diff --git "a/downstream/seg/fpn.log" "b/downstream/seg/fpn.log" new file mode 100644--- /dev/null +++ "b/downstream/seg/fpn.log" @@ -0,0 +1,6420 @@ +2024/10/27 18:04:56 - mmengine - INFO - +------------------------------------------------------------ +System environment: + sys.platform: linux + Python: 3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0] + CUDA available: True + MUSA available: False + numpy_random_seed: 2049099036 + GPU 0,1,2,3: Tesla V100-SXM2-32GB + CUDA_HOME: /usr/local/cuda + NVCC: Cuda compilation tools, release 11.8, V11.8.89 + GCC: gcc (GCC) 7.3.1 20180303 (Red Hat 7.3.1-5) + PyTorch: 2.1.2+cu118 + PyTorch compiling details: PyTorch built with: + - GCC 9.3 + - C++ Version: 201703 + - Intel(R) oneAPI Math Kernel Library Version 2022.2-Product Build 20220804 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v3.1.1 (Git Hash 64f6bcbcbab628e96f33a62c3e975f8535a7bde4) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX512 + - CUDA Runtime 11.8 + - NVCC architecture flags: -gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_90,code=sm_90 + - CuDNN 8.7 + - Magma 2.6.1 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.8, CUDNN_VERSION=8.7.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -DNDEBUG -DUSE_KINETO -DLIBKINETO_NOROCTRACER -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wall -Wextra -Werror=return-type -Werror=non-virtual-dtor -Werror=bool-operation -Wnarrowing -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=old-style-cast -Wno-invalid-partial-specialization -Wno-unused-private-field -Wno-aligned-allocation-unavailable -Wno-missing-braces -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_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.1.2, 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=1, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + + TorchVision: 0.16.2+cu118 + OpenCV: 4.9.0 + MMEngine: 0.10.5 + +Runtime environment: + cudnn_benchmark: True + mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} + dist_cfg: {'backend': 'nccl'} + seed: 2049099036 + Distributed launcher: pytorch + Distributed training: True + GPU number: 4 +------------------------------------------------------------ + +2024/10/27 18:04:57 - mmengine - INFO - Config: +bs_ratio = 4 +crop_size = ( + 512, + 512, +) +data_preprocessor = dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor') +data_root = 'data/ade/ADEChallengeData2016' +dataset_type = 'ADE20KDataset' +default_hooks = dict( + checkpoint=dict(by_epoch=False, interval=16000, type='CheckpointHook'), + logger=dict(interval=50, log_metric_by_epoch=False, type='LoggerHook'), + param_scheduler=dict(type='ParamSchedulerHook'), + sampler_seed=dict(type='DistSamplerSeedHook'), + timer=dict(type='IterTimerHook'), + visualization=dict(type='SegVisualizationHook')) +default_scope = 'mmseg' +env_cfg = dict( + cudnn_benchmark=True, + dist_cfg=dict(backend='nccl'), + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) +img_ratios = [ + 0.5, + 0.75, + 1.0, + 1.25, + 1.5, + 1.75, +] +launcher = 'pytorch' +load_from = None +log_level = 'INFO' +log_processor = dict(by_epoch=False) +max_iters = 160000 +model = dict( + backbone=dict( + depth=[ + 2, + 3, + 2, + ], + distillation=False, + down_ops=[ + [ + 'subsample', + 2, + ], + [ + 'subsample', + 2, + ], + [ + '', + ], + ], + drop_path=0.03, + embed_dim=[ + 200, + 376, + 448, + ], + forward_type='v052d', + frozen_stages=-1, + global_ratio=[ + 0.8, + 0.7, + 0.6, + ], + img_size=224, + in_chans=3, + kernels=[ + 7, + 5, + 3, + ], + local_ratio=[ + 0.2, + 0.2, + 0.3, + ], + norm_eval=False, + num_classes=80, + num_heads=[ + 4, + 4, + 4, + ], + out_indices=( + 1, + 2, + 3, + ), + patch_size=16, + pretrained= + '../../weights/MobileMamba_B4/mobilemamba_b4.pth', + ssm_ratio=2, + stages=[ + 's', + 's', + 's', + ], + sync_bn=False, + type='MobileMamba', + window_size=[ + 7, + 7, + 7, + ]), + data_preprocessor=dict( + bgr_to_rgb=True, + mean=[ + 123.675, + 116.28, + 103.53, + ], + pad_val=0, + seg_pad_val=255, + size=( + 512, + 512, + ), + std=[ + 58.395, + 57.12, + 57.375, + ], + type='SegDataPreProcessor'), + decode_head=dict( + align_corners=False, + channels=128, + dropout_ratio=0.1, + feature_strides=[ + 16, + 32, + 64, + ], + in_channels=[ + 256, + 256, + 256, + ], + in_index=[ + 0, + 1, + 2, + ], + loss_decode=dict( + loss_weight=1.0, type='CrossEntropyLoss', use_sigmoid=False), + norm_cfg=dict(requires_grad=True, type='SyncBN'), + num_classes=150, + type='FPNHead'), + neck=dict( + in_channels=[ + 200, + 376, + 448, + ], + num_outs=3, + out_channels=256, + type='FPN'), + pretrained=None, + test_cfg=dict(mode='whole'), + train_cfg=dict(), + type='EncoderDecoder') +norm_cfg = dict(requires_grad=True, type='SyncBN') +optim_wrapper = dict( + optimizer=dict( + betas=( + 0.9, + 0.999, + ), lr=0.00012, type='AdamW', weight_decay=0.05), + paramwise_cfg=dict( + custom_keys=dict( + head=dict(lr_mult=10.0), + norm=dict(decay_mult=0.0), + pos_block=dict(decay_mult=0.0))), + type='OptimWrapper') +optimizer = dict(lr=0.01, momentum=0.9, type='SGD', weight_decay=0.0005) +param_scheduler = [ + dict( + begin=0, by_epoch=False, end=1500, start_factor=1e-06, + type='LinearLR'), + dict( + T_max=80000, + begin=80000, + by_epoch=False, + end=160000, + eta_min=0, + type='CosineAnnealingLR'), +] +ratio = 1 +resume = True +test_cfg = dict(type='TestLoop') +test_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/ade/ADEChallengeData2016', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +test_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), +] +train_cfg = dict( + max_iters=160000, type='IterBasedTrainLoop', val_interval=16000) +train_dataloader = dict( + batch_size=8, + dataset=dict( + data_prefix=dict( + img_path='images/training', seg_map_path='annotations/training'), + data_root='data/ade/ADEChallengeData2016', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict( + cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=8, + persistent_workers=True, + sampler=dict(shuffle=True, type='InfiniteSampler')) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict( + keep_ratio=True, + ratio_range=( + 0.5, + 2.0, + ), + scale=( + 2048, + 512, + ), + type='RandomResize'), + dict(cat_max_ratio=0.75, crop_size=( + 512, + 512, + ), type='RandomCrop'), + dict(prob=0.5, type='RandomFlip'), + dict(type='PhotoMetricDistortion'), + dict(type='PackSegInputs'), +] +tta_model = dict(type='SegTTAModel') +tta_pipeline = [ + dict(backend_args=None, type='LoadImageFromFile'), + dict( + transforms=[ + [ + dict(keep_ratio=True, scale_factor=0.5, type='Resize'), + dict(keep_ratio=True, scale_factor=0.75, type='Resize'), + dict(keep_ratio=True, scale_factor=1.0, type='Resize'), + dict(keep_ratio=True, scale_factor=1.25, type='Resize'), + dict(keep_ratio=True, scale_factor=1.5, type='Resize'), + dict(keep_ratio=True, scale_factor=1.75, type='Resize'), + ], + [ + dict(direction='horizontal', prob=0.0, type='RandomFlip'), + dict(direction='horizontal', prob=1.0, type='RandomFlip'), + ], + [ + dict(type='LoadAnnotations'), + ], + [ + dict(type='PackSegInputs'), + ], + ], + type='TestTimeAug'), +] +val_cfg = dict(type='ValLoop') +val_dataloader = dict( + batch_size=1, + dataset=dict( + data_prefix=dict( + img_path='images/validation', + seg_map_path='annotations/validation'), + data_root='data/ade/ADEChallengeData2016', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(keep_ratio=True, scale=( + 2048, + 512, + ), type='Resize'), + dict(reduce_zero_label=True, type='LoadAnnotations'), + dict(type='PackSegInputs'), + ], + type='ADE20KDataset'), + num_workers=2, + persistent_workers=True, + sampler=dict(shuffle=False, type='DefaultSampler')) +val_evaluator = dict( + iou_metrics=[ + 'mIoU', + ], type='IoUMetric') +vis_backends = [ + dict(type='LocalVisBackend'), +] +visualizer = dict( + name='visualizer', + type='SegLocalVisualizer', + vis_backends=[ + dict(type='LocalVisBackend'), + ]) +work_dir = './work_dirs/fpn_mobilemamba_B4P-160k_ade20k-512x512' + +2024/10/27 18:05:00 - mmengine - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) RuntimeInfoHook +(BELOW_NORMAL) LoggerHook + -------------------- +before_train: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_train_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) DistSamplerSeedHook + -------------------- +before_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook + -------------------- +after_train_iter: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_train_epoch: +(NORMAL ) IterTimerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +before_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_val_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_val_iter: +(NORMAL ) IterTimerHook + -------------------- +after_val_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_val_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook +(LOW ) ParamSchedulerHook +(VERY_LOW ) CheckpointHook + -------------------- +after_val: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_train: +(VERY_HIGH ) RuntimeInfoHook +(VERY_LOW ) CheckpointHook + -------------------- +before_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +before_test_epoch: +(NORMAL ) IterTimerHook + -------------------- +before_test_iter: +(NORMAL ) IterTimerHook + -------------------- +after_test_iter: +(NORMAL ) IterTimerHook +(NORMAL ) SegVisualizationHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test_epoch: +(VERY_HIGH ) RuntimeInfoHook +(NORMAL ) IterTimerHook +(BELOW_NORMAL) LoggerHook + -------------------- +after_test: +(VERY_HIGH ) RuntimeInfoHook + -------------------- +after_run: +(BELOW_NORMAL) LoggerHook + -------------------- +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks1.0.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks1.0.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks1.1.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks1.1.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks2.3.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks2.3.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks2.4.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks2.4.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks2.5.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks2.5.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks3.3.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks3.3.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks3.4.mixer.m.attn.global_op.wt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - WARNING - backbone.blocks3.4.mixer.m.attn.global_op.iwt_filter is skipped since its requires_grad=False +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:lr=0.00012 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:weight_decay=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias:decay_mult=0.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.conv_seg.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.conv_seg.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.conv_seg.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.conv_seg.bias:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.conv_seg.bias:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.conv_seg.bias:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.conv.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.conv.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.conv.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.bn.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.bn.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.bn.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.bn.bias:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.bn.bias:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.0.0.bn.bias:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.conv.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.conv.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.conv.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.bn.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.bn.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.bn.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.bn.bias:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.bn.bias:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.1.0.bn.bias:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.conv.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.conv.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.conv.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.bn.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.bn.