | 55.29 | | cushion | 64.11 | 76.4 | | base | 33.8 | 53.81 | | box | 35.78 | 46.03 | | column | 54.6 | 68.47 | | signboard | 37.07 | 50.64 | | chest of drawers | 45.41 | 65.32 | | counter | 53.08 | 67.09 | | sand | 62.68 | 87.96 | | sink | 77.59 | 84.64 | | skyscraper | 47.73 | 59.77 | | fireplace | 74.53 | 88.0 | | refrigerator | 84.77 | 92.5 | | grandstand | 43.99 | 85.25 | | path | 29.11 | 37.97 | | stairs | 30.61 | 38.55 | | runway | 68.91 | 87.94 | | case | 61.93 | 87.45 | | pool table | 93.68 | 97.96 | | pillow | 58.63 | 67.42 | | screen door | 83.57 | 86.78 | | stairway | 40.11 | 53.01 | | river | 23.43 | 47.8 | | bridge | 76.26 | 86.03 | | bookcase | 44.98 | 55.17 | | blind | 39.36 | 43.64 | | coffee table | 69.18 | 84.99 | | toilet | 89.06 | 92.92 | | flower | 41.34 | 63.92 | | book | 49.11 | 74.76 | | hill | 6.1 | 10.02 | | bench | 55.41 | 63.28 | | countertop | 63.15 | 75.92 | | stove | 84.74 | 90.65 | | palm | 49.99 | 81.12 | | kitchen island | 44.75 | 70.51 | | computer | 77.74 | 87.34 | | swivel chair | 48.37 | 71.61 | | boat | 70.7 | 88.34 | | bar | 74.43 | 80.68 | | arcade machine | 73.06 | 76.34 | | hovel | 27.14 | 30.49 | | bus | 93.26 | 96.2 | | towel | 72.3 | 83.48 | | light | 47.62 | 55.31 | | truck | 51.67 | 65.19 | | tower | 20.7 | 28.18 | | chandelier | 67.44 | 82.9 | | awning | 39.59 | 48.78 | | streetlight | 26.88 | 38.71 | | booth | 63.78 | 66.9 | | television receiver | 73.98 | 88.66 | | airplane | 76.55 | 83.72 | | dirt track | 13.2 | 17.58 | | apparel | 49.65 | 68.82 | | pole | 24.42 | 31.32 | | land | 10.31 | 14.61 | | bannister | 13.93 | 20.24 | | escalator | 63.41 | 80.49 | | ottoman | 55.45 | 68.84 | | bottle | 27.51 | 36.62 | | buffet | 65.8 | 76.67 | | poster | 37.8 | 47.38 | | stage | 21.28 | 42.67 | | van | 51.76 | 73.87 | | ship | 28.86 | 30.04 | | fountain | 32.84 | 33.4 | | conveyer belt | 85.15 | 92.21 | | canopy | 37.44 | 43.05 | | washer | 84.37 | 90.09 | | plaything | 31.4 | 45.02 | | swimming pool | 52.99 | 76.84 | | stool | 50.02 | 67.73 | | barrel | 29.17 | 88.51 | | basket | 37.51 | 49.2 | | waterfall | 43.91 | 56.8 | | tent | 90.16 | 98.05 | | bag | 24.41 | 29.53 | | minibike | 72.97 | 87.74 | | cradle | 86.58 | 97.42 | | oven | 65.35 | 75.9 | | ball | 25.82 | 26.65 | | food | 51.4 | 53.86 | | step | 17.29 | 19.95 | | tank | 56.1 | 63.57 | | trade name | 27.73 | 33.85 | | microwave | 89.05 | 93.07 | | pot | 56.15 | 63.61 | | animal | 74.04 | 77.54 | | bicycle | 59.35 | 76.34 | | lake | 55.69 | 63.65 | | dishwasher | 70.04 | 75.2 | | screen | 61.08 | 79.26 | | blanket | 31.69 | 36.93 | | sculpture | 75.62 | 87.0 | | hood | 64.13 | 72.31 | | sconce | 53.92 | 67.0 | | vase | 44.86 | 60.59 | | traffic light | 38.7 | 54.98 | | tray | 21.96 | 29.03 | | ashcan | 51.79 | 63.91 | | fan | 63.03 | 73.42 | | pier | 37.99 | 43.92 | | crt screen | 15.18 | 26.87 | | plate | 54.51 | 75.12 | | monitor | 54.72 | 68.5 | | bulletin board | 50.34 | 60.31 | | shower | 2.28 | 4.15 | | radiator | 67.74 | 82.68 | | glass | 19.02 | 21.59 | | clock | 31.66 | 36.98 | | flag | 66.65 | 74.04 | +---------------------+-------+-------+ 2023-11-03 05:56:00,890 - mmseg - INFO - Summary: 2023-11-03 05:56:00,890 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.34 | 55.58 | 67.59 | +-------+-------+-------+ 2023-11-03 05:56:00,891 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 05:56:00,892 - mmseg - INFO - Iter(val) [250] aAcc: 0.8534, mIoU: 0.5558, mAcc: 0.6759, IoU.wall: 0.8086, IoU.building: 0.8353, IoU.sky: 0.9449, IoU.floor: 0.8363, IoU.tree: 0.7659, IoU.ceiling: 0.8510, IoU.road: 0.8604, IoU.bed : 0.9123, IoU.windowpane: 0.6398, IoU.grass: 0.6897, IoU.cabinet: 0.6589, IoU.sidewalk: 0.7027, IoU.person: 0.8246, IoU.earth: 0.3411, IoU.door: 0.5647, IoU.table: 0.6933, IoU.mountain: 0.6190, IoU.plant: 0.5525, IoU.curtain: 0.7719, IoU.chair: 0.6147, IoU.car: 0.8607, IoU.water: 0.6588, IoU.painting: 0.7929, IoU.sofa: 0.8169, IoU.shelf: 0.4829, IoU.house: 0.4470, IoU.sea: 0.7028, IoU.mirror: 0.7288, IoU.rug: 0.6569, IoU.field: 0.3735, IoU.armchair: 0.5816, IoU.seat: 0.6361, IoU.fence: 0.4819, IoU.desk: 0.5735, IoU.rock: 0.5635, IoU.wardrobe: 0.5203, IoU.lamp: 0.6788, IoU.bathtub: 0.8936, IoU.railing: 0.4294, IoU.cushion: 0.6411, IoU.base: 0.3380, IoU.box: 0.3578, IoU.column: 0.5460, IoU.signboard: 0.3707, IoU.chest of drawers: 0.4541, IoU.counter: 0.5308, IoU.sand: 0.6268, IoU.sink: 0.7759, IoU.skyscraper: 0.4773, IoU.fireplace: 0.7453, IoU.refrigerator: 0.8477, IoU.grandstand: 0.4399, IoU.path: 0.2911, IoU.stairs: 0.3061, IoU.runway: 0.6891, IoU.case: 0.6193, IoU.pool table: 0.9368, IoU.pillow: 0.5863, IoU.screen door: 0.8357, IoU.stairway: 0.4011, IoU.river: 0.2343, IoU.bridge: 0.7626, IoU.bookcase: 0.4498, IoU.blind: 0.3936, IoU.coffee table: 0.6918, IoU.toilet: 0.8906, IoU.flower: 0.4134, IoU.book: 0.4911, IoU.hill: 0.0610, IoU.bench: 0.5541, IoU.countertop: 0.6315, IoU.stove: 0.8474, IoU.palm: 0.4999, IoU.kitchen island: 0.4475, IoU.computer: 0.7774, IoU.swivel chair: 0.4837, IoU.boat: 0.7070, IoU.bar: 0.7443, IoU.arcade machine: 0.7306, IoU.hovel: 0.2714, IoU.bus: 0.9326, IoU.towel: 0.7230, IoU.light: 0.4762, IoU.truck: 0.5167, IoU.tower: 0.2070, IoU.chandelier: 0.6744, IoU.awning: 0.3959, IoU.streetlight: 0.2688, IoU.booth: 0.6378, IoU.television receiver: 0.7398, IoU.airplane: 0.7655, IoU.dirt track: 0.1320, IoU.apparel: 0.4965, IoU.pole: 0.2442, IoU.land: 0.1031, IoU.bannister: 0.1393, IoU.escalator: 0.6341, IoU.ottoman: 0.5545, IoU.bottle: 0.2751, IoU.buffet: 0.6580, IoU.poster: 0.3780, IoU.stage: 0.2128, IoU.van: 0.5176, IoU.ship: 0.2886, IoU.fountain: 0.3284, IoU.conveyer belt: 0.8515, IoU.canopy: 0.3744, IoU.washer: 0.8437, IoU.plaything: 0.3140, IoU.swimming pool: 0.5299, IoU.stool: 0.5002, IoU.barrel: 0.2917, IoU.basket: 0.3751, IoU.waterfall: 0.4391, IoU.tent: 0.9016, IoU.bag: 0.2441, IoU.minibike: 0.7297, IoU.cradle: 0.8658, IoU.oven: 0.6535, IoU.ball: 0.2582, IoU.food: 0.5140, IoU.step: 0.1729, IoU.tank: 0.5610, IoU.trade name: 0.2773, IoU.microwave: 0.8905, IoU.pot: 0.5615, IoU.animal: 0.7404, IoU.bicycle: 0.5935, IoU.lake: 0.5569, IoU.dishwasher: 0.7004, IoU.screen: 0.6108, IoU.blanket: 0.3169, IoU.sculpture: 0.7562, IoU.hood: 0.6413, IoU.sconce: 0.5392, IoU.vase: 0.4486, IoU.traffic light: 0.3870, IoU.tray: 0.2196, IoU.ashcan: 0.5179, IoU.fan: 0.6303, IoU.pier: 0.3799, IoU.crt screen: 0.1518, IoU.plate: 0.5451, IoU.monitor: 0.5472, IoU.bulletin board: 0.5034, IoU.shower: 0.0228, IoU.radiator: 0.6774, IoU.glass: 0.1902, IoU.clock: 0.3166, IoU.flag: 0.6665, Acc.wall: 0.8898, Acc.building: 0.9276, Acc.sky: 0.9723, Acc.floor: 0.9110, Acc.tree: 0.9003, Acc.ceiling: 0.9417, Acc.road: 0.9234, Acc.bed : 0.9707, Acc.windowpane: 0.7822, Acc.grass: 0.8221, Acc.cabinet: 0.7697, Acc.sidewalk: 0.8335, Acc.person: 0.9299, Acc.earth: 0.4485, Acc.door: 0.7195, Acc.table: 0.8340, Acc.mountain: 0.7834, Acc.plant: 0.6654, Acc.curtain: 0.9066, Acc.chair: 0.7375, Acc.car: 0.9354, Acc.water: 0.8048, Acc.painting: 0.8909, Acc.sofa: 0.8964, Acc.shelf: 0.6824, Acc.house: 0.5709, Acc.sea: 0.8089, Acc.mirror: 0.8173, Acc.rug: 0.7812, Acc.field: 0.6736, Acc.armchair: 0.7832, Acc.seat: 0.8838, Acc.fence: 0.6602, Acc.desk: 0.7195, Acc.rock: 0.7658, Acc.wardrobe: 0.7379, Acc.lamp: 0.7963, Acc.bathtub: 0.9277, Acc.railing: 0.5529, Acc.cushion: 0.7640, Acc.base: 0.5381, Acc.box: 0.4603, Acc.column: 0.6847, Acc.signboard: 0.5064, Acc.chest of drawers: 0.6532, Acc.counter: 0.6709, Acc.sand: 0.8796, Acc.sink: 0.8464, Acc.skyscraper: 0.5977, Acc.fireplace: 0.8800, Acc.refrigerator: 0.9250, Acc.grandstand: 0.8525, Acc.path: 0.3797, Acc.stairs: 0.3855, Acc.runway: 0.8794, Acc.case: 0.8745, Acc.pool table: 0.9796, Acc.pillow: 0.6742, Acc.screen door: 0.8678, Acc.stairway: 0.5301, Acc.river: 0.4780, Acc.bridge: 0.8603, Acc.bookcase: 0.5517, Acc.blind: 0.4364, Acc.coffee table: 0.8499, Acc.toilet: 0.9292, Acc.flower: 0.6392, Acc.book: 0.7476, Acc.hill: 0.1002, Acc.bench: 0.6328, Acc.countertop: 0.7592, Acc.stove: 0.9065, Acc.palm: 0.8112, Acc.kitchen island: 0.7051, Acc.computer: 0.8734, Acc.swivel chair: 0.7161, Acc.boat: 0.8834, Acc.bar: 0.8068, Acc.arcade machine: 0.7634, Acc.hovel: 0.3049, Acc.bus: 0.9620, Acc.towel: 0.8348, Acc.light: 0.5531, Acc.truck: 0.6519, Acc.tower: 0.2818, Acc.chandelier: 0.8290, Acc.awning: 0.4878, Acc.streetlight: 0.3871, Acc.booth: 0.6690, Acc.television receiver: 0.8866, Acc.airplane: 0.8372, Acc.dirt track: 0.1758, Acc.apparel: 0.6882, Acc.pole: 0.3132, Acc.land: 0.1461, Acc.bannister: 0.2024, Acc.escalator: 0.8049, Acc.ottoman: 0.6884, Acc.bottle: 0.3662, Acc.buffet: 0.7667, Acc.poster: 0.4738, Acc.stage: 0.4267, Acc.van: 0.7387, Acc.ship: 0.3004, Acc.fountain: 0.3340, Acc.conveyer belt: 0.9221, Acc.canopy: 0.4305, Acc.washer: 0.9009, Acc.plaything: 0.4502, Acc.swimming pool: 0.7684, Acc.stool: 0.6773, Acc.barrel: 0.8851, Acc.basket: 0.4920, Acc.waterfall: 0.5680, Acc.tent: 0.9805, Acc.bag: 0.2953, Acc.minibike: 0.8774, Acc.cradle: 0.9742, Acc.oven: 0.7590, Acc.ball: 0.2665, Acc.food: 0.5386, Acc.step: 0.1995, Acc.tank: 0.6357, Acc.trade name: 0.3385, Acc.microwave: 0.9307, Acc.pot: 0.6361, Acc.animal: 0.7754, Acc.bicycle: 0.7634, Acc.lake: 0.6365, Acc.dishwasher: 0.7520, Acc.screen: 0.7926, Acc.blanket: 0.3693, Acc.sculpture: 0.8700, Acc.hood: 0.7231, Acc.sconce: 0.6700, Acc.vase: 0.6059, Acc.traffic light: 0.5498, Acc.tray: 0.2903, Acc.ashcan: 0.6391, Acc.fan: 0.7342, Acc.pier: 0.4392, Acc.crt screen: 0.2687, Acc.plate: 0.7512, Acc.monitor: 0.6850, Acc.bulletin board: 0.6031, Acc.shower: 0.0415, Acc.radiator: 0.8268, Acc.glass: 0.2159, Acc.clock: 0.3698, Acc.flag: 0.7404 2023-11-03 05:57:01,710 - mmseg - INFO - Iter [30050/40000] lr: 8.060e-07, eta: 3:41:47, time: 2.448, data_time: 1.239, memory: 38534, decode.loss_ce: 0.1570, decode.acc_seg: 93.1952, loss: 0.1570 2023-11-03 05:58:02,446 - mmseg - INFO - Iter [30100/40000] lr: 8.019e-07, eta: 3:40:38, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1579, decode.acc_seg: 93.1613, loss: 0.1579 2023-11-03 05:59:03,171 - mmseg - INFO - Iter [30150/40000] lr: 7.979e-07, eta: 3:39:29, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1525, decode.acc_seg: 93.6105, loss: 0.1525 2023-11-03 06:00:03,843 - mmseg - INFO - Iter [30200/40000] lr: 7.938e-07, eta: 3:38:20, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1555, decode.acc_seg: 93.2551, loss: 0.1555 2023-11-03 06:01:04,510 - mmseg - INFO - Iter [30250/40000] lr: 7.898e-07, eta: 3:37:12, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1536, decode.acc_seg: 93.3866, loss: 0.1536 2023-11-03 06:02:05,150 - mmseg - INFO - Iter [30300/40000] lr: 7.857e-07, eta: 3:36:03, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1601, decode.acc_seg: 93.2401, loss: 0.1601 2023-11-03 06:03:08,139 - mmseg - INFO - Iter [30350/40000] lr: 7.817e-07, eta: 3:34:55, time: 1.260, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1491, decode.acc_seg: 93.5246, loss: 0.1491 2023-11-03 06:04:08,806 - mmseg - INFO - Iter [30400/40000] lr: 7.776e-07, eta: 3:33:46, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1643, decode.acc_seg: 93.2137, loss: 0.1643 2023-11-03 06:05:09,467 - mmseg - INFO - Iter [30450/40000] lr: 7.736e-07, eta: 3:32:37, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1523, decode.acc_seg: 93.6415, loss: 0.1523 2023-11-03 06:06:10,128 - mmseg - INFO - Iter [30500/40000] lr: 7.695e-07, eta: 3:31:29, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1505, decode.acc_seg: 93.6350, loss: 0.1505 2023-11-03 06:07:10,798 - mmseg - INFO - Iter [30550/40000] lr: 7.655e-07, eta: 3:30:20, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1527, decode.acc_seg: 93.4197, loss: 0.1527 2023-11-03 06:08:11,453 - mmseg - INFO - Iter [30600/40000] lr: 7.614e-07, eta: 3:29:11, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1584, decode.acc_seg: 93.2679, loss: 0.1584 2023-11-03 06:09:12,116 - mmseg - INFO - Iter [30650/40000] lr: 7.574e-07, eta: 3:28:03, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1556, decode.acc_seg: 93.3827, loss: 0.1556 2023-11-03 06:10:12,827 - mmseg - INFO - Iter [30700/40000] lr: 7.533e-07, eta: 3:26:54, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1555, decode.acc_seg: 93.3901, loss: 0.1555 2023-11-03 06:11:13,510 - mmseg - INFO - Iter [30750/40000] lr: 7.493e-07, eta: 3:25:45, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1602, decode.acc_seg: 93.2144, loss: 0.1602 2023-11-03 06:12:14,171 - mmseg - INFO - Iter [30800/40000] lr: 7.452e-07, eta: 3:24:37, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1577, decode.acc_seg: 93.3778, loss: 0.1577 2023-11-03 06:13:14,889 - mmseg - INFO - Iter [30850/40000] lr: 7.412e-07, eta: 3:23:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1523, decode.acc_seg: 93.4278, loss: 0.1523 2023-11-03 06:14:15,547 - mmseg - INFO - Iter [30900/40000] lr: 7.371e-07, eta: 3:22:20, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1601, decode.acc_seg: 93.2275, loss: 0.1601 2023-11-03 06:15:16,223 - mmseg - INFO - Iter [30950/40000] lr: 7.331e-07, eta: 3:21:11, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1538, decode.acc_seg: 93.5019, loss: 0.1538 2023-11-03 06:16:20,133 - mmseg - INFO - Saving checkpoint at 31000 iterations 2023-11-03 06:17:15,309 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 06:17:15,309 - mmseg - INFO - Iter [31000/40000] lr: 7.290e-07, eta: 3:20:20, time: 2.382, data_time: 0.068, memory: 38534, decode.loss_ce: 0.1506, decode.acc_seg: 93.5577, loss: 0.1506 2023-11-03 06:18:18,532 - mmseg - INFO - per class results: 2023-11-03 06:18:18,537 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.72 | 89.94 | | building | 83.24 | 93.69 | | sky | 94.27 | 97.33 | | floor | 83.22 | 91.18 | | tree | 75.8 | 86.85 | | ceiling | 84.52 | 91.96 | | road | 86.5 | 92.36 | | bed | 91.23 | 96.74 | | windowpane | 65.47 | 80.27 | | grass | 67.94 | 85.45 | | cabinet | 65.81 | 76.46 | | sidewalk | 69.99 | 81.92 | | person | 82.45 | 92.94 | | earth | 34.58 | 47.07 | | door | 58.52 | 75.37 | | table | 69.95 | 82.2 | | mountain | 63.09 | 76.43 | | plant | 55.46 | 67.07 | | curtain | 76.66 | 85.98 | | chair | 61.92 | 74.35 | | car | 86.47 | 92.9 | | water | 65.5 | 82.19 | | painting | 78.27 | 88.18 | | sofa | 77.63 | 92.42 | | shelf | 48.12 | 65.94 | | house | 36.39 | 43.7 | | sea | 70.6 | 81.44 | | mirror | 74.33 | 83.85 | | rug | 65.42 | 76.51 | | field | 30.89 | 48.49 | | armchair | 52.14 | 65.73 | | seat | 63.93 | 87.73 | | fence | 47.69 | 63.38 | | desk | 55.45 | 72.41 | | rock | 57.22 | 77.77 | | wardrobe | 51.79 | 71.81 | | lamp | 68.59 | 79.75 | | bathtub | 89.18 | 92.4 | | railing | 42.39 | 55.6 | | cushion | 63.31 | 78.0 | | base | 34.01 | 53.28 | | box | 37.86 | 52.05 | | column | 54.56 | 66.11 | | signboard | 37.18 | 52.03 | | chest of drawers | 47.35 | 70.68 | | counter | 52.33 | 62.02 | | sand | 60.57 | 89.04 | | sink | 77.03 | 83.35 | | skyscraper | 46.66 | 56.04 | | fireplace | 75.69 | 88.35 | | refrigerator | 84.86 | 90.66 | | grandstand | 46.43 | 84.79 | | path | 30.18 | 41.1 | | stairs | 30.08 | 37.95 | | runway | 69.01 | 88.35 | | case | 62.2 | 85.2 | | pool table | 93.46 | 97.85 | | pillow | 58.72 | 67.85 | | screen door | 83.2 | 88.14 | | stairway | 34.6 | 45.37 | | river | 17.0 | 32.21 | | bridge | 74.32 | 84.8 | | bookcase | 44.88 | 55.22 | | blind | 38.21 | 41.66 | | coffee table | 68.37 | 86.43 | | toilet | 89.12 | 92.81 | | flower | 42.0 | 61.15 | | book | 48.76 | 72.94 | | hill | 10.6 | 18.2 | | bench | 55.64 | 62.38 | | countertop | 63.93 | 78.67 | | stove | 84.82 | 90.93 | | palm | 47.77 | 81.38 | | kitchen island | 49.67 | 71.73 | | computer | 77.6 | 87.6 | | swivel chair | 46.46 | 65.53 | | boat | 67.31 | 82.04 | | bar | 76.4 | 82.98 | | arcade machine | 71.41 | 73.78 | | hovel | 20.89 | 23.43 | | bus | 93.08 | 95.86 | | towel | 72.15 | 82.58 | | light | 49.24 | 60.97 | | truck | 49.96 | 60.3 | | tower | 32.21 | 44.2 | | chandelier | 66.87 | 80.6 | | awning | 38.99 | 46.39 | | streetlight | 25.15 | 34.4 | | booth | 58.49 | 59.43 | | television receiver | 74.37 | 87.94 | | airplane | 76.29 | 81.97 | | dirt track | 9.93 | 19.63 | | apparel | 47.53 | 62.34 | | pole | 24.94 | 32.83 | | land | 7.63 | 15.65 | | bannister | 14.89 | 20.92 | | escalator | 60.33 | 81.15 | | ottoman | 55.13 | 69.57 | | bottle | 25.86 | 34.63 | | buffet | 59.61 | 78.78 | | poster | 37.05 | 42.94 | | stage | 21.23 | 43.4 | | van | 49.73 | 72.72 | | ship | 14.19 | 15.76 | | fountain | 32.8 | 33.96 | | conveyer belt | 84.88 | 92.15 | | canopy | 31.56 | 37.67 | | washer | 83.76 | 88.88 | | plaything | 31.06 | 42.92 | | swimming pool | 55.99 | 70.33 | | stool | 52.12 | 64.02 | | barrel | 70.91 | 85.19 | | basket | 35.12 | 44.85 | | waterfall | 43.48 | 56.79 | | tent | 90.65 | 97.93 | | bag | 24.61 | 28.39 | | minibike | 74.09 | 86.68 | | cradle | 84.23 | 98.01 | | oven | 64.48 | 78.02 | | ball | 47.35 | 50.84 | | food | 54.95 | 58.72 | | step | 20.41 | 26.46 | | tank | 54.9 | 64.9 | | trade name | 25.75 | 30.4 | | microwave | 89.31 | 94.18 | | pot | 55.55 | 62.22 | | animal | 73.76 | 77.21 | | bicycle | 59.12 | 76.1 | | lake | 55.74 | 63.59 | | dishwasher | 74.88 | 84.47 | | screen | 66.88 | 86.36 | | blanket | 30.07 | 35.2 | | sculpture | 77.13 | 87.19 | | hood | 69.05 | 78.92 | | sconce | 53.26 | 65.38 | | vase | 44.68 | 60.96 | | traffic light | 37.79 | 59.48 | | tray | 21.65 | 30.01 | | ashcan | 51.16 | 66.06 | | fan | 62.3 | 72.69 | | pier | 36.9 | 41.27 | | crt screen | 12.9 | 21.31 | | plate | 55.25 | 73.62 | | monitor | 52.56 | 66.23 | | bulletin board | 50.76 | 60.08 | | shower | 6.13 | 6.54 | | radiator | 66.39 | 83.47 | | glass | 18.53 | 20.87 | | clock | 33.07 | 38.07 | | flag | 65.55 | 72.26 | +---------------------+-------+-------+ 2023-11-03 06:18:18,537 - mmseg - INFO - Summary: 2023-11-03 06:18:18,538 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.19 | 55.57 | 67.05 | +-------+-------+-------+ 2023-11-03 06:18:18,538 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 06:18:18,539 - mmseg - INFO - Iter(val) [250] aAcc: 0.8519, mIoU: 0.5557, mAcc: 0.6705, IoU.wall: 0.8072, IoU.building: 0.8324, IoU.sky: 0.9427, IoU.floor: 0.8322, IoU.tree: 0.7580, IoU.ceiling: 0.8452, IoU.road: 0.8650, IoU.bed : 0.9123, IoU.windowpane: 0.6547, IoU.grass: 0.6794, IoU.cabinet: 0.6581, IoU.sidewalk: 0.6999, IoU.person: 0.8245, IoU.earth: 0.3458, IoU.door: 0.5852, IoU.table: 0.6995, IoU.mountain: 0.6309, IoU.plant: 0.5546, IoU.curtain: 0.7666, IoU.chair: 0.6192, IoU.car: 0.8647, IoU.water: 0.6550, IoU.painting: 0.7827, IoU.sofa: 0.7763, IoU.shelf: 0.4812, IoU.house: 0.3639, IoU.sea: 0.7060, IoU.mirror: 0.7433, IoU.rug: 0.6542, IoU.field: 0.3089, IoU.armchair: 0.5214, IoU.seat: 0.6393, IoU.fence: 0.4769, IoU.desk: 0.5545, IoU.rock: 0.5722, IoU.wardrobe: 0.5179, IoU.lamp: 0.6859, IoU.bathtub: 0.8918, IoU.railing: 0.4239, IoU.cushion: 0.6331, IoU.base: 0.3401, IoU.box: 0.3786, IoU.column: 0.5456, IoU.signboard: 0.3718, IoU.chest of drawers: 0.4735, IoU.counter: 0.5233, IoU.sand: 0.6057, IoU.sink: 0.7703, IoU.skyscraper: 0.4666, IoU.fireplace: 0.7569, IoU.refrigerator: 0.8486, IoU.grandstand: 0.4643, IoU.path: 0.3018, IoU.stairs: 0.3008, IoU.runway: 0.6901, IoU.case: 0.6220, IoU.pool table: 0.9346, IoU.pillow: 0.5872, IoU.screen door: 0.8320, IoU.stairway: 0.3460, IoU.river: 0.1700, IoU.bridge: 0.7432, IoU.bookcase: 0.4488, IoU.blind: 0.3821, IoU.coffee table: 0.6837, IoU.toilet: 0.8912, IoU.flower: 0.4200, IoU.book: 0.4876, IoU.hill: 0.1060, IoU.bench: 0.5564, IoU.countertop: 0.6393, IoU.stove: 