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.bn.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.bn.bias:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.bn.bias:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.0.bn.bias:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.conv.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.conv.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.conv.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.bn.weight:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.bn.weight:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.bn.weight:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.bn.bias:lr=0.0012000000000000001 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.bn.bias:weight_decay=0.05 +2024/10/27 18:05:03 - mmengine - INFO - paramwise_options -- decode_head.scale_heads.2.2.bn.bias:lr_mult=10.0 +2024/10/27 18:05:03 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. +Name of parameter - Initialization information + +backbone.patch_embed.0.c.weight - torch.Size([25, 3, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.0.bn.weight - torch.Size([25]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.0.bn.bias - torch.Size([25]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.2.c.weight - torch.Size([50, 25, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.2.bn.weight - torch.Size([50]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.2.bn.bias - torch.Size([50]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.4.c.weight - torch.Size([100, 50, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.4.bn.weight - torch.Size([100]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.4.bn.bias - torch.Size([100]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.6.c.weight - torch.Size([200, 100, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.6.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.patch_embed.6.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.dw0.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.dw0.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.dw0.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn0.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn0.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn0.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn0.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn0.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn0.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([40, 1, 7, 7]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.local_op.bn1.weight - torch.Size([40]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.local_op.bn1.bias - torch.Size([40]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([40, 1, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.local_op.bn2.weight - torch.Size([40]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.local_op.bn2.bias - torch.Size([40]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.wt_filter - torch.Size([640, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.iwt_filter - torch.Size([640, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 12, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.Ds - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([640, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 320, 10]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([640, 160, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([160, 320, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([160]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([160]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 160, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([640, 1, 7, 7]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 640, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.proj.1.c.weight - torch.Size([200, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.proj.1.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.mixer.m.attn.proj.1.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.dw1.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.dw1.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.dw1.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn1.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn1.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn1.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.0.ffn1.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.dw0.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.dw0.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.dw0.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn0.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn0.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn0.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn0.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn0.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn0.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([40, 1, 7, 7]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.local_op.bn1.weight - torch.Size([40]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.local_op.bn1.bias - torch.Size([40]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([40, 1, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.local_op.bn2.weight - torch.Size([40]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.local_op.bn2.bias - torch.Size([40]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.wt_filter - torch.Size([640, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.iwt_filter - torch.Size([640, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 12, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.Ds - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([640, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 320, 10]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([640, 160, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([160, 320, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([160]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([160]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 160, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([640, 1, 7, 7]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 640, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.proj.1.c.weight - torch.Size([200, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.proj.1.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.mixer.m.attn.proj.1.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.dw1.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.dw1.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.dw1.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn1.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn1.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn1.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks1.1.ffn1.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.0.m.c.weight - torch.Size([200, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.0.m.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.0.m.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.1.m.pw1.c.weight - torch.Size([400, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.1.m.pw1.bn.weight - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.1.m.pw1.bn.bias - torch.Size([400]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.1.m.pw2.c.weight - torch.Size([200, 400, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.1.m.pw2.bn.weight - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.0.1.m.pw2.bn.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv1.c.weight - torch.Size([800, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv1.bn.weight - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv1.bn.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv2.c.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv2.bn.weight - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv2.bn.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.se.fc1.weight - torch.Size([200, 800, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.se.fc1.bias - torch.Size([200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.se.fc2.weight - torch.Size([800, 200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.se.fc2.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv3.c.weight - torch.Size([376, 800, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv3.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.1.conv3.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.2.1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.dw0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.dw0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.dw0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn0.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn0.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn0.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn0.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn0.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn0.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([75, 1, 5, 5]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.local_op.bn1.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.local_op.bn1.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([75, 1, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.local_op.bn2.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.local_op.bn2.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.wt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.iwt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 18, 512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1024, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 512, 16]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1024, 256, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([512, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([256, 512, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 256, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1024, 1, 5, 5]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1024, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.proj.1.c.weight - torch.Size([376, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.proj.1.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.mixer.m.attn.proj.1.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.dw1.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.dw1.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.dw1.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.3.ffn1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.dw0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.dw0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.dw0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn0.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn0.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn0.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn0.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn0.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn0.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([75, 1, 5, 5]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.local_op.bn1.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.local_op.bn1.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([75, 1, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.local_op.bn2.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.local_op.bn2.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.wt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.iwt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 18, 512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1024, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 512, 16]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1024, 256, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([512, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([256, 512, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 256, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1024, 1, 5, 5]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1024, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.proj.1.c.weight - torch.Size([376, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.proj.1.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.mixer.m.attn.proj.1.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.dw1.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.dw1.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.dw1.