0.8482, IoU.palm: 0.4777, IoU.kitchen island: 0.4967, IoU.computer: 0.7760, IoU.swivel chair: 0.4646, IoU.boat: 0.6731, IoU.bar: 0.7640, IoU.arcade machine: 0.7141, IoU.hovel: 0.2089, IoU.bus: 0.9308, IoU.towel: 0.7215, IoU.light: 0.4924, IoU.truck: 0.4996, IoU.tower: 0.3221, IoU.chandelier: 0.6687, IoU.awning: 0.3899, IoU.streetlight: 0.2515, IoU.booth: 0.5849, IoU.television receiver: 0.7437, IoU.airplane: 0.7629, IoU.dirt track: 0.0993, IoU.apparel: 0.4753, IoU.pole: 0.2494, IoU.land: 0.0763, IoU.bannister: 0.1489, IoU.escalator: 0.6033, IoU.ottoman: 0.5513, IoU.bottle: 0.2586, IoU.buffet: 0.5961, IoU.poster: 0.3705, IoU.stage: 0.2123, IoU.van: 0.4973, IoU.ship: 0.1419, IoU.fountain: 0.3280, IoU.conveyer belt: 0.8488, IoU.canopy: 0.3156, IoU.washer: 0.8376, IoU.plaything: 0.3106, IoU.swimming pool: 0.5599, IoU.stool: 0.5212, IoU.barrel: 0.7091, IoU.basket: 0.3512, IoU.waterfall: 0.4348, IoU.tent: 0.9065, IoU.bag: 0.2461, IoU.minibike: 0.7409, IoU.cradle: 0.8423, IoU.oven: 0.6448, IoU.ball: 0.4735, IoU.food: 0.5495, IoU.step: 0.2041, IoU.tank: 0.5490, IoU.trade name: 0.2575, IoU.microwave: 0.8931, IoU.pot: 0.5555, IoU.animal: 0.7376, IoU.bicycle: 0.5912, IoU.lake: 0.5574, IoU.dishwasher: 0.7488, IoU.screen: 0.6688, IoU.blanket: 0.3007, IoU.sculpture: 0.7713, IoU.hood: 0.6905, IoU.sconce: 0.5326, IoU.vase: 0.4468, IoU.traffic light: 0.3779, IoU.tray: 0.2165, IoU.ashcan: 0.5116, IoU.fan: 0.6230, IoU.pier: 0.3690, IoU.crt screen: 0.1290, IoU.plate: 0.5525, IoU.monitor: 0.5256, IoU.bulletin board: 0.5076, IoU.shower: 0.0613, IoU.radiator: 0.6639, IoU.glass: 0.1853, IoU.clock: 0.3307, IoU.flag: 0.6555, Acc.wall: 0.8994, Acc.building: 0.9369, Acc.sky: 0.9733, Acc.floor: 0.9118, Acc.tree: 0.8685, Acc.ceiling: 0.9196, Acc.road: 0.9236, Acc.bed : 0.9674, Acc.windowpane: 0.8027, Acc.grass: 0.8545, Acc.cabinet: 0.7646, Acc.sidewalk: 0.8192, Acc.person: 0.9294, Acc.earth: 0.4707, Acc.door: 0.7537, Acc.table: 0.8220, Acc.mountain: 0.7643, Acc.plant: 0.6707, Acc.curtain: 0.8598, Acc.chair: 0.7435, Acc.car: 0.9290, Acc.water: 0.8219, Acc.painting: 0.8818, Acc.sofa: 0.9242, Acc.shelf: 0.6594, Acc.house: 0.4370, Acc.sea: 0.8144, Acc.mirror: 0.8385, Acc.rug: 0.7651, Acc.field: 0.4849, Acc.armchair: 0.6573, Acc.seat: 0.8773, Acc.fence: 0.6338, Acc.desk: 0.7241, Acc.rock: 0.7777, Acc.wardrobe: 0.7181, Acc.lamp: 0.7975, Acc.bathtub: 0.9240, Acc.railing: 0.5560, Acc.cushion: 0.7800, Acc.base: 0.5328, Acc.box: 0.5205, Acc.column: 0.6611, Acc.signboard: 0.5203, Acc.chest of drawers: 0.7068, Acc.counter: 0.6202, Acc.sand: 0.8904, Acc.sink: 0.8335, Acc.skyscraper: 0.5604, Acc.fireplace: 0.8835, Acc.refrigerator: 0.9066, Acc.grandstand: 0.8479, Acc.path: 0.4110, Acc.stairs: 0.3795, Acc.runway: 0.8835, Acc.case: 0.8520, Acc.pool table: 0.9785, Acc.pillow: 0.6785, Acc.screen door: 0.8814, Acc.stairway: 0.4537, Acc.river: 0.3221, Acc.bridge: 0.8480, Acc.bookcase: 0.5522, Acc.blind: 0.4166, Acc.coffee table: 0.8643, Acc.toilet: 0.9281, Acc.flower: 0.6115, Acc.book: 0.7294, Acc.hill: 0.1820, Acc.bench: 0.6238, Acc.countertop: 0.7867, Acc.stove: 0.9093, Acc.palm: 0.8138, Acc.kitchen island: 0.7173, Acc.computer: 0.8760, Acc.swivel chair: 0.6553, Acc.boat: 0.8204, Acc.bar: 0.8298, Acc.arcade machine: 0.7378, Acc.hovel: 0.2343, Acc.bus: 0.9586, Acc.towel: 0.8258, Acc.light: 0.6097, Acc.truck: 0.6030, Acc.tower: 0.4420, Acc.chandelier: 0.8060, Acc.awning: 0.4639, Acc.streetlight: 0.3440, Acc.booth: 0.5943, Acc.television receiver: 0.8794, Acc.airplane: 0.8197, Acc.dirt track: 0.1963, Acc.apparel: 0.6234, Acc.pole: 0.3283, Acc.land: 0.1565, Acc.bannister: 0.2092, Acc.escalator: 0.8115, Acc.ottoman: 0.6957, Acc.bottle: 0.3463, Acc.buffet: 0.7878, Acc.poster: 0.4294, Acc.stage: 0.4340, Acc.van: 0.7272, Acc.ship: 0.1576, Acc.fountain: 0.3396, Acc.conveyer belt: 0.9215, Acc.canopy: 0.3767, Acc.washer: 0.8888, Acc.plaything: 0.4292, Acc.swimming pool: 0.7033, Acc.stool: 0.6402, Acc.barrel: 0.8519, Acc.basket: 0.4485, Acc.waterfall: 0.5679, Acc.tent: 0.9793, Acc.bag: 0.2839, Acc.minibike: 0.8668, Acc.cradle: 0.9801, Acc.oven: 0.7802, Acc.ball: 0.5084, Acc.food: 0.5872, Acc.step: 0.2646, Acc.tank: 0.6490, Acc.trade name: 0.3040, Acc.microwave: 0.9418, Acc.pot: 0.6222, Acc.animal: 0.7721, Acc.bicycle: 0.7610, Acc.lake: 0.6359, Acc.dishwasher: 0.8447, Acc.screen: 0.8636, Acc.blanket: 0.3520, Acc.sculpture: 0.8719, Acc.hood: 0.7892, Acc.sconce: 0.6538, Acc.vase: 0.6096, Acc.traffic light: 0.5948, Acc.tray: 0.3001, Acc.ashcan: 0.6606, Acc.fan: 0.7269, Acc.pier: 0.4127, Acc.crt screen: 0.2131, Acc.plate: 0.7362, Acc.monitor: 0.6623, Acc.bulletin board: 0.6008, Acc.shower: 0.0654, Acc.radiator: 0.8347, Acc.glass: 0.2087, Acc.clock: 0.3807, Acc.flag: 0.7226 2023-11-03 06:19:19,285 - mmseg - INFO - Iter [31050/40000] lr: 7.250e-07, eta: 3:19:30, time: 2.480, data_time: 1.272, memory: 38534, decode.loss_ce: 0.1566, decode.acc_seg: 93.1930, loss: 0.1566 2023-11-03 06:20:19,945 - mmseg - INFO - Iter [31100/40000] lr: 7.209e-07, eta: 3:18:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1480, decode.acc_seg: 93.5985, loss: 0.1480 2023-11-03 06:21:20,562 - mmseg - INFO - Iter [31150/40000] lr: 7.169e-07, eta: 3:17:12, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1491, decode.acc_seg: 93.5374, loss: 0.1491 2023-11-03 06:22:21,226 - mmseg - INFO - Iter [31200/40000] lr: 7.128e-07, eta: 3:16:04, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1601, decode.acc_seg: 93.3125, loss: 0.1601 2023-11-03 06:23:21,854 - mmseg - INFO - Iter [31250/40000] lr: 7.088e-07, eta: 3:14:55, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1536, decode.acc_seg: 93.5019, loss: 0.1536 2023-11-03 06:24:22,460 - mmseg - INFO - Iter [31300/40000] lr: 7.047e-07, eta: 3:13:47, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1553, decode.acc_seg: 93.3535, loss: 0.1553 2023-11-03 06:25:23,099 - mmseg - INFO - Iter [31350/40000] lr: 7.007e-07, eta: 3:12:38, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1618, decode.acc_seg: 93.0020, loss: 0.1618 2023-11-03 06:26:23,786 - mmseg - INFO - Iter [31400/40000] lr: 6.966e-07, eta: 3:11:30, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1494, decode.acc_seg: 93.5970, loss: 0.1494 2023-11-03 06:27:24,427 - mmseg - INFO - Iter [31450/40000] lr: 6.926e-07, eta: 3:10:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1461, decode.acc_seg: 93.7419, loss: 0.1461 2023-11-03 06:28:25,103 - mmseg - INFO - Iter [31500/40000] lr: 6.885e-07, eta: 3:09:13, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1589, decode.acc_seg: 93.1910, loss: 0.1589 2023-11-03 06:29:25,765 - mmseg - INFO - Iter [31550/40000] lr: 6.845e-07, eta: 3:08:04, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1517, decode.acc_seg: 93.5329, loss: 0.1517 2023-11-03 06:30:26,389 - mmseg - INFO - Iter [31600/40000] lr: 6.804e-07, eta: 3:06:56, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1577, decode.acc_seg: 93.4189, loss: 0.1577 2023-11-03 06:31:29,487 - mmseg - INFO - Iter [31650/40000] lr: 6.764e-07, eta: 3:05:48, time: 1.262, data_time: 0.055, memory: 38534, decode.loss_ce: 0.1416, decode.acc_seg: 94.0025, loss: 0.1416 2023-11-03 06:32:30,098 - mmseg - INFO - Iter [31700/40000] lr: 6.723e-07, eta: 3:04:40, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1422, decode.acc_seg: 93.9526, loss: 0.1422 2023-11-03 06:33:30,741 - mmseg - INFO - Iter [31750/40000] lr: 6.683e-07, eta: 3:03:31, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1497, decode.acc_seg: 93.7346, loss: 0.1497 2023-11-03 06:34:31,368 - mmseg - INFO - Iter [31800/40000] lr: 6.642e-07, eta: 3:02:23, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1518, decode.acc_seg: 93.5222, loss: 0.1518 2023-11-03 06:35:32,061 - mmseg - INFO - Iter [31850/40000] lr: 6.602e-07, eta: 3:01:15, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1525, decode.acc_seg: 93.5674, loss: 0.1525 2023-11-03 06:36:32,707 - mmseg - INFO - Iter [31900/40000] lr: 6.561e-07, eta: 3:00:07, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1505, decode.acc_seg: 93.4575, loss: 0.1505 2023-11-03 06:37:33,389 - mmseg - INFO - Iter [31950/40000] lr: 6.521e-07, eta: 2:58:58, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1658, decode.acc_seg: 93.2051, loss: 0.1658 2023-11-03 06:38:34,139 - mmseg - INFO - Saving checkpoint at 32000 iterations 2023-11-03 06:39:35,499 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 06:39:35,499 - mmseg - INFO - Iter [32000/40000] lr: 6.480e-07, eta: 2:58:05, time: 2.442, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1545, decode.acc_seg: 93.3769, loss: 0.1545 2023-11-03 06:40:33,573 - mmseg - INFO - per class results: 2023-11-03 06:40:33,579 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 81.04 | 89.61 | | building | 83.81 | 92.91 | | sky | 94.35 | 97.23 | | floor | 83.14 | 92.1 | | tree | 75.92 | 88.89 | | ceiling | 84.84 | 92.4 | | road | 84.99 | 92.07 | | bed | 91.4 | 96.8 | | windowpane | 65.46 | 80.72 | | grass | 68.77 | 82.71 | | cabinet | 66.48 | 76.77 | | sidewalk | 69.09 | 82.76 | | person | 82.38 | 92.65 | | earth | 36.05 | 48.6 | | door | 57.99 | 73.65 | | table | 69.59 | 83.18 | | mountain | 62.58 | 75.89 | | plant | 55.72 | 66.03 | | curtain | 77.53 | 88.82 | | chair | 61.59 | 73.54 | | car | 86.26 | 93.18 | | water | 64.97 | 80.74 | | painting | 78.81 | 89.05 | | sofa | 82.15 | 90.81 | | shelf | 48.29 | 65.33 | | house | 45.05 | 56.69 | | sea | 70.63 | 80.92 | | mirror | 75.76 | 85.55 | | rug | 61.03 | 68.86 | | field | 37.73 | 62.38 | | armchair | 58.74 | 76.68 | | seat | 64.18 | 88.63 | | fence | 46.9 | 61.13 | | desk | 56.76 | 76.62 | | rock | 56.89 | 75.8 | | wardrobe | 52.41 | 72.07 | | lamp | 67.98 | 79.89 | | bathtub | 89.04 | 92.18 | | railing | 43.0 | 58.13 | | cushion | 64.0 | 78.27 | | base | 33.76 | 51.78 | | box | 37.47 | 47.65 | | column | 55.1 | 67.73 | | signboard | 36.68 | 52.71 | | chest of drawers | 46.69 | 69.73 | | counter | 49.83 | 59.76 | | sand | 62.15 | 89.27 | | sink | 77.27 | 85.25 | | skyscraper | 47.62 | 59.37 | | fireplace | 75.1 | 90.31 | | refrigerator | 84.91 | 91.74 | | grandstand | 46.2 | 83.74 | | path | 27.27 | 36.13 | | stairs | 30.1 | 39.04 | | runway | 69.19 | 87.89 | | case | 60.62 | 86.66 | | pool table | 93.92 | 97.53 | | pillow | 59.13 | 68.58 | | screen door | 78.66 | 82.14 | | stairway | 32.31 | 42.59 | | river | 20.13 | 38.03 | | bridge | 77.12 | 84.38 | | bookcase | 44.89 | 54.49 | | blind | 40.8 | 45.86 | | coffee table | 67.92 | 86.89 | | toilet | 89.3 | 92.83 | | flower | 42.97 | 62.92 | | book | 49.13 | 73.81 | | hill | 8.9 | 19.17 | | bench | 55.23 | 63.61 | | countertop | 62.51 | 75.94 | | stove | 84.9 | 89.81 | | palm | 47.33 | 81.09 | | kitchen island | 49.32 | 67.6 | | computer | 77.56 | 88.61 | | swivel chair | 45.49 | 66.01 | | boat | 67.03 | 81.97 | | bar | 74.54 | 80.37 | | arcade machine | 72.77 | 75.76 | | hovel | 21.8 | 24.37 | | bus | 92.86 | 96.65 | | towel | 72.13 | 83.95 | | light | 46.49 | 55.65 | | truck | 49.22 | 62.26 | | tower | 30.2 | 41.48 | | chandelier | 67.28 | 83.54 | | awning | 44.66 | 56.04 | | streetlight | 25.63 | 35.14 | | booth | 55.39 | 57.07 | | television receiver | 74.88 | 89.93 | | airplane | 83.66 | 93.47 | | dirt track | 13.08 | 17.21 | | apparel | 48.81 | 68.23 | | pole | 24.67 | 32.44 | | land | 10.57 | 15.29 | | bannister | 15.01 | 19.87 | | escalator | 59.89 | 83.66 | | ottoman | 53.71 | 70.92 | | bottle | 29.76 | 44.27 | | buffet | 58.86 | 78.33 | | poster | 36.86 | 45.75 | | stage | 22.57 | 44.87 | | van | 47.86 | 69.38 | | ship | 10.24 | 11.46 | | fountain | 33.38 | 34.13 | | conveyer belt | 84.29 | 91.64 | | canopy | 31.88 | 37.32 | | washer | 85.62 | 91.55 | | plaything | 30.49 | 41.68 | | swimming pool | 49.12 | 69.36 | | stool | 50.95 | 63.53 | | barrel | 70.31 | 86.64 | | basket | 36.53 | 49.01 | | waterfall | 46.47 | 62.92 | | tent | 94.61 | 97.77 | | bag | 24.76 | 29.28 | | minibike | 74.39 | 87.98 | | cradle | 85.4 | 97.24 | | oven | 65.23 | 76.57 | | ball | 49.51 | 53.1 | | food | 49.9 | 52.39 | | step | 12.66 | 14.0 | | tank | 54.29 | 65.25 | | trade name | 27.09 | 32.62 | | microwave | 89.51 | 94.68 | | pot | 55.3 | 61.77 | | animal | 73.22 | 76.43 | | bicycle | 59.72 | 80.91 | | lake | 55.81 | 63.58 | | dishwasher | 73.59 | 81.75 | | screen | 70.2 | 92.31 | | blanket | 31.74 | 36.82 | | sculpture | 75.63 | 88.19 | | hood | 66.87 | 77.88 | | sconce | 54.69 | 70.46 | | vase | 43.72 | 57.89 | | traffic light | 36.95 | 58.97 | | tray | 21.59 | 28.5 | | ashcan | 50.47 | 64.27 | | fan | 62.21 | 74.5 | | pier | 37.2 | 42.24 | | crt screen | 12.03 | 21.63 | | plate | 56.48 | 72.66 | | monitor | 38.93 | 47.82 | | bulletin board | 50.46 | 61.67 | | shower | 9.21 | 10.34 | | radiator | 66.71 | 83.2 | | glass | 18.59 | 20.77 | | clock | 33.76 | 39.23 | | flag | 66.19 | 73.7 | +---------------------+-------+-------+ 2023-11-03 06:40:33,579 - mmseg - INFO - Summary: 2023-11-03 06:40:33,579 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.33 | 55.66 | 67.44 | +-------+-------+-------+ 2023-11-03 06:40:33,580 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 06:40:33,580 - mmseg - INFO - Iter(val) [250] aAcc: 0.8533, mIoU: 0.5566, mAcc: 0.6744, IoU.wall: 0.8104, IoU.building: 0.8381, IoU.sky: 0.9435, IoU.floor: 0.8314, IoU.tree: 0.7592, IoU.ceiling: 0.8484, IoU.road: 0.8499, IoU.bed : 0.9140, IoU.windowpane: 0.6546, IoU.grass: 0.6877, IoU.cabinet: 0.6648, IoU.sidewalk: 0.6909, IoU.person: 0.8238, IoU.earth: 0.3605, IoU.door: 0.5799, IoU.table: 0.6959, IoU.mountain: 0.6258, IoU.plant: 0.5572, IoU.curtain: 0.7753, IoU.chair: 0.6159, IoU.car: 0.8626, IoU.water: 0.6497, IoU.painting: 0.7881, IoU.sofa: 0.8215, IoU.shelf: 0.4829, IoU.house: 0.4505, IoU.sea: 0.7063, IoU.mirror: 0.7576, IoU.rug: 0.6103, IoU.field: 0.3773, IoU.armchair: 0.5874, IoU.seat: 0.6418, IoU.fence: 0.4690, IoU.desk: 0.5676, IoU.rock: 0.5689, IoU.wardrobe: 0.5241, IoU.lamp: 0.6798, IoU.bathtub: 0.8904, IoU.railing: 0.4300, IoU.cushion: 0.6400, IoU.base: 0.3376, IoU.box: 0.3747, IoU.column: 0.5510, IoU.signboard: 0.3668, IoU.chest of drawers: 0.4669, IoU.counter: 0.4983, IoU.sand: 0.6215, IoU.sink: 0.7727, IoU.skyscraper: 0.4762, IoU.fireplace: 0.7510, IoU.refrigerator: 0.8491, IoU.grandstand: 0.4620, IoU.path: 0.2727, IoU.stairs: 0.3010, IoU.runway: 0.6919, IoU.case: 0.6062, IoU.pool table: 0.9392, IoU.pillow: 0.5913, IoU.screen door: 0.7866, IoU.stairway: 0.3231, IoU.river: 0.2013, IoU.bridge: 0.7712, IoU.bookcase: 0.4489, IoU.blind: 0.4080, IoU.coffee table: 0.6792, IoU.toilet: 0.8930, IoU.flower: 0.4297, IoU.book: 0.4913, IoU.hill: 0.0890, IoU.bench: 0.5523, IoU.countertop: 0.6251, IoU.stove: 0.8490, IoU.palm: 0.4733, IoU.kitchen island: 0.4932, IoU.computer: 0.7756, IoU.swivel chair: 0.4549, IoU.boat: 0.6703, IoU.bar: 0.7454, IoU.arcade machine: 0.7277, IoU.hovel: 0.2180, IoU.bus: 0.9286, IoU.towel: 0.7213, IoU.light: 0.4649, IoU.truck: 0.4922, IoU.tower: 0.3020, IoU.chandelier: 0.6728, IoU.awning: 0.4466, IoU.streetlight: 0.2563, IoU.booth: 0.5539, IoU.television receiver: 0.7488, IoU.airplane: 0.8366, IoU.dirt track: 0.1308, IoU.apparel: 0.4881, IoU.pole: 0.2467, IoU.land: 0.1057, IoU.bannister: 0.1501, IoU.escalator: 0.5989, IoU.ottoman: 0.5371, IoU.bottle: 0.2976, IoU.buffet: 0.5886, IoU.poster: 0.3686, IoU.stage: 0.2257, IoU.van: 0.4786, IoU.ship: 0.1024, IoU.fountain: 0.3338, IoU.conveyer belt: 0.8429, IoU.canopy: 0.3188, IoU.washer: 0.8562, IoU.plaything: 0.3049, IoU.swimming pool: 0.4912, IoU.stool: 0.5095, IoU.barrel: 0.7031, IoU.basket: 0.3653, IoU.waterfall: 0.4647, IoU.tent: 0.9461, IoU.bag: 0.2476, IoU.minibike: 0.7439, IoU.cradle: 0.8540, IoU.oven: 0.6523, IoU.ball: 0.4951, IoU.food: 0.4990, IoU.step: 0.1266, IoU.tank: 0.5429, IoU.trade name: 0.2709, IoU.microwave: 0.8951, IoU.pot: 0.5530, IoU.animal: 0.7322, IoU.bicycle: 0.5972, IoU.lake: 0.5581, IoU.dishwasher: 0.7359, IoU.screen: 0.7020, IoU.blanket: 0.3174, IoU.sculpture: 0.7563, IoU.hood: 0.6687, IoU.sconce: 0.5469, IoU.vase: 0.4372, IoU.traffic light: 0.3695, IoU.tray: 0.2159, IoU.ashcan: 0.5047, IoU.fan: 0.6221, IoU.pier: 0.3720, IoU.crt screen: 0.1203, IoU.plate: 0.5648, IoU.monitor: 0.3893, IoU.bulletin board: 0.5046, IoU.shower: 0.0921, IoU.radiator: 0.6671, IoU.glass: 0.1859, IoU.clock: 0.3376, IoU.flag: 0.6619, Acc.wall: 0.8961, Acc.building: 0.9291, Acc.sky: 0.9723, Acc.floor: 0.9210, Acc.tree: 0.8889, Acc.ceiling: 0.9240, Acc.road: 0.9207, Acc.bed : 0.9680, Acc.windowpane: 0.8072, Acc.grass: 0.8271, Acc.cabinet: 0.7677, Acc.sidewalk: 0.8276, Acc.person: 0.9265, Acc.earth: 0.4860, Acc.door: 0.7365, Acc.table: 0.8318, Acc.mountain: 0.7589, Acc.plant: 0.6603, Acc.curtain: 0.8882, Acc.chair: 0.7354, Acc.car: 0.9318, Acc.water: 0.8074, Acc.painting: 0.8905, Acc.sofa: 0.9081, Acc.shelf: 0.6533, Acc.house: 0.5669, Acc.sea: 0.8092, Acc.mirror: 0.8555, Acc.rug: 0.6886, Acc.field: 0.6238, Acc.armchair: 0.7668, Acc.seat: 0.8863, Acc.fence: 0.6113, Acc.desk: 0.7662, Acc.rock: 0.7580, Acc.wardrobe: 0.7207, Acc.lamp: 0.7989, Acc.bathtub: 0.9218, Acc.railing: 0.5813, Acc.cushion: 0.7827, Acc.base: 0.5178, Acc.box: 0.4765, Acc.column: 0.6773, Acc.signboard: 0.5271, Acc.chest of drawers: 0.6973, Acc.counter: 0.5976, Acc.sand: 0.8927, Acc.sink: 0.8525, Acc.skyscraper: 0.5937, Acc.fireplace: 0.9031, Acc.refrigerator: 0.9174, Acc.grandstand: 0.8374, Acc.path: 0.3613, Acc.stairs: 0.3904, Acc.runway: 0.8789, Acc.case: 0.8666, Acc.pool table: 0.9753, Acc.pillow: 0.6858, Acc.screen door: 0.8214, Acc.stairway: 0.4259, Acc.river: 0.3803, Acc.bridge: 0.8438, Acc.bookcase: 0.5449, Acc.blind: 0.4586, Acc.coffee table: 0.8689, Acc.toilet: 0.9283, Acc.flower: 0.6292, Acc.book: 0.7381, Acc.hill: 0.1917, Acc.bench: 0.6361, Acc.countertop: 0.7594, Acc.stove: 0.8981, Acc.palm: 0.8109, Acc.kitchen island: 0.6760, Acc.computer: 0.8861, Acc.swivel chair: 0.6601, Acc.boat: 0.8197, Acc.bar: 0.8037, Acc.arcade machine: 0.7576, Acc.hovel: 0.2437, Acc.bus: 0.9665, Acc.towel: 0.8395, Acc.light: 0.5565, Acc.truck: 0.6226, Acc.tower: 0.4148, Acc.chandelier: 0.8354, Acc.awning: 0.5604, Acc.streetlight: 0.3514, Acc.booth: 0.5707, Acc.television receiver: 0.8993, Acc.airplane: 0.9347, Acc.dirt track: 0.1721, Acc.apparel: 0.6823, Acc.pole: 0.3244, Acc.land: 0.1529, Acc.bannister: 0.1987, Acc.escalator: 0.8366, Acc.ottoman: 0.7092, Acc.bottle: 0.4427, Acc.buffet: 0.7833, Acc.poster: 0.4575, Acc.stage: 0.4487, Acc.van: 0.6938, Acc.ship: 0.1146, Acc.fountain: 0.3413, Acc.conveyer belt: 0.9164, Acc.canopy: 0.3732, Acc.washer: 0.9155, Acc.plaything: 0.4168, Acc.swimming pool: 0.6936, Acc.stool: 0.6353, Acc.barrel: 0.8664, Acc.basket: 0.4901, Acc.waterfall: 0.6292, Acc.tent: 0.9777, Acc.bag: 0.2928, Acc.minibike: 0.8798, Acc.cradle: 0.9724, Acc.oven: 0.7657, Acc.ball: 0.5310, Acc.food: 0.5239, Acc.step: 0.1400, Acc.tank: 0.6525, Acc.trade name: 0.3262, Acc.microwave: 0.9468, Acc.pot: 0.6177, Acc.animal: 0.7643, Acc.bicycle: 0.8091, Acc.lake: 0.6358, Acc.dishwasher: 0.8175, Acc.screen: 0.9231, Acc.blanket: 0.3682, Acc.sculpture: 0.8819, Acc.hood: 0.7788, Acc.sconce: 0.7046, Acc.vase: 0.5789, Acc.traffic light: 0.5897, Acc.tray: 0.2850, Acc.ashcan: 0.6427, Acc.fan: 0.7450, Acc.pier: 0.4224, Acc.crt screen: 0.2163, Acc.plate: 0.7266, Acc.monitor: 0.4782, Acc.bulletin board: 0.6167, Acc.shower: 0.1034, Acc.radiator: 0.8320, Acc.glass: 0.2077, Acc.clock: 0.3923, Acc.flag: 0.7370 2023-11-03 06:41:34,355 - mmseg - INFO - Iter [32050/40000] lr: 6.440e-07, eta: 2:57:12, time: 2.377, data_time: 1.169, memory: 38534, decode.loss_ce: 0.1555, decode.acc_seg: 93.5184, loss: 0.1555 