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.4.ffn1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.dw0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.dw0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.dw0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn0.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn0.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn0.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn0.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn0.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn0.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([75, 1, 5, 5]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.local_op.bn1.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.local_op.bn1.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([75, 1, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.local_op.bn2.weight - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.local_op.bn2.bias - torch.Size([75]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.wt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.iwt_filter - torch.Size([1024, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 18, 512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1024, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 512, 16]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1024, 256, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1024]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([512, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([512]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([256, 512, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 256, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1024, 1, 5, 5]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1024, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.proj.1.c.weight - torch.Size([376, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.proj.1.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.mixer.m.attn.proj.1.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.dw1.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.dw1.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.dw1.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks2.5.ffn1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.0.m.c.weight - torch.Size([376, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.0.m.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.0.m.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.1.m.pw1.c.weight - torch.Size([752, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.1.m.pw1.bn.weight - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.1.m.pw1.bn.bias - torch.Size([752]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.1.m.pw2.c.weight - torch.Size([376, 752, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.1.m.pw2.bn.weight - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.0.1.m.pw2.bn.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv1.c.weight - torch.Size([1504, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv1.bn.weight - torch.Size([1504]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv1.bn.bias - torch.Size([1504]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv2.c.weight - torch.Size([1504, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv2.bn.weight - torch.Size([1504]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv2.bn.bias - torch.Size([1504]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.se.fc1.weight - torch.Size([376, 1504, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.se.fc1.bias - torch.Size([376]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.se.fc2.weight - torch.Size([1504, 376, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.se.fc2.bias - torch.Size([1504]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv3.c.weight - torch.Size([448, 1504, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv3.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.1.conv3.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.0.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.0.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.0.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.1.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.1.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.1.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.1.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.1.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.2.1.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.dw0.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.dw0.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.dw0.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn0.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn0.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn0.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn0.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn0.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn0.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([134, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.local_op.bn1.weight - torch.Size([134]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.local_op.bn1.bias - torch.Size([134]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([134, 1, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.local_op.bn2.weight - torch.Size([134]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.local_op.bn2.bias - torch.Size([134]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.wt_filter - torch.Size([1088, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.iwt_filter - torch.Size([1088, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 19, 544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1088]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1088, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 544, 17]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1088, 272, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1088]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1088]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([544, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([272, 544, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([272]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([272]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 272, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1088, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1088, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.proj.1.c.weight - torch.Size([448, 448, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.proj.1.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.mixer.m.attn.proj.1.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.dw1.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.dw1.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.dw1.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn1.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn1.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn1.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn1.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn1.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.3.ffn1.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.dw0.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.dw0.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.dw0.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn0.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn0.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn0.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn0.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn0.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn0.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.local_op.dwconv3x3.weight - torch.Size([134, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.local_op.bn1.weight - torch.Size([134]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.local_op.bn1.bias - torch.Size([134]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.local_op.dwconv1x1.weight - torch.Size([134, 1, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.local_op.bn2.weight - torch.Size([134]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.local_op.bn2.bias - torch.Size([134]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.wt_filter - torch.Size([1088, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.iwt_filter - torch.Size([1088, 1, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.x_proj_weight - torch.Size([2, 19, 544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.Ds - torch.Size([1088]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.A_logs - torch.Size([1088, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.dt_projs_weight - torch.Size([2, 544, 17]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.dt_projs_bias - torch.Size([2, 544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.weight - torch.Size([544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_norm.bias - torch.Size([544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.in_proj.c.weight - torch.Size([1088, 272, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.in_proj.bn.weight - torch.Size([1088]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.in_proj.bn.bias - torch.Size([1088]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.conv2d.weight - torch.Size([544, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.conv2d.bias - torch.Size([544]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_proj.c.weight - torch.Size([272, 544, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_proj.bn.weight - torch.Size([272]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.global_atten.out_proj.bn.bias - torch.Size([272]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.base_scale.weight - torch.Size([1, 272, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.wavelet_convs.0.weight - torch.Size([1088, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.global_op.wavelet_scale.0.weight - torch.Size([1, 1088, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.proj.1.c.weight - torch.Size([448, 448, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.proj.1.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.mixer.m.attn.proj.1.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.dw1.m.c.weight - torch.Size([448, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.dw1.m.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.dw1.m.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn1.m.pw1.c.weight - torch.Size([896, 448, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn1.m.pw1.bn.weight - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn1.m.pw1.bn.bias - torch.Size([896]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn1.m.pw2.c.weight - torch.Size([448, 896, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn1.m.pw2.bn.weight - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.blocks3.4.ffn1.m.pw2.bn.bias - torch.Size([448]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +neck.lateral_convs.0.conv.weight - torch.Size([256, 200, 1, 1]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.lateral_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +neck.lateral_convs.1.conv.weight - torch.Size([256, 376, 1, 1]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.lateral_convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +neck.lateral_convs.2.conv.weight - torch.Size([256, 448, 1, 1]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.lateral_convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.fpn_convs.0.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.fpn_convs.1.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): +XavierInit: gain=1, distribution=uniform, bias=0 + +neck.fpn_convs.2.conv.bias - torch.Size([256]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.conv_seg.weight - torch.Size([150, 128, 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.scale_heads.0.0.conv.weight - torch.Size([128, 256, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.0.0.bn.weight - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.0.0.bn.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.1.0.conv.weight - torch.Size([128, 256, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.1.0.bn.weight - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.1.0.bn.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.2.0.conv.weight - torch.Size([128, 256, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.2.0.bn.weight - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.2.0.bn.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.2.2.conv.weight - torch.Size([128, 128, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.2.2.bn.weight - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.scale_heads.2.2.bn.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder +2024/10/27 18:05:07 - mmengine - INFO - Auto resumed from the latest checkpoint work_dirs/fpn_mobilemamba_B4P-160k_ade20k-512x512/iter_16000.pth. +2024/10/27 18:05:13 - mmengine - INFO - Load checkpoint from work_dirs/fpn_mobilemamba_B4P-160k_ade20k-512x512/iter_16000.pth +2024/10/27 18:05:13 - mmengine - INFO - resumed epoch: 0, iter: 16000 +2024/10/27 18:05:13 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io +2024/10/27 18:05:13 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. +2024/10/27 18:05:13 - mmengine - INFO - Checkpoints will be saved to work_dirs/fpn_mobilemamba_B4P-160k_ade20k-512x512. +2024/10/27 18:05:13 - mmengine - WARNING - Advance dataloader 16000 steps to skip data that has already been trained +2024/10/27 18:13:30 - mmengine - INFO - Iter(train) [ 16050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 16 days, 13:32:44 time: 0.3671 data_time: 0.0160 memory: 14742 loss: 0.4980 decode.loss_ce: 0.4980 decode.acc_seg: 73.8264 +2024/10/27 18:13:48 - mmengine - INFO - Iter(train) [ 16100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8 days, 14:01:21 time: 0.3660 data_time: 0.0163 memory: 5384 loss: 0.5454 decode.loss_ce: 0.5454 decode.acc_seg: 78.2960 +2024/10/27 18:14:07 - mmengine - INFO - Iter(train) [ 16150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 5 days, 22:11:20 time: 0.3678 data_time: 0.0147 memory: 5384 loss: 0.4822 decode.loss_ce: 0.4822 decode.acc_seg: 89.1267 +2024/10/27 18:14:25 - mmengine - INFO - Iter(train) [ 16200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 4 days, 14:17:43 time: 0.3650 data_time: 0.0165 memory: 5384 loss: 0.5370 decode.loss_ce: 0.5370 decode.acc_seg: 79.5284 +2024/10/27 18:14:44 - mmengine - INFO - Iter(train) [ 16250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 3 days, 19:06:57 time: 0.3642 data_time: 0.0166 memory: 5386 loss: 0.5102 decode.loss_ce: 0.5102 decode.acc_seg: 81.8464 +2024/10/27 18:15:02 - mmengine - INFO - Iter(train) [ 16300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 3 days, 6:20:29 time: 0.3657 data_time: 0.0168 memory: 5384 loss: 0.5073 decode.loss_ce: 0.5073 decode.acc_seg: 89.8018 +2024/10/27 18:15:24 - mmengine - INFO - Iter(train) [ 16350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 2 days, 21:37:31 time: 0.3682 data_time: 0.0161 memory: 5384 loss: 0.5683 decode.loss_ce: 0.5683 decode.acc_seg: 68.0908 +2024/10/27 18:15:42 - mmengine - INFO - Iter(train) [ 16400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 2 days, 14:43:29 time: 0.3653 data_time: 0.0155 memory: 5384 loss: 0.6090 decode.loss_ce: 0.6090 decode.acc_seg: 86.0747 +2024/10/27 18:16:00 - mmengine - INFO - Iter(train) [ 16450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 2 days, 9:21:23 time: 0.3655 data_time: 0.0157 memory: 5384 loss: 0.5360 decode.loss_ce: 0.5360 decode.acc_seg: 73.3498 +2024/10/27 18:16:19 - mmengine - INFO - Iter(train) [ 16500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 2 days, 5:04:15 time: 0.3673 data_time: 0.0158 memory: 5384 loss: 0.4854 decode.loss_ce: 0.4854 decode.acc_seg: 77.5677 +2024/10/27 18:16:37 - mmengine - INFO - Iter(train) [ 16550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 2 days, 1:33:38 time: 0.3686 data_time: 0.0151 memory: 5385 loss: 0.4837 decode.loss_ce: 0.4837 decode.acc_seg: 71.9330 +2024/10/27 18:16:57 - mmengine - INFO - Iter(train) [ 16600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 22:42:25 time: 0.3900 data_time: 0.0141 memory: 5384 loss: 0.5597 decode.loss_ce: 0.5597 decode.acc_seg: 77.1700 +2024/10/27 18:17:16 - mmengine - INFO - Iter(train) [ 16650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 20:17:19 time: 0.3888 data_time: 0.0138 memory: 5385 loss: 0.6185 decode.loss_ce: 0.6185 decode.acc_seg: 80.4883 +2024/10/27 18:17:36 - mmengine - INFO - Iter(train) [ 16700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 18:13:49 time: 0.3897 data_time: 0.0145 memory: 5384 loss: 0.5323 decode.loss_ce: 0.5323 decode.acc_seg: 83.1896 +2024/10/27 18:17:55 - mmengine - INFO - Iter(train) [ 16750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 16:26:42 time: 0.3911 data_time: 0.0140 memory: 5383 loss: 0.5602 decode.loss_ce: 0.5602 decode.acc_seg: 83.6363 +2024/10/27 18:18:15 - mmengine - INFO - Iter(train) [ 16800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 14:52:30 time: 0.3868 data_time: 0.0119 memory: 5383 loss: 0.4969 decode.loss_ce: 0.4969 decode.acc_seg: 87.1134 +2024/10/27 18:18:34 - mmengine - INFO - Iter(train) [ 16850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 13:28:56 time: 0.3903 data_time: 0.0123 memory: 5384 loss: 0.5626 decode.loss_ce: 0.5626 decode.acc_seg: 91.4762 +2024/10/27 18:18:54 - mmengine - INFO - Iter(train) [ 16900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 12:14:59 time: 0.3915 data_time: 0.0122 memory: 5383 loss: 0.4273 decode.loss_ce: 0.4273 decode.acc_seg: 82.4785 +2024/10/27 18:19:13 - mmengine - INFO - Iter(train) [ 16950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 11:08:48 time: 0.3896 data_time: 0.0122 memory: 5385 loss: 0.5789 decode.loss_ce: 0.5789 decode.acc_seg: 78.2029 +2024/10/27 18:19:32 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 18:19:32 - mmengine - INFO - Iter(train) [ 17000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 10:07:45 time: 0.3618 data_time: 0.0129 memory: 5384 loss: 0.5085 decode.loss_ce: 0.5085 decode.acc_seg: 82.2953 +2024/10/27 18:19:51 - mmengine - INFO - Iter(train) [ 17050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 9:11:06 time: 0.3686 data_time: 0.0150 memory: 5385 loss: 0.4396 decode.loss_ce: 0.4396 decode.acc_seg: 77.2140 +2024/10/27 18:20:09 - mmengine - INFO - Iter(train) [ 17100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 8:20:18 time: 0.3878 data_time: 0.0142 memory: 5384 loss: 0.4996 decode.loss_ce: 0.4996 decode.acc_seg: 80.6623 +2024/10/27 18:20:29 - mmengine - INFO - Iter(train) [ 17150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 7:35:52 time: 0.3901 data_time: 0.0140 memory: 5384 loss: 0.5212 decode.loss_ce: 0.5212 decode.acc_seg: 84.2882 +2024/10/27 18:20:48 - mmengine - INFO - Iter(train) [ 17200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 6:54:24 time: 0.3619 data_time: 0.0146 memory: 5383 loss: 0.5786 decode.loss_ce: 0.5786 decode.acc_seg: 79.5618 +2024/10/27 18:21:06 - mmengine - INFO - Iter(train) [ 17250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 6:14:25 time: 0.3695 data_time: 0.0154 memory: 5384 loss: 0.5736 decode.loss_ce: 0.5736 decode.acc_seg: 82.5392 +2024/10/27 18:21:25 - mmengine - INFO - Iter(train) [ 17300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 5:37:54 time: 0.3640 data_time: 0.0153 memory: 5384 loss: 0.4453 decode.loss_ce: 0.4453 decode.acc_seg: 76.9139 +2024/10/27 18:21:43 - mmengine - INFO - Iter(train) [ 17350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 5:04:01 time: 0.3654 data_time: 0.0160 memory: 5384 loss: 0.5201 decode.loss_ce: 0.5201 decode.acc_seg: 78.0416 +2024/10/27 18:22:02 - mmengine - INFO - Iter(train) [ 17400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 4:32:07 time: 0.3627 data_time: 0.0136 memory: 5385 loss: 0.4625 decode.loss_ce: 0.4625 decode.acc_seg: 87.3270 +2024/10/27 18:22:24 - mmengine - INFO - Iter(train) [ 17450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 4:09:47 time: 0.3628 data_time: 0.0139 memory: 5384 loss: 0.5212 decode.loss_ce: 0.5212 decode.acc_seg: 86.1775 +2024/10/27 18:22:43 - mmengine - INFO - Iter(train) [ 17500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 3:41:59 time: 0.3667 data_time: 0.0154 memory: 5385 loss: 0.5580 decode.loss_ce: 0.5580 decode.acc_seg: 81.5772 +2024/10/27 18:23:02 - mmengine - INFO - Iter(train) [ 17550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 3:17:46 time: 0.3920 data_time: 0.0154 memory: 5384 loss: 0.4954 decode.loss_ce: 0.4954 decode.acc_seg: 79.6832 +2024/10/27 18:23:24 - mmengine - INFO - Iter(train) [ 17600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 2:58:31 time: 0.3647 data_time: 0.0153 memory: 5384 loss: 0.5371 decode.loss_ce: 0.5371 decode.acc_seg: 80.6204 +2024/10/27 18:23:43 - mmengine - INFO - Iter(train) [ 17650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 2:35:23 time: 0.3654 data_time: 0.0148 memory: 5384 loss: 0.5074 decode.loss_ce: 0.5074 decode.acc_seg: 85.3510 +2024/10/27 18:24:01 - mmengine - INFO - Iter(train) [ 17700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 2:13:34 time: 0.3669 data_time: 0.0154 memory: 5384 loss: 0.5673 decode.loss_ce: 0.5673 decode.acc_seg: 78.0349 +2024/10/27 18:24:24 - mmengine - INFO - Iter(train) [ 17750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 1:58:52 time: 0.3663 data_time: 0.0163 memory: 5384 loss: 0.4884 decode.loss_ce: 0.4884 decode.acc_seg: 79.9710 +2024/10/27 18:24:42 - mmengine - INFO - Iter(train) [ 17800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 1:39:04 time: 0.3651 data_time: 0.0157 memory: 5384 loss: 0.4341 decode.loss_ce: 0.4341 decode.acc_seg: 82.9926 +2024/10/27 18:25:00 - mmengine - INFO - Iter(train) [ 17850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 1:20:24 time: 0.3660 data_time: 0.0153 memory: 5385 loss: 0.4853 decode.loss_ce: 0.4853 decode.acc_seg: 84.2191 +2024/10/27 18:25:19 - mmengine - INFO - Iter(train) [ 17900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 1:02:47 time: 0.3669 data_time: 0.0157 memory: 5384 loss: 0.5462 decode.loss_ce: 0.5462 decode.acc_seg: 70.0703 +2024/10/27 18:25:37 - mmengine - INFO - Iter(train) [ 17950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 0:46:01 time: 0.3650 data_time: 0.0144 memory: 5385 loss: 0.5301 decode.loss_ce: 0.5301 decode.acc_seg: 80.4753 +2024/10/27 18:25:55 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 18:25:55 - mmengine - INFO - Iter(train) [ 18000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 0:29:47 time: 0.3606 data_time: 0.0148 memory: 5384 loss: 0.5370 decode.loss_ce: 0.5370 decode.acc_seg: 76.7680 +2024/10/27 18:26:14 - mmengine - INFO - Iter(train) [ 18050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 1 day, 0:14:31 time: 0.3639 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data_time: 0.0139 memory: 5384 loss: 0.4898 decode.loss_ce: 0.4898 decode.acc_seg: 74.8107 +2024/10/27 19:14:09 - mmengine - INFO - Iter(train) [ 25800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 15:43:40 time: 0.3665 data_time: 0.0155 memory: 5384 loss: 0.4093 decode.loss_ce: 0.4093 decode.acc_seg: 89.3786 +2024/10/27 19:14:27 - mmengine - INFO - Iter(train) [ 25850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 15:42:40 time: 0.3611 data_time: 0.0160 memory: 5385 loss: 0.5101 decode.loss_ce: 0.5101 decode.acc_seg: 82.0045 +2024/10/27 19:14:46 - mmengine - INFO - Iter(train) [ 25900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 15:41:40 time: 0.3659 data_time: 0.0151 memory: 5384 loss: 0.4400 decode.loss_ce: 0.4400 decode.acc_seg: 79.6569 +2024/10/27 19:15:04 - mmengine - INFO - Iter(train) [ 25950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 15:40:43 time: 0.3734 data_time: 0.0156 memory: 5384 loss: 0.4458 decode.loss_ce: 0.4458 decode.acc_seg: 78.8896 +2024/10/27 19:15:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 19:15:24 - mmengine - INFO - Iter(train) [ 26000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 15:40:08 time: 0.3674 data_time: 0.0157 memory: 5384 loss: 0.5249 decode.loss_ce: 0.5249 decode.acc_seg: 75.4288 +2024/10/27 19:15:42 - mmengine - INFO - Iter(train) [ 26050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 15:39:10 time: 0.3645 data_time: 0.0182 memory: 5383 loss: 0.4971 decode.loss_ce: 0.4971 decode.acc_seg: 82.1544 +2024/10/27 19:16:01 - mmengine - INFO - Iter(train) [ 26100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 15:38:12 time: 0.3689 data_time: 0.0175 memory: 5384 loss: 0.3862 decode.loss_ce: 0.3862 decode.acc_seg: 84.1036 +2024/10/27 19:16:19 - mmengine - INFO - Iter(train) [ 26150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 15:37:18 time: 0.3701 data_time: 0.0170 memory: 5383 loss: 0.5579 decode.loss_ce: 0.5579 decode.acc_seg: 80.2422 +2024/10/27 19:16:37 - 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- mmengine - INFO - Iter(train) [ 31700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:24:42 time: 0.3706 data_time: 0.0155 memory: 5384 loss: 0.5160 decode.loss_ce: 0.5160 decode.acc_seg: 84.8491 +2024/10/27 19:51:24 - mmengine - INFO - Iter(train) [ 31750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:24:21 time: 0.3620 data_time: 0.0155 memory: 5384 loss: 0.3982 decode.loss_ce: 0.3982 decode.acc_seg: 87.3608 +2024/10/27 19:51:43 - mmengine - INFO - Iter(train) [ 31800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:23:45 time: 0.3669 data_time: 0.0156 memory: 5385 loss: 0.4001 decode.loss_ce: 0.4001 decode.acc_seg: 90.7601 +2024/10/27 19:52:01 - mmengine - INFO - Iter(train) [ 31850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:23:10 time: 0.3647 data_time: 0.0159 memory: 5384 loss: 0.3844 decode.loss_ce: 0.3844 decode.acc_seg: 83.8113 +2024/10/27 19:52:19 - mmengine - INFO - Iter(train) [ 31900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:22:35 time: 0.3652 data_time: 0.0150 memory: 5383 loss: 0.5254 decode.loss_ce: 0.5254 decode.acc_seg: 84.0350 +2024/10/27 19:52:38 - mmengine - INFO - Iter(train) [ 31950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:22:00 time: 0.3658 data_time: 0.0163 memory: 5384 loss: 0.4557 decode.loss_ce: 0.4557 decode.acc_seg: 84.1897 +2024/10/27 19:52:56 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 19:52:56 - mmengine - INFO - Iter(train) [ 32000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:21:25 time: 0.3650 data_time: 0.0159 memory: 5384 loss: 0.4226 decode.loss_ce: 0.4226 decode.acc_seg: 89.7397 +2024/10/27 19:52:56 - mmengine - INFO - Saving checkpoint at 32000 iterations +2024/10/27 19:53:43 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:06:41 time: 0.2082 data_time: 0.0017 memory: 20667 +2024/10/27 19:54:00 - mmengine - INFO - Iter(val) [100/500] eta: 0:04:04 time: 0.0426 data_time: 0.0014 memory: 20669 +2024/10/27 19:54:04 - mmengine - INFO - Iter(val) [150/500] eta: 0:02:32 time: 0.0318 data_time: 0.0020 memory: 5483 +2024/10/27 19:54:12 - mmengine - INFO - Iter(val) [200/500] eta: 0:01:49 time: 0.0494 data_time: 0.0016 memory: 5487 +2024/10/27 19:54:15 - mmengine - INFO - Iter(val) [250/500] eta: 0:01:16 time: 0.0500 data_time: 0.0020 memory: 5488 +2024/10/27 19:54:21 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:54 time: 0.0624 data_time: 0.0019 memory: 5490 +2024/10/27 19:54:23 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:36 time: 0.0402 data_time: 0.0014 memory: 767 +2024/10/27 19:54:26 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:21 time: 0.0337 data_time: 0.0018 memory: 5483 +2024/10/27 19:54:30 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:10 time: 0.0380 data_time: 0.0014 memory: 5485 +2024/10/27 19:54:39 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.2139 data_time: 0.0016 memory: 20664 +2024/10/27 19:54:50 - mmengine - INFO - per class results: +2024/10/27 19:54:50 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 69.06 | 84.97 | +| building | 76.87 | 91.05 | +| sky | 91.69 | 94.83 | +| floor | 73.88 | 85.78 | +| tree | 69.97 | 83.8 | +| ceiling | 78.12 | 89.46 | +| road | 78.64 | 85.64 | +| bed | 77.46 | 94.38 | +| windowpane | 53.11 | 74.75 | +| grass | 61.91 | 79.14 | +| cabinet | 52.8 | 64.88 | +| sidewalk | 57.38 | 74.45 | +| person | 69.8 | 83.06 | +| earth | 33.71 | 52.1 | +| door | 33.09 | 45.79 | +| table | 46.07 | 59.31 | +| mountain | 53.61 | 73.85 | +| plant | 48.08 | 59.21 | +| curtain | 56.29 | 62.64 | +| chair | 44.86 | 61.94 | +| car | 75.85 | 85.03 | +| water | 41.39 | 57.34 | +| painting | 62.83 | 73.56 | +| sofa | 54.58 | 83.15 | +| shelf | 31.37 | 44.11 | +| house | 37.29 | 50.26 | +| sea | 41.88 | 65.8 | +| mirror | 51.86 | 60.14 | +| rug | 59.29 | 72.73 | +| field | 18.42 | 26.15 | +| armchair | 31.22 | 39.03 | +| seat | 53.18 | 63.97 | +| fence | 15.98 | 19.33 | +| desk | 30.68 | 43.07 | +| rock | 34.1 | 50.75 | +| wardrobe | 43.13 | 57.27 | +| lamp | 45.63 | 54.88 | +| bathtub | 66.35 | 74.88 | +| railing | 25.23 | 39.15 | +| cushion | 35.38 | 41.2 | +| base | 26.42 | 39.4 | +| box | 14.38 | 23.66 | +| column | 36.58 | 48.76 | +| signboard | 19.63 | 25.43 | +| chest of drawers | 28.94 | 44.35 | +| counter | 29.84 | 40.89 | +| sand | 45.39 | 62.93 | +| sink | 55.3 | 61.26 | +| skyscraper | 17.48 | 19.91 | +| fireplace | 65.07 | 78.3 | +| refrigerator | 57.64 | 78.56 | +| grandstand | 22.51 | 49.87 | +| path | 20.94 | 25.67 | +| stairs | 25.01 | 36.3 | +| runway | 62.87 | 93.53 | +| case | 41.84 | 63.13 | +| pool table | 7.61 | 8.02 | +| pillow | 38.71 | 46.12 | +| screen door | 41.08 | 44.96 | +| stairway | 23.87 | 37.38 | +| river | 8.38 | 13.57 | +| bridge | 38.15 | 57.95 | +| bookcase | 29.88 | 59.03 | +| blind | 32.2 | 42.46 | +| coffee table | 44.49 | 76.55 | +| toilet | 65.11 | 86.74 | +| flower | 20.71 | 26.08 | +| book | 29.54 | 43.23 | +| hill | 2.57 | 4.18 | +| bench | 29.26 | 46.17 | +| countertop | 48.4 | 66.19 | +| stove | 53.78 | 59.73 | +| palm | 39.2 | 62.3 | +| kitchen island | 31.51 | 71.46 | +| computer | 34.18 | 39.91 | +| swivel chair | 32.16 | 56.25 | +| boat | 37.61 | 51.91 | +| bar | 30.28 | 45.28 | +| arcade machine | 46.5 | 60.1 | +| hovel | 33.1 | 40.76 | +| bus | 71.86 | 92.53 | +| towel | 39.45 | 42.3 | +| light | 20.18 | 21.55 | +| truck | 26.41 | 30.92 | +| tower | 29.21 | 50.65 | +| chandelier | 49.33 | 66.37 | +| awning | 9.69 | 11.49 | +| streetlight | 9.11 | 11.48 | +| booth | 19.22 | 62.96 | +| television receiver | 54.99 | 65.1 | +| airplane | 47.85 | 58.06 | +| dirt track | 8.23 | 65.15 | +| apparel | 26.55 | 34.42 | +| pole | 8.18 | 9.88 | +| land | 0.0 | 0.01 | +| bannister | 2.11 | 2.67 | +| escalator | 29.51 | 52.25 | +| ottoman | 35.89 | 37.29 | +| bottle | 27.43 | 44.19 | +| buffet | 37.82 | 45.23 | +| poster | 20.37 | 34.37 | +| stage | 7.69 | 15.04 | +| van | 36.86 | 46.51 | +| ship | 4.72 | 6.06 | +| fountain | 19.21 | 19.34 | +| conveyer belt | 48.89 | 91.22 | +| canopy | 15.81 | 20.22 | +| washer | 66.57 | 68.48 | +| plaything | 7.03 | 8.76 | +| swimming pool | 30.06 | 65.53 | +| stool | 24.44 | 52.94 | +| barrel | 42.38 | 63.91 | +| basket | 17.64 | 20.93 | +| waterfall | 29.82 | 30.98 | +| tent | 71.5 | 97.65 | +| bag | 1.11 | 1.18 | +| minibike | 47.56 | 56.72 | +| cradle | 69.17 | 92.15 | +| oven | 24.11 | 52.95 | +| ball | 8.73 | 9.92 | +| food | 11.12 | 11.73 | +| step | 0.15 | 0.19 | +| tank | 41.67 | 46.48 | +| trade name | 5.47 | 5.79 | +| microwave | 34.37 | 40.05 | +| pot | 29.41 | 35.73 | +| animal | 47.44 | 50.68 | +| bicycle | 41.62 | 55.63 | +| lake | 59.71 | 59.83 | +| dishwasher | 35.08 | 54.58 | +| screen | 56.78 | 79.27 | +| blanket | 1.44 | 1.53 | +| sculpture | 34.24 | 62.8 | +| hood | 45.96 | 51.16 | +| sconce | 25.55 | 27.54 | +| vase | 18.47 | 21.02 | +| traffic light | 15.02 | 24.36 | +| tray | 1.68 | 4.43 | +| ashcan | 23.05 | 28.71 | +| fan | 28.97 | 34.34 | +| pier | 25.77 | 30.45 | +| crt screen | 4.06 | 7.26 | +| plate | 24.83 | 28.16 | +| monitor | 41.38 | 50.35 | +| bulletin board | 26.56 | 37.29 | +| shower | 0.0 | 0.0 | +| radiator | 39.95 | 47.63 | +| glass | 0.64 | 0.67 | +| clock | 21.63 | 31.47 | +| flag | 16.07 | 16.67 | ++---------------------+-------+-------+ +2024/10/27 19:54:50 - mmengine - INFO - Iter(val) [500/500] aAcc: 77.0400 mIoU: 35.7000 mAcc: 47.6400 data_time: 0.0020 time: 0.1997 +2024/10/27 19:55:09 - mmengine - INFO - Iter(train) [ 32050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:22:28 time: 0.3739 data_time: 0.0155 memory: 5384 loss: 0.4017 decode.loss_ce: 0.4017 decode.acc_seg: 89.7535 +2024/10/27 19:55:29 - mmengine - INFO - Iter(train) [ 32100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:22:02 time: 0.4001 data_time: 0.0137 memory: 5385 loss: 0.4902 decode.loss_ce: 0.4902 decode.acc_seg: 81.4030 +2024/10/27 19:55:48 - mmengine - INFO - Iter(train) [ 32150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:21:35 time: 0.3774 data_time: 0.0143 memory: 5384 loss: 0.3983 decode.loss_ce: 0.3983 decode.acc_seg: 89.5977 +2024/10/27 19:56:08 - mmengine - INFO - Iter(train) [ 32200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:21:09 time: 0.3963 data_time: 0.0134 memory: 5384 loss: 0.4998 decode.loss_ce: 0.4998 decode.acc_seg: 90.8406 +2024/10/27 19:56:28 - mmengine - INFO - Iter(train) [ 32250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:20:47 time: 0.4012 data_time: 0.0132 memory: 5385 loss: 0.3571 decode.loss_ce: 0.3571 decode.acc_seg: 87.0924 +2024/10/27 19:56:46 - mmengine - INFO - Iter(train) [ 32300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:20:16 time: 0.3769 data_time: 0.0154 memory: 5384 loss: 0.4257 decode.loss_ce: 0.4257 decode.acc_seg: 81.8645 +2024/10/27 19:57:05 - mmengine - INFO - Iter(train) [ 32350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:19:46 time: 0.3794 data_time: 0.0149 memory: 5384 loss: 0.4592 decode.loss_ce: 0.4592 decode.acc_seg: 84.1361 +2024/10/27 19:57:25 - mmengine - INFO - Iter(train) [ 32400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:19:21 time: 0.3850 data_time: 0.0152 memory: 5385 loss: 0.3765 decode.loss_ce: 0.3765 decode.acc_seg: 90.0913 +2024/10/27 19:57:44 - mmengine - INFO - Iter(train) [ 32450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:18:49 time: 0.3715 data_time: 0.0153 memory: 5384 loss: 0.4930 decode.loss_ce: 0.4930 decode.acc_seg: 76.0182 +2024/10/27 19:58:02 - mmengine - INFO - Iter(train) [ 32500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:18:17 time: 0.3789 data_time: 0.0143 memory: 5385 loss: 0.5066 decode.loss_ce: 0.5066 decode.acc_seg: 72.9126 +2024/10/27 19:58:25 - mmengine - INFO - Iter(train) [ 32550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:18:14 time: 0.3759 data_time: 0.0165 memory: 5384 loss: 0.4778 decode.loss_ce: 0.4778 decode.acc_seg: 91.4094 +2024/10/27 19:58:43 - mmengine - INFO - Iter(train) [ 32600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:17:41 time: 0.3718 data_time: 0.0152 memory: 5384 loss: 0.4805 decode.loss_ce: 0.4805 decode.acc_seg: 91.9336 +2024/10/27 19:59:02 - mmengine - INFO - Iter(train) [ 32650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:17:09 time: 0.3731 data_time: 0.0151 memory: 5384 loss: 0.5262 decode.loss_ce: 0.5262 decode.acc_seg: 83.0767 +2024/10/27 19:59:25 - mmengine - INFO - Iter(train) [ 32700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:17:06 time: 0.3727 data_time: 0.0147 memory: 5384 loss: 0.4503 decode.loss_ce: 0.4503 decode.acc_seg: 85.1017 +2024/10/27 19:59:43 - mmengine - INFO - Iter(train) [ 32750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:16:35 time: 0.3763 data_time: 0.0151 memory: 5384 loss: 0.4960 decode.loss_ce: 0.4960 decode.acc_seg: 75.4393 +2024/10/27 20:00:02 - mmengine - INFO - Iter(train) [ 32800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:16:05 time: 0.3794 data_time: 0.0151 memory: 5384 loss: 0.4667 decode.loss_ce: 0.4667 decode.acc_seg: 81.6075 +2024/10/27 20:00:25 - mmengine - INFO - Iter(train) [ 32850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:16:02 time: 0.3725 data_time: 0.0157 memory: 5384 loss: 0.3940 decode.loss_ce: 0.3940 decode.acc_seg: 88.7751 +2024/10/27 20:00:43 - mmengine - INFO - Iter(train) [ 32900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:15:31 time: 0.3729 data_time: 0.0148 memory: 5385 loss: 0.4734 decode.loss_ce: 0.4734 decode.acc_seg: 83.1573 +2024/10/27 20:01:02 - mmengine - INFO - Iter(train) [ 32950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:14:59 time: 0.3758 data_time: 0.0146 memory: 5384 loss: 0.4064 decode.loss_ce: 0.4064 decode.acc_seg: 83.6597 +2024/10/27 20:01:26 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 20:01:26 - mmengine - INFO - Iter(train) [ 33000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:15:05 time: 0.4015 data_time: 0.0143 memory: 5384 loss: 0.4197 decode.loss_ce: 0.4197 decode.acc_seg: 81.0279 +2024/10/27 20:01:45 - mmengine - INFO - Iter(train) [ 33050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:14:40 time: 0.3731 data_time: 0.0149 memory: 5384 loss: 0.4227 decode.loss_ce: 0.4227 decode.acc_seg: 85.2486 +2024/10/27 20:02:04 - mmengine - INFO - Iter(train) [ 33100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:14:11 time: 0.3770 data_time: 0.0156 memory: 5384 loss: 0.4102 decode.loss_ce: 0.4102 decode.acc_seg: 69.1607 +2024/10/27 20:02:24 - mmengine - INFO - Iter(train) [ 33150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:13:50 time: 0.3757 data_time: 0.0149 memory: 5384 loss: 0.4611 