2023-11-03 06:42:35,076 - mmseg - INFO - Iter [32100/40000] lr: 6.399e-07, eta: 2:56:03, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1424, decode.acc_seg: 93.8924, loss: 0.1424 2023-11-03 06:43:35,773 - mmseg - INFO - Iter [32150/40000] lr: 6.359e-07, eta: 2:54:55, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1509, decode.acc_seg: 93.6445, loss: 0.1509 2023-11-03 06:44:36,464 - mmseg - INFO - Iter [32200/40000] lr: 6.318e-07, eta: 2:53:47, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1516, decode.acc_seg: 93.4292, loss: 0.1516 2023-11-03 06:45:39,412 - mmseg - INFO - Iter [32250/40000] lr: 6.278e-07, eta: 2:52:39, time: 1.259, data_time: 0.050, memory: 38534, decode.loss_ce: 0.1585, decode.acc_seg: 93.3561, loss: 0.1585 2023-11-03 06:46:40,107 - mmseg - INFO - Iter [32300/40000] lr: 6.237e-07, eta: 2:51:30, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1519, decode.acc_seg: 93.4913, loss: 0.1519 2023-11-03 06:47:40,789 - mmseg - INFO - Iter [32350/40000] lr: 6.197e-07, eta: 2:50:22, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1499, decode.acc_seg: 93.6584, loss: 0.1499 2023-11-03 06:48:41,454 - mmseg - INFO - Iter [32400/40000] lr: 6.156e-07, eta: 2:49:14, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1534, decode.acc_seg: 93.5700, loss: 0.1534 2023-11-03 06:49:42,129 - mmseg - INFO - Iter [32450/40000] lr: 6.116e-07, eta: 2:48:06, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1462, decode.acc_seg: 93.8393, loss: 0.1462 2023-11-03 06:50:42,798 - mmseg - INFO - Iter [32500/40000] lr: 6.075e-07, eta: 2:46:57, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1498, decode.acc_seg: 93.6758, loss: 0.1498 2023-11-03 06:51:43,478 - mmseg - INFO - Iter [32550/40000] lr: 6.035e-07, eta: 2:45:49, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1551, decode.acc_seg: 93.3690, loss: 0.1551 2023-11-03 06:52:44,111 - mmseg - INFO - Iter [32600/40000] lr: 5.994e-07, eta: 2:44:41, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1397, decode.acc_seg: 93.9659, loss: 0.1397 2023-11-03 06:53:44,737 - mmseg - INFO - Iter [32650/40000] lr: 5.954e-07, eta: 2:43:33, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1495, decode.acc_seg: 93.5933, loss: 0.1495 2023-11-03 06:54:45,371 - mmseg - INFO - Iter [32700/40000] lr: 5.913e-07, eta: 2:42:25, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1492, decode.acc_seg: 93.6148, loss: 0.1492 2023-11-03 06:55:46,014 - mmseg - INFO - Iter [32750/40000] lr: 5.873e-07, eta: 2:41:17, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1482, decode.acc_seg: 93.7186, loss: 0.1482 2023-11-03 06:56:46,718 - mmseg - INFO - Iter [32800/40000] lr: 5.832e-07, eta: 2:40:09, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1517, decode.acc_seg: 93.6063, loss: 0.1517 2023-11-03 06:57:47,409 - mmseg - INFO - Iter [32850/40000] lr: 5.792e-07, eta: 2:39:01, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1476, decode.acc_seg: 93.7091, loss: 0.1476 2023-11-03 06:58:50,578 - mmseg - INFO - Iter [32900/40000] lr: 5.751e-07, eta: 2:37:53, time: 1.263, data_time: 0.055, memory: 38534, decode.loss_ce: 0.1511, decode.acc_seg: 93.4856, loss: 0.1511 2023-11-03 06:59:51,229 - mmseg - INFO - Iter [32950/40000] lr: 5.711e-07, eta: 2:36:45, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1498, decode.acc_seg: 93.6032, loss: 0.1498 2023-11-03 07:00:51,909 - mmseg - INFO - Saving checkpoint at 33000 iterations 2023-11-03 07:01:47,920 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 07:01:47,920 - mmseg - INFO - Iter [33000/40000] lr: 5.670e-07, eta: 2:35:49, time: 2.334, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1480, decode.acc_seg: 93.5975, loss: 0.1480 2023-11-03 07:02:45,796 - mmseg - INFO - per class results: 2023-11-03 07:02:45,801 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.8 | 90.49 | | building | 83.66 | 93.03 | | sky | 94.24 | 97.71 | | floor | 83.42 | 90.93 | | tree | 75.85 | 87.97 | | ceiling | 84.67 | 92.79 | | road | 85.37 | 91.5 | | bed | 91.56 | 96.49 | | windowpane | 65.21 | 80.12 | | grass | 68.47 | 84.59 | | cabinet | 66.54 | 76.71 | | sidewalk | 69.7 | 84.48 | | person | 82.65 | 92.76 | | earth | 36.46 | 48.31 | | door | 57.98 | 73.39 | | table | 69.75 | 81.69 | | mountain | 62.15 | 74.38 | | plant | 55.33 | 66.99 | | curtain | 76.18 | 87.33 | | chair | 61.62 | 75.33 | | car | 86.3 | 93.17 | | water | 65.61 | 81.96 | | painting | 78.5 | 87.53 | | sofa | 80.69 | 91.88 | | shelf | 49.45 | 69.83 | | house | 48.33 | 60.51 | | sea | 73.76 | 84.97 | | mirror | 74.5 | 84.34 | | rug | 65.22 | 75.6 | | field | 31.8 | 47.57 | | armchair | 56.43 | 71.99 | | seat | 64.76 | 88.19 | | fence | 47.78 | 63.81 | | desk | 57.71 | 72.75 | | rock | 57.49 | 78.86 | | wardrobe | 51.05 | 68.41 | | lamp | 67.52 | 77.37 | | bathtub | 89.12 | 92.82 | | railing | 42.69 | 58.79 | | cushion | 63.2 | 75.93 | | base | 33.99 | 54.26 | | box | 37.32 | 48.42 | | column | 53.5 | 64.27 | | signboard | 36.25 | 51.5 | | chest of drawers | 47.31 | 66.9 | | counter | 51.88 | 60.63 | | sand | 61.16 | 89.01 | | sink | 76.76 | 84.02 | | skyscraper | 48.11 | 55.76 | | fireplace | 73.46 | 86.46 | | refrigerator | 83.75 | 91.08 | | grandstand | 47.18 | 80.91 | | path | 27.79 | 37.4 | | stairs | 29.61 | 36.92 | | runway | 68.73 | 89.03 | | case | 61.21 | 84.88 | | pool table | 93.87 | 97.69 | | pillow | 59.62 | 68.77 | | screen door | 76.38 | 78.28 | | stairway | 36.06 | 46.98 | | river | 19.22 | 33.67 | | bridge | 76.73 | 86.7 | | bookcase | 46.1 | 55.77 | | blind | 38.97 | 43.29 | | coffee table | 68.72 | 86.27 | | toilet | 89.07 | 93.29 | | flower | 43.19 | 62.28 | | book | 49.88 | 70.11 | | hill | 9.06 | 18.37 | | bench | 55.28 | 61.71 | | countertop | 63.28 | 75.44 | | stove | 83.99 | 88.09 | | palm | 47.52 | 79.35 | | kitchen island | 48.3 | 72.67 | | computer | 78.04 | 86.84 | | swivel chair | 44.41 | 61.25 | | boat | 56.73 | 67.82 | | bar | 73.28 | 82.33 | | arcade machine | 73.3 | 75.81 | | hovel | 16.97 | 18.66 | | bus | 93.04 | 96.15 | | towel | 72.29 | 81.9 | | light | 46.77 | 54.2 | | truck | 50.56 | 62.57 | | tower | 25.02 | 34.04 | | chandelier | 66.87 | 82.29 | | awning | 44.23 | 54.68 | | streetlight | 25.81 | 35.66 | | booth | 55.02 | 55.61 | | television receiver | 77.27 | 89.0 | | airplane | 78.04 | 83.77 | | dirt track | 14.68 | 17.9 | | apparel | 48.13 | 68.52 | | pole | 24.72 | 32.33 | | land | 8.27 | 15.41 | | bannister | 14.29 | 19.69 | | escalator | 61.71 | 81.47 | | ottoman | 52.93 | 73.76 | | bottle | 25.49 | 33.32 | | buffet | 60.94 | 77.28 | | poster | 37.18 | 44.33 | | stage | 22.01 | 40.73 | | van | 49.48 | 69.91 | | ship | 22.91 | 29.39 | | fountain | 30.13 | 30.31 | | conveyer belt | 84.63 | 90.32 | | canopy | 28.59 | 31.46 | | washer | 84.31 | 89.71 | | plaything | 30.89 | 41.51 | | swimming pool | 53.88 | 68.54 | | stool | 49.67 | 63.54 | | barrel | 70.48 | 83.57 | | basket | 36.12 | 47.8 | | waterfall | 46.14 | 58.14 | | tent | 94.65 | 97.43 | | bag | 24.42 | 28.33 | | minibike | 74.65 | 84.73 | | cradle | 86.36 | 96.32 | | oven | 65.39 | 76.93 | | ball | 21.33 | 21.97 | | food | 55.08 | 58.57 | | step | 20.12 | 23.6 | | tank | 54.2 | 64.91 | | trade name | 27.38 | 33.04 | | microwave | 89.0 | 93.32 | | pot | 57.56 | 65.56 | | animal | 72.45 | 75.76 | | bicycle | 60.12 | 77.41 | | lake | 55.98 | 63.48 | | dishwasher | 74.49 | 85.13 | | screen | 64.56 | 85.07 | | blanket | 32.29 | 37.05 | | sculpture | 77.48 | 86.77 | | hood | 71.44 | 80.97 | | sconce | 52.82 | 65.71 | | vase | 44.49 | 57.21 | | traffic light | 38.36 | 57.08 | | tray | 21.44 | 27.15 | | ashcan | 50.25 | 65.9 | | fan | 59.35 | 67.0 | | pier | 36.01 | 39.28 | | crt screen | 11.61 | 22.79 | | plate | 55.64 | 75.3 | | monitor | 42.82 | 52.13 | | bulletin board | 48.55 | 60.25 | | shower | 5.78 | 6.05 | | radiator | 68.56 | 79.98 | | glass | 18.92 | 21.48 | | clock | 34.06 | 38.28 | | flag | 65.2 | 71.48 | +---------------------+-------+-------+ 2023-11-03 07:02:45,801 - mmseg - INFO - Summary: 2023-11-03 07:02:45,801 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.33 | 55.43 | 66.46 | +-------+-------+-------+ 2023-11-03 07:02:45,802 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 07:02:45,802 - mmseg - INFO - Iter(val) [250] aAcc: 0.8533, mIoU: 0.5543, mAcc: 0.6646, IoU.wall: 0.8080, IoU.building: 0.8366, IoU.sky: 0.9424, IoU.floor: 0.8342, IoU.tree: 0.7585, IoU.ceiling: 0.8467, IoU.road: 0.8537, IoU.bed : 0.9156, IoU.windowpane: 0.6521, IoU.grass: 0.6847, IoU.cabinet: 0.6654, IoU.sidewalk: 0.6970, IoU.person: 0.8265, IoU.earth: 0.3646, IoU.door: 0.5798, IoU.table: 0.6975, IoU.mountain: 0.6215, IoU.plant: 0.5533, IoU.curtain: 0.7618, IoU.chair: 0.6162, IoU.car: 0.8630, IoU.water: 0.6561, IoU.painting: 0.7850, IoU.sofa: 0.8069, IoU.shelf: 0.4945, IoU.house: 0.4833, IoU.sea: 0.7376, IoU.mirror: 0.7450, IoU.rug: 0.6522, IoU.field: 0.3180, IoU.armchair: 0.5643, IoU.seat: 0.6476, IoU.fence: 0.4778, IoU.desk: 0.5771, IoU.rock: 0.5749, IoU.wardrobe: 0.5105, IoU.lamp: 0.6752, IoU.bathtub: 0.8912, IoU.railing: 0.4269, IoU.cushion: 0.6320, IoU.base: 0.3399, IoU.box: 0.3732, IoU.column: 0.5350, IoU.signboard: 0.3625, IoU.chest of drawers: 0.4731, IoU.counter: 0.5188, IoU.sand: 0.6116, IoU.sink: 0.7676, IoU.skyscraper: 0.4811, IoU.fireplace: 0.7346, IoU.refrigerator: 0.8375, IoU.grandstand: 0.4718, IoU.path: 0.2779, IoU.stairs: 0.2961, IoU.runway: 0.6873, IoU.case: 0.6121, IoU.pool table: 0.9387, IoU.pillow: 0.5962, IoU.screen door: 0.7638, IoU.stairway: 0.3606, IoU.river: 0.1922, IoU.bridge: 0.7673, IoU.bookcase: 0.4610, IoU.blind: 0.3897, IoU.coffee table: 0.6872, IoU.toilet: 0.8907, IoU.flower: 0.4319, IoU.book: 0.4988, IoU.hill: 0.0906, IoU.bench: 0.5528, IoU.countertop: 0.6328, IoU.stove: 0.8399, IoU.palm: 0.4752, IoU.kitchen island: 0.4830, IoU.computer: 0.7804, IoU.swivel chair: 0.4441, IoU.boat: 0.5673, IoU.bar: 0.7328, IoU.arcade machine: 0.7330, IoU.hovel: 0.1697, IoU.bus: 0.9304, IoU.towel: 0.7229, IoU.light: 0.4677, IoU.truck: 0.5056, IoU.tower: 0.2502, IoU.chandelier: 0.6687, IoU.awning: 0.4423, IoU.streetlight: 0.2581, IoU.booth: 0.5502, IoU.television receiver: 0.7727, IoU.airplane: 0.7804, IoU.dirt track: 0.1468, IoU.apparel: 0.4813, IoU.pole: 0.2472, IoU.land: 0.0827, IoU.bannister: 0.1429, IoU.escalator: 0.6171, IoU.ottoman: 0.5293, IoU.bottle: 0.2549, IoU.buffet: 0.6094, IoU.poster: 0.3718, IoU.stage: 0.2201, IoU.van: 0.4948, IoU.ship: 0.2291, IoU.fountain: 0.3013, IoU.conveyer belt: 0.8463, IoU.canopy: 0.2859, IoU.washer: 0.8431, IoU.plaything: 0.3089, IoU.swimming pool: 0.5388, IoU.stool: 0.4967, IoU.barrel: 0.7048, IoU.basket: 0.3612, IoU.waterfall: 0.4614, IoU.tent: 0.9465, IoU.bag: 0.2442, IoU.minibike: 0.7465, IoU.cradle: 0.8636, IoU.oven: 0.6539, IoU.ball: 0.2133, IoU.food: 0.5508, IoU.step: 0.2012, IoU.tank: 0.5420, IoU.trade name: 0.2738, IoU.microwave: 0.8900, IoU.pot: 0.5756, IoU.animal: 0.7245, IoU.bicycle: 0.6012, IoU.lake: 0.5598, IoU.dishwasher: 0.7449, IoU.screen: 0.6456, IoU.blanket: 0.3229, IoU.sculpture: 0.7748, IoU.hood: 0.7144, IoU.sconce: 0.5282, IoU.vase: 0.4449, IoU.traffic light: 0.3836, IoU.tray: 0.2144, IoU.ashcan: 0.5025, IoU.fan: 0.5935, IoU.pier: 0.3601, IoU.crt screen: 0.1161, IoU.plate: 0.5564, IoU.monitor: 0.4282, IoU.bulletin board: 0.4855, IoU.shower: 0.0578, IoU.radiator: 0.6856, IoU.glass: 0.1892, IoU.clock: 0.3406, IoU.flag: 0.6520, Acc.wall: 0.9049, Acc.building: 0.9303, Acc.sky: 0.9771, Acc.floor: 0.9093, Acc.tree: 0.8797, Acc.ceiling: 0.9279, Acc.road: 0.9150, Acc.bed : 0.9649, Acc.windowpane: 0.8012, Acc.grass: 0.8459, Acc.cabinet: 0.7671, Acc.sidewalk: 0.8448, Acc.person: 0.9276, Acc.earth: 0.4831, Acc.door: 0.7339, Acc.table: 0.8169, Acc.mountain: 0.7438, Acc.plant: 0.6699, Acc.curtain: 0.8733, Acc.chair: 0.7533, Acc.car: 0.9317, Acc.water: 0.8196, Acc.painting: 0.8753, Acc.sofa: 0.9188, Acc.shelf: 0.6983, Acc.house: 0.6051, Acc.sea: 0.8497, Acc.mirror: 0.8434, Acc.rug: 0.7560, Acc.field: 0.4757, Acc.armchair: 0.7199, Acc.seat: 0.8819, Acc.fence: 0.6381, Acc.desk: 0.7275, Acc.rock: 0.7886, Acc.wardrobe: 0.6841, Acc.lamp: 0.7737, Acc.bathtub: 0.9282, Acc.railing: 0.5879, Acc.cushion: 0.7593, Acc.base: 0.5426, Acc.box: 0.4842, Acc.column: 0.6427, Acc.signboard: 0.5150, Acc.chest of drawers: 0.6690, Acc.counter: 0.6063, Acc.sand: 0.8901, Acc.sink: 0.8402, Acc.skyscraper: 0.5576, Acc.fireplace: 0.8646, Acc.refrigerator: 0.9108, Acc.grandstand: 0.8091, Acc.path: 0.3740, Acc.stairs: 0.3692, Acc.runway: 0.8903, Acc.case: 0.8488, Acc.pool table: 0.9769, Acc.pillow: 0.6877, Acc.screen door: 0.7828, Acc.stairway: 0.4698, Acc.river: 0.3367, Acc.bridge: 0.8670, Acc.bookcase: 0.5577, Acc.blind: 0.4329, Acc.coffee table: 0.8627, Acc.toilet: 0.9329, Acc.flower: 0.6228, Acc.book: 0.7011, Acc.hill: 0.1837, Acc.bench: 0.6171, Acc.countertop: 0.7544, Acc.stove: 0.8809, Acc.palm: 0.7935, Acc.kitchen island: 0.7267, Acc.computer: 0.8684, Acc.swivel chair: 0.6125, Acc.boat: 0.6782, Acc.bar: 0.8233, Acc.arcade machine: 0.7581, Acc.hovel: 0.1866, Acc.bus: 0.9615, Acc.towel: 0.8190, Acc.light: 0.5420, Acc.truck: 0.6257, Acc.tower: 0.3404, Acc.chandelier: 0.8229, Acc.awning: 0.5468, Acc.streetlight: 0.3566, Acc.booth: 0.5561, Acc.television receiver: 0.8900, Acc.airplane: 0.8377, Acc.dirt track: 0.1790, Acc.apparel: 0.6852, Acc.pole: 0.3233, Acc.land: 0.1541, Acc.bannister: 0.1969, Acc.escalator: 0.8147, Acc.ottoman: 0.7376, Acc.bottle: 0.3332, Acc.buffet: 0.7728, Acc.poster: 0.4433, Acc.stage: 0.4073, Acc.van: 0.6991, Acc.ship: 0.2939, Acc.fountain: 0.3031, Acc.conveyer belt: 0.9032, Acc.canopy: 0.3146, Acc.washer: 0.8971, Acc.plaything: 0.4151, Acc.swimming pool: 0.6854, Acc.stool: 0.6354, Acc.barrel: 0.8357, Acc.basket: 0.4780, Acc.waterfall: 0.5814, Acc.tent: 0.9743, Acc.bag: 0.2833, Acc.minibike: 0.8473, Acc.cradle: 0.9632, Acc.oven: 0.7693, Acc.ball: 0.2197, Acc.food: 0.5857, Acc.step: 0.2360, Acc.tank: 0.6491, Acc.trade name: 0.3304, Acc.microwave: 0.9332, Acc.pot: 0.6556, Acc.animal: 0.7576, Acc.bicycle: 0.7741, Acc.lake: 0.6348, Acc.dishwasher: 0.8513, Acc.screen: 0.8507, Acc.blanket: 0.3705, Acc.sculpture: 0.8677, Acc.hood: 0.8097, Acc.sconce: 0.6571, Acc.vase: 0.5721, Acc.traffic light: 0.5708, Acc.tray: 0.2715, Acc.ashcan: 0.6590, Acc.fan: 0.6700, Acc.pier: 0.3928, Acc.crt screen: 0.2279, Acc.plate: 0.7530, Acc.monitor: 0.5213, Acc.bulletin board: 0.6025, Acc.shower: 0.0605, Acc.radiator: 0.7998, Acc.glass: 0.2148, Acc.clock: 0.3828, Acc.flag: 0.7148 2023-11-03 07:03:46,566 - mmseg - INFO - Iter [33050/40000] lr: 5.630e-07, eta: 2:34:53, time: 2.373, data_time: 1.165, memory: 38534, decode.loss_ce: 0.1539, decode.acc_seg: 93.2188, loss: 0.1539 2023-11-03 07:04:47,215 - mmseg - INFO - Iter [33100/40000] lr: 5.589e-07, eta: 2:33:45, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1461, decode.acc_seg: 93.7805, loss: 0.1461 2023-11-03 07:05:47,853 - mmseg - INFO - Iter [33150/40000] lr: 5.549e-07, eta: 2:32:37, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1517, decode.acc_seg: 93.5867, loss: 0.1517 2023-11-03 07:06:48,493 - mmseg - INFO - Iter [33200/40000] lr: 5.508e-07, eta: 2:31:29, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1485, decode.acc_seg: 93.7106, loss: 0.1485 2023-11-03 07:07:49,155 - mmseg - INFO - Iter [33250/40000] lr: 5.468e-07, eta: 2:30:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1458, decode.acc_seg: 93.7403, loss: 0.1458 2023-11-03 07:08:49,779 - mmseg - INFO - Iter [33300/40000] lr: 5.427e-07, eta: 2:29:13, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1523, decode.acc_seg: 93.6369, loss: 0.1523 2023-11-03 07:09:50,489 - mmseg - INFO - Iter [33350/40000] lr: 5.387e-07, eta: 2:28:05, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1459, decode.acc_seg: 93.6859, loss: 0.1459 2023-11-03 07:10:51,166 - mmseg - INFO - Iter [33400/40000] lr: 5.346e-07, eta: 2:26:57, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1474, decode.acc_seg: 93.6242, loss: 0.1474 2023-11-03 07:11:51,843 - mmseg - INFO - Iter [33450/40000] lr: 5.306e-07, eta: 2:25:49, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1521, decode.acc_seg: 93.5338, loss: 0.1521 2023-11-03 07:12:54,902 - mmseg - INFO - Iter [33500/40000] lr: 5.265e-07, eta: 2:24:41, time: 1.261, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1480, decode.acc_seg: 93.6062, loss: 0.1480 2023-11-03 07:13:55,601 - mmseg - INFO - Iter [33550/40000] lr: 5.225e-07, eta: 2:23:33, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1458, decode.acc_seg: 93.7339, loss: 0.1458 2023-11-03 07:14:56,342 - mmseg - INFO - Iter [33600/40000] lr: 5.184e-07, eta: 2:22:25, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1433, decode.acc_seg: 93.8117, loss: 0.1433 2023-11-03 07:15:57,018 - mmseg - INFO - Iter [33650/40000] lr: 5.144e-07, eta: 2:21:17, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1397, decode.acc_seg: 93.9439, loss: 0.1397 2023-11-03 07:16:57,714 - mmseg - INFO - Iter [33700/40000] lr: 5.103e-07, eta: 2:20:09, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1477, decode.acc_seg: 93.6816, loss: 0.1477 2023-11-03 07:17:58,369 - mmseg - INFO - Iter [33750/40000] lr: 5.063e-07, eta: 2:19:02, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1486, decode.acc_seg: 93.5418, loss: 0.1486 2023-11-03 07:18:59,020 - mmseg - INFO - Iter [33800/40000] lr: 5.022e-07, eta: 2:17:54, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1467, decode.acc_seg: 93.7209, loss: 0.1467 2023-11-03 07:19:59,649 - mmseg - INFO - Iter [33850/40000] lr: 4.982e-07, eta: 2:16:46, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1499, decode.acc_seg: 93.5802, loss: 0.1499 2023-11-03 07:21:00,328 - mmseg - INFO - Iter [33900/40000] lr: 4.941e-07, eta: 2:15:38, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1445, decode.acc_seg: 93.7009, loss: 0.1445 2023-11-03 07:22:01,006 - mmseg - INFO - Iter [33950/40000] lr: 4.901e-07, eta: 2:14:30, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1456, decode.acc_seg: 93.8448, loss: 0.1456 2023-11-03 07:23:01,668 - mmseg - INFO - Saving checkpoint at 34000 iterations 2023-11-03 07:24:03,118 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 07:24:03,118 - mmseg - INFO - Iter [34000/40000] lr: 4.860e-07, eta: 2:13:33, time: 2.442, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1518, decode.acc_seg: 93.6689, loss: 0.1518 2023-11-03 07:25:02,601 - mmseg - INFO - per class results: 2023-11-03 07:25:02,606 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 81.01 | 89.24 | | building | 83.81 | 93.56 | | sky | 94.3 | 97.28 | | floor | 83.61 | 91.63 | | tree | 75.91 | 89.63 | | ceiling | 84.92 | 93.03 | | road | 85.93 | 91.48 | | bed | 91.65 | 96.86 | | windowpane | 64.95 | 78.78 | | grass | 69.6 | 84.79 | | cabinet | 66.49 | 76.38 | | sidewalk | 69.74 | 84.8 | | person | 82.46 | 92.93 | | earth | 36.13 | 47.1 | | door | 58.25 | 72.22 | | table | 69.96 | 82.5 | | mountain | 62.11 | 74.85 | | plant | 54.94 | 65.84 | | curtain | 76.09 | 90.42 | | chair | 62.0 | 76.18 | | car | 86.14 | 92.89 | | water | 65.58 | 80.13 | | painting | 78.8 | 88.62 | | sofa | 81.53 | 91.75 | | shelf | 48.74 | 69.66 | | house | 49.2 | 60.97 | | sea | 72.03 | 83.07 | | mirror | 74.82 | 83.56 | | rug | 65.53 | 76.1 | | field | 35.5 | 57.37 | | armchair | 58.46 | 74.67 | | seat | 64.81 | 88.01 | | fence | 47.48 | 61.63 | | desk | 57.99 | 76.64 | | rock | 55.19 | 78.17 | | wardrobe | 52.81 | 74.41 | | lamp | 68.09 | 80.11 | | bathtub | 89.25 | 92.23 | | railing | 43.27 | 58.45 | | cushion | 62.83 | 75.21 | | base | 34.7 | 