decode.loss_ce: 0.4611 decode.acc_seg: 76.6742 +2024/10/27 20:02:43 - mmengine - INFO - Iter(train) [ 33200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:13:19 time: 0.3715 data_time: 0.0162 memory: 5384 loss: 0.4793 decode.loss_ce: 0.4793 decode.acc_seg: 78.6366 +2024/10/27 20:03:02 - mmengine - INFO - Iter(train) [ 33250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:12:47 time: 0.3773 data_time: 0.0165 memory: 5386 loss: 0.3596 decode.loss_ce: 0.3596 decode.acc_seg: 88.4508 +2024/10/27 20:03:25 - mmengine - INFO - Iter(train) [ 33300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:12:48 time: 0.3752 data_time: 0.0161 memory: 5384 loss: 0.3828 decode.loss_ce: 0.3828 decode.acc_seg: 89.6442 +2024/10/27 20:03:44 - mmengine - INFO - Iter(train) [ 33350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:12:19 time: 0.3749 data_time: 0.0165 memory: 5384 loss: 0.4155 decode.loss_ce: 0.4155 decode.acc_seg: 86.5485 +2024/10/27 20:04:03 - mmengine - INFO - Iter(train) [ 33400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:11:50 time: 0.3765 data_time: 0.0157 memory: 5384 loss: 0.4540 decode.loss_ce: 0.4540 decode.acc_seg: 80.8239 +2024/10/27 20:04:24 - mmengine - INFO - Iter(train) [ 33450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:11:37 time: 0.3787 data_time: 0.0157 memory: 5384 loss: 0.5812 decode.loss_ce: 0.5812 decode.acc_seg: 77.1106 +2024/10/27 20:04:43 - mmengine - INFO - Iter(train) [ 33500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:11:08 time: 0.3769 data_time: 0.0172 memory: 5384 loss: 0.5280 decode.loss_ce: 0.5280 decode.acc_seg: 85.0985 +2024/10/27 20:05:02 - mmengine - INFO - Iter(train) [ 33550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:10:40 time: 0.3777 data_time: 0.0177 memory: 5384 loss: 0.3859 decode.loss_ce: 0.3859 decode.acc_seg: 81.8503 +2024/10/27 20:05:25 - mmengine - INFO - Iter(train) [ 33600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:10:37 time: 0.3718 data_time: 0.0175 memory: 5386 loss: 0.5256 decode.loss_ce: 0.5256 decode.acc_seg: 75.1988 +2024/10/27 20:05:43 - mmengine - INFO - Iter(train) [ 33650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:10:06 time: 0.3740 data_time: 0.0173 memory: 5384 loss: 0.4546 decode.loss_ce: 0.4546 decode.acc_seg: 77.4036 +2024/10/27 20:06:03 - mmengine - INFO - Iter(train) [ 33700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:09:38 time: 0.3806 data_time: 0.0171 memory: 5385 loss: 0.4405 decode.loss_ce: 0.4405 decode.acc_seg: 89.5265 +2024/10/27 20:06:25 - mmengine - INFO - Iter(train) [ 33750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:09:33 time: 0.3777 data_time: 0.0171 memory: 5384 loss: 0.5392 decode.loss_ce: 0.5392 decode.acc_seg: 77.3217 +2024/10/27 20:06:44 - mmengine - INFO - Iter(train) [ 33800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:09:08 time: 0.3733 data_time: 0.0152 memory: 5385 loss: 0.4645 decode.loss_ce: 0.4645 decode.acc_seg: 78.1800 +2024/10/27 20:07:04 - mmengine - INFO - Iter(train) [ 33850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:08:40 time: 0.3807 data_time: 0.0160 memory: 5384 loss: 0.4960 decode.loss_ce: 0.4960 decode.acc_seg: 83.6675 +2024/10/27 20:07:24 - mmengine - INFO - Iter(train) [ 33900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:08:25 time: 0.3772 data_time: 0.0159 memory: 5383 loss: 0.4307 decode.loss_ce: 0.4307 decode.acc_seg: 85.8665 +2024/10/27 20:07:44 - mmengine - INFO - Iter(train) [ 33950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:07:59 time: 0.3775 data_time: 0.0160 memory: 5383 loss: 0.4679 decode.loss_ce: 0.4679 decode.acc_seg: 87.8250 +2024/10/27 20:08:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 20:08:03 - mmengine - INFO - Iter(train) [ 34000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:07:30 time: 0.3786 data_time: 0.0162 memory: 5386 loss: 0.4527 decode.loss_ce: 0.4527 decode.acc_seg: 84.9365 +2024/10/27 20:08:25 - mmengine - INFO - Iter(train) [ 34050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:07:23 time: 0.3768 data_time: 0.0161 memory: 5384 loss: 0.4061 decode.loss_ce: 0.4061 decode.acc_seg: 86.8253 +2024/10/27 20:08:44 - mmengine - INFO - Iter(train) [ 34100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:06:54 time: 0.3771 data_time: 0.0160 memory: 5384 loss: 0.3842 decode.loss_ce: 0.3842 decode.acc_seg: 84.5252 +2024/10/27 20:09:03 - mmengine - INFO - Iter(train) [ 34150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:06:25 time: 0.3803 data_time: 0.0165 memory: 5384 loss: 0.4760 decode.loss_ce: 0.4760 decode.acc_seg: 77.8148 +2024/10/27 20:09:25 - mmengine - INFO - Iter(train) [ 34200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:06:20 time: 0.3754 data_time: 0.0156 memory: 5384 loss: 0.4417 decode.loss_ce: 0.4417 decode.acc_seg: 74.7032 +2024/10/27 20:09:44 - mmengine - INFO - Iter(train) [ 34250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:05:51 time: 0.3775 data_time: 0.0160 memory: 5384 loss: 0.4719 decode.loss_ce: 0.4719 decode.acc_seg: 85.7127 +2024/10/27 20:10:03 - mmengine - INFO - Iter(train) [ 34300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 14:05:23 time: 0.3787 data_time: 0.0167 memory: 5384 loss: 0.4103 decode.loss_ce: 0.4103 decode.acc_seg: 87.7965 +2024/10/27 20:10:25 - mmengine - INFO - Iter(train) [ 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fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 21:27:28 - mmengine - INFO - Iter(train) [ 46000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:41:43 time: 0.3821 data_time: 0.0164 memory: 5384 loss: 0.4335 decode.loss_ce: 0.4335 decode.acc_seg: 77.9852 +2024/10/27 21:27:47 - mmengine - INFO - Iter(train) [ 46050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:41:19 time: 0.3779 data_time: 0.0182 memory: 5385 loss: 0.4523 decode.loss_ce: 0.4523 decode.acc_seg: 82.6348 +2024/10/27 21:28:06 - mmengine - INFO - Iter(train) [ 46100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:40:54 time: 0.3790 data_time: 0.0157 memory: 5384 loss: 0.4188 decode.loss_ce: 0.4188 decode.acc_seg: 88.6033 +2024/10/27 21:28:26 - mmengine - INFO - Iter(train) [ 46150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:40:34 time: 0.4024 data_time: 0.0143 memory: 5385 loss: 0.4058 decode.loss_ce: 0.4058 decode.acc_seg: 81.5381 +2024/10/27 21:28:45 - mmengine - INFO - Iter(train) [ 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0.3847 decode.loss_ce: 0.3847 decode.acc_seg: 81.6560 +2024/10/27 21:40:29 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 21:40:29 - mmengine - INFO - Iter(train) [ 48000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:27:05 time: 0.3726 data_time: 0.0168 memory: 5384 loss: 0.3978 decode.loss_ce: 0.3978 decode.acc_seg: 87.1630 +2024/10/27 21:40:29 - mmengine - INFO - Saving checkpoint at 48000 iterations +2024/10/27 21:40:34 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:00:15 time: 0.0340 data_time: 0.0016 memory: 980 +2024/10/27 21:40:36 - mmengine - INFO - Iter(val) [100/500] eta: 0:00:13 time: 0.0333 data_time: 0.0015 memory: 1050 +2024/10/27 21:40:37 - mmengine - INFO - Iter(val) [150/500] eta: 0:00:11 time: 0.0346 data_time: 0.0017 memory: 767 +2024/10/27 21:40:39 - mmengine - INFO - Iter(val) [200/500] eta: 0:00:10 time: 0.0345 data_time: 0.0016 memory: 800 +2024/10/27 21:40:41 - mmengine - INFO - Iter(val) [250/500] eta: 0:00:08 time: 0.0332 data_time: 0.0016 memory: 839 +2024/10/27 21:40:42 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:06 time: 0.0345 data_time: 0.0019 memory: 1961 +2024/10/27 21:40:44 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:05 time: 0.0327 data_time: 0.0015 memory: 765 +2024/10/27 21:40:46 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:03 time: 0.0332 data_time: 0.0016 memory: 837 +2024/10/27 21:40:47 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:01 time: 0.0329 data_time: 0.0014 memory: 772 +2024/10/27 21:40:49 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.0330 data_time: 0.0014 memory: 822 +2024/10/27 21:40:51 - mmengine - INFO - per class results: +2024/10/27 21:40:51 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 69.49 | 84.07 | +| building | 77.99 | 88.95 | +| sky | 92.4 | 95.98 | +| floor | 74.55 | 87.69 | +| tree | 70.1 | 86.24 | +| ceiling | 77.87 | 88.3 | +| road | 77.87 | 84.96 | +| bed | 83.71 | 91.11 | +| windowpane | 53.05 | 67.68 | +| grass | 61.2 | 75.24 | +| cabinet | 50.54 | 66.45 | +| sidewalk | 55.58 | 79.38 | +| person | 69.19 | 84.95 | +| earth | 32.31 | 50.71 | +| door | 37.69 | 62.71 | +| table | 49.29 | 69.41 | +| mountain | 44.81 | 51.73 | +| plant | 45.31 | 59.26 | +| curtain | 63.44 | 79.6 | +| chair | 44.51 | 55.94 | +| car | 75.4 | 84.8 | +| water | 38.26 | 42.66 | +| painting | 63.93 | 75.17 | +| sofa | 58.13 | 70.38 | +| shelf | 33.63 | 47.18 | +| house | 35.97 | 46.8 | +| sea | 45.82 | 76.83 | +| mirror | 52.44 | 59.86 | +| rug | 52.16 | 71.45 | +| field | 24.72 | 33.63 | +| armchair | 39.66 | 67.74 | +| seat | 49.24 | 76.32 | +| fence | 32.19 | 47.86 | +| desk | 35.78 | 47.3 | +| rock | 35.14 | 54.84 | +| wardrobe | 40.59 | 65.65 | +| lamp | 46.18 | 59.55 | +| bathtub | 69.23 | 75.3 | +| railing | 26.22 | 45.97 | +| cushion | 42.69 | 56.65 | +| base | 15.31 | 19.58 | +| box | 16.1 | 22.83 | +| column | 34.71 | 52.9 | +| signboard | 25.63 | 32.53 | +| chest of drawers | 34.05 | 48.98 | +| counter | 20.07 | 23.26 | +| sand | 31.16 | 53.17 | +| sink | 57.46 | 67.45 | +| skyscraper | 55.24 | 70.16 | +| fireplace | 68.25 | 87.62 | +| refrigerator | 55.05 | 74.02 | +| grandstand | 13.61 | 25.41 | +| path | 10.09 | 12.77 | +| stairs | 24.39 | 33.91 | +| runway | 66.52 | 86.37 | +| case | 39.49 | 52.54 | +| pool table | 69.38 | 73.18 | +| pillow | 45.59 | 56.15 | +| screen door | 47.46 | 56.15 | +| stairway | 18.92 | 26.26 | +| river | 5.87 | 13.88 | +| bridge | 28.78 | 31.81 | +| bookcase | 33.06 | 43.61 | +| blind | 30.04 | 34.31 | +| coffee table | 51.89 | 77.24 | +| toilet | 70.9 | 86.32 | +| flower | 24.87 | 39.77 | +| book | 36.3 | 56.69 | +| hill | 3.05 | 7.57 | +| bench | 36.87 | 45.69 | +| countertop | 48.04 | 72.82 | +| stove | 52.94 | 58.8 | +| palm | 41.03 | 55.03 | +| kitchen island | 23.82 | 56.53 | +| computer | 46.03 | 60.39 | +| swivel chair | 24.65 | 31.72 | +| boat | 64.72 | 79.6 | +| bar | 38.92 | 60.33 | +| arcade machine | 14.61 | 18.9 | +| hovel | 29.87 | 38.21 | +| bus | 65.31 | 83.93 | +| towel | 50.32 | 63.32 | +| light | 36.67 | 44.71 | +| truck | 24.4 | 34.69 | +| tower | 25.42 | 52.72 | +| chandelier | 50.31 | 72.48 | +| awning | 18.83 | 22.11 | +| streetlight | 10.27 | 12.96 | +| booth | 44.4 | 60.82 | +| television receiver | 57.6 | 76.62 | +| airplane | 50.35 | 59.27 | +| dirt track | 17.32 | 52.16 | +| apparel | 16.19 | 22.09 | +| pole | 10.26 | 13.88 | +| land | 0.0 | 0.0 | +| bannister | 4.34 | 5.82 | +| escalator | 21.42 | 65.59 | +| ottoman | 34.52 | 50.43 | +| bottle | 22.86 | 44.4 | +| buffet | 40.13 | 47.8 | +| poster | 20.93 | 25.91 | +| stage | 7.16 | 23.9 | +| van | 33.53 | 40.17 | +| ship | 9.15 | 14.26 | +| fountain | 19.26 | 19.59 | +| conveyer belt | 52.47 | 60.59 | +| canopy | 8.54 | 10.57 | +| washer | 46.97 | 54.04 | +| plaything | 9.15 | 19.43 | +| swimming pool | 33.36 | 61.36 | +| stool | 29.71 | 40.8 | +| barrel | 26.11 | 63.77 | +| basket | 14.44 | 16.46 | +| waterfall | 58.96 | 76.32 | +| tent | 56.66 | 96.96 | +| bag | 3.51 | 4.19 | +| minibike | 36.29 | 46.04 | +| cradle | 69.77 | 93.53 | +| oven | 39.91 | 57.94 | +| ball | 8.02 | 9.3 | +| food | 37.02 | 50.54 | +| step | 0.08 | 0.09 | +| tank | 33.79 | 38.1 | +| trade name | 9.53 | 9.99 | +| microwave | 33.09 | 36.58 | +| pot | 29.34 | 36.64 | +| animal | 47.65 | 49.73 | +| bicycle | 33.26 | 64.03 | +| lake | 33.16 | 58.05 | +| dishwasher | 31.38 | 32.24 | +| screen | 57.34 | 87.44 | +| blanket | 3.44 | 3.93 | +| sculpture | 29.43 | 51.1 | +| hood | 50.06 | 54.48 | +| sconce | 26.88 | 29.22 | +| vase | 18.36 | 27.48 | +| traffic light | 13.26 | 24.56 | +| tray | 4.94 | 8.07 | +| ashcan | 30.54 | 39.55 | +| fan | 33.26 | 40.87 | +| pier | 29.14 | 40.23 | +| crt screen | 0.06 | 0.06 | +| plate | 28.39 | 33.93 | +| monitor | 42.47 | 51.58 | +| bulletin board | 36.78 | 40.91 | +| shower | 0.0 | 0.0 | +| radiator | 38.89 | 43.89 | +| glass | 1.3 | 1.6 | +| clock | 10.74 | 15.64 | +| flag | 33.98 | 39.12 | ++---------------------+-------+-------+ +2024/10/27 21:40:51 - mmengine - INFO - Iter(val) [500/500] aAcc: 77.3500 mIoU: 36.9700 mAcc: 49.2800 data_time: 0.0016 time: 0.0339 +2024/10/27 21:41:10 - mmengine - INFO - Iter(train) [ 