55.82 | | box | 37.41 | 46.66 | | column | 53.81 | 65.85 | | signboard | 36.79 | 51.72 | | chest of drawers | 45.99 | 66.71 | | counter | 49.01 | 60.9 | | sand | 61.12 | 89.36 | | sink | 77.11 | 84.7 | | skyscraper | 47.81 | 58.35 | | fireplace | 74.53 | 89.08 | | refrigerator | 84.22 | 91.22 | | grandstand | 46.7 | 82.44 | | path | 25.92 | 34.09 | | stairs | 30.95 | 40.14 | | runway | 68.37 | 89.63 | | case | 60.79 | 85.09 | | pool table | 93.66 | 97.92 | | pillow | 59.21 | 69.09 | | screen door | 81.0 | 83.15 | | stairway | 33.63 | 44.32 | | river | 22.96 | 41.74 | | bridge | 76.66 | 87.88 | | bookcase | 45.31 | 56.85 | | blind | 40.27 | 45.6 | | coffee table | 68.76 | 87.68 | | toilet | 88.96 | 93.45 | | flower | 43.88 | 63.76 | | book | 48.92 | 73.4 | | hill | 9.26 | 17.02 | | bench | 55.18 | 63.52 | | countertop | 63.99 | 78.73 | | stove | 84.6 | 89.81 | | palm | 47.23 | 78.86 | | kitchen island | 48.6 | 67.4 | | computer | 77.8 | 87.83 | | swivel chair | 44.91 | 62.78 | | boat | 61.35 | 76.04 | | bar | 76.25 | 86.53 | | arcade machine | 72.8 | 75.82 | | hovel | 17.78 | 19.62 | | bus | 93.18 | 96.53 | | towel | 70.68 | 80.03 | | light | 48.53 | 59.1 | | truck | 50.74 | 62.95 | | tower | 24.11 | 32.94 | | chandelier | 67.37 | 83.18 | | awning | 41.08 | 50.36 | | streetlight | 24.69 | 33.26 | | booth | 57.24 | 57.83 | | television receiver | 77.39 | 89.38 | | airplane | 84.5 | 92.17 | | dirt track | 10.86 | 20.37 | | apparel | 49.14 | 67.13 | | pole | 23.51 | 29.94 | | land | 10.27 | 14.63 | | bannister | 13.94 | 18.89 | | escalator | 61.13 | 81.7 | | ottoman | 54.09 | 71.62 | | bottle | 25.49 | 34.24 | | buffet | 61.38 | 75.04 | | poster | 37.13 | 44.52 | | stage | 22.95 | 46.0 | | van | 49.7 | 72.0 | | ship | 19.85 | 23.73 | | fountain | 31.03 | 31.59 | | conveyer belt | 82.79 | 92.19 | | canopy | 34.06 | 38.42 | | washer | 86.47 | 92.51 | | plaything | 29.32 | 43.14 | | swimming pool | 49.8 | 70.8 | | stool | 48.15 | 63.39 | | barrel | 67.73 | 84.22 | | basket | 36.33 | 47.12 | | waterfall | 48.33 | 63.82 | | tent | 91.93 | 97.38 | | bag | 24.92 | 29.63 | | minibike | 73.22 | 85.86 | | cradle | 86.96 | 96.39 | | oven | 65.61 | 75.68 | | ball | 50.34 | 53.7 | | food | 55.65 | 60.15 | | step | 17.51 | 20.79 | | tank | 53.95 | 65.64 | | trade name | 26.32 | 31.66 | | microwave | 89.68 | 95.2 | | pot | 55.59 | 62.53 | | animal | 74.16 | 77.56 | | bicycle | 59.35 | 79.2 | | lake | 55.94 | 63.39 | | dishwasher | 74.66 | 82.81 | | screen | 67.56 | 88.42 | | blanket | 33.65 | 39.35 | | sculpture | 76.24 | 88.69 | | hood | 66.86 | 76.39 | | sconce | 53.62 | 68.93 | | vase | 43.99 | 58.77 | | traffic light | 37.82 | 53.65 | | tray | 20.72 | 28.06 | | ashcan | 50.29 | 65.01 | | fan | 62.27 | 74.97 | | pier | 36.72 | 41.14 | | crt screen | 12.28 | 22.23 | | plate | 55.58 | 76.06 | | monitor | 43.56 | 52.97 | | bulletin board | 48.43 | 59.84 | | shower | 4.0 | 9.51 | | radiator | 67.98 | 82.42 | | glass | 17.75 | 19.63 | | clock | 35.28 | 41.24 | | flag | 65.71 | 72.62 | +---------------------+-------+-------+ 2023-11-03 07:25:02,606 - mmseg - INFO - Summary: 2023-11-03 07:25:02,607 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.44 | 55.73 | 67.44 | +-------+-------+-------+ 2023-11-03 07:25:02,607 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 07:25:02,608 - mmseg - INFO - Iter(val) [250] aAcc: 0.8544, mIoU: 0.5573, mAcc: 0.6744, IoU.wall: 0.8101, IoU.building: 0.8381, IoU.sky: 0.9430, IoU.floor: 0.8361, IoU.tree: 0.7591, IoU.ceiling: 0.8492, IoU.road: 0.8593, IoU.bed : 0.9165, IoU.windowpane: 0.6495, IoU.grass: 0.6960, IoU.cabinet: 0.6649, IoU.sidewalk: 0.6974, IoU.person: 0.8246, IoU.earth: 0.3613, IoU.door: 0.5825, IoU.table: 0.6996, IoU.mountain: 0.6211, IoU.plant: 0.5494, IoU.curtain: 0.7609, IoU.chair: 0.6200, IoU.car: 0.8614, IoU.water: 0.6558, IoU.painting: 0.7880, IoU.sofa: 0.8153, IoU.shelf: 0.4874, IoU.house: 0.4920, IoU.sea: 0.7203, IoU.mirror: 0.7482, IoU.rug: 0.6553, IoU.field: 0.3550, IoU.armchair: 0.5846, IoU.seat: 0.6481, IoU.fence: 0.4748, IoU.desk: 0.5799, IoU.rock: 0.5519, IoU.wardrobe: 0.5281, IoU.lamp: 0.6809, IoU.bathtub: 0.8925, IoU.railing: 0.4327, IoU.cushion: 0.6283, IoU.base: 0.3470, IoU.box: 0.3741, IoU.column: 0.5381, IoU.signboard: 0.3679, IoU.chest of drawers: 0.4599, IoU.counter: 0.4901, IoU.sand: 0.6112, IoU.sink: 0.7711, IoU.skyscraper: 0.4781, IoU.fireplace: 0.7453, IoU.refrigerator: 0.8422, IoU.grandstand: 0.4670, IoU.path: 0.2592, IoU.stairs: 0.3095, IoU.runway: 0.6837, IoU.case: 0.6079, IoU.pool table: 0.9366, IoU.pillow: 0.5921, IoU.screen door: 0.8100, IoU.stairway: 0.3363, IoU.river: 0.2296, IoU.bridge: 0.7666, IoU.bookcase: 0.4531, IoU.blind: 0.4027, IoU.coffee table: 0.6876, IoU.toilet: 0.8896, IoU.flower: 0.4388, IoU.book: 0.4892, IoU.hill: 0.0926, IoU.bench: 0.5518, IoU.countertop: 0.6399, IoU.stove: 0.8460, IoU.palm: 0.4723, IoU.kitchen island: 0.4860, IoU.computer: 0.7780, IoU.swivel chair: 0.4491, IoU.boat: 0.6135, IoU.bar: 0.7625, IoU.arcade machine: 0.7280, IoU.hovel: 0.1778, IoU.bus: 0.9318, IoU.towel: 0.7068, IoU.light: 0.4853, IoU.truck: 0.5074, IoU.tower: 0.2411, IoU.chandelier: 0.6737, IoU.awning: 0.4108, IoU.streetlight: 0.2469, IoU.booth: 0.5724, IoU.television receiver: 0.7739, IoU.airplane: 0.8450, IoU.dirt track: 0.1086, IoU.apparel: 0.4914, IoU.pole: 0.2351, IoU.land: 0.1027, IoU.bannister: 0.1394, IoU.escalator: 0.6113, IoU.ottoman: 0.5409, IoU.bottle: 0.2549, IoU.buffet: 0.6138, IoU.poster: 0.3713, IoU.stage: 0.2295, IoU.van: 0.4970, IoU.ship: 0.1985, IoU.fountain: 0.3103, IoU.conveyer belt: 0.8279, IoU.canopy: 0.3406, IoU.washer: 0.8647, IoU.plaything: 0.2932, IoU.swimming pool: 0.4980, IoU.stool: 0.4815, IoU.barrel: 0.6773, IoU.basket: 0.3633, IoU.waterfall: 0.4833, IoU.tent: 0.9193, IoU.bag: 0.2492, IoU.minibike: 0.7322, IoU.cradle: 0.8696, IoU.oven: 0.6561, IoU.ball: 0.5034, IoU.food: 0.5565, IoU.step: 0.1751, IoU.tank: 0.5395, IoU.trade name: 0.2632, IoU.microwave: 0.8968, IoU.pot: 0.5559, IoU.animal: 0.7416, IoU.bicycle: 0.5935, IoU.lake: 0.5594, IoU.dishwasher: 0.7466, IoU.screen: 0.6756, IoU.blanket: 0.3365, IoU.sculpture: 0.7624, IoU.hood: 0.6686, IoU.sconce: 0.5362, IoU.vase: 0.4399, IoU.traffic light: 0.3782, IoU.tray: 0.2072, IoU.ashcan: 0.5029, IoU.fan: 0.6227, IoU.pier: 0.3672, IoU.crt screen: 0.1228, IoU.plate: 0.5558, IoU.monitor: 0.4356, IoU.bulletin board: 0.4843, IoU.shower: 0.0400, IoU.radiator: 0.6798, IoU.glass: 0.1775, IoU.clock: 0.3528, IoU.flag: 0.6571, Acc.wall: 0.8924, Acc.building: 0.9356, Acc.sky: 0.9728, Acc.floor: 0.9163, Acc.tree: 0.8963, Acc.ceiling: 0.9303, Acc.road: 0.9148, Acc.bed : 0.9686, Acc.windowpane: 0.7878, Acc.grass: 0.8479, Acc.cabinet: 0.7638, Acc.sidewalk: 0.8480, Acc.person: 0.9293, Acc.earth: 0.4710, Acc.door: 0.7222, Acc.table: 0.8250, Acc.mountain: 0.7485, Acc.plant: 0.6584, Acc.curtain: 0.9042, Acc.chair: 0.7618, Acc.car: 0.9289, Acc.water: 0.8013, Acc.painting: 0.8862, Acc.sofa: 0.9175, Acc.shelf: 0.6966, Acc.house: 0.6097, Acc.sea: 0.8307, Acc.mirror: 0.8356, Acc.rug: 0.7610, Acc.field: 0.5737, Acc.armchair: 0.7467, Acc.seat: 0.8801, Acc.fence: 0.6163, Acc.desk: 0.7664, Acc.rock: 0.7817, Acc.wardrobe: 0.7441, Acc.lamp: 0.8011, Acc.bathtub: 0.9223, Acc.railing: 0.5845, Acc.cushion: 0.7521, Acc.base: 0.5582, Acc.box: 0.4666, Acc.column: 0.6585, Acc.signboard: 0.5172, Acc.chest of drawers: 0.6671, Acc.counter: 0.6090, Acc.sand: 0.8936, Acc.sink: 0.8470, Acc.skyscraper: 0.5835, Acc.fireplace: 0.8908, Acc.refrigerator: 0.9122, Acc.grandstand: 0.8244, Acc.path: 0.3409, Acc.stairs: 0.4014, Acc.runway: 0.8963, Acc.case: 0.8509, Acc.pool table: 0.9792, Acc.pillow: 0.6909, Acc.screen door: 0.8315, Acc.stairway: 0.4432, Acc.river: 0.4174, Acc.bridge: 0.8788, Acc.bookcase: 0.5685, Acc.blind: 0.4560, Acc.coffee table: 0.8768, Acc.toilet: 0.9345, Acc.flower: 0.6376, Acc.book: 0.7340, Acc.hill: 0.1702, Acc.bench: 0.6352, Acc.countertop: 0.7873, Acc.stove: 0.8981, Acc.palm: 0.7886, Acc.kitchen island: 0.6740, Acc.computer: 0.8783, Acc.swivel chair: 0.6278, Acc.boat: 0.7604, Acc.bar: 0.8653, Acc.arcade machine: 0.7582, Acc.hovel: 0.1962, Acc.bus: 0.9653, Acc.towel: 0.8003, Acc.light: 0.5910, Acc.truck: 0.6295, Acc.tower: 0.3294, Acc.chandelier: 0.8318, Acc.awning: 0.5036, Acc.streetlight: 0.3326, Acc.booth: 0.5783, Acc.television receiver: 0.8938, Acc.airplane: 0.9217, Acc.dirt track: 0.2037, Acc.apparel: 0.6713, Acc.pole: 0.2994, Acc.land: 0.1463, Acc.bannister: 0.1889, Acc.escalator: 0.8170, Acc.ottoman: 0.7162, Acc.bottle: 0.3424, Acc.buffet: 0.7504, Acc.poster: 0.4452, Acc.stage: 0.4600, Acc.van: 0.7200, Acc.ship: 0.2373, Acc.fountain: 0.3159, Acc.conveyer belt: 0.9219, Acc.canopy: 0.3842, Acc.washer: 0.9251, Acc.plaything: 0.4314, Acc.swimming pool: 0.7080, Acc.stool: 0.6339, Acc.barrel: 0.8422, Acc.basket: 0.4712, Acc.waterfall: 0.6382, Acc.tent: 0.9738, Acc.bag: 0.2963, Acc.minibike: 0.8586, Acc.cradle: 0.9639, Acc.oven: 0.7568, Acc.ball: 0.5370, Acc.food: 0.6015, Acc.step: 0.2079, Acc.tank: 0.6564, Acc.trade name: 0.3166, Acc.microwave: 0.9520, Acc.pot: 0.6253, Acc.animal: 0.7756, Acc.bicycle: 0.7920, Acc.lake: 0.6339, Acc.dishwasher: 0.8281, Acc.screen: 0.8842, Acc.blanket: 0.3935, Acc.sculpture: 0.8869, Acc.hood: 0.7639, Acc.sconce: 0.6893, Acc.vase: 0.5877, Acc.traffic light: 0.5365, Acc.tray: 0.2806, Acc.ashcan: 0.6501, Acc.fan: 0.7497, Acc.pier: 0.4114, Acc.crt screen: 0.2223, Acc.plate: 0.7606, Acc.monitor: 0.5297, Acc.bulletin board: 0.5984, Acc.shower: 0.0951, Acc.radiator: 0.8242, Acc.glass: 0.1963, Acc.clock: 0.4124, Acc.flag: 0.7262 2023-11-03 07:26:03,366 - mmseg - INFO - Iter [34050/40000] lr: 4.820e-07, eta: 2:12:36, time: 2.405, data_time: 1.197, memory: 38534, decode.loss_ce: 0.1392, decode.acc_seg: 93.8575, loss: 0.1392 2023-11-03 07:27:03,973 - mmseg - INFO - Iter [34100/40000] lr: 4.779e-07, eta: 2:11:28, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1536, decode.acc_seg: 93.6469, loss: 0.1536 2023-11-03 07:28:06,950 - mmseg - INFO - Iter [34150/40000] lr: 4.739e-07, eta: 2:10:20, time: 1.260, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1512, decode.acc_seg: 93.6558, loss: 0.1512 2023-11-03 07:29:07,591 - mmseg - INFO - Iter [34200/40000] lr: 4.699e-07, eta: 2:09:13, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1417, decode.acc_seg: 93.9641, loss: 0.1417 2023-11-03 07:30:08,258 - mmseg - INFO - Iter [34250/40000] lr: 4.658e-07, eta: 2:08:05, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1499, decode.acc_seg: 93.6662, loss: 0.1499 2023-11-03 07:31:08,913 - mmseg - INFO - Iter [34300/40000] lr: 4.618e-07, eta: 2:06:57, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1382, decode.acc_seg: 94.1326, loss: 0.1382 2023-11-03 07:32:09,587 - mmseg - INFO - Iter [34350/40000] lr: 4.577e-07, eta: 2:05:49, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1448, decode.acc_seg: 93.8058, loss: 0.1448 2023-11-03 07:33:10,261 - mmseg - INFO - Iter [34400/40000] lr: 4.537e-07, eta: 2:04:41, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1440, decode.acc_seg: 93.8405, loss: 0.1440 2023-11-03 07:34:10,884 - mmseg - INFO - Iter [34450/40000] lr: 4.496e-07, eta: 2:03:33, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1502, decode.acc_seg: 93.6041, loss: 0.1502 2023-11-03 07:35:11,535 - mmseg - INFO - Iter [34500/40000] lr: 4.456e-07, eta: 2:02:26, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1511, decode.acc_seg: 93.6468, loss: 0.1511 2023-11-03 07:36:12,230 - mmseg - INFO - Iter [34550/40000] lr: 4.415e-07, eta: 2:01:18, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1412, decode.acc_seg: 93.8390, loss: 0.1412 2023-11-03 07:37:12,866 - mmseg - INFO - Iter [34600/40000] lr: 4.375e-07, eta: 2:00:10, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1458, decode.acc_seg: 93.6946, loss: 0.1458 2023-11-03 07:38:13,521 - mmseg - INFO - Iter [34650/40000] lr: 4.334e-07, eta: 1:59:02, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1517, decode.acc_seg: 93.6498, loss: 0.1517 2023-11-03 07:39:14,179 - mmseg - INFO - Iter [34700/40000] lr: 4.294e-07, eta: 1:57:55, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1350, decode.acc_seg: 94.0716, loss: 0.1350 2023-11-03 07:40:14,830 - mmseg - INFO - Iter [34750/40000] lr: 4.253e-07, eta: 1:56:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1429, decode.acc_seg: 93.9312, loss: 0.1429 2023-11-03 07:41:17,882 - mmseg - INFO - Iter [34800/40000] lr: 4.213e-07, eta: 1:55:40, time: 1.261, data_time: 0.051, memory: 38534, decode.loss_ce: 0.1346, decode.acc_seg: 94.0938, loss: 0.1346 2023-11-03 07:42:18,595 - mmseg - INFO - Iter [34850/40000] lr: 4.172e-07, eta: 1:54:32, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1378, decode.acc_seg: 94.0421, loss: 0.1378 2023-11-03 07:43:19,326 - mmseg - INFO - Iter [34900/40000] lr: 4.132e-07, eta: 1:53:25, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1382, decode.acc_seg: 93.8988, loss: 0.1382 2023-11-03 07:44:19,985 - mmseg - INFO - Iter [34950/40000] lr: 4.091e-07, eta: 1:52:17, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1415, decode.acc_seg: 93.7864, loss: 0.1415 2023-11-03 07:45:20,611 - mmseg - INFO - Saving checkpoint at 35000 iterations 2023-11-03 07:46:17,378 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 07:46:17,379 - mmseg - INFO - Iter [35000/40000] lr: 4.051e-07, eta: 1:51:17, time: 2.348, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1427, decode.acc_seg: 93.9295, loss: 0.1427 2023-11-03 07:47:15,383 - mmseg - INFO - per class results: 2023-11-03 07:47:15,389 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 80.98 | 89.8 | | building | 83.61 | 93.34 | | sky | 94.33 | 97.38 | | floor | 83.34 | 92.02 | | tree | 75.55 | 87.83 | | ceiling | 84.76 | 92.76 | | road | 85.55 | 91.93 | | bed | 91.63 | 97.18 | | windowpane | 65.46 | 80.46 | | grass | 69.04 | 85.59 | | cabinet | 66.51 | 75.74 | | sidewalk | 69.67 | 83.33 | | person | 82.5 | 92.84 | | earth | 34.07 | 43.7 | | door | 57.53 | 72.73 | | table | 69.86 | 82.48 | | mountain | 62.48 | 75.93 | | plant | 53.65 | 63.19 | | curtain | 77.05 | 88.73 | | chair | 62.98 | 77.92 | | car | 86.12 | 93.27 | | water | 65.72 | 79.7 | | painting | 78.39 | 89.07 | | sofa | 81.32 | 91.95 | | shelf | 49.2 | 69.48 | | house | 47.23 | 59.65 | | sea | 70.85 | 80.93 | | mirror | 75.09 | 84.16 | | rug | 64.22 | 73.22 | | field | 34.32 | 55.27 | | armchair | 59.57 | 72.32 | | seat | 64.51 | 88.68 | | fence | 47.65 | 60.61 | | desk | 56.58 | 76.37 | | rock | 55.07 | 79.94 | | wardrobe | 53.05 | 75.05 | | lamp | 67.95 | 80.43 | | bathtub | 89.38 | 92.49 | | railing | 42.62 | 56.71 | | cushion | 63.24 | 76.28 | | base | 34.14 | 52.08 | | box | 37.86 | 48.2 | | column | 52.88 | 63.04 | | signboard | 37.04 | 52.77 | | chest of drawers | 47.34 | 68.92 | | counter | 48.83 | 56.69 | | sand | 61.22 | 89.27 | | sink | 77.22 | 84.04 | | skyscraper | 47.83 | 61.98 | | fireplace | 74.52 | 90.74 | | refrigerator | 85.13 | 92.72 | | grandstand | 47.37 | 82.57 | | path | 24.79 | 33.86 | | stairs | 30.54 | 38.76 | | runway | 68.43 | 89.61 | | case | 59.63 | 85.74 | | pool table | 93.78 | 97.64 | | pillow | 58.13 | 66.95 | | screen door | 80.62 | 83.23 | | stairway | 33.59 | 44.32 | | river | 21.41 | 44.14 | | bridge | 78.25 | 87.44 | | bookcase | 45.61 | 53.29 | | blind | 39.22 | 44.26 | | coffee table | 67.41 | 88.16 | | toilet | 89.02 | 93.34 | | flower | 43.28 | 65.39 | | book | 50.48 | 73.49 | | hill | 9.23 | 16.96 | | bench | 55.42 | 63.04 | | countertop | 60.17 | 76.58 | | stove | 85.07 | 90.36 | | palm | 46.26 | 82.78 | | kitchen island | 48.25 | 65.48 | | computer | 77.66 | 87.7 | | swivel chair | 44.03 | 57.59 | | boat | 62.68 | 75.6 | | bar | 72.57 | 78.07 | | arcade machine | 70.94 | 73.35 | | hovel | 21.05 | 23.47 | | bus | 93.19 | 96.51 | | towel | 71.7 | 82.16 | | light | 48.72 | 58.61 | | truck | 48.73 | 59.95 | | tower | 23.31 | 31.32 | | chandelier | 67.36 | 82.79 | | awning | 39.08 | 48.97 | | streetlight | 25.57 | 34.74 | | booth | 58.27 | 61.14 | | television receiver | 76.87 | 89.3 | | airplane | 83.92 | 92.08 | | dirt track | 13.5 | 18.68 | | apparel | 47.2 | 65.31 | | pole | 24.57 | 32.25 | | land | 8.34 | 15.52 | | bannister | 14.14 | 19.28 | | escalator | 61.46 | 81.15 | | ottoman | 55.02 | 70.89 | | bottle | 26.31 | 35.51 | | buffet | 64.23 | 76.49 | | poster | 36.7 | 44.88 | | stage | 22.06 | 40.33 | | van | 47.24 | 68.69 | | ship | 9.87 | 11.73 | | fountain | 32.35 | 33.52 | | conveyer belt | 85.65 | 90.88 | | canopy | 34.67 | 39.27 | | washer | 86.9 | 92.81 | | plaything | 30.78 | 43.16 | | swimming pool | 51.85 | 72.13 | | stool | 51.14 | 63.19 | | barrel | 69.9 | 83.08 | | basket | 36.91 | 45.56 | | waterfall | 51.53 | 67.4 | | tent | 95.38 | 97.38 | | bag | 25.22 | 30.18 | | minibike | 73.43 | 85.78 | | cradle | 86.89 | 96.94 | | oven | 64.68 | 78.07 | | ball | 35.17 | 36.61 | | food | 50.61 | 53.51 | | step | 16.39 | 19.5 | | tank | 54.51 | 65.02 | | trade name | 27.1 | 33.48 | | microwave | 89.18 | 94.02 | | pot | 57.0 | 65.66 | | animal | 74.6 | 78.55 | | bicycle | 58.9 | 74.88 | | lake | 55.96 | 63.47 | | dishwasher | 74.99 | 82.91 | | screen | 68.78 | 91.56 | | blanket | 31.89 | 37.19 | | sculpture | 76.74 | 88.09 | | hood | 68.14 | 77.84 | | sconce | 52.9 | 69.06 | | vase | 44.25 | 60.0 | | traffic light | 38.39 | 57.37 | | tray | 20.76 | 27.6 | | ashcan | 50.95 | 64.99 | | fan | 62.37 | 75.52 | | pier | 37.25 | 42.67 | | crt screen | 13.52 | 20.31 | | plate | 57.37 | 74.23 | | monitor | 50.85 | 63.14 | | bulletin board | 50.05 | 59.99 | | shower | 5.81 | 6.33 | | radiator | 68.12 | 81.22 | | glass | 19.37 | 22.13 | | clock | 34.25 | 39.63 | | flag | 65.75 | 74.18 | +---------------------+-------+-------+ 2023-11-03 07:47:15,389 - mmseg - INFO - Summary: 2023-11-03 07:47:15,389 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.34 | 55.65 | 67.08 | +-------+-------+-------+ 2023-11-03 07:47:15,390 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 07:47:15,390 - mmseg - INFO - Iter(val) [250] aAcc: 0.8534, mIoU: 0.5565, mAcc: 0.6708, IoU.wall: 0.8098, IoU.building: 0.8361, IoU.sky: 0.9433, IoU.floor: 0.8334, IoU.tree: 0.7555, IoU.ceiling: 0.8476, IoU.road: 0.8555, IoU.bed : 0.9163, IoU.windowpane: 0.6546, IoU.grass: 0.6904, IoU.cabinet: 0.6651, IoU.sidewalk: 0.6967, IoU.person: 0.8250, IoU.earth: 0.3407, IoU.door: 0.5753, IoU.table: 0.6986, IoU.mountain: 0.6248, IoU.plant: 0.5365, IoU.curtain: 0.7705, IoU.chair: 0.6298, IoU.car: 0.8612, IoU.water: 0.6572, IoU.painting: 0.7839, IoU.sofa: 0.8132, IoU.shelf: 0.4920, IoU.house: 0.4723, IoU.sea: 0.7085, IoU.mirror: 0.7509, IoU.rug: 0.6422, IoU.field: 0.3432, IoU.armchair: 0.5957, IoU.seat: 0.6451, IoU.fence: 0.4765, IoU.desk: 0.5658, IoU.rock: 0.5507, IoU.wardrobe: 0.5305, IoU.lamp: 0.6795, IoU.bathtub: 0.8938, IoU.railing: 0.4262, IoU.cushion: 0.6324, IoU.base: 0.3414, IoU.box: 0.3786, IoU.column: 0.5288, IoU.signboard: 0.3704, IoU.chest of drawers: 0.4734, IoU.counter: 0.4883, IoU.sand: 0.6122, IoU.sink: 0.7722, IoU.skyscraper: 0.4783, IoU.fireplace: 0.7452, IoU.refrigerator: 0.8513, IoU.grandstand: 0.4737, IoU.path: 0.2479, IoU.stairs: 0.3054, IoU.runway: 0.6843, IoU.case: 0.5963, IoU.pool table: 0.9378, IoU.pillow: 0.5813, IoU.screen door: 0.8062, IoU.stairway: 0.3359, IoU.river: 0.2141, IoU.bridge: 0.7825, IoU.bookcase: 0.4561, IoU.blind: 0.3922, IoU.coffee table: 0.6741, IoU.toilet: 0.8902, IoU.flower: 0.4328, IoU.book: 0.5048, IoU.hill: 0.0923, IoU.bench: 0.5542, IoU.countertop: 0.6017, IoU.stove: 0.8507, IoU.palm: 0.4626, IoU.kitchen island: 0.4825, IoU.computer: 0.7766, IoU.swivel chair: 0.4403, IoU.boat: 0.6268, IoU.bar: 0.7257, IoU.arcade machine: 0.7094, IoU.hovel: 0.2105, IoU.bus: 0.9319, IoU.towel: 0.7170, IoU.light: 0.4872, IoU.truck: 0.4873, IoU.tower: 0.2331, IoU.chandelier: 0.6736, IoU.awning: 0.3908, IoU.streetlight: 0.2557, IoU.booth: 0.5827, IoU.television receiver: 0.7687, IoU.airplane: 0.8392, IoU.dirt track: 0.1350, IoU.apparel: 0.4720, IoU.pole: 0.2457, IoU.land: 0.0834, IoU.bannister: 0.1414, IoU.escalator: 0.6146, IoU.ottoman: 0.5502, IoU.bottle: 0.2631, IoU.buffet: 0.6423, IoU.poster: 0.3670, IoU.stage: 0.2206, IoU.van: 0.4724, IoU.ship: 0.0987, IoU.fountain: 0.3235, IoU.conveyer belt: 0.8565, IoU.canopy: 0.3467, IoU.washer: 0.8690, IoU.plaything: 0.3078, IoU.swimming pool: 0.5185, IoU.stool: 0.5114, IoU.barrel: 0.6990, IoU.basket: 0.3691, IoU.waterfall: 0.5153, IoU.tent: 0.9538, IoU.bag: 0.2522, IoU.minibike: 0.7343, IoU.cradle: 0.8689, IoU.oven: 0.6468, IoU.ball: 0.3517, IoU.food: 0.5061, IoU.step: 0.1639, IoU.tank: 0.5451, IoU.trade name: 0.2710, IoU.microwave: 0.8918, IoU.pot: 0.5700, IoU.animal: 0.7460, IoU.bicycle: 0.5890, IoU.lake: 0.5596, IoU.dishwasher: 0.7499, IoU.screen: 0.6878, IoU.blanket: 0.3189, IoU.sculpture: 0.7674, IoU.hood: 0.6814, IoU.sconce: 0.5290, IoU.vase: 0.4425, IoU.traffic light: 0.3839, IoU.tray: 0.2076, IoU.ashcan: 0.5095, IoU.fan: 0.6237, IoU.pier: 0.3725, IoU.crt screen: 0.1352, IoU.plate: 0.5737, IoU.monitor: 0.5085, IoU.bulletin board: 0.5005, IoU.shower: 0.0581, IoU.radiator: 0.6812, IoU.glass: 0.1937, IoU.clock: 0.3425, IoU.flag: 0.6575, Acc.wall: 0.8980, Acc.building: 0.9334, Acc.sky: 0.9738, Acc.floor: 0.9202, Acc.tree: 0.8783, Acc.ceiling: 0.9276, Acc.road: 0.9193, Acc.bed : 0.9718, Acc.windowpane: 0.8046, Acc.grass: 0.8559, Acc.cabinet: 0.7574, Acc.sidewalk: 0.8333, Acc.person: 0.9284, Acc.earth: 0.4370, Acc.door: 0.7273, Acc.table: 0.8248, Acc.mountain: 0.7593, Acc.plant: 0.6319, Acc.curtain: 0.8873, Acc.chair: 0.7792, Acc.car: 0.9327, Acc.water: 0.7970, Acc.painting: 0.8907, Acc.sofa: 0.9195, Acc.shelf: 0.6948, Acc.house: 0.5965, Acc.sea: 0.8093, Acc.mirror: 0.8416, Acc.rug: 0.7322, Acc.field: 0.5527, Acc.armchair: 0.7232, Acc.seat: 0.8868, Acc.fence: 0.6061, Acc.desk: 0.7637, Acc.rock: 0.7994, Acc.wardrobe: 0.7505, Acc.lamp: 0.8043, Acc.bathtub: 0.9249, Acc.railing: 0.5671, Acc.cushion: 0.7628, Acc.base: 0.5208, Acc.box: 0.4820, Acc.column: 0.6304, Acc.signboard: 0.5277, Acc.chest of drawers: 0.6892, Acc.counter: 0.5669, Acc.sand: 0.8927, Acc.sink: 0.8404, Acc.skyscraper: 0.6198, Acc.fireplace: 0.9074, Acc.refrigerator: 0.9272, Acc.grandstand: 0.8257, Acc.path: 0.3386, Acc.stairs: 0.3876, Acc.runway: 0.8961, Acc.case: 0.8574, Acc.pool table: 0.9764, Acc.pillow: 0.6695, Acc.screen door: 0.8323, Acc.stairway: 0.4432, Acc.river: 0.4414, Acc.bridge: 0.8744, Acc.bookcase: 0.5329, Acc.blind: 0.4426, Acc.coffee table: 0.8816, Acc.toilet: 0.9334, Acc.flower: 0.6539, Acc.book: 0.7349, Acc.hill: 0.1696, Acc.bench: 0.6304, Acc.countertop: 0.7658, Acc.stove: 0.9036, Acc.palm: 0.8278, Acc.kitchen island: 0.6548, Acc.computer: 0.8770, Acc.swivel chair: 0.5759, Acc.boat: 0.7560, Acc.bar: 0.7807, Acc.arcade machine: 0.7335, Acc.hovel: 0.2347, Acc.bus: 0.9651, Acc.towel: 0.8216, Acc.light: 0.5861, Acc.truck: 0.5995, Acc.tower: 0.3132, Acc.chandelier: 0.8279, Acc.awning: 0.4897, Acc.streetlight: 0.3474, Acc.booth: 0.6114, Acc.television receiver: 0.8930, Acc.airplane: 0.9208, Acc.dirt track: 0.1868, Acc.apparel: 0.6531, Acc.pole: 0.3225, Acc.land: 0.1552, Acc.bannister: 0.1928, Acc.escalator: 0.8115, Acc.ottoman: 0.7089, Acc.bottle: 0.3551, Acc.buffet: 0.7649, Acc.poster: 0.4488, Acc.stage: 0.4033, Acc.van: 0.6869, Acc.ship: 0.1173, Acc.fountain: 0.3352, Acc.conveyer belt: 0.9088, Acc.canopy: 0.3927, Acc.washer: 0.9281, Acc.plaything: 0.4316, Acc.swimming pool: 0.7213, Acc.stool: 0.6319, Acc.barrel: 0.8308, Acc.basket: 0.4556, Acc.waterfall: 0.6740, Acc.tent: 0.9738, Acc.bag: 0.3018, Acc.minibike: 0.8578, Acc.cradle: 0.9694, Acc.oven: 0.7807, Acc.ball: 0.3661, Acc.food: 0.5351, Acc.step: 0.1950, Acc.tank: 0.6502, Acc.trade name: 0.3348, Acc.microwave: 0.9402, Acc.pot: 0.6566, Acc.animal: 0.7855, Acc.bicycle: 0.7488, Acc.lake: 0.6347, Acc.dishwasher: 0.8291, Acc.screen: 0.9156, Acc.blanket: 0.3719, Acc.sculpture: 0.8809, Acc.hood: 0.7784, Acc.sconce: 0.6906, Acc.vase: 0.6000, Acc.traffic light: 0.5737, Acc.tray: 0.2760, Acc.ashcan: 0.6499, Acc.fan: 0.7552, Acc.pier: 0.4267, Acc.crt screen: 0.2031, Acc.plate: 0.7423, Acc.monitor: 0.6314, Acc.bulletin board: 0.5999, Acc.shower: 0.0633, Acc.radiator: 0.8122, Acc.glass: 0.2213, Acc.clock: 0.3963, Acc.flag: 0.7418 2023-11-03 07:48:16,127 - mmseg - INFO - Iter [35050/40000] lr: 4.010e-07, eta: 1:50:18, time: 2.375, data_time: 1.168, memory: 38534, decode.loss_ce: 0.1462, decode.acc_seg: 93.7795, loss: 0.1462 2023-11-03 07:49:16,792 - mmseg - INFO - Iter [35100/40000] lr: 3.970e-07, eta: 1:49:10, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1462, decode.acc_seg: 93.6564, loss: 0.1462 2023-11-03 07:50:17,462 - mmseg - INFO - Iter [35150/40000] lr: 3.929e-07, eta: 1:48:03, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1443, decode.acc_seg: 93.8245, loss: 0.1443 2023-11-03 07:51:18,174 - mmseg - INFO - Iter [35200/40000] lr: 3.889e-07, eta: 1:46:55, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1398, decode.acc_seg: 94.0540, loss: 0.1398 2023-11-03 07:52:18,848 - mmseg - INFO - Iter [35250/40000] lr: 3.848e-07, eta: 1:45:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1428, decode.acc_seg: 93.8996, loss: 0.1428 2023-11-03 07:53:19,473 - mmseg - INFO - Iter [35300/40000] lr: 3.808e-07, eta: 1:44:40, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1474, decode.acc_seg: 93.7074, loss: 0.1474 2023-11-03 07:54:20,193 - mmseg - INFO - Iter [35350/40000] lr: 3.767e-07, eta: 1:43:32, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1456, decode.acc_seg: 93.7997, loss: 0.1456 2023-11-03 07:55:23,268 - mmseg - INFO - Iter [35400/40000] lr: 3.727e-07, eta: 1:42:25, time: 1.261, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1408, decode.acc_seg: 93.9732, loss: 0.1408 2023-11-03 07:56:23,964 - mmseg - INFO - Iter [35450/40000] lr: 3.686e-07, eta: 1:41:17, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1320, decode.acc_seg: 94.2968, loss: 0.1320 2023-11-03 07:57:24,642 - mmseg - INFO - Iter [35500/40000] lr: 3.646e-07, eta: 1:40:10, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1418, decode.acc_seg: 93.8638, loss: 0.1418 2023-11-03 07:58:25,368 - mmseg - INFO - Iter [35550/40000] lr: 3.605e-07, eta: 1:39:02, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1418, decode.acc_seg: 93.9568, loss: 0.1418 2023-11-03 07:59:26,062 - mmseg - INFO - Iter [35600/40000] lr: 3.565e-07, eta: 1:37:55, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1330, decode.acc_seg: 94.3391, loss: 0.1330 2023-11-03 08:00:26,688 - mmseg - INFO - Iter [35650/40000] lr: 3.524e-07, eta: 1:36:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1475, decode.acc_seg: 93.8748, loss: 0.1475 2023-11-03 08:01:27,345 - mmseg - INFO - Iter [35700/40000] lr: 3.484e-07, eta: 1:35:40, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1398, decode.acc_seg: 93.9696, loss: 0.1398 2023-11-03 08:02:28,042 - mmseg - INFO - Iter [35750/40000] lr: 3.443e-07, eta: 1:34:32, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1404, decode.acc_seg: 93.8519, loss: 0.1404 2023-11-03 08:03:28,662 - mmseg - INFO - Iter [35800/40000] lr: 3.403e-07, eta: 1:33:25, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1415, decode.acc_seg: 93.9284, loss: 0.1415 2023-11-03 08:04:29,328 - mmseg - INFO - Iter [35850/40000] lr: 3.362e-07, eta: 1:32:17, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.1441, decode.acc_seg: 93.7642, loss: 0.1441 2023-11-03 08:05:29,998 - mmseg - INFO - Iter [35900/40000] lr: 3.322e-07, eta: 1:31:10, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1487, decode.acc_seg: 93.5263, loss: 0.1487 2023-11-03 08:06:30,638 - mmseg - INFO - Iter [35950/40000] lr: 3.281e-07, eta: 1:30:02, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1459, decode.acc_seg: 93.7038, loss: 0.1459 2023-11-03 08:07:31,311 - mmseg - INFO - Saving checkpoint at 36000 iterations 2023-11-03 08:08:28,127 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 08:08:28,127 - mmseg - INFO - Iter [36000/40000] lr: 3.241e-07, eta: 1:29:01, time: 2.350, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1426, decode.acc_seg: 93.9621, loss: 0.1426 2023-11-03 08:09:30,149 - mmseg - INFO - per class results: 2023-11-03 08:09:30,154 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 81.1 | 89.39 | | building | 83.96 | 93.25 | | sky | 94.33 | 97.18 | | floor | 83.36 | 91.06 | | tree | 75.77 | 88.53 | | ceiling | 85.15 | 93.25 | | road | 85.38 | 91.97 | | bed | 91.46 | 97.11 | | windowpane | 65.37 | 80.47 | | grass | 68.94 | 84.46 | | cabinet | 66.55 | 76.32 | | sidewalk | 68.96 | 82.05 | | person | 82.44 | 93.09 | | earth | 35.74 | 49.19 | | door | 58.13 | 73.99 | | table | 69.4 | 81.96 | | mountain | 62.3 | 75.36 | | plant | 53.76 | 63.94 | | curtain | 76.74 | 89.17 | | chair | 61.94 | 76.12 | | car | 86.17 | 93.31 | | water | 65.49 | 80.36 | | painting | 78.36 | 88.47 | | sofa | 81.26 | 91.6 | | shelf | 49.07 | 68.22 | | house | 49.67 | 62.82 | | sea | 70.47 | 80.95 | | mirror | 75.29 | 85.47 | | rug | 65.51 | 75.51 | | field | 30.72 | 47.2 | | armchair | 58.62 | 75.26 | | seat | 63.94 | 88.67 | | fence | 48.59 | 61.8 | | desk | 56.22 | 73.29 | | rock | 55.58 | 79.41 | | wardrobe | 53.97 | 73.02 | | lamp | 68.48 | 80.18 | | bathtub | 89.2 | 92.22 | | railing | 43.9 | 58.48 | | cushion | 62.81 | 74.44 | | base | 34.28 | 53.59 | | box | 37.97 | 50.73 | | column | 54.44 | 66.86 | | signboard | 37.89 | 54.43 | | chest of drawers | 45.91 | 71.76 | | counter | 50.86 | 60.37 | | sand | 61.91 | 89.35 | | sink | 77.06 | 83.78 | | skyscraper | 48.62 | 62.66 | | fireplace | 73.91 | 89.78 | | refrigerator | 84.96 | 92.56 | | grandstand | 47.35 | 82.63 | | path | 26.52 | 35.46 | | stairs | 30.15 | 38.71 | | runway | 67.73 | 89.71 | | case | 61.12 | 85.2 | | pool table | 93.56 | 98.05 | | pillow | 57.68 | 66.21 | | screen door | 83.58 | 86.56 | | stairway | 33.54 | 43.74 | | river | 21.04 | 41.76 | | bridge | 77.18 | 87.07 | | bookcase | 46.55 | 56.16 | | blind | 39.77 | 45.23 | | coffee table | 67.45 | 87.83 | | toilet | 89.35 | 93.17 | | flower | 42.55 | 64.82 | | book | 49.8 | 73.34 | | hill | 9.25 | 17.5 | | bench | 55.76 | 62.33 | | countertop | 60.8 | 78.38 | | stove | 85.08 | 89.87 | | palm | 47.04 | 82.1 | | kitchen island | 47.03 | 68.38 | | computer | 77.44 | 88.01 | | swivel chair | 44.08 | 60.38 | | boat | 63.38 | 81.25 | | bar | 72.92 | 79.22 | | arcade machine | 71.11 | 74.11 | | hovel | 22.3 | 24.81 | | bus | 92.93 | 95.88 | | towel | 71.81 | 80.68 | | light | 48.84 | 59.75 | | truck | 49.56 | 63.61 | | tower | 22.88 | 30.86 | | chandelier | 67.08 | 82.77 | | awning | 38.54 | 47.85 | | streetlight | 25.42 | 35.23 | | booth | 60.28 | 63.07 | | television receiver | 76.51 | 89.8 | | airplane | 83.21 | 91.94 | | dirt track | 11.22 | 20.84 | | apparel | 49.22 | 68.9 | | pole | 22.36 | 28.98 | | land | 7.7 | 14.72 | | bannister | 14.1 | 19.39 | | escalator | 60.89 | 82.21 | | ottoman | 55.04 | 71.87 | | bottle | 27.5 | 38.04 | | buffet | 62.88 | 74.74 | | poster | 36.89 | 45.31 | | stage | 22.82 | 39.01 | | van | 48.66 | 69.11 | | ship | 18.78 | 21.54 | | fountain | 31.14 | 32.15 | | conveyer belt | 84.81 | 92.67 | | canopy | 38.12 | 43.86 | | washer | 85.99 | 91.8 | | plaything | 30.64 | 41.89 | | swimming pool | 51.56 | 72.06 | | stool | 50.34 | 62.32 | | barrel | 68.67 | 83.21 | | basket | 37.54 | 49.07 | | waterfall | 48.58 | 62.45 | | tent | 94.03 | 97.47 | | bag | 24.13 | 28.27 | | minibike | 73.28 | 86.39 | | cradle | 86.08 | 97.29 | | oven | 65.51 | 77.7 | | ball | 37.57 | 39.22 | | food | 52.1 | 55.18 | | step | 14.04 | 16.33 | | tank | 54.64 | 65.18 | | trade name | 28.2 | 35.23 | | microwave | 89.5 | 94.74 | | pot | 55.8 | 63.41 | | animal | 75.1 | 79.03 | | bicycle | 59.18 | 78.48 | | lake | 55.73 | 63.57 | | dishwasher | 74.42 | 81.91 | | screen | 64.86 | 84.59 | | blanket | 30.85 | 35.66 | | sculpture | 74.76 | 89.03 | | hood | 67.95 | 78.24 | | sconce | 52.69 | 69.83 | | vase | 43.75 | 60.9 | | traffic light | 37.52 | 58.07 | | tray | 20.0 | 26.02 | | ashcan | 50.87 | 64.95 | | fan | 62.06 | 73.74 | | pier | 36.76 | 43.48 | | crt screen | 12.17 | 22.88 | | plate | 55.64 | 76.11 | | monitor | 44.78 | 55.31 | | bulletin board | 50.73 | 63.73 | | shower | 6.21 | 8.19 | | radiator | 67.9 | 83.75 | | glass | 18.73 | 20.98 | | clock | 34.45 | 40.13 | | flag | 66.13 | 73.68 | +---------------------+-------+-------+ 2023-11-03 08:09:30,154 - mmseg - INFO - Summary: 2023-11-03 08:09:30,155 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.34 | 55.62 | 67.42 | +-------+-------+-------+ 2023-11-03 08:09:30,155 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 08:09:30,156 - mmseg - INFO - Iter(val) [250] aAcc: 0.8534, mIoU: 0.5562, mAcc: 0.6742, IoU.wall: 0.8110, IoU.building: 0.8396, IoU.sky: 0.9433, IoU.floor: 0.8336, IoU.tree: 0.7577, IoU.ceiling: 0.8515, IoU.road: 0.8538, IoU.bed : 0.9146, IoU.windowpane: 0.6537, IoU.grass: 0.6894, IoU.cabinet: 0.6655, IoU.sidewalk: 0.6896, IoU.person: 0.8244, IoU.earth: 0.3574, IoU.door: 0.5813, IoU.table: 0.6940, IoU.mountain: 0.6230, IoU.plant: 0.5376, IoU.curtain: 0.7674, IoU.chair: 0.6194, IoU.car: 0.8617, IoU.water: 0.6549, IoU.painting: 0.7836, IoU.sofa: 0.8126, IoU.shelf: 0.4907, IoU.house: 0.4967, IoU.sea: 0.7047, IoU.mirror: 0.7529, IoU.rug: 0.6551, IoU.field: 0.3072, IoU.armchair: 0.5862, IoU.seat: 0.6394, IoU.fence: 0.4859, IoU.desk: 0.5622, IoU.rock: 0.5558, IoU.wardrobe: 0.5397, IoU.lamp: 0.6848, IoU.bathtub: 0.8920, IoU.railing: 0.4390, IoU.cushion: 0.6281, IoU.base: 0.3428, IoU.box: 0.3797, IoU.column: 0.5444, IoU.signboard: 0.3789, IoU.chest of drawers: 0.4591, IoU.counter: 0.5086, IoU.sand: 0.6191, IoU.sink: 0.7706, IoU.skyscraper: 0.4862, IoU.fireplace: 0.7391, IoU.refrigerator: 0.8496, IoU.grandstand: 0.4735, IoU.path: 0.2652, IoU.stairs: 0.3015, IoU.runway: 0.6773, IoU.case: 0.6112, IoU.pool table: 0.9356, IoU.pillow: 0.5768, IoU.screen door: 0.8358, IoU.stairway: 0.3354, IoU.river: 0.2104, IoU.bridge: 0.7718, IoU.bookcase: 0.4655, IoU.blind: 0.3977, IoU.coffee table: 0.6745, IoU.toilet: 0.8935, IoU.flower: 0.4255, IoU.book: 0.4980, IoU.hill: 0.0925, IoU.bench: 0.5576, IoU.countertop: 0.6080, IoU.stove: 0.8508, IoU.palm: 0.4704, IoU.kitchen island: 0.4703, IoU.computer: 0.7744, IoU.swivel chair: 0.4408, IoU.boat: 0.6338, IoU.bar: 0.7292, IoU.arcade machine: 0.7111, IoU.hovel: 0.2230, IoU.bus: 0.9293, IoU.towel: 0.7181, IoU.light: 0.4884, IoU.truck: 0.4956, IoU.tower: 0.2288, IoU.chandelier: 0.6708, IoU.awning: 0.3854, IoU.streetlight: 0.2542, IoU.booth: 0.6028, IoU.television receiver: 0.7651, IoU.airplane: 0.8321, IoU.dirt track: 0.1122, IoU.apparel: 0.4922, IoU.pole: 0.2236, IoU.land: 0.0770, IoU.bannister: 0.1410, IoU.escalator: 0.6089, IoU.ottoman: 0.5504, IoU.bottle: 0.2750, IoU.buffet: 0.6288, IoU.poster: 0.3689, IoU.stage: 0.2282, IoU.van: 0.4866, IoU.ship: 0.1878, IoU.fountain: 0.3114, IoU.conveyer belt: 0.8481, IoU.canopy: 0.3812, IoU.washer: 0.8599, IoU.plaything: 0.3064, IoU.swimming pool: 0.5156, IoU.stool: 0.5034, IoU.barrel: 0.6867, IoU.basket: 0.3754, IoU.waterfall: 0.4858, IoU.tent: 0.9403, IoU.bag: 0.2413, IoU.minibike: 0.7328, IoU.cradle: 0.8608, IoU.oven: 0.6551, IoU.ball: 0.3757, IoU.food: 0.5210, IoU.step: 0.1404, IoU.tank: 0.5464, IoU.trade name: 0.2820, IoU.microwave: 0.8950, IoU.pot: 0.5580, IoU.animal: 0.7510, IoU.bicycle: 0.5918, IoU.lake: 0.5573, IoU.dishwasher: 0.7442, IoU.screen: 0.6486, IoU.blanket: 0.3085, IoU.sculpture: 0.7476, IoU.hood: 0.6795, IoU.sconce: 0.5269, IoU.vase: 0.4375, IoU.traffic light: 0.3752, IoU.tray: 0.2000, IoU.ashcan: 0.5087, IoU.fan: 0.6206, IoU.pier: 0.3676, IoU.crt screen: 0.1217, IoU.plate: 0.5564, IoU.monitor: 0.4478, IoU.bulletin board: 0.5073, IoU.shower: 0.0621, IoU.radiator: 0.6790, IoU.glass: 0.1873, IoU.clock: 0.3445, IoU.flag: 0.6613, Acc.wall: 0.8939, Acc.building: 0.9325, Acc.sky: 0.9718, Acc.floor: 0.9106, Acc.tree: 0.8853, Acc.ceiling: 0.9325, Acc.road: 0.9197, Acc.bed : 0.9711, Acc.windowpane: 0.8047, Acc.grass: 0.8446, Acc.cabinet: 0.7632, Acc.sidewalk: 0.8205, Acc.person: 0.9309, Acc.earth: 0.4919, Acc.door: 0.7399, Acc.table: 0.8196, Acc.mountain: 0.7536, Acc.plant: 0.6394, Acc.curtain: 0.8917, Acc.chair: 0.7612, Acc.car: 0.9331, Acc.water: 0.8036, Acc.painting: 0.8847, Acc.sofa: 0.9160, Acc.shelf: 0.6822, Acc.house: 0.6282, Acc.sea: 0.8095, Acc.mirror: 0.8547, Acc.rug: 0.7551, Acc.field: 0.4720, Acc.armchair: 0.7526, Acc.seat: 0.8867, Acc.fence: 0.6180, Acc.desk: 0.7329, Acc.rock: 0.7941, Acc.wardrobe: 0.7302, Acc.lamp: 0.8018, Acc.bathtub: 0.9222, Acc.railing: 0.5848, Acc.cushion: 0.7444, Acc.base: 0.5359, Acc.box: 0.5073, Acc.column: 0.6686, Acc.signboard: 0.5443, Acc.chest of drawers: 0.7176, Acc.counter: 0.6037, Acc.sand: 0.8935, Acc.sink: 0.8378, Acc.skyscraper: 0.6266, Acc.fireplace: 0.8978, Acc.refrigerator: 0.9256, Acc.grandstand: 0.8263, Acc.path: 0.3546, Acc.stairs: 0.3871, Acc.runway: 0.8971, Acc.case: 0.8520, Acc.pool table: 0.9805, Acc.pillow: 0.6621, Acc.screen door: 0.8656, Acc.stairway: 0.4374, Acc.river: 0.4176, Acc.bridge: 0.8707, Acc.bookcase: 0.5616, Acc.blind: 0.4523, Acc.coffee table: 0.8783, Acc.toilet: 0.9317, Acc.flower: 0.6482, Acc.book: 0.7334, Acc.hill: 0.1750, Acc.bench: 0.6233, Acc.countertop: 0.7838, Acc.stove: 0.8987, Acc.palm: 0.8210, Acc.kitchen island: 0.6838, Acc.computer: 0.8801, Acc.swivel chair: 0.6038, Acc.boat: 0.8125, Acc.bar: 0.7922, Acc.arcade machine: 0.7411, Acc.hovel: 0.2481, Acc.bus: 0.9588, Acc.towel: 0.8068, Acc.light: 0.5975, Acc.truck: 0.6361, Acc.tower: 0.3086, Acc.chandelier: 0.8277, Acc.awning: 0.4785, Acc.streetlight: 0.3523, Acc.booth: 0.6307, Acc.television receiver: 0.8980, Acc.airplane: 0.9194, Acc.dirt track: 0.2084, Acc.apparel: 0.6890, Acc.pole: 0.2898, Acc.land: 0.1472, Acc.bannister: 0.1939, Acc.escalator: 0.8221, Acc.ottoman: 0.7187, Acc.bottle: 0.3804, Acc.buffet: 0.7474, Acc.poster: 0.4531, Acc.stage: 0.3901, Acc.van: 0.6911, Acc.ship: 0.2154, Acc.fountain: 0.3215, Acc.conveyer belt: 0.9267, Acc.canopy: 0.4386, Acc.washer: 0.9180, Acc.plaything: 0.4189, Acc.swimming pool: 0.7206, Acc.stool: 0.6232, Acc.barrel: 0.8321, Acc.basket: 0.4907, Acc.waterfall: 0.6245, Acc.tent: 0.9747, Acc.bag: 0.2827, Acc.minibike: 0.8639, Acc.cradle: 0.9729, Acc.oven: 0.7770, Acc.ball: 0.3922, Acc.food: 0.5518, Acc.step: 0.1633, Acc.tank: 0.6518, Acc.trade name: 0.3523, Acc.microwave: 0.9474, Acc.pot: 0.6341, Acc.animal: 0.7903, Acc.bicycle: 0.7848, Acc.lake: 0.6357, Acc.dishwasher: 0.8191, Acc.screen: 0.8459, Acc.blanket: 0.3566, Acc.sculpture: 0.8903, Acc.hood: 0.7824, Acc.sconce: 0.6983, Acc.vase: 0.6090, Acc.traffic light: 0.5807, Acc.tray: 0.2602, Acc.ashcan: 0.6495, Acc.fan: 0.7374, Acc.pier: 0.4348, Acc.crt screen: 0.2288, Acc.plate: 0.7611, Acc.monitor: 0.5531, Acc.bulletin board: 0.6373, Acc.shower: 0.0819, Acc.radiator: 0.8375, Acc.glass: 0.2098, Acc.clock: 0.4013, Acc.flag: 0.7368 2023-11-03 08:10:33,286 - mmseg - INFO - Iter [36050/40000] lr: 3.200e-07, eta: 1:28:01, time: 2.503, data_time: 1.295, memory: 38534, decode.loss_ce: 0.1404, decode.acc_seg: 93.9383, loss: 0.1404 2023-11-03 08:11:33,989 - mmseg - INFO - Iter [36100/40000] lr: 3.160e-07, eta: 1:26:53, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1442, decode.acc_seg: 93.7540, loss: 0.1442 2023-11-03 08:12:34,720 - mmseg - INFO - Iter [36150/40000] lr: 3.119e-07, eta: 1:25:46, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1371, decode.acc_seg: 94.1819, loss: 0.1371 2023-11-03 08:13:35,447 - mmseg - INFO - Iter [36200/40000] lr: 3.079e-07, eta: 1:24:38, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1439, decode.acc_seg: 93.9083, loss: 0.1439 2023-11-03 08:14:36,123 - mmseg - INFO - Iter [36250/40000] lr: 3.038e-07, eta: 1:23:31, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1427, decode.acc_seg: 93.8591, loss: 0.1427 2023-11-03 08:15:36,713 - mmseg - INFO - Iter [36300/40000] lr: 2.998e-07, eta: 1:22:24, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1434, decode.acc_seg: 93.7514, loss: 0.1434 2023-11-03 08:16:37,380 - mmseg - INFO - Iter [36350/40000] lr: 2.957e-07, eta: 1:21:16, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1409, decode.acc_seg: 93.9894, loss: 0.1409 2023-11-03 08:17:38,050 - mmseg - INFO - Iter [36400/40000] lr: 2.917e-07, eta: 1:20:09, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1444, decode.acc_seg: 93.6881, loss: 0.1444 2023-11-03 08:18:38,673 - mmseg - INFO - Iter [36450/40000] lr: 2.876e-07, eta: 1:19:01, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1393, decode.acc_seg: 93.9668, loss: 0.1393 2023-11-03 08:19:39,418 - mmseg - INFO - Iter [36500/40000] lr: 2.836e-07, eta: 1:17:54, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1360, decode.acc_seg: 94.0660, loss: 0.1360 2023-11-03 08:20:40,157 - mmseg - INFO - Iter [36550/40000] lr: 2.795e-07, eta: 1:16:47, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1413, decode.acc_seg: 93.9178, loss: 0.1413 2023-11-03 08:21:40,800 - mmseg - INFO - Iter [36600/40000] lr: 2.755e-07, eta: 1:15:39, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1497, decode.acc_seg: 93.7406, loss: 0.1497 2023-11-03 08:22:41,417 - mmseg - INFO - Iter [36650/40000] lr: 2.714e-07, eta: 1:14:32, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1461, decode.acc_seg: 93.6274, loss: 0.1461 2023-11-03 08:23:44,363 - mmseg - INFO - Iter [36700/40000] lr: 2.674e-07, eta: 1:13:25, time: 1.259, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1440, decode.acc_seg: 93.7916, loss: 0.1440 2023-11-03 08:24:45,016 - mmseg - INFO - Iter [36750/40000] lr: 2.633e-07, eta: 1:12:18, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1362, decode.acc_seg: 94.0441, loss: 0.1362 2023-11-03 08:25:45,677 - mmseg - INFO - Iter [36800/40000] lr: 2.593e-07, eta: 1:11:10, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1422, decode.acc_seg: 93.9136, loss: 0.1422 2023-11-03 08:26:46,355 - mmseg - INFO - Iter [36850/40000] lr: 2.552e-07, eta: 1:10:03, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1481, decode.acc_seg: 93.6320, loss: 0.1481 2023-11-03 08:27:47,129 - mmseg - INFO - Iter [36900/40000] lr: 2.512e-07, eta: 1:08:56, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1491, decode.acc_seg: 93.7773, loss: 0.1491 2023-11-03 08:28:47,830 - mmseg - INFO - Iter [36950/40000] lr: 2.471e-07, eta: 1:07:49, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1438, decode.acc_seg: 93.8825, loss: 0.1438 2023-11-03 08:29:48,531 - mmseg - INFO - Saving checkpoint at 37000 iterations 2023-11-03 08:30:49,258 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 08:30:49,258 - mmseg - INFO - Iter [37000/40000] lr: 2.431e-07, eta: 1:06:46, time: 2.429, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1441, decode.acc_seg: 93.7765, loss: 0.1441 2023-11-03 08:31:48,516 - mmseg - INFO - per class results: 2023-11-03 08:31:48,522 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 81.16 | 90.4 | | building | 83.8 | 93.4 | | sky | 94.34 | 97.1 | | floor | 83.65 | 91.25 | | tree | 76.12 | 88.9 | | ceiling | 85.25 | 92.78 | | road | 85.5 | 92.15 | | bed | 91.55 | 96.81 | | windowpane | 65.62 | 80.63 | | grass | 69.37 | 84.96 | | cabinet | 66.18 | 75.81 | | sidewalk | 69.08 | 82.13 | | person | 82.4 | 93.01 | | earth | 35.43 | 47.03 | | door | 58.53 | 72.41 | | table | 69.68 | 81.59 | | mountain | 62.53 | 74.01 | | plant | 54.78 | 66.14 | | curtain | 77.12 | 88.97 | | chair | 61.75 | 74.73 | | car | 86.36 | 92.93 | | water | 66.03 | 82.37 | | painting | 78.5 | 87.82 | | sofa | 81.72 | 90.85 | | shelf | 47.64 | 64.62 | | house | 46.92 | 58.71 | | sea | 70.71 | 80.57 | | mirror | 75.24 | 85.1 | | rug | 65.54 | 75.86 | | field | 34.47 | 54.15 | | armchair | 58.68 | 77.25 | | seat | 64.44 | 88.28 | | fence | 48.35 | 62.42 | | desk | 55.44 | 76.44 | | rock | 57.53 | 79.98 | | wardrobe | 53.64 | 74.65 | | lamp | 67.8 | 78.61 | | bathtub | 88.73 | 91.82 | | railing | 42.51 | 56.85 | | cushion | 63.33 | 74.46 | | base | 33.8 | 50.97 | | box | 38.24 | 48.44 | | column | 53.98 | 65.3 | | signboard | 36.89 | 52.68 | | chest of drawers | 46.7 | 68.87 | | counter | 51.2 | 59.8 | | sand | 61.33 | 89.37 | | sink | 76.76 | 84.1 | | skyscraper | 48.6 | 62.77 | | fireplace | 74.15 | 90.01 | | refrigerator | 85.27 | 91.27 | | grandstand | 47.0 | 82.64 | | path | 25.29 | 33.59 | | stairs | 30.92 | 40.18 | | runway | 68.49 | 89.92 | | case | 59.88 | 85.68 | | pool table | 93.76 | 97.81 | | pillow | 58.86 | 68.31 | | screen door | 82.58 | 85.26 | | stairway | 33.58 | 44.12 | | river | 20.61 | 40.09 | | bridge | 77.23 | 86.32 | | bookcase | 44.59 | 52.53 | | blind | 40.54 | 45.78 | | coffee table | 67.29 | 87.64 | | toilet | 89.68 | 93.52 | | flower | 41.7 | 60.96 | | book | 49.68 | 75.71 | | hill | 9.12 | 17.69 | | bench | 55.72 | 61.15 | | countertop | 62.2 | 77.73 | | stove | 84.63 | 89.79 | | palm | 47.18 | 82.13 | | kitchen island | 47.36 | 71.66 | | computer | 77.32 | 87.03 | | swivel chair | 46.14 | 67.52 | | boat | 61.95 | 75.11 | | bar | 75.57 | 83.33 | | arcade machine | 73.88 | 76.64 | | hovel | 19.94 | 22.23 | | bus | 93.06 | 96.21 | | towel | 71.38 | 80.18 | | light | 47.68 | 56.32 | | truck | 48.25 | 59.15 | | tower | 22.0 | 29.14 | | chandelier | 67.35 | 82.34 | | awning | 34.67 | 42.18 | | streetlight | 24.88 | 33.65 | | booth | 59.57 | 62.94 | | television receiver | 76.81 | 88.88 | | airplane | 81.85 | 89.72 | | dirt track | 14.44 | 19.12 | | apparel | 48.86 | 66.27 | | pole | 23.76 | 31.41 | | land | 7.82 | 14.12 | | bannister | 14.28 | 19.82 | | escalator | 61.33 | 82.45 | | ottoman | 55.67 | 69.7 | | bottle | 26.87 | 38.06 | | buffet | 60.34 | 76.19 | | poster | 37.12 | 45.39 | | stage | 22.02 | 41.86 | | van | 47.45 | 72.12 | | ship | 13.61 | 16.31 | | fountain | 28.54 | 29.58 | | conveyer belt | 84.72 | 92.7 | | canopy | 26.28 | 29.51 | | washer | 87.11 | 92.71 | | plaything | 30.41 | 40.28 | | swimming pool | 55.52 | 71.3 | | stool | 49.9 | 64.62 | | barrel | 71.63 | 82.89 | | basket | 36.85 | 45.94 | | waterfall | 46.9 | 62.86 | | tent | 93.26 | 97.5 | | bag | 25.1 | 29.21 | | minibike | 73.82 | 85.22 | | cradle | 86.81 | 97.04 | | oven | 65.44 | 77.75 | | ball | 40.34 | 42.09 | | food | 50.41 | 53.2 | | step | 14.96 | 17.17 | | tank | 54.05 | 65.17 | | trade name | 25.32 | 30.26 | | microwave | 89.01 | 94.24 | | pot | 55.41 | 62.9 | | animal | 73.29 | 76.54 | | bicycle | 58.25 | 74.31 | | lake | 55.86 | 63.56 | | dishwasher | 74.59 | 82.96 | | screen | 69.56 | 90.58 | | blanket | 32.02 | 37.37 | | sculpture | 77.61 | 87.56 | | hood | 67.87 | 78.45 | | sconce | 52.41 | 65.8 | | vase | 43.48 | 58.72 | | traffic light | 37.03 | 54.76 | | tray | 19.79 | 26.81 | | ashcan | 51.0 | 65.15 | | fan | 62.32 | 73.37 | | pier | 36.44 | 41.52 | | crt screen | 14.39 | 22.06 | | plate | 55.7 | 75.22 | | monitor | 52.38 | 63.67 | | bulletin board | 48.01 | 57.64 | | shower | 5.71 | 6.66 | | radiator | 67.67 | 82.94 | | glass | 19.73 | 22.7 | | clock | 34.16 | 39.72 | | flag | 65.88 | 72.27 | +---------------------+-------+-------+ 2023-11-03 08:31:48,522 - mmseg - INFO - Summary: 2023-11-03 08:31:48,522 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.42 | 55.57 | 66.94 | +-------+-------+-------+ 2023-11-03 08:31:48,523 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 08:31:48,523 - mmseg - INFO - Iter(val) [250] aAcc: 0.8542, mIoU: 0.5557, mAcc: 0.6694, IoU.wall: 0.8116, IoU.building: 0.8380, IoU.sky: 0.9434, IoU.floor: 0.8365, IoU.tree: 0.7612, IoU.ceiling: 0.8525, IoU.road: 0.8550, IoU.bed : 0.9155, IoU.windowpane: 0.6562, IoU.grass: 0.6937, IoU.cabinet: 0.6618, IoU.sidewalk: 0.6908, IoU.person: 0.8240, IoU.earth: 0.3543, IoU.door: 0.5853, IoU.table: 0.6968, IoU.mountain: 0.6253, IoU.plant: 0.5478, IoU.curtain: 0.7712, IoU.chair: 0.6175, IoU.car: 0.8636, IoU.water: 0.6603, IoU.painting: 0.7850, IoU.sofa: 0.8172, IoU.shelf: 0.4764, IoU.house: 0.4692, IoU.sea: 0.7071, IoU.mirror: 0.7524, IoU.rug: 0.6554, IoU.field: 0.3447, IoU.armchair: 0.5868, IoU.seat: 0.6444, IoU.fence: 0.4835, IoU.desk: 0.5544, IoU.rock: 0.5753, IoU.wardrobe: 0.5364, IoU.lamp: 0.6780, IoU.bathtub: 0.8873, IoU.railing: 0.4251, IoU.cushion: 0.6333, IoU.base: 0.3380, IoU.box: 0.3824, IoU.column: 0.5398, IoU.signboard: 0.3689, IoU.chest of drawers: 0.4670, IoU.counter: 0.5120, IoU.sand: 0.6133, IoU.sink: 0.7676, IoU.skyscraper: 0.4860, IoU.fireplace: 0.7415, IoU.refrigerator: 0.8527, IoU.grandstand: 0.4700, IoU.path: 0.2529, IoU.stairs: 0.3092, IoU.runway: 0.6849, IoU.case: 0.5988, IoU.pool table: 0.9376, IoU.pillow: 0.5886, IoU.screen door: 0.8258, IoU.stairway: 0.3358, IoU.river: 0.2061, IoU.bridge: 0.7723, IoU.bookcase: 0.4459, IoU.blind: 0.4054, IoU.coffee table: 0.6729, IoU.toilet: 0.8968, IoU.flower: 0.4170, IoU.book: 0.4968, IoU.hill: 0.0912, IoU.bench: 0.5572, IoU.countertop: 0.6220, IoU.stove: 0.8463, IoU.palm: 0.4718, IoU.kitchen island: 0.4736, IoU.computer: 0.7732, IoU.swivel chair: 0.4614, IoU.boat: 0.6195, IoU.bar: 0.7557, IoU.arcade machine: 0.7388, IoU.hovel: 0.1994, IoU.bus: 0.9306, IoU.towel: 0.7138, IoU.light: 0.4768, IoU.truck: 0.4825, IoU.tower: 0.2200, IoU.chandelier: 0.6735, IoU.awning: 0.3467, IoU.streetlight: 0.2488, IoU.booth: 0.5957, IoU.television receiver: 0.7681, IoU.airplane: 0.8185, IoU.dirt track: 0.1444, IoU.apparel: 0.4886, IoU.pole: 0.2376, IoU.land: 0.0782, IoU.bannister: 0.1428, IoU.escalator: 0.6133, IoU.ottoman: 0.5567, IoU.bottle: 0.2687, IoU.buffet: 0.6034, IoU.poster: 0.3712, IoU.stage: 0.2202, IoU.van: 0.4745, IoU.ship: 0.1361, IoU.fountain: 0.2854, IoU.conveyer belt: 0.8472, IoU.canopy: 0.2628, IoU.washer: 0.8711, IoU.plaything: 0.3041, IoU.swimming pool: 0.5552, IoU.stool: 0.4990, IoU.barrel: 0.7163, IoU.basket: 0.3685, IoU.waterfall: 0.4690, IoU.tent: 0.9326, IoU.bag: 0.2510, IoU.minibike: 0.7382, IoU.cradle: 0.8681, IoU.oven: 0.6544, IoU.ball: 0.4034, IoU.food: 0.5041, IoU.step: 0.1496, IoU.tank: 0.5405, IoU.trade name: 0.2532, IoU.microwave: 0.8901, IoU.pot: 0.5541, IoU.animal: 0.7329, IoU.bicycle: 0.5825, IoU.lake: 0.5586, IoU.dishwasher: 0.7459, IoU.screen: 0.6956, IoU.blanket: 0.3202, IoU.sculpture: 0.7761, IoU.hood: 0.6787, IoU.sconce: 0.5241, IoU.vase: 0.4348, IoU.traffic light: 0.3703, IoU.tray: 0.1979, IoU.ashcan: 0.5100, IoU.fan: 0.6232, IoU.pier: 0.3644, IoU.crt screen: 0.1439, IoU.plate: 0.5570, IoU.monitor: 0.5238, IoU.bulletin board: 0.4801, IoU.shower: 0.0571, IoU.radiator: 0.6767, IoU.glass: 0.1973, IoU.clock: 0.3416, IoU.flag: 0.6588, Acc.wall: 0.9040, Acc.building: 0.9340, Acc.sky: 0.9710, Acc.floor: 0.9125, Acc.tree: 0.8890, Acc.ceiling: 0.9278, Acc.road: 0.9215, Acc.bed : 0.9681, Acc.windowpane: 0.8063, Acc.grass: 0.8496, Acc.cabinet: 0.7581, Acc.sidewalk: 0.8213, Acc.person: 0.9301, Acc.earth: 0.4703, Acc.door: 0.7241, Acc.table: 0.8159, Acc.mountain: 0.7401, Acc.plant: 0.6614, Acc.curtain: 0.8897, Acc.chair: 0.7473, Acc.car: 0.9293, Acc.water: 0.8237, Acc.painting: 0.8782, Acc.sofa: 0.9085, Acc.shelf: 0.6462, Acc.house: 0.5871, Acc.sea: 0.8057, Acc.mirror: 0.8510, Acc.rug: 0.7586, Acc.field: 0.5415, Acc.armchair: 0.7725, Acc.seat: 0.8828, Acc.fence: 0.6242, Acc.desk: 0.7644, Acc.rock: 0.7998, Acc.wardrobe: 0.7465, Acc.lamp: 0.7861, Acc.bathtub: 0.9182, Acc.railing: 0.5685, Acc.cushion: 0.7446, Acc.base: 0.5097, Acc.box: 0.4844, Acc.column: 0.6530, Acc.signboard: 0.5268, Acc.chest of drawers: 0.6887, Acc.counter: 0.5980, Acc.sand: 0.8937, Acc.sink: 0.8410, Acc.skyscraper: 0.6277, Acc.fireplace: 0.9001, Acc.refrigerator: 0.9127, Acc.grandstand: 0.8264, Acc.path: 0.3359, Acc.stairs: 0.4018, Acc.runway: 0.8992, Acc.case: 0.8568, Acc.pool table: 0.9781, Acc.pillow: 0.6831, Acc.screen door: 0.8526, Acc.stairway: 0.4412, Acc.river: 0.4009, Acc.bridge: 0.8632, Acc.bookcase: 0.5253, Acc.blind: 0.4578, Acc.coffee table: 0.8764, Acc.toilet: 0.9352, Acc.flower: 0.6096, Acc.book: 0.7571, Acc.hill: 0.1769, Acc.bench: 0.6115, Acc.countertop: 0.7773, Acc.stove: 0.8979, Acc.palm: 0.8213, Acc.kitchen island: 0.7166, Acc.computer: 0.8703, Acc.swivel chair: 0.6752, Acc.boat: 0.7511, Acc.bar: 0.8333, Acc.arcade machine: 0.7664, Acc.hovel: 0.2223, Acc.bus: 0.9621, Acc.towel: 0.8018, Acc.light: 0.5632, Acc.truck: 0.5915, Acc.tower: 0.2914, Acc.chandelier: 0.8234, Acc.awning: 0.4218, Acc.streetlight: 0.3365, Acc.booth: 0.6294, Acc.television receiver: 0.8888, Acc.airplane: 0.8972, Acc.dirt track: 0.1912, Acc.apparel: 0.6627, Acc.pole: 0.3141, Acc.land: 0.1412, Acc.bannister: 0.1982, Acc.escalator: 0.8245, Acc.ottoman: 0.6970, Acc.bottle: 0.3806, Acc.buffet: 0.7619, Acc.poster: 0.4539, Acc.stage: 0.4186, Acc.van: 0.7212, Acc.ship: 0.1631, Acc.fountain: 0.2958, Acc.conveyer belt: 0.9270, Acc.canopy: 0.2951, Acc.washer: 0.9271, Acc.plaything: 0.4028, Acc.swimming pool: 0.7130, Acc.stool: 0.6462, Acc.barrel: 0.8289, Acc.basket: 0.4594, Acc.waterfall: 0.6286, Acc.tent: 0.9750, Acc.bag: 0.2921, Acc.minibike: 0.8522, Acc.cradle: 0.9704, Acc.oven: 0.7775, Acc.ball: 0.4209, Acc.food: 0.5320, Acc.step: 0.1717, Acc.tank: 0.6517, Acc.trade name: 0.3026, Acc.microwave: 0.9424, Acc.pot: 0.6290, Acc.animal: 0.7654, Acc.bicycle: 0.7431, Acc.lake: 0.6356, Acc.dishwasher: 0.8296, Acc.screen: 0.9058, Acc.blanket: 0.3737, Acc.sculpture: 0.8756, Acc.hood: 0.7845, Acc.sconce: 0.6580, Acc.vase: 0.5872, Acc.traffic light: 0.5476, Acc.tray: 0.2681, Acc.ashcan: 0.6515, Acc.fan: 0.7337, Acc.pier: 0.4152, Acc.crt screen: 0.2206, Acc.plate: 0.7522, Acc.monitor: 0.6367, Acc.bulletin board: 0.5764, Acc.shower: 0.0666, Acc.radiator: 0.8294, Acc.glass: 0.2270, Acc.clock: 0.3972, Acc.flag: 0.7227 2023-11-03 08:32:49,268 - mmseg - INFO - Iter [37050/40000] lr: 2.390e-07, eta: 1:05:44, time: 2.400, data_time: 1.193, memory: 38534, decode.loss_ce: 0.1400, decode.acc_seg: 93.9836, loss: 0.1400 2023-11-03 08:33:49,955 - mmseg - INFO - Iter [37100/40000] lr: 2.350e-07, eta: 1:04:36, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1383, decode.acc_seg: 93.8754, loss: 0.1383 2023-11-03 08:34:50,661 - mmseg - INFO - Iter [37150/40000] lr: 2.309e-07, eta: 1:03:29, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1461, decode.acc_seg: 93.7370, loss: 0.1461 2023-11-03 08:35:51,400 - mmseg - INFO - Iter [37200/40000] lr: 2.269e-07, eta: 1:02:22, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1346, decode.acc_seg: 94.2108, loss: 0.1346 2023-11-03 08:36:52,132 - mmseg - INFO - Iter [37250/40000] lr: 2.228e-07, eta: 1:01:15, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1368, decode.acc_seg: 94.0945, loss: 0.1368 2023-11-03 08:37:55,115 - mmseg - INFO - Iter [37300/40000] lr: 2.188e-07, eta: 1:00:07, time: 1.260, data_time: 0.051, memory: 38534, decode.loss_ce: 0.1402, decode.acc_seg: 93.9938, loss: 0.1402 2023-11-03 08:38:55,863 - mmseg - INFO - Iter [37350/40000] lr: 2.147e-07, eta: 0:59:00, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1351, decode.acc_seg: 94.1664, loss: 0.1351 2023-11-03 08:39:56,625 - mmseg - INFO - Iter [37400/40000] lr: 2.107e-07, eta: 0:57:53, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1423, decode.acc_seg: 93.8975, loss: 0.1423 2023-11-03 08:40:57,364 - mmseg - INFO - Iter [37450/40000] lr: 2.066e-07, eta: 0:56:46, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1411, decode.acc_seg: 93.9777, loss: 0.1411 2023-11-03 08:41:58,021 - mmseg - INFO - Iter [37500/40000] lr: 2.026e-07, eta: 0:55:39, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1440, decode.acc_seg: 93.8013, loss: 0.1440 2023-11-03 08:42:58,640 - mmseg - INFO - Iter [37550/40000] lr: 1.985e-07, eta: 0:54:31, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1448, decode.acc_seg: 93.8243, loss: 0.1448 2023-11-03 08:43:59,294 - mmseg - INFO - Iter [37600/40000] lr: 1.945e-07, eta: 0:53:24, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1383, decode.acc_seg: 94.1749, loss: 0.1383 2023-11-03 08:44:59,922 - mmseg - INFO - Iter [37650/40000] lr: 1.904e-07, eta: 0:52:17, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1480, decode.acc_seg: 93.6132, loss: 0.1480 2023-11-03 08:46:00,624 - mmseg - INFO - Iter [37700/40000] lr: 1.864e-07, eta: 0:51:10, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1429, decode.acc_seg: 93.6270, loss: 0.1429 2023-11-03 08:47:01,305 - mmseg - INFO - Iter [37750/40000] lr: 1.823e-07, eta: 0:50:03, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1409, decode.acc_seg: 93.9346, loss: 0.1409 2023-11-03 08:48:01,964 - mmseg - INFO - Iter [37800/40000] lr: 1.783e-07, eta: 0:48:56, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1357, decode.acc_seg: 94.1543, loss: 0.1357 2023-11-03 08:49:02,638 - mmseg - INFO - Iter [37850/40000] lr: 1.742e-07, eta: 0:47:49, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1322, decode.acc_seg: 94.2927, loss: 0.1322 2023-11-03 08:50:03,282 - mmseg - INFO - Iter [37900/40000] lr: 1.702e-07, eta: 0:46:42, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1395, decode.acc_seg: 94.0369, loss: 0.1395 2023-11-03 08:51:06,384 - mmseg - INFO - Iter [37950/40000] lr: 1.661e-07, eta: 0:45:35, time: 1.262, data_time: 0.055, memory: 38534, decode.loss_ce: 0.1392, decode.acc_seg: 93.9727, loss: 0.1392 2023-11-03 08:52:07,044 - mmseg - INFO - Saving checkpoint at 38000 iterations 2023-11-03 08:53:02,388 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 08:53:02,388 - mmseg - INFO - Iter [38000/40000] lr: 1.621e-07, eta: 0:44:31, time: 2.320, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1342, decode.acc_seg: 94.1309, loss: 0.1342 2023-11-03 08:54:02,141 - mmseg - INFO - per class results: 2023-11-03 08:54:02,146 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 81.14 | 89.72 | | building | 83.81 | 93.32 | | sky | 94.25 | 97.32 | | floor | 83.58 | 91.79 | | tree | 76.16 | 89.33 | | ceiling | 85.3 | 93.27 | | road | 86.14 | 91.11 | | bed | 91.57 | 96.93 | | windowpane | 65.91 | 80.83 | | grass | 69.3 | 84.35 | | cabinet | 67.06 | 76.79 | | sidewalk | 69.93 | 84.97 | | person | 82.61 | 92.22 | | earth | 35.9 | 47.73 | | door | 58.73 | 74.38 | | table | 69.95 | 82.6 | | mountain | 62.89 | 74.35 | | plant | 55.42 | 67.93 | | curtain | 76.54 | 89.36 | | chair | 62.37 | 76.8 | | car | 86.28 | 93.01 | | water | 65.57 | 81.31 | | painting | 78.57 | 88.18 | | sofa | 81.23 | 91.97 | | shelf | 49.08 | 68.98 | | house | 48.82 | 61.09 | | sea | 70.94 | 80.56 | | mirror | 74.6 | 83.62 | | rug | 64.64 | 73.88 | | field | 33.39 | 51.37 | | armchair | 59.68 | 75.48 | | seat | 64.49 | 88.82 | | fence | 48.19 | 62.48 | | desk | 55.72 | 74.27 | | rock | 56.07 | 78.51 | | wardrobe | 54.51 | 73.75 | | lamp | 68.01 | 79.81 | | bathtub | 89.16 | 92.56 | | railing | 43.02 | 57.49 | | cushion | 63.22 | 74.23 | | base | 34.35 | 54.58 | | box | 38.56 | 50.04 | | column | 53.25 | 64.01 | | signboard | 36.93 | 51.5 | | chest of drawers | 46.12 | 66.3 | | counter | 52.17 | 61.11 | | sand | 62.01 | 89.27 | | sink | 77.19 | 84.26 | | skyscraper | 48.4 | 59.89 | | fireplace | 74.23 | 90.53 | | refrigerator | 84.92 | 92.41 | | grandstand | 47.04 | 83.5 | | path | 22.93 | 31.43 | | stairs | 30.78 | 39.51 | | runway | 68.35 | 89.95 | | case | 60.64 | 86.06 | | pool table | 93.75 | 97.82 | | pillow | 58.88 | 68.2 | | screen door | 79.44 | 81.98 | | stairway | 32.93 | 42.73 | | river | 20.5 | 39.84 | | bridge | 76.98 | 85.21 | | bookcase | 45.26 | 52.74 | | blind | 40.06 | 45.01 | | coffee table | 67.92 | 85.8 | | toilet | 89.47 | 93.31 | | flower | 43.05 | 63.34 | | book | 50.46 | 74.95 | | hill | 9.53 | 19.71 | | bench | 55.74 | 62.15 | | countertop | 63.63 | 76.67 | | stove | 84.86 | 89.66 | | palm | 46.24 | 78.93 | | kitchen island | 47.64 | 69.51 | | computer | 77.43 | 87.59 | | swivel chair | 45.11 | 64.51 | | boat | 62.17 | 74.29 | | bar | 77.23 | 84.47 | | arcade machine | 73.48 | 76.23 | | hovel | 18.36 | 20.3 | | bus | 93.16 | 96.41 | | towel | 71.89 | 81.29 | | light | 48.15 | 58.07 | | truck | 48.59 | 59.23 | | tower | 25.68 | 35.18 | | chandelier | 67.2 | 82.26 | | awning | 33.79 | 40.64 | | streetlight | 24.42 | 32.89 | | booth | 58.98 | 60.98 | | television receiver | 76.61 | 88.81 | | airplane | 80.95 | 89.13 | | dirt track | 15.27 | 20.51 | | apparel | 49.33 | 68.41 | | pole | 21.74 | 27.92 | | land | 7.77 | 15.15 | | bannister | 13.82 | 18.3 | | escalator | 62.59 | 82.31 | | ottoman | 55.46 | 70.72 | | bottle | 26.27 | 35.1 | | buffet | 61.23 | 75.92 | | poster | 37.45 | 44.8 | | stage | 22.89 | 40.49 | | van | 47.72 | 71.12 | | ship | 10.8 | 12.96 | | fountain | 27.59 | 28.6 | | conveyer belt | 84.53 | 92.29 | | canopy | 24.25 | 26.71 | | washer | 85.59 | 91.26 | | plaything | 30.78 | 41.18 | | swimming pool | 50.35 | 70.1 | | stool | 50.98 | 64.25 | | barrel | 71.23 | 83.01 | | basket | 37.25 | 47.62 | | waterfall | 46.9 | 62.13 | | tent | 93.93 | 97.44 | | bag | 24.36 | 28.38 | | minibike | 73.59 | 85.72 | | cradle | 86.16 | 97.52 | | oven | 65.97 | 76.03 | | ball | 42.71 | 44.59 | | food | 48.96 | 51.46 | | step | 12.5 | 14.35 | | tank | 53.96 | 65.0 | | trade name | 25.49 | 30.59 | | microwave | 89.24 | 94.21 | | pot | 55.53 | 63.07 | | animal | 73.53 | 76.78 | | bicycle | 58.28 | 75.29 | | lake | 55.85 | 63.57 | | dishwasher | 74.49 | 82.0 | | screen | 68.12 | 89.43 | | blanket | 31.87 | 37.31 | | sculpture | 75.39 | 88.58 | | hood | 68.14 | 78.24 | | sconce | 53.3 | 67.26 | | vase | 44.33 | 60.09 | | traffic light | 36.77 | 56.61 | | tray | 20.09 | 26.42 | | ashcan | 50.99 | 64.68 | | fan | 62.61 | 74.43 | | pier | 36.87 | 41.08 | | crt screen | 14.51 | 22.14 | | plate | 56.59 | 75.32 | | monitor | 53.1 | 65.63 | | bulletin board | 50.35 | 62.63 | | shower | 6.98 | 7.71 | | radiator | 67.8 | 82.24 | | glass | 19.1 | 21.57 | | clock | 34.61 | 40.57 | | flag | 65.72 | 72.8 | +---------------------+-------+-------+ 2023-11-03 08:54:02,147 - mmseg - INFO - Summary: 2023-11-03 08:54:02,147 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.47 | 55.57 | 66.88 | +-------+-------+-------+ 2023-11-03 08:54:02,147 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 08:54:02,148 - mmseg - INFO - Iter(val) [250] aAcc: 0.8547, mIoU: 0.5557, mAcc: 0.6688, IoU.wall: 0.8114, IoU.building: 0.8381, IoU.sky: 0.9425, IoU.floor: 0.8358, IoU.tree: 0.7616, IoU.ceiling: 0.8530, IoU.road: 0.8614, IoU.bed : 0.9157, IoU.windowpane: 0.6591, IoU.grass: 0.6930, IoU.cabinet: 0.6706, IoU.sidewalk: 0.6993, IoU.person: 0.8261, IoU.earth: 0.3590, IoU.door: 0.5873, IoU.table: 0.6995, IoU.mountain: 0.6289, IoU.plant: 0.5542, IoU.curtain: 0.7654, IoU.chair: 0.6237, IoU.car: 0.8628, IoU.water: 0.6557, IoU.painting: 0.7857, IoU.sofa: 0.8123, IoU.shelf: 0.4908, IoU.house: 0.4882, IoU.sea: 0.7094, IoU.mirror: 0.7460, IoU.rug: 0.6464, IoU.field: 0.3339, IoU.armchair: 0.5968, IoU.seat: 0.6449, IoU.fence: 0.4819, IoU.desk: 0.5572, IoU.rock: 0.5607, IoU.wardrobe: 0.5451, IoU.lamp: 0.6801, IoU.bathtub: 0.8916, IoU.railing: 0.4302, IoU.cushion: 0.6322, IoU.base: 0.3435, IoU.box: 0.3856, IoU.column: 0.5325, IoU.signboard: 0.3693, IoU.chest of drawers: 0.4612, IoU.counter: 0.5217, IoU.sand: 0.6201, IoU.sink: 0.7719, IoU.skyscraper: 0.4840, IoU.fireplace: 0.7423, IoU.refrigerator: 0.8492, IoU.grandstand: 0.4704, IoU.path: 0.2293, IoU.stairs: 0.3078, IoU.runway: 0.6835, IoU.case: 0.6064, IoU.pool table: 0.9375, IoU.pillow: 0.5888, IoU.screen door: 0.7944, IoU.stairway: 0.3293, IoU.river: 0.2050, IoU.bridge: 0.7698, IoU.bookcase: 0.4526, IoU.blind: 0.4006, IoU.coffee table: 0.6792, IoU.toilet: 0.8947, IoU.flower: 0.4305, IoU.book: 0.5046, IoU.hill: 0.0953, IoU.bench: 0.5574, IoU.countertop: 0.6363, IoU.stove: 0.8486, IoU.palm: 0.4624, IoU.kitchen island: 0.4764, IoU.computer: 0.7743, IoU.swivel chair: 0.4511, IoU.boat: 0.6217, IoU.bar: 0.7723, IoU.arcade machine: 0.7348, IoU.hovel: 0.1836, IoU.bus: 0.9316, IoU.towel: 0.7189, IoU.light: 0.4815, IoU.truck: 0.4859, IoU.tower: 0.2568, IoU.chandelier: 0.6720, IoU.awning: 0.3379, IoU.streetlight: 0.2442, IoU.booth: 0.5898, IoU.television receiver: 0.7661, IoU.airplane: 0.8095, IoU.dirt track: 0.1527, IoU.apparel: 0.4933, IoU.pole: 0.2174, IoU.land: 0.0777, IoU.bannister: 0.1382, IoU.escalator: 0.6259, IoU.ottoman: 0.5546, IoU.bottle: 0.2627, IoU.buffet: 0.6123, IoU.poster: 0.3745, IoU.stage: 0.2289, IoU.van: 0.4772, IoU.ship: 0.1080, IoU.fountain: 0.2759, IoU.conveyer belt: 0.8453, IoU.canopy: 0.2425, IoU.washer: 0.8559, IoU.plaything: 0.3078, IoU.swimming pool: 0.5035, IoU.stool: 0.5098, IoU.barrel: 0.7123, IoU.basket: 0.3725, IoU.waterfall: 0.4690, IoU.tent: 0.9393, IoU.bag: 0.2436, IoU.minibike: 0.7359, IoU.cradle: 0.8616, IoU.oven: 0.6597, IoU.ball: 0.4271, IoU.food: 0.4896, IoU.step: 0.1250, IoU.tank: 0.5396, IoU.trade name: 0.2549, IoU.microwave: 0.8924, IoU.pot: 0.5553, IoU.animal: 0.7353, IoU.bicycle: 0.5828, IoU.lake: 0.5585, IoU.dishwasher: 0.7449, IoU.screen: 0.6812, IoU.blanket: 0.3187, IoU.sculpture: 0.7539, IoU.hood: 0.6814, IoU.sconce: 0.5330, IoU.vase: 0.4433, IoU.traffic light: 0.3677, IoU.tray: 0.2009, IoU.ashcan: 0.5099, IoU.fan: 0.6261, IoU.pier: 0.3687, IoU.crt screen: 0.1451, IoU.plate: 0.5659, IoU.monitor: 0.5310, IoU.bulletin board: 0.5035, IoU.shower: 0.0698, IoU.radiator: 0.6780, IoU.glass: 0.1910, IoU.clock: 0.3461, IoU.flag: 0.6572, Acc.wall: 0.8972, Acc.building: 0.9332, Acc.sky: 0.9732, Acc.floor: 0.9179, Acc.tree: 0.8933, Acc.ceiling: 0.9327, Acc.road: 0.9111, Acc.bed : 0.9693, Acc.windowpane: 0.8083, Acc.grass: 0.8435, Acc.cabinet: 0.7679, Acc.sidewalk: 0.8497, Acc.person: 0.9222, Acc.earth: 0.4773, Acc.door: 0.7438, Acc.table: 0.8260, Acc.mountain: 0.7435, Acc.plant: 0.6793, Acc.curtain: 0.8936, Acc.chair: 0.7680, Acc.car: 0.9301, Acc.water: 0.8131, Acc.painting: 0.8818, Acc.sofa: 0.9197, Acc.shelf: 0.6898, Acc.house: 0.6109, Acc.sea: 0.8056, Acc.mirror: 0.8362, Acc.rug: 0.7388, Acc.field: 0.5137, Acc.armchair: 0.7548, Acc.seat: 0.8882, Acc.fence: 0.6248, Acc.desk: 0.7427, Acc.rock: 0.7851, Acc.wardrobe: 0.7375, Acc.lamp: 0.7981, Acc.bathtub: 0.9256, Acc.railing: 0.5749, Acc.cushion: 0.7423, Acc.base: 0.5458, Acc.box: 0.5004, Acc.column: 0.6401, Acc.signboard: 0.5150, Acc.chest of drawers: 0.6630, Acc.counter: 0.6111, Acc.sand: 0.8927, Acc.sink: 0.8426, Acc.skyscraper: 0.5989, Acc.fireplace: 0.9053, Acc.refrigerator: 0.9241, Acc.grandstand: 0.8350, Acc.path: 0.3143, Acc.stairs: 0.3951, Acc.runway: 0.8995, Acc.case: 0.8606, Acc.pool table: 0.9782, Acc.pillow: 0.6820, Acc.screen door: 0.8198, Acc.stairway: 0.4273, Acc.river: 0.3984, Acc.bridge: 0.8521, Acc.bookcase: 0.5274, Acc.blind: 0.4501, Acc.coffee table: 0.8580, Acc.toilet: 0.9331, Acc.flower: 0.6334, Acc.book: 0.7495, Acc.hill: 0.1971, Acc.bench: 0.6215, Acc.countertop: 0.7667, Acc.stove: 0.8966, Acc.palm: 0.7893, Acc.kitchen island: 0.6951, Acc.computer: 0.8759, Acc.swivel chair: 0.6451, Acc.boat: 0.7429, Acc.bar: 0.8447, Acc.arcade machine: 0.7623, Acc.hovel: 0.2030, Acc.bus: 0.9641, Acc.towel: 0.8129, Acc.light: 0.5807, Acc.truck: 0.5923, Acc.tower: 0.3518, Acc.chandelier: 0.8226, Acc.awning: 0.4064, Acc.streetlight: 0.3289, Acc.booth: 0.6098, Acc.television receiver: 0.8881, Acc.airplane: 0.8913, Acc.dirt track: 0.2051, Acc.apparel: 0.6841, Acc.pole: 0.2792, Acc.land: 0.1515, Acc.bannister: 0.1830, Acc.escalator: 0.8231, Acc.ottoman: 0.7072, Acc.bottle: 0.3510, Acc.buffet: 0.7592, Acc.poster: 0.4480, Acc.stage: 0.4049, Acc.van: 0.7112, Acc.ship: 0.1296, Acc.fountain: 0.2860, Acc.conveyer belt: 0.9229, Acc.canopy: 0.2671, Acc.washer: 0.9126, Acc.plaything: 0.4118, Acc.swimming pool: 0.7010, Acc.stool: 0.6425, Acc.barrel: 0.8301, Acc.basket: 0.4762, Acc.waterfall: 0.6213, Acc.tent: 0.9744, Acc.bag: 0.2838, Acc.minibike: 0.8572, Acc.cradle: 0.9752, Acc.oven: 0.7603, Acc.ball: 0.4459, Acc.food: 0.5146, Acc.step: 0.1435, Acc.tank: 0.6500, Acc.trade name: 0.3059, Acc.microwave: 0.9421, Acc.pot: 0.6307, Acc.animal: 0.7678, Acc.bicycle: 0.7529, Acc.lake: 0.6357, Acc.dishwasher: 0.8200, Acc.screen: 0.8943, Acc.blanket: 0.3731, Acc.sculpture: 0.8858, Acc.hood: 0.7824, Acc.sconce: 0.6726, Acc.vase: 0.6009, Acc.traffic light: 0.5661, Acc.tray: 0.2642, Acc.ashcan: 0.6468, Acc.fan: 0.7443, Acc.pier: 0.4108, Acc.crt screen: 0.2214, Acc.plate: 0.7532, Acc.monitor: 0.6563, Acc.bulletin board: 0.6263, Acc.shower: 0.0771, Acc.radiator: 0.8224, Acc.glass: 0.2157, Acc.clock: 0.4057, Acc.flag: 0.7280 2023-11-03 08:55:02,853 - mmseg - INFO - Iter [38050/40000] lr: 1.580e-07, eta: 0:43:27, time: 2.409, data_time: 1.202, memory: 38534, decode.loss_ce: 0.1398, decode.acc_seg: 93.9907, loss: 0.1398 2023-11-03 08:56:03,510 - mmseg - INFO - Iter [38100/40000] lr: 1.540e-07, eta: 0:42:19, time: 1.213, data_time: 0.008, memory: 38534, decode.loss_ce: 0.1457, decode.acc_seg: 93.6600, loss: 0.1457 2023-11-03 08:57:04,176 - mmseg - INFO - Iter [38150/40000] lr: 1.499e-07, eta: 0:41:12, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1487, decode.acc_seg: 93.4616, loss: 0.1487 2023-11-03 08:58:04,871 - mmseg - INFO - Iter [38200/40000] lr: 1.459e-07, eta: 0:40:05, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1323, decode.acc_seg: 94.2103, loss: 0.1323 2023-11-03 08:59:05,500 - mmseg - INFO - Iter [38250/40000] lr: 1.418e-07, eta: 0:38:58, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1309, decode.acc_seg: 94.2901, loss: 0.1309 2023-11-03 09:00:06,155 - mmseg - INFO - Iter [38300/40000] lr: 1.378e-07, eta: 0:37:51, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1434, decode.acc_seg: 93.8318, loss: 0.1434 2023-11-03 09:01:06,773 - mmseg - INFO - Iter [38350/40000] lr: 1.337e-07, eta: 0:36:44, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1416, decode.acc_seg: 93.8058, loss: 0.1416 2023-11-03 09:02:07,433 - mmseg - INFO - Iter [38400/40000] lr: 1.297e-07, eta: 0:35:37, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1419, decode.acc_seg: 93.8863, loss: 0.1419 2023-11-03 09:03:08,113 - mmseg - INFO - Iter [38450/40000] lr: 1.256e-07, eta: 0:34:30, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1352, decode.acc_seg: 94.0823, loss: 0.1352 2023-11-03 09:04:08,804 - mmseg - INFO - Iter [38500/40000] lr: 1.216e-07, eta: 0:33:23, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1326, decode.acc_seg: 94.3284, loss: 0.1326 2023-11-03 09:05:09,544 - mmseg - INFO - Iter [38550/40000] lr: 1.175e-07, eta: 0:32:16, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1440, decode.acc_seg: 93.8742, loss: 0.1440 2023-11-03 09:06:12,566 - mmseg - INFO - Iter [38600/40000] lr: 1.135e-07, eta: 0:31:09, time: 1.260, data_time: 0.054, memory: 38534, decode.loss_ce: 0.1349, decode.acc_seg: 94.0576, loss: 0.1349 2023-11-03 09:07:13,193 - mmseg - INFO - Iter [38650/40000] lr: 1.094e-07, eta: 0:30:02, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1373, decode.acc_seg: 94.0770, loss: 0.1373 2023-11-03 09:08:13,866 - mmseg - INFO - Iter [38700/40000] lr: 1.054e-07, eta: 0:28:55, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1402, decode.acc_seg: 94.0408, loss: 0.1402 2023-11-03 09:09:14,558 - mmseg - INFO - Iter [38750/40000] lr: 1.013e-07, eta: 0:27:48, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1338, decode.acc_seg: 94.1885, loss: 0.1338 2023-11-03 09:10:15,181 - mmseg - INFO - Iter [38800/40000] lr: 9.727e-08, eta: 0:26:41, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1404, decode.acc_seg: 93.8920, loss: 0.1404 2023-11-03 09:11:15,912 - mmseg - INFO - Iter [38850/40000] lr: 9.322e-08, eta: 0:25:34, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1434, decode.acc_seg: 93.8614, loss: 0.1434 2023-11-03 09:12:16,682 - mmseg - INFO - Iter [38900/40000] lr: 8.918e-08, eta: 0:24:27, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1392, decode.acc_seg: 94.0136, loss: 0.1392 2023-11-03 09:13:17,411 - mmseg - INFO - Iter [38950/40000] lr: 8.513e-08, eta: 0:23:20, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1463, decode.acc_seg: 93.6429, loss: 0.1463 2023-11-03 09:14:18,032 - mmseg - INFO - Saving checkpoint at 39000 iterations 2023-11-03 09:15:17,528 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 09:15:17,528 - mmseg - INFO - Iter [39000/40000] lr: 8.108e-08, eta: 0:22:15, time: 2.402, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1367, decode.acc_seg: 94.0816, loss: 0.1367 2023-11-03 09:16:23,100 - mmseg - INFO - per class results: 2023-11-03 09:16:23,106 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 81.17 | 89.64 | | building | 83.85 | 93.62 | | sky | 94.29 | 97.23 | | floor | 83.63 | 91.72 | | tree | 76.09 | 88.17 | | ceiling | 85.38 | 93.23 | | road | 86.05 | 91.64 | | bed | 91.7 | 96.83 | | windowpane | 66.2 | 81.14 | | grass | 69.28 | 85.01 | | cabinet | 67.27 | 76.95 | | sidewalk | 69.94 | 84.37 | | person | 82.44 | 92.99 | | earth | 36.15 | 47.68 | | door | 59.02 | 74.67 | | table | 69.9 | 82.74 | | mountain | 62.9 | 75.38 | | plant | 55.35 | 66.65 | | curtain | 77.19 | 89.22 | | chair | 62.44 | 77.39 | | car | 86.26 | 93.2 | | water | 65.35 | 80.89 | | painting | 78.38 | 88.37 | | sofa | 81.52 | 91.46 | | shelf | 48.51 | 66.75 | | house | 48.7 | 60.35 | | sea | 71.67 | 82.03 | | mirror | 75.12 | 84.54 | | rug | 64.83 | 74.23 | | field | 33.29 | 50.85 | | armchair | 59.73 | 75.88 | | seat | 64.77 | 88.72 | | fence | 48.52 | 62.44 | | desk | 55.8 | 74.79 | | rock | 55.93 | 77.61 | | wardrobe | 54.46 | 73.64 | | lamp | 68.27 | 80.65 | | bathtub | 89.35 | 92.81 | | railing | 43.34 | 57.73 | | cushion | 63.18 | 75.75 | | base | 34.23 | 51.9 | | box | 39.13 | 50.59 | | column | 53.95 | 65.23 | | signboard | 36.84 | 52.03 | | chest of drawers | 47.16 | 68.32 | | counter | 50.25 | 58.7 | | sand | 61.64 | 89.35 | | sink | 77.29 | 84.1 | | skyscraper | 48.63 | 59.89 | | fireplace | 74.63 | 90.53 | | refrigerator | 84.92 | 92.23 | | grandstand | 47.22 | 83.37 | | path | 23.07 | 31.12 | | stairs | 30.26 | 38.71 | | runway | 67.95 | 89.97 | | case | 60.52 | 86.71 | | pool table | 93.69 | 97.9 | | pillow | 59.04 | 68.61 | | screen door | 81.98 | 84.84 | | stairway | 33.43 | 44.19 | | river | 21.69 | 41.39 | | bridge | 77.66 | 86.54 | | bookcase | 45.87 | 54.99 | | blind | 40.45 | 45.53 | | coffee table | 67.63 | 86.95 | | toilet | 89.5 | 93.79 | | flower | 42.92 | 64.57 | | book | 49.83 | 74.22 | | hill | 9.18 | 18.07 | | bench | 55.48 | 62.36 | | countertop | 63.8 | 76.82 | | stove | 85.09 | 90.01 | | palm | 46.42 | 80.93 | | kitchen island | 47.38 | 66.03 | | computer | 77.46 | 87.76 | | swivel chair | 46.12 | 67.58 | | boat | 62.67 | 77.37 | | bar | 77.46 | 85.2 | | arcade machine | 74.06 | 76.85 | | hovel | 19.0 | 21.01 | | bus | 93.04 | 96.67 | | towel | 72.0 | 81.93 | | light | 48.16 | 57.68 | | truck | 48.38 | 59.89 | | tower | 24.88 | 33.55 | | chandelier | 67.45 | 82.95 | | awning | 35.8 | 44.21 | | streetlight | 25.27 | 34.35 | | booth | 58.88 | 60.93 | | television receiver | 76.97 | 88.13 | | airplane | 83.88 | 92.89 | | dirt track | 15.12 | 20.49 | | apparel | 48.42 | 65.83 | | pole | 21.05 | 27.02 | | land | 7.7 | 15.59 | | bannister | 14.24 | 19.36 | | escalator | 62.03 | 82.57 | | ottoman | 55.33 | 70.1 | | bottle | 26.82 | 35.97 | | buffet | 60.75 | 76.59 | | poster | 36.86 | 46.07 | | stage | 22.41 | 40.81 | | van | 47.29 | 70.71 | | ship | 12.85 | 15.17 | | fountain | 30.85 | 32.05 | | conveyer belt | 84.74 | 92.6 | | canopy | 33.55 | 38.9 | | washer | 86.72 | 92.6 | | plaything | 30.45 | 42.04 | | swimming pool | 49.76 | 71.5 | | stool | 51.0 | 63.88 | | barrel | 71.3 | 86.02 | | basket | 37.33 | 47.54 | | waterfall | 46.95 | 62.32 | | tent | 91.39 | 97.73 | | bag | 25.29 | 30.08 | | minibike | 73.5 | 86.64 | | cradle | 85.93 | 97.6 | | oven | 65.5 | 77.3 | | ball | 47.51 | 49.95 | | food | 50.44 | 53.16 | | step | 14.59 | 16.87 | | tank | 53.8 | 65.23 | | trade name | 27.22 | 33.39 | | microwave | 89.23 | 94.44 | | pot | 55.47 | 62.88 | | animal | 73.37 | 76.56 | | bicycle | 58.37 | 76.48 | | lake | 55.85 | 63.57 | | dishwasher | 74.89 | 83.3 | | screen | 69.02 | 91.72 | | blanket | 31.38 | 36.62 | | sculpture | 76.1 | 88.47 | | hood | 67.45 | 77.56 | | sconce | 53.38 | 67.63 | | vase | 44.17 | 61.68 | | traffic light | 35.72 | 59.22 | | tray | 19.96 | 25.84 | | ashcan | 50.69 | 65.1 | | fan | 63.17 | 76.63 | | pier | 36.88 | 41.33 | | crt screen | 14.84 | 22.21 | | plate | 56.42 | 75.63 | | monitor | 53.12 | 65.6 | | bulletin board | 50.52 | 61.96 | | shower | 8.24 | 9.64 | | radiator | 67.71 | 82.58 | | glass | 18.9 | 21.26 | | clock | 35.16 | 41.03 | | flag | 66.12 | 73.69 | +---------------------+-------+-------+ 2023-11-03 09:16:23,106 - mmseg - INFO - Summary: 2023-11-03 09:16:23,106 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.52 | 55.81 | 67.43 | +-------+-------+-------+ 2023-11-03 09:16:23,107 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 09:16:23,107 - mmseg - INFO - Iter(val) [250] aAcc: 0.8552, mIoU: 0.5581, mAcc: 0.6743, IoU.wall: 0.8117, IoU.building: 0.8385, IoU.sky: 0.9429, IoU.floor: 0.8363, IoU.tree: 0.7609, IoU.ceiling: 0.8538, IoU.road: 0.8605, IoU.bed : 0.9170, IoU.windowpane: 0.6620, IoU.grass: 0.6928, IoU.cabinet: 0.6727, IoU.sidewalk: 0.6994, IoU.person: 0.8244, IoU.earth: 0.3615, IoU.door: 0.5902, IoU.table: 0.6990, IoU.mountain: 0.6290, IoU.plant: 0.5535, IoU.curtain: 0.7719, IoU.chair: 0.6244, IoU.car: 0.8626, IoU.water: 0.6535, IoU.painting: 0.7838, IoU.sofa: 0.8152, IoU.shelf: 0.4851, IoU.house: 0.4870, IoU.sea: 0.7167, IoU.mirror: 0.7512, IoU.rug: 0.6483, IoU.field: 0.3329, IoU.armchair: 0.5973, IoU.seat: 0.6477, IoU.fence: 0.4852, IoU.desk: 0.5580, IoU.rock: 0.5593, IoU.wardrobe: 0.5446, IoU.lamp: 0.6827, IoU.bathtub: 0.8935, IoU.railing: 0.4334, IoU.cushion: 0.6318, IoU.base: 0.3423, IoU.box: 0.3913, IoU.column: 0.5395, IoU.signboard: 0.3684, IoU.chest of drawers: 0.4716, IoU.counter: 0.5025, IoU.sand: 0.6164, IoU.sink: 0.7729, IoU.skyscraper: 0.4863, IoU.fireplace: 0.7463, IoU.refrigerator: 0.8492, IoU.grandstand: 0.4722, IoU.path: 0.2307, IoU.stairs: 0.3026, IoU.runway: 0.6795, IoU.case: 0.6052, IoU.pool table: 0.9369, IoU.pillow: 0.5904, IoU.screen door: 0.8198, IoU.stairway: 0.3343, IoU.river: 0.2169, IoU.bridge: 0.7766, IoU.bookcase: 0.4587, IoU.blind: 0.4045, IoU.coffee table: 0.6763, IoU.toilet: 0.8950, IoU.flower: 0.4292, IoU.book: 0.4983, IoU.hill: 0.0918, IoU.bench: 0.5548, IoU.countertop: 0.6380, IoU.stove: 0.8509, IoU.palm: 0.4642, IoU.kitchen island: 0.4738, IoU.computer: 0.7746, IoU.swivel chair: 0.4612, IoU.boat: 0.6267, IoU.bar: 0.7746, IoU.arcade machine: 0.7406, IoU.hovel: 0.1900, IoU.bus: 0.9304, IoU.towel: 0.7200, IoU.light: 0.4816, IoU.truck: 0.4838, IoU.tower: 0.2488, IoU.chandelier: 0.6745, IoU.awning: 0.3580, IoU.streetlight: 0.2527, IoU.booth: 0.5888, IoU.television receiver: 0.7697, IoU.airplane: 0.8388, IoU.dirt track: 0.1512, IoU.apparel: 0.4842, IoU.pole: 0.2105, IoU.land: 0.0770, IoU.bannister: 0.1424, IoU.escalator: 0.6203, IoU.ottoman: 0.5533, IoU.bottle: 0.2682, IoU.buffet: 0.6075, IoU.poster: 0.3686, IoU.stage: 0.2241, IoU.van: 0.4729, IoU.ship: 0.1285, IoU.fountain: 0.3085, IoU.conveyer belt: 0.8474, IoU.canopy: 0.3355, IoU.washer: 0.8672, IoU.plaything: 0.3045, IoU.swimming pool: 0.4976, IoU.stool: 0.5100, IoU.barrel: 0.7130, IoU.basket: 0.3733, IoU.waterfall: 0.4695, IoU.tent: 0.9139, IoU.bag: 0.2529, IoU.minibike: 0.7350, IoU.cradle: 0.8593, IoU.oven: 0.6550, IoU.ball: 0.4751, IoU.food: 0.5044, IoU.step: 0.1459, IoU.tank: 0.5380, IoU.trade name: 0.2722, IoU.microwave: 0.8923, IoU.pot: 0.5547, IoU.animal: 0.7337, IoU.bicycle: 0.5837, IoU.lake: 0.5585, IoU.dishwasher: 0.7489, IoU.screen: 0.6902, IoU.blanket: 0.3138, IoU.sculpture: 0.7610, IoU.hood: 0.6745, IoU.sconce: 0.5338, IoU.vase: 0.4417, IoU.traffic light: 0.3572, IoU.tray: 0.1996, IoU.ashcan: 0.5069, IoU.fan: 0.6317, IoU.pier: 0.3688, IoU.crt screen: 0.1484, IoU.plate: 0.5642, IoU.monitor: 0.5312, IoU.bulletin board: 0.5052, IoU.shower: 0.0824, IoU.radiator: 0.6771, IoU.glass: 0.1890, IoU.clock: 0.3516, IoU.flag: 0.6612, Acc.wall: 0.8964, Acc.building: 0.9362, Acc.sky: 0.9723, Acc.floor: 0.9172, Acc.tree: 0.8817, Acc.ceiling: 0.9323, Acc.road: 0.9164, Acc.bed : 0.9683, Acc.windowpane: 0.8114, Acc.grass: 0.8501, Acc.cabinet: 0.7695, Acc.sidewalk: 0.8437, Acc.person: 0.9299, Acc.earth: 0.4768, Acc.door: 0.7467, Acc.table: 0.8274, Acc.mountain: 0.7538, Acc.plant: 0.6665, Acc.curtain: 0.8922, Acc.chair: 0.7739, Acc.car: 0.9320, Acc.water: 0.8089, Acc.painting: 0.8837, Acc.sofa: 0.9146, Acc.shelf: 0.6675, Acc.house: 0.6035, Acc.sea: 0.8203, Acc.mirror: 0.8454, Acc.rug: 0.7423, Acc.field: 0.5085, Acc.armchair: 0.7588, Acc.seat: 0.8872, Acc.fence: 0.6244, Acc.desk: 0.7479, Acc.rock: 0.7761, Acc.wardrobe: 0.7364, Acc.lamp: 0.8065, Acc.bathtub: 0.9281, Acc.railing: 0.5773, Acc.cushion: 0.7575, Acc.base: 0.5190, Acc.box: 0.5059, Acc.column: 0.6523, Acc.signboard: 0.5203, Acc.chest of drawers: 0.6832, Acc.counter: 0.5870, Acc.sand: 0.8935, Acc.sink: 0.8410, Acc.skyscraper: 0.5989, Acc.fireplace: 0.9053, Acc.refrigerator: 0.9223, Acc.grandstand: 0.8337, Acc.path: 0.3112, Acc.stairs: 0.3871, Acc.runway: 0.8997, Acc.case: 0.8671, Acc.pool table: 0.9790, Acc.pillow: 0.6861, Acc.screen door: 0.8484, Acc.stairway: 0.4419, Acc.river: 0.4139, Acc.bridge: 0.8654, Acc.bookcase: 0.5499, Acc.blind: 0.4553, Acc.coffee table: 0.8695, Acc.toilet: 0.9379, Acc.flower: 0.6457, Acc.book: 0.7422, Acc.hill: 0.1807, Acc.bench: 0.6236, Acc.countertop: 0.7682, Acc.stove: 0.9001, Acc.palm: 0.8093, Acc.kitchen island: 0.6603, Acc.computer: 0.8776, Acc.swivel chair: 0.6758, Acc.boat: 0.7737, Acc.bar: 0.8520, Acc.arcade machine: 0.7685, Acc.hovel: 0.2101, Acc.bus: 0.9667, Acc.towel: 0.8193, Acc.light: 0.5768, Acc.truck: 0.5989, Acc.tower: 0.3355, Acc.chandelier: 0.8295, Acc.awning: 0.4421, Acc.streetlight: 0.3435, Acc.booth: 0.6093, Acc.television receiver: 0.8813, Acc.airplane: 0.9289, Acc.dirt track: 0.2049, Acc.apparel: 0.6583, Acc.pole: 0.2702, Acc.land: 0.1559, Acc.bannister: 0.1936, Acc.escalator: 0.8257, Acc.ottoman: 0.7010, Acc.bottle: 0.3597, Acc.buffet: 0.7659, Acc.poster: 0.4607, Acc.stage: 0.4081, Acc.van: 0.7071, Acc.ship: 0.1517, Acc.fountain: 0.3205, Acc.conveyer belt: 0.9260, Acc.canopy: 0.3890, Acc.washer: 0.9260, Acc.plaything: 0.4204, Acc.swimming pool: 0.7150, Acc.stool: 0.6388, Acc.barrel: 0.8602, Acc.basket: 0.4754, Acc.waterfall: 0.6232, Acc.tent: 0.9773, Acc.bag: 0.3008, Acc.minibike: 0.8664, Acc.cradle: 0.9760, Acc.oven: 0.7730, Acc.ball: 0.4995, Acc.food: 0.5316, Acc.step: 0.1687, Acc.tank: 0.6523, Acc.trade name: 0.3339, Acc.microwave: 0.9444, Acc.pot: 0.6288, Acc.animal: 0.7656, Acc.bicycle: 0.7648, Acc.lake: 0.6357, Acc.dishwasher: 0.8330, Acc.screen: 0.9172, Acc.blanket: 0.3662, Acc.sculpture: 0.8847, Acc.hood: 0.7756, Acc.sconce: 0.6763, Acc.vase: 0.6168, Acc.traffic light: 0.5922, Acc.tray: 0.2584, Acc.ashcan: 0.6510, Acc.fan: 0.7663, Acc.pier: 0.4133, Acc.crt screen: 0.2221, Acc.plate: 0.7563, Acc.monitor: 0.6560, Acc.bulletin board: 0.6196, Acc.shower: 0.0964, Acc.radiator: 0.8258, Acc.glass: 0.2126, Acc.clock: 0.4103, Acc.flag: 0.7369 2023-11-03 09:17:23,830 - mmseg - INFO - Iter [39050/40000] lr: 7.703e-08, eta: 0:21:10, time: 2.526, data_time: 1.319, memory: 38534, decode.loss_ce: 0.1399, decode.acc_seg: 93.9896, loss: 0.1399 2023-11-03 09:18:24,464 - mmseg - INFO - Iter [39100/40000] lr: 7.298e-08, eta: 0:20:03, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1380, decode.acc_seg: 94.0770, loss: 0.1380 2023-11-03 09:19:25,058 - mmseg - INFO - Iter [39150/40000] lr: 6.893e-08, eta: 0:18:56, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1510, decode.acc_seg: 93.5130, loss: 0.1510 2023-11-03 09:20:28,119 - mmseg - INFO - Iter [39200/40000] lr: 6.488e-08, eta: 0:17:49, time: 1.261, data_time: 0.052, memory: 38534, decode.loss_ce: 0.1336, decode.acc_seg: 94.2749, loss: 0.1336 2023-11-03 09:21:30,972 - mmseg - INFO - Iter [39250/40000] lr: 6.083e-08, eta: 0:16:42, time: 1.257, data_time: 0.050, memory: 38534, decode.loss_ce: 0.1373, decode.acc_seg: 94.0836, loss: 0.1373 2023-11-03 09:22:31,580 - mmseg - INFO - Iter [39300/40000] lr: 5.678e-08, eta: 0:15:35, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1435, decode.acc_seg: 93.9192, loss: 0.1435 2023-11-03 09:23:32,260 - mmseg - INFO - Iter [39350/40000] lr: 5.273e-08, eta: 0:14:28, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1448, decode.acc_seg: 93.8010, loss: 0.1448 2023-11-03 09:24:32,894 - mmseg - INFO - Iter [39400/40000] lr: 4.868e-08, eta: 0:13:21, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1452, decode.acc_seg: 93.7679, loss: 0.1452 2023-11-03 09:25:33,537 - mmseg - INFO - Iter [39450/40000] lr: 4.463e-08, eta: 0:12:14, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1364, decode.acc_seg: 94.0032, loss: 0.1364 2023-11-03 09:26:34,179 - mmseg - INFO - Iter [39500/40000] lr: 4.058e-08, eta: 0:11:07, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1388, decode.acc_seg: 93.9935, loss: 0.1388 2023-11-03 09:27:34,822 - mmseg - INFO - Iter [39550/40000] lr: 3.653e-08, eta: 0:10:01, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1455, decode.acc_seg: 93.6999, loss: 0.1455 2023-11-03 09:28:35,431 - mmseg - INFO - Iter [39600/40000] lr: 3.248e-08, eta: 0:08:54, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1348, decode.acc_seg: 94.1736, loss: 0.1348 2023-11-03 09:29:36,097 - mmseg - INFO - Iter [39650/40000] lr: 2.843e-08, eta: 0:07:47, time: 1.213, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1396, decode.acc_seg: 93.9912, loss: 0.1396 2023-11-03 09:30:36,784 - mmseg - INFO - Iter [39700/40000] lr: 2.438e-08, eta: 0:06:40, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1371, decode.acc_seg: 94.2121, loss: 0.1371 2023-11-03 09:31:37,515 - mmseg - INFO - Iter [39750/40000] lr: 2.033e-08, eta: 0:05:33, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1445, decode.acc_seg: 93.8765, loss: 0.1445 2023-11-03 09:32:38,244 - mmseg - INFO - Iter [39800/40000] lr: 1.628e-08, eta: 0:04:26, time: 1.215, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1364, decode.acc_seg: 94.1761, loss: 0.1364 2023-11-03 09:33:41,191 - mmseg - INFO - Iter [39850/40000] lr: 1.223e-08, eta: 0:03:20, time: 1.259, data_time: 0.053, memory: 38534, decode.loss_ce: 0.1336, decode.acc_seg: 94.1423, loss: 0.1336 2023-11-03 09:34:41,889 - mmseg - INFO - Iter [39900/40000] lr: 8.180e-09, eta: 0:02:13, time: 1.214, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1305, decode.acc_seg: 94.4030, loss: 0.1305 2023-11-03 09:35:42,486 - mmseg - INFO - Iter [39950/40000] lr: 4.131e-09, eta: 0:01:06, time: 1.212, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1330, decode.acc_seg: 94.2784, loss: 0.1330 2023-11-03 09:36:43,077 - mmseg - INFO - Saving checkpoint at 40000 iterations 2023-11-03 09:37:39,902 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 09:37:39,902 - mmseg - INFO - Iter [40000/40000] lr: 8.099e-11, eta: 0:00:00, time: 2.348, data_time: 0.007, memory: 38534, decode.loss_ce: 0.1359, decode.acc_seg: 94.1741, loss: 0.1359 2023-11-03 09:38:38,114 - mmseg - INFO - per class results: 2023-11-03 09:38:38,119 - mmseg - INFO - +---------------------+-------+-------+ | Class | IoU | Acc | +---------------------+-------+-------+ | wall | 81.15 | 89.78 | | building | 83.9 | 93.51 | | sky | 94.26 | 97.32 | | floor | 83.69 | 91.45 | | tree | 75.96 | 88.95 | | ceiling | 85.37 | 93.5 | | road | 86.03 | 91.64 | | bed | 91.59 | 96.94 | | windowpane | 65.98 | 81.12 | | grass | 69.24 | 85.13 | | cabinet | 67.43 | 77.06 | | sidewalk | 69.9 | 84.78 | | person | 82.47 | 92.97 | | earth | 35.85 | 47.09 | | door | 58.95 | 73.98 | | table | 69.98 | 82.11 | | mountain | 62.84 | 75.58 | | plant | 54.64 | 64.79 | | curtain | 77.35 | 88.87 | | chair | 62.26 | 75.84 | | car | 86.35 | 92.82 | | water | 65.42 | 80.91 | | painting | 78.41 | 88.37 | | sofa | 81.24 | 91.67 | | shelf | 48.88 | 68.72 | | house | 49.36 | 61.63 | | sea | 72.45 | 83.21 | | mirror | 74.94 | 83.78 | | rug | 64.87 | 74.38 | | field | 33.12 | 50.46 | | armchair | 59.23 | 76.47 | | seat | 64.72 | 88.43 | | fence | 48.07 | 61.46 | | desk | 56.22 | 74.87 | | rock | 55.38 | 77.93 | | wardrobe | 54.71 | 74.47 | | lamp | 68.38 | 80.68 | | bathtub | 89.36 | 92.73 | | railing | 43.6 | 58.08 | | cushion | 63.06 | 75.4 | | base | 33.63 | 51.19 | | box | 38.77 | 49.24 | | column | 53.64 | 64.71 | | signboard | 36.64 | 52.15 | | chest of drawers | 47.2 | 67.1 | | counter | 51.87 | 61.14 | | sand | 61.38 | 89.59 | | sink | 77.32 | 84.08 | | skyscraper | 48.45 | 59.0 | | fireplace | 74.5 | 90.22 | | refrigerator | 84.85 | 92.56 | | grandstand | 47.29 | 83.66 | | path | 22.9 | 30.14 | | stairs | 30.34 | 39.08 | | runway | 67.69 | 90.07 | | case | 60.65 | 86.4 | | pool table | 93.59 | 98.03 | | pillow | 58.29 | 67.34 | | screen door | 82.38 | 85.17 | | stairway | 32.85 | 42.85 | | river | 22.15 | 41.06 | | bridge | 77.49 | 86.59 | | bookcase | 46.11 | 53.94 | | blind | 39.84 | 44.63 | | coffee table | 67.65 | 87.1 | | toilet | 89.52 | 93.67 | | flower | 42.87 | 64.21 | | book | 50.21 | 74.25 | | hill | 9.01 | 17.73 | | bench | 55.34 | 62.38 | | countertop | 64.0 | 77.23 | | stove | 85.1 | 90.17 | | palm | 46.15 | 79.82 | | kitchen island | 47.82 | 67.85 | | computer | 77.58 | 87.53 | | swivel chair | 46.05 | 67.17 | | boat | 62.19 | 76.14 | | bar | 77.91 | 85.72 | | arcade machine | 74.55 | 77.59 | | hovel | 19.03 | 21.21 | | bus | 93.08 | 96.68 | | towel | 72.09 | 82.33 | | light | 47.8 | 56.85 | | truck | 48.87 | 60.61 | | tower | 24.86 | 33.12 | | chandelier | 67.2 | 81.73 | | awning | 35.92 | 44.58 | | streetlight | 25.07 | 33.88 | | booth | 60.01 | 62.52 | | television receiver | 76.85 | 88.52 | | airplane | 83.73 | 92.5 | | dirt track | 14.84 | 20.48 | | apparel | 48.61 | 65.62 | | pole | 20.7 | 26.34 | | land | 7.78 | 14.85 | | bannister | 14.11 | 18.73 | | escalator | 62.41 | 82.77 | | ottoman | 54.92 | 69.75 | | bottle | 25.9 | 33.98 | | buffet | 62.17 | 75.93 | | poster | 36.99 | 46.18 | | stage | 21.78 | 41.5 | | van | 47.86 | 71.68 | | ship | 14.16 | 16.84 | | fountain | 30.05 | 31.12 | | conveyer belt | 84.67 | 93.05 | | canopy | 35.45 | 40.93 | | washer | 87.13 | 93.24 | | plaything | 30.72 | 42.18 | | swimming pool | 49.88 | 71.4 | | stool | 50.39 | 65.37 | | barrel | 69.32 | 85.52 | | basket | 37.51 | 47.98 | | waterfall | 47.33 | 62.63 | | tent | 91.17 | 97.63 | | bag | 25.26 | 29.9 | | minibike | 73.61 | 85.71 | | cradle | 86.06 | 97.62 | | oven | 65.38 | 77.41 | | ball | 48.2 | 50.96 | | food | 49.94 | 52.58 | | step | 14.16 | 16.19 | | tank | 53.86 | 65.29 | | trade name | 27.01 | 33.07 | | microwave | 89.19 | 94.37 | | pot | 55.43 | 62.81 | | animal | 74.02 | 77.53 | | bicycle | 58.12 | 75.96 | | lake | 55.85 | 63.57 | | dishwasher | 74.86 | 83.3 | | screen | 69.29 | 91.88 | | blanket | 31.07 | 36.24 | | sculpture | 76.17 | 88.22 | | hood | 68.81 | 79.56 | | sconce | 53.39 | 67.83 | | vase | 44.33 | 61.16 | | traffic light | 35.95 | 58.56 | | tray | 20.28 | 27.23 | | ashcan | 50.57 | 64.59 | | fan | 62.43 | 74.35 | | pier | 37.27 | 41.72 | | crt screen | 14.95 | 21.84 | | plate | 56.27 | 76.31 | | monitor | 54.22 | 66.75 | | bulletin board | 49.91 | 60.64 | | shower | 7.56 | 8.19 | | radiator | 67.76 | 82.16 | | glass | 19.36 | 21.97 | | clock | 34.55 | 40.01 | | flag | 65.96 | 72.59 | +---------------------+-------+-------+ 2023-11-03 09:38:38,119 - mmseg - INFO - Summary: 2023-11-03 09:38:38,119 - mmseg - INFO - +-------+-------+-------+ | aAcc | mIoU | mAcc | +-------+-------+-------+ | 85.52 | 55.83 | 67.37 | +-------+-------+-------+ 2023-11-03 09:38:38,120 - mmseg - INFO - Exp name: segmenter_linear_intern_vit_6b_504_40k_ade20k_bs16_lr4e-5_1of2.py 2023-11-03 09:38:38,120 - mmseg - INFO - Iter(val) [250] aAcc: 0.8552, mIoU: 0.5583, mAcc: 0.6737, IoU.wall: 0.8115, IoU.building: 0.8390, IoU.sky: 0.9426, IoU.floor: 0.8369, IoU.tree: 0.7596, IoU.ceiling: 0.8537, IoU.road: 0.8603, IoU.bed : 0.9159, IoU.windowpane: 0.6598, IoU.grass: 0.6924, IoU.cabinet: 0.6743, IoU.sidewalk: 0.6990, IoU.person: 0.8247, IoU.earth: 0.3585, IoU.door: 0.5895, IoU.table: 0.6998, IoU.mountain: 0.6284, IoU.plant: 0.5464, IoU.curtain: 0.7735, IoU.chair: 0.6226, IoU.car: 0.8635, IoU.water: 0.6542, IoU.painting: 0.7841, IoU.sofa: 0.8124, IoU.shelf: 0.4888, IoU.house: 0.4936, IoU.sea: 0.7245, IoU.mirror: 0.7494, IoU.rug: 0.6487, IoU.field: 0.3312, IoU.armchair: 0.5923, IoU.seat: 0.6472, IoU.fence: 0.4807, IoU.desk: 0.5622, IoU.rock: 0.5538, IoU.wardrobe: 0.5471, IoU.lamp: 0.6838, IoU.bathtub: 0.8936, IoU.railing: 0.4360, IoU.cushion: 0.6306, IoU.base: 0.3363, IoU.box: 0.3877, IoU.column: 0.5364, IoU.signboard: 0.3664, IoU.chest of drawers: 0.4720, IoU.counter: 0.5187, IoU.sand: 0.6138, IoU.sink: 0.7732, IoU.skyscraper: 0.4845, IoU.fireplace: 0.7450, IoU.refrigerator: 0.8485, IoU.grandstand: 0.4729, IoU.path: 0.2290, IoU.stairs: 0.3034, IoU.runway: 0.6769, IoU.case: 0.6065, IoU.pool table: 0.9359, IoU.pillow: 0.5829, IoU.screen door: 0.8238, IoU.stairway: 0.3285, IoU.river: 0.2215, IoU.bridge: 0.7749, IoU.bookcase: 0.4611, IoU.blind: 0.3984, IoU.coffee table: 0.6765, IoU.toilet: 0.8952, IoU.flower: 0.4287, IoU.book: 0.5021, IoU.hill: 0.0901, IoU.bench: 0.5534, IoU.countertop: 0.6400, IoU.stove: 0.8510, IoU.palm: 0.4615, IoU.kitchen island: 0.4782, IoU.computer: 0.7758, IoU.swivel chair: 0.4605, IoU.boat: 0.6219, IoU.bar: 0.7791, IoU.arcade machine: 0.7455, IoU.hovel: 0.1903, IoU.bus: 0.9308, IoU.towel: 0.7209, IoU.light: 0.4780, IoU.truck: 0.4887, IoU.tower: 0.2486, IoU.chandelier: 0.6720, IoU.awning: 0.3592, IoU.streetlight: 0.2507, IoU.booth: 0.6001, IoU.television receiver: 0.7685, IoU.airplane: 0.8373, IoU.dirt track: 0.1484, IoU.apparel: 0.4861, IoU.pole: 0.2070, IoU.land: 0.0778, IoU.bannister: 0.1411, IoU.escalator: 0.6241, IoU.ottoman: 0.5492, IoU.bottle: 0.2590, IoU.buffet: 0.6217, IoU.poster: 0.3699, IoU.stage: 0.2178, IoU.van: 0.4786, IoU.ship: 0.1416, IoU.fountain: 0.3005, IoU.conveyer belt: 0.8467, IoU.canopy: 0.3545, IoU.washer: 0.8713, IoU.plaything: 0.3072, IoU.swimming pool: 0.4988, IoU.stool: 0.5039, IoU.barrel: 0.6932, IoU.basket: 0.3751, IoU.waterfall: 0.4733, IoU.tent: 0.9117, IoU.bag: 0.2526, IoU.minibike: 0.7361, IoU.cradle: 0.8606, IoU.oven: 0.6538, IoU.ball: 0.4820, IoU.food: 0.4994, IoU.step: 0.1416, IoU.tank: 0.5386, IoU.trade name: 0.2701, IoU.microwave: 0.8919, IoU.pot: 0.5543, IoU.animal: 0.7402, IoU.bicycle: 0.5812, IoU.lake: 0.5585, IoU.dishwasher: 0.7486, IoU.screen: 0.6929, IoU.blanket: 0.3107, IoU.sculpture: 0.7617, IoU.hood: 0.6881, IoU.sconce: 0.5339, IoU.vase: 0.4433, IoU.traffic light: 0.3595, IoU.tray: 0.2028, IoU.ashcan: 0.5057, IoU.fan: 0.6243, IoU.pier: 0.3727, IoU.crt screen: 0.1495, IoU.plate: 0.5627, IoU.monitor: 0.5422, IoU.bulletin board: 0.4991, IoU.shower: 0.0756, IoU.radiator: 0.6776, IoU.glass: 0.1936, IoU.clock: 0.3455, IoU.flag: 0.6596, Acc.wall: 0.8978, Acc.building: 0.9351, Acc.sky: 0.9732, Acc.floor: 0.9145, Acc.tree: 0.8895, Acc.ceiling: 0.9350, Acc.road: 0.9164, Acc.bed : 0.9694, Acc.windowpane: 0.8112, Acc.grass: 0.8513, Acc.cabinet: 0.7706, Acc.sidewalk: 0.8478, Acc.person: 0.9297, Acc.earth: 0.4709, Acc.door: 0.7398, Acc.table: 0.8211, Acc.mountain: 0.7558, Acc.plant: 0.6479, Acc.curtain: 0.8887, Acc.chair: 0.7584, Acc.car: 0.9282, Acc.water: 0.8091, Acc.painting: 0.8837, Acc.sofa: 0.9167, Acc.shelf: 0.6872, Acc.house: 0.6163, Acc.sea: 0.8321, Acc.mirror: 0.8378, Acc.rug: 0.7438, Acc.field: 0.5046, Acc.armchair: 0.7647, Acc.seat: 0.8843, Acc.fence: 0.6146, Acc.desk: 0.7487, Acc.rock: 0.7793, Acc.wardrobe: 0.7447, Acc.lamp: 0.8068, Acc.bathtub: 0.9273, Acc.railing: 0.5808, Acc.cushion: 0.7540, Acc.base: 0.5119, Acc.box: 0.4924, Acc.column: 0.6471, Acc.signboard: 0.5215, Acc.chest of drawers: 0.6710, Acc.counter: 0.6114, Acc.sand: 0.8959, Acc.sink: 0.8408, Acc.skyscraper: 0.5900, Acc.fireplace: 0.9022, Acc.refrigerator: 0.9256, Acc.grandstand: 0.8366, Acc.path: 0.3014, Acc.stairs: 0.3908, Acc.runway: 0.9007, Acc.case: 0.8640, Acc.pool table: 0.9803, Acc.pillow: 0.6734, Acc.screen door: 0.8517, Acc.stairway: 0.4285, Acc.river: 0.4106, Acc.bridge: 0.8659, Acc.bookcase: 0.5394, Acc.blind: 0.4463, Acc.coffee table: 0.8710, Acc.toilet: 0.9367, Acc.flower: 0.6421, Acc.book: 0.7425, Acc.hill: 0.1773, Acc.bench: 0.6238, Acc.countertop: 0.7723, Acc.stove: 0.9017, Acc.palm: 0.7982, Acc.kitchen island: 0.6785, Acc.computer: 0.8753, Acc.swivel chair: 0.6717, Acc.boat: 0.7614, Acc.bar: 0.8572, Acc.arcade machine: 0.7759, Acc.hovel: 0.2121, Acc.bus: 0.9668, Acc.towel: 0.8233, Acc.light: 0.5685, Acc.truck: 0.6061, Acc.tower: 0.3312, Acc.chandelier: 0.8173, Acc.awning: 0.4458, Acc.streetlight: 0.3388, Acc.booth: 0.6252, Acc.television receiver: 0.8852, Acc.airplane: 0.9250, Acc.dirt track: 0.2048, Acc.apparel: 0.6562, Acc.pole: 0.2634, Acc.land: 0.1485, Acc.bannister: 0.1873, Acc.escalator: 0.8277, Acc.ottoman: 0.6975, Acc.bottle: 0.3398, Acc.buffet: 0.7593, Acc.poster: 0.4618, Acc.stage: 0.4150, Acc.van: 0.7168, Acc.ship: 0.1684, Acc.fountain: 0.3112, Acc.conveyer belt: 0.9305, Acc.canopy: 0.4093, Acc.washer: 0.9324, Acc.plaything: 0.4218, Acc.swimming pool: 0.7140, Acc.stool: 0.6537, Acc.barrel: 0.8552, Acc.basket: 0.4798, Acc.waterfall: 0.6263, Acc.tent: 0.9763, Acc.bag: 0.2990, Acc.minibike: 0.8571, Acc.cradle: 0.9762, Acc.oven: 0.7741, Acc.ball: 0.5096, Acc.food: 0.5258, Acc.step: 0.1619, Acc.tank: 0.6529, Acc.trade name: 0.3307, Acc.microwave: 0.9437, Acc.pot: 0.6281, Acc.animal: 0.7753, Acc.bicycle: 0.7596, Acc.lake: 0.6357, Acc.dishwasher: 0.8330, Acc.screen: 0.9188, Acc.blanket: 0.3624, Acc.sculpture: 0.8822, Acc.hood: 0.7956, Acc.sconce: 0.6783, Acc.vase: 0.6116, Acc.traffic light: 0.5856, Acc.tray: 0.2723, Acc.ashcan: 0.6459, Acc.fan: 0.7435, Acc.pier: 0.4172, Acc.crt screen: 0.2184, Acc.plate: 0.7631, Acc.monitor: 0.6675, Acc.bulletin board: 0.6064, Acc.shower: 0.0819, Acc.radiator: 0.8216, Acc.glass: 0.2197, Acc.clock: 0.4001, Acc.flag: 0.7259