48050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:26:48 time: 0.3777 data_time: 0.0168 memory: 5384 loss: 0.3544 decode.loss_ce: 0.3544 decode.acc_seg: 86.1006 +2024/10/27 21:41:29 - mmengine - INFO - Iter(train) [ 48100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:26:25 time: 0.3821 data_time: 0.0176 memory: 5384 loss: 0.4424 decode.loss_ce: 0.4424 decode.acc_seg: 80.7882 +2024/10/27 21:41:48 - mmengine - INFO - Iter(train) [ 48150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:26:01 time: 0.3743 data_time: 0.0171 memory: 5384 loss: 0.3903 decode.loss_ce: 0.3903 decode.acc_seg: 80.0123 +2024/10/27 21:42:07 - mmengine - INFO - Iter(train) [ 48200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:25:38 time: 0.3805 data_time: 0.0186 memory: 5384 loss: 0.4147 decode.loss_ce: 0.4147 decode.acc_seg: 81.6397 +2024/10/27 21:42:27 - mmengine - INFO - Iter(train) [ 48250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:25:15 time: 0.3742 data_time: 0.0174 memory: 5385 loss: 0.4428 decode.loss_ce: 0.4428 decode.acc_seg: 83.6416 +2024/10/27 21:42:45 - mmengine - INFO - Iter(train) [ 48300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:24:51 time: 0.3748 data_time: 0.0171 memory: 5384 loss: 0.4093 decode.loss_ce: 0.4093 decode.acc_seg: 84.8838 +2024/10/27 21:43:04 - mmengine - INFO - Iter(train) [ 48350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:24:27 time: 0.3775 data_time: 0.0161 memory: 5384 loss: 0.3626 decode.loss_ce: 0.3626 decode.acc_seg: 93.3063 +2024/10/27 21:43:25 - mmengine - INFO - Iter(train) [ 48400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:24:08 time: 0.3743 data_time: 0.0164 memory: 5384 loss: 0.4845 decode.loss_ce: 0.4845 decode.acc_seg: 85.5760 +2024/10/27 21:43:44 - mmengine - INFO - Iter(train) [ 48450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:23:44 time: 0.3779 data_time: 0.0170 memory: 5384 loss: 0.3951 decode.loss_ce: 0.3951 decode.acc_seg: 87.3014 +2024/10/27 21:44:03 - mmengine - INFO - Iter(train) [ 48500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:23:21 time: 0.3825 data_time: 0.0173 memory: 5384 loss: 0.4273 decode.loss_ce: 0.4273 decode.acc_seg: 85.6580 +2024/10/27 21:44:24 - mmengine - INFO - Iter(train) [ 48550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:23:07 time: 0.3794 data_time: 0.0160 memory: 5383 loss: 0.4979 decode.loss_ce: 0.4979 decode.acc_seg: 88.6600 +2024/10/27 21:44:43 - mmengine - INFO - Iter(train) [ 48600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:22:43 time: 0.3793 data_time: 0.0160 memory: 5383 loss: 0.4300 decode.loss_ce: 0.4300 decode.acc_seg: 80.0786 +2024/10/27 21:45:02 - mmengine - INFO - Iter(train) [ 48650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:22:20 time: 0.3823 data_time: 0.0162 memory: 5384 loss: 0.3700 decode.loss_ce: 0.3700 decode.acc_seg: 80.6677 +2024/10/27 21:45:24 - mmengine - INFO - Iter(train) [ 48700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:22:05 time: 0.3806 data_time: 0.0158 memory: 5384 loss: 0.3641 decode.loss_ce: 0.3641 decode.acc_seg: 83.0311 +2024/10/27 21:45:43 - mmengine - INFO - Iter(train) [ 48750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:21:43 time: 0.3828 data_time: 0.0160 memory: 5386 loss: 0.4287 decode.loss_ce: 0.4287 decode.acc_seg: 85.3727 +2024/10/27 21:46:03 - mmengine - INFO - Iter(train) [ 48800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:21:21 time: 0.3818 data_time: 0.0170 memory: 5384 loss: 0.3927 decode.loss_ce: 0.3927 decode.acc_seg: 81.3687 +2024/10/27 21:46:24 - mmengine - INFO - Iter(train) [ 48850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:21:06 time: 0.3817 data_time: 0.0166 memory: 5386 loss: 0.3629 decode.loss_ce: 0.3629 decode.acc_seg: 84.9109 +2024/10/27 21:46:43 - mmengine - INFO - Iter(train) [ 48900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:20:43 time: 0.3768 data_time: 0.0175 memory: 5384 loss: 0.3868 decode.loss_ce: 0.3868 decode.acc_seg: 91.5181 +2024/10/27 21:47:02 - mmengine - INFO - Iter(train) [ 48950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:20:20 time: 0.3789 data_time: 0.0170 memory: 5386 loss: 0.4443 decode.loss_ce: 0.4443 decode.acc_seg: 80.2266 +2024/10/27 21:47:23 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 21:47:23 - mmengine - INFO - Iter(train) [ 49000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:20:03 time: 0.3799 data_time: 0.0170 memory: 5384 loss: 0.4452 decode.loss_ce: 0.4452 decode.acc_seg: 82.5937 +2024/10/27 21:47:43 - mmengine - INFO - Iter(train) [ 49050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:19:40 time: 0.3767 data_time: 0.0144 memory: 5384 loss: 0.3153 decode.loss_ce: 0.3153 decode.acc_seg: 83.1151 +2024/10/27 21:48:01 - mmengine - INFO - Iter(train) [ 49100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:19:17 time: 0.3783 data_time: 0.0156 memory: 5384 loss: 0.3641 decode.loss_ce: 0.3641 decode.acc_seg: 88.8104 +2024/10/27 21:48:24 - mmengine - INFO - Iter(train) [ 49150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:19:06 time: 0.3817 data_time: 0.0161 memory: 5384 loss: 0.4424 decode.loss_ce: 0.4424 decode.acc_seg: 72.7049 +2024/10/27 21:48:44 - mmengine - INFO - Iter(train) [ 49200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:18:44 time: 0.3795 data_time: 0.0147 memory: 5384 loss: 0.3578 decode.loss_ce: 0.3578 decode.acc_seg: 89.0058 +2024/10/27 21:49:03 - mmengine - INFO - Iter(train) [ 49250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:18:21 time: 0.3811 data_time: 0.0160 memory: 5384 loss: 0.3676 decode.loss_ce: 0.3676 decode.acc_seg: 89.0747 +2024/10/27 21:49:25 - mmengine - INFO - Iter(train) [ 49300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:18:07 time: 0.4008 data_time: 0.0157 memory: 5384 loss: 0.3839 decode.loss_ce: 0.3839 decode.acc_seg: 88.2090 +2024/10/27 21:49:44 - mmengine - INFO - Iter(train) [ 49350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:17:44 time: 0.3778 data_time: 0.0158 memory: 5384 loss: 0.4142 decode.loss_ce: 0.4142 decode.acc_seg: 89.8831 +2024/10/27 21:50:03 - mmengine - INFO - Iter(train) [ 49400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:17:21 time: 0.3807 data_time: 0.0158 memory: 5383 loss: 0.3616 decode.loss_ce: 0.3616 decode.acc_seg: 86.1522 +2024/10/27 21:50:25 - mmengine - INFO - Iter(train) [ 49450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:17:06 time: 0.3787 data_time: 0.0149 memory: 5385 loss: 0.4162 decode.loss_ce: 0.4162 decode.acc_seg: 82.7182 +2024/10/27 21:50:44 - mmengine - INFO - Iter(train) [ 49500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:16:46 time: 0.3985 data_time: 0.0135 memory: 5384 loss: 0.4133 decode.loss_ce: 0.4133 decode.acc_seg: 85.6548 +2024/10/27 21:51:04 - mmengine - INFO - Iter(train) [ 49550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:16:25 time: 0.3850 data_time: 0.0152 memory: 5384 loss: 0.3430 decode.loss_ce: 0.3430 decode.acc_seg: 89.8512 +2024/10/27 21:51:24 - mmengine - INFO - Iter(train) [ 49600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:16:05 time: 0.3759 data_time: 0.0157 memory: 5384 loss: 0.4143 decode.loss_ce: 0.4143 decode.acc_seg: 92.5954 +2024/10/27 21:51:43 - mmengine - INFO - Iter(train) [ 49650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:15:41 time: 0.3782 data_time: 0.0161 memory: 5385 loss: 0.3937 decode.loss_ce: 0.3937 decode.acc_seg: 80.8421 +2024/10/27 21:52:02 - mmengine - INFO - Iter(train) [ 49700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:15:17 time: 0.3777 data_time: 0.0159 memory: 5384 loss: 0.3705 decode.loss_ce: 0.3705 decode.acc_seg: 80.0485 +2024/10/27 21:52:25 - mmengine - INFO - Iter(train) [ 49750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:15:07 time: 0.3770 data_time: 0.0164 memory: 5384 loss: 0.3755 decode.loss_ce: 0.3755 decode.acc_seg: 83.6819 +2024/10/27 21:52:44 - mmengine - INFO - Iter(train) [ 49800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:14:43 time: 0.3804 data_time: 0.0162 memory: 5384 loss: 0.3940 decode.loss_ce: 0.3940 decode.acc_seg: 91.2143 +2024/10/27 21:53:03 - mmengine - INFO - Iter(train) [ 49850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:14:20 time: 0.3791 data_time: 0.0161 memory: 5384 loss: 0.4069 decode.loss_ce: 0.4069 decode.acc_seg: 84.5965 +2024/10/27 21:53:24 - mmengine - INFO - Iter(train) [ 49900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:14:05 time: 0.3774 data_time: 0.0164 memory: 5383 loss: 0.4382 decode.loss_ce: 0.4382 decode.acc_seg: 85.4730 +2024/10/27 21:53:43 - mmengine - INFO - Iter(train) [ 49950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:13:41 time: 0.3771 data_time: 0.0161 memory: 5384 loss: 0.3596 decode.loss_ce: 0.3596 decode.acc_seg: 89.2979 +2024/10/27 21:54:02 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 21:54:02 - mmengine - INFO - Iter(train) [ 50000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:13:18 time: 0.3779 data_time: 0.0163 memory: 5384 loss: 0.3708 decode.loss_ce: 0.3708 decode.acc_seg: 81.9493 +2024/10/27 21:54:25 - mmengine - INFO - Iter(train) [ 50050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:13:06 time: 0.3755 data_time: 0.0168 memory: 5384 loss: 0.4131 decode.loss_ce: 0.4131 decode.acc_seg: 78.4432 +2024/10/27 21:54:44 - mmengine - INFO - Iter(train) [ 50100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:12:43 time: 0.3788 data_time: 0.0179 memory: 5384 loss: 0.4451 decode.loss_ce: 0.4451 decode.acc_seg: 84.3790 +2024/10/27 21:55:03 - mmengine - INFO - Iter(train) [ 50150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:12:20 time: 0.3799 data_time: 0.0175 memory: 5384 loss: 0.3665 decode.loss_ce: 0.3665 decode.acc_seg: 85.2988 +2024/10/27 21:55:25 - mmengine - INFO - Iter(train) [ 50200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:12:07 time: 0.3777 data_time: 0.0167 memory: 5383 loss: 0.4129 decode.loss_ce: 0.4129 decode.acc_seg: 81.8093 +2024/10/27 21:55:44 - mmengine - INFO - Iter(train) [ 50250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:11:44 time: 0.3761 data_time: 0.0155 memory: 5384 loss: 0.4574 decode.loss_ce: 0.4574 decode.acc_seg: 79.9349 +2024/10/27 21:56:03 - mmengine - INFO - Iter(train) [ 50300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:11:21 time: 0.3800 data_time: 0.0156 memory: 5384 loss: 0.4118 decode.loss_ce: 0.4118 decode.acc_seg: 84.3207 +2024/10/27 21:56:25 - mmengine - INFO - Iter(train) [ 50350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:11:07 time: 0.3798 data_time: 0.0152 memory: 5384 loss: 0.3500 decode.loss_ce: 0.3500 decode.acc_seg: 88.5551 +2024/10/27 21:56:44 - mmengine - INFO - Iter(train) [ 50400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:10:45 time: 0.3805 data_time: 0.0162 memory: 5384 loss: 0.5305 decode.loss_ce: 0.5305 decode.acc_seg: 84.5421 +2024/10/27 21:57:04 - mmengine - INFO - Iter(train) [ 50450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:10:23 time: 0.3806 data_time: 0.0161 memory: 5384 loss: 0.3609 decode.loss_ce: 0.3609 decode.acc_seg: 88.7589 +2024/10/27 21:57:24 - mmengine - INFO - Iter(train) [ 50500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:10:03 time: 0.3712 data_time: 0.0149 memory: 5383 loss: 0.4354 decode.loss_ce: 0.4354 decode.acc_seg: 82.4257 +2024/10/27 21:57:43 - mmengine - INFO - Iter(train) [ 50550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:09:40 time: 0.3738 data_time: 0.0150 memory: 5383 loss: 0.4129 decode.loss_ce: 0.4129 decode.acc_seg: 80.9388 +2024/10/27 21:58:03 - mmengine - INFO - Iter(train) [ 50600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:09:20 time: 0.3823 data_time: 0.0167 memory: 5384 loss: 0.4703 decode.loss_ce: 0.4703 decode.acc_seg: 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12:06:16 time: 0.3768 data_time: 0.0169 memory: 5384 loss: 0.4236 decode.loss_ce: 0.4236 decode.acc_seg: 88.0834 +2024/10/27 22:01:25 - mmengine - INFO - Iter(train) [ 51100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:06:06 time: 0.3762 data_time: 0.0168 memory: 5385 loss: 0.3519 decode.loss_ce: 0.3519 decode.acc_seg: 84.6778 +2024/10/27 22:01:44 - mmengine - INFO - Iter(train) [ 51150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:05:42 time: 0.3788 data_time: 0.0176 memory: 5384 loss: 0.4293 decode.loss_ce: 0.4293 decode.acc_seg: 74.4032 +2024/10/27 22:02:03 - mmengine - INFO - Iter(train) [ 51200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:05:19 time: 0.3781 data_time: 0.0177 memory: 5384 loss: 0.4188 decode.loss_ce: 0.4188 decode.acc_seg: 78.6253 +2024/10/27 22:02:25 - mmengine - INFO - Iter(train) [ 51250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:05:04 time: 0.3788 data_time: 0.0155 memory: 5384 loss: 0.4232 decode.loss_ce: 0.4232 decode.acc_seg: 78.8581 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12:03:20 time: 0.3806 data_time: 0.0169 memory: 5384 loss: 0.3557 decode.loss_ce: 0.3557 decode.acc_seg: 86.5449 +2024/10/27 22:04:25 - mmengine - INFO - Iter(train) [ 51550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:03:06 time: 0.3826 data_time: 0.0170 memory: 5383 loss: 0.4009 decode.loss_ce: 0.4009 decode.acc_seg: 82.1124 +2024/10/27 22:04:45 - mmengine - INFO - Iter(train) [ 51600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:02:46 time: 0.3964 data_time: 0.0152 memory: 5384 loss: 0.4000 decode.loss_ce: 0.4000 decode.acc_seg: 87.4827 +2024/10/27 22:05:05 - mmengine - INFO - Iter(train) [ 51650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:02:24 time: 0.3832 data_time: 0.0156 memory: 5385 loss: 0.4136 decode.loss_ce: 0.4136 decode.acc_seg: 83.1045 +2024/10/27 22:05:24 - mmengine - INFO - Iter(train) [ 51700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:02:04 time: 0.3795 data_time: 0.0167 memory: 5384 loss: 0.4484 decode.loss_ce: 0.4484 decode.acc_seg: 74.6256 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12:00:19 time: 0.3815 data_time: 0.0163 memory: 5385 loss: 0.3834 decode.loss_ce: 0.3834 decode.acc_seg: 83.6665 +2024/10/27 22:07:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 22:07:24 - mmengine - INFO - Iter(train) [ 52000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 12:00:03 time: 0.3763 data_time: 0.0158 memory: 5383 loss: 0.3214 decode.loss_ce: 0.3214 decode.acc_seg: 84.2067 +2024/10/27 22:07:44 - mmengine - INFO - Iter(train) [ 52050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:59:41 time: 0.3810 data_time: 0.0167 memory: 5384 loss: 0.3518 decode.loss_ce: 0.3518 decode.acc_seg: 84.2905 +2024/10/27 22:08:03 - mmengine - INFO - Iter(train) [ 52100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:59:19 time: 0.3825 data_time: 0.0159 memory: 5385 loss: 0.3573 decode.loss_ce: 0.3573 decode.acc_seg: 83.8741 +2024/10/27 22:08:24 - mmengine - INFO - Iter(train) [ 52150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 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11:44:16 time: 0.3799 data_time: 0.0164 memory: 5384 loss: 0.3389 decode.loss_ce: 0.3389 decode.acc_seg: 82.3362 +2024/10/27 22:23:24 - mmengine - INFO - Iter(train) [ 54400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:44:01 time: 0.3942 data_time: 0.0162 memory: 5384 loss: 0.3623 decode.loss_ce: 0.3623 decode.acc_seg: 82.9903 +2024/10/27 22:23:44 - mmengine - INFO - Iter(train) [ 54450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:43:40 time: 0.3769 data_time: 0.0170 memory: 5383 loss: 0.4657 decode.loss_ce: 0.4657 decode.acc_seg: 86.1037 +2024/10/27 22:24:03 - mmengine - INFO - Iter(train) [ 54500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:43:18 time: 0.3853 data_time: 0.0160 memory: 5384 loss: 0.4144 decode.loss_ce: 0.4144 decode.acc_seg: 79.8317 +2024/10/27 22:24:25 - mmengine - INFO - Iter(train) [ 54550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:43:03 time: 0.3800 data_time: 0.0164 memory: 5384 loss: 0.4335 decode.loss_ce: 0.4335 decode.acc_seg: 89.3098 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11:41:15 time: 0.3815 data_time: 0.0178 memory: 5384 loss: 0.3839 decode.loss_ce: 0.3839 decode.acc_seg: 89.0791 +2024/10/27 22:26:24 - mmengine - INFO - Iter(train) [ 54850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:41:01 time: 0.3727 data_time: 0.0145 memory: 5384 loss: 0.3171 decode.loss_ce: 0.3171 decode.acc_seg: 94.3786 +2024/10/27 22:26:44 - mmengine - INFO - Iter(train) [ 54900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:40:39 time: 0.3815 data_time: 0.0158 memory: 5382 loss: 0.4087 decode.loss_ce: 0.4087 decode.acc_seg: 87.6444 +2024/10/27 22:27:03 - mmengine - INFO - Iter(train) [ 54950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:40:16 time: 0.3802 data_time: 0.0158 memory: 5385 loss: 0.4516 decode.loss_ce: 0.4516 decode.acc_seg: 86.2222 +2024/10/27 22:27:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 22:27:25 - mmengine - INFO - Iter(train) [ 55000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:40:03 time: 0.4021 data_time: 0.0142 memory: 5384 loss: 0.3947 decode.loss_ce: 0.3947 decode.acc_seg: 91.3883 +2024/10/27 22:27:45 - mmengine - INFO - Iter(train) [ 55050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:39:43 time: 0.3983 data_time: 0.0138 memory: 5384 loss: 0.3965 decode.loss_ce: 0.3965 decode.acc_seg: 85.2145 +2024/10/27 22:28:05 - mmengine - INFO - Iter(train) [ 55100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:39:23 time: 0.4008 data_time: 0.0138 memory: 5385 loss: 0.3873 decode.loss_ce: 0.3873 decode.acc_seg: 76.3216 +2024/10/27 22:28:25 - mmengine - INFO - Iter(train) [ 55150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:39:02 time: 0.3775 data_time: 0.0170 memory: 5386 loss: 0.4014 decode.loss_ce: 0.4014 decode.acc_seg: 80.4819 +2024/10/27 22:28:45 - mmengine - INFO - Iter(train) [ 55200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:38:41 time: 0.3767 data_time: 0.0167 memory: 5383 loss: 0.3875 decode.loss_ce: 0.3875 decode.acc_seg: 83.8942 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11:37:02 time: 0.3798 data_time: 0.0171 memory: 5385 loss: 0.3496 decode.loss_ce: 0.3496 decode.acc_seg: 87.5312 +2024/10/27 22:30:44 - mmengine - INFO - Iter(train) [ 55500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:36:39 time: 0.3807 data_time: 0.0166 memory: 5384 loss: 0.3448 decode.loss_ce: 0.3448 decode.acc_seg: 87.6606 +2024/10/27 22:31:03 - mmengine - INFO - Iter(train) [ 55550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:36:17 time: 0.3803 data_time: 0.0164 memory: 5384 loss: 0.3417 decode.loss_ce: 0.3417 decode.acc_seg: 85.7408 +2024/10/27 22:31:24 - mmengine - INFO - Iter(train) [ 55600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:36:00 time: 0.3752 data_time: 0.0154 memory: 5383 loss: 0.3661 decode.loss_ce: 0.3661 decode.acc_seg: 91.7741 +2024/10/27 22:31:45 - mmengine - INFO - Iter(train) [ 55650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:35:41 time: 0.3787 data_time: 0.0172 memory: 5384 loss: 0.3380 decode.loss_ce: 0.3380 decode.acc_seg: 85.8385 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11:32:37 time: 0.3799 data_time: 0.0161 memory: 5384 loss: 0.4379 decode.loss_ce: 0.4379 decode.acc_seg: 86.1055 +2024/10/27 22:35:03 - mmengine - INFO - Iter(train) [ 56150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:32:15 time: 0.3799 data_time: 0.0157 memory: 5384 loss: 0.3540 decode.loss_ce: 0.3540 decode.acc_seg: 87.2376 +2024/10/27 22:35:26 - mmengine - INFO - Iter(train) [ 56200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:32:03 time: 0.4000 data_time: 0.0147 memory: 5384 loss: 0.3442 decode.loss_ce: 0.3442 decode.acc_seg: 88.4929 +2024/10/27 22:35:45 - mmengine - INFO - Iter(train) [ 56250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:31:41 time: 0.3818 data_time: 0.0160 memory: 5384 loss: 0.3519 decode.loss_ce: 0.3519 decode.acc_seg: 86.6400 +2024/10/27 22:36:04 - mmengine - INFO - Iter(train) [ 56300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:31:19 time: 0.3838 data_time: 0.0159 memory: 5383 loss: 0.3456 decode.loss_ce: 0.3456 decode.acc_seg: 87.1557 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11:29:36 time: 0.3725 data_time: 0.0138 memory: 5384 loss: 0.3757 decode.loss_ce: 0.3757 decode.acc_seg: 86.5718 +2024/10/27 22:38:02 - mmengine - INFO - Iter(train) [ 56600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:29:13 time: 0.3797 data_time: 0.0135 memory: 5384 loss: 0.4255 decode.loss_ce: 0.4255 decode.acc_seg: 84.4256 +2024/10/27 22:38:25 - mmengine - INFO - Iter(train) [ 56650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:29:01 time: 0.4056 data_time: 0.0134 memory: 5384 loss: 0.3951 decode.loss_ce: 0.3951 decode.acc_seg: 77.1652 +2024/10/27 22:38:44 - mmengine - INFO - Iter(train) [ 56700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:28:39 time: 0.3807 data_time: 0.0160 memory: 5384 loss: 0.3957 decode.loss_ce: 0.3957 decode.acc_seg: 90.5074 +2024/10/27 22:39:04 - mmengine - INFO - Iter(train) [ 56750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:28:19 time: 0.4027 data_time: 0.0133 memory: 5384 loss: 0.3020 decode.loss_ce: 0.3020 decode.acc_seg: 89.2850 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11:22:22 time: 0.4038 data_time: 0.0135 memory: 5387 loss: 0.3994 decode.loss_ce: 0.3994 decode.acc_seg: 89.9074 +2024/10/27 22:45:26 - mmengine - INFO - Iter(train) [ 57700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:22:02 time: 0.3964 data_time: 0.0163 memory: 5384 loss: 0.3628 decode.loss_ce: 0.3628 decode.acc_seg: 92.1077 +2024/10/27 22:45:45 - mmengine - INFO - Iter(train) [ 57750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:21:40 time: 0.3785 data_time: 0.0166 memory: 5384 loss: 0.3656 decode.loss_ce: 0.3656 decode.acc_seg: 87.8497 +2024/10/27 22:46:04 - mmengine - INFO - Iter(train) [ 57800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:21:18 time: 0.3818 data_time: 0.0164 memory: 5385 loss: 0.3461 decode.loss_ce: 0.3461 decode.acc_seg: 87.5169 +2024/10/27 22:46:26 - mmengine - INFO - Iter(train) [ 57850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:21:01 time: 0.4017 data_time: 0.0152 memory: 5384 loss: 0.3354 decode.loss_ce: 0.3354 decode.acc_seg: 86.7932 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11:17:58 time: 0.3744 data_time: 0.0179 memory: 5383 loss: 0.4102 decode.loss_ce: 0.4102 decode.acc_seg: 88.5940 +2024/10/27 22:49:44 - mmengine - INFO - Iter(train) [ 58350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:17:37 time: 0.3770 data_time: 0.0157 memory: 5384 loss: 0.4151 decode.loss_ce: 0.4151 decode.acc_seg: 89.9683 +2024/10/27 22:50:03 - mmengine - INFO - Iter(train) [ 58400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:17:14 time: 0.3777 data_time: 0.0157 memory: 5386 loss: 0.3347 decode.loss_ce: 0.3347 decode.acc_seg: 87.7447 +2024/10/27 22:50:25 - mmengine - INFO - Iter(train) [ 58450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:16:59 time: 0.3774 data_time: 0.0165 memory: 5384 loss: 0.3916 decode.loss_ce: 0.3916 decode.acc_seg: 90.7319 +2024/10/27 22:50:44 - mmengine - INFO - Iter(train) [ 58500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:16:36 time: 0.3942 data_time: 0.0165 memory: 5384 loss: 0.3548 decode.loss_ce: 0.3548 decode.acc_seg: 83.9223 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11:14:59 time: 0.3794 data_time: 0.0163 memory: 5384 loss: 0.3324 decode.loss_ce: 0.3324 decode.acc_seg: 88.8928 +2024/10/27 22:52:44 - mmengine - INFO - Iter(train) [ 58800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:14:36 time: 0.3783 data_time: 0.0164 memory: 5383 loss: 0.3711 decode.loss_ce: 0.3711 decode.acc_seg: 88.5612 +2024/10/27 22:53:03 - mmengine - INFO - Iter(train) [ 58850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:14:14 time: 0.3801 data_time: 0.0157 memory: 5384 loss: 0.3656 decode.loss_ce: 0.3656 decode.acc_seg: 79.6315 +2024/10/27 22:53:25 - mmengine - INFO - Iter(train) [ 58900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:14:00 time: 0.3798 data_time: 0.0152 memory: 5384 loss: 0.3875 decode.loss_ce: 0.3875 decode.acc_seg: 87.6780 +2024/10/27 22:53:45 - mmengine - INFO - Iter(train) [ 58950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:13:38 time: 0.3961 data_time: 0.0152 memory: 5385 loss: 0.3197 decode.loss_ce: 0.3197 decode.acc_seg: 87.2751 +2024/10/27 22:54:04 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 22:54:04 - mmengine - INFO - Iter(train) [ 59000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:13:16 time: 0.3827 data_time: 0.0162 memory: 5385 loss: 0.3819 decode.loss_ce: 0.3819 decode.acc_seg: 86.0058 +2024/10/27 22:54:24 - mmengine - INFO - Iter(train) [ 59050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:12:57 time: 0.3728 data_time: 0.0140 memory: 5384 loss: 0.3513 decode.loss_ce: 0.3513 decode.acc_seg: 84.6229 +2024/10/27 22:54:43 - mmengine - INFO - Iter(train) [ 59100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:12:35 time: 0.3776 data_time: 0.0140 memory: 5383 loss: 0.3457 decode.loss_ce: 0.3457 decode.acc_seg: 83.9640 +2024/10/27 22:55:02 - mmengine - INFO - Iter(train) [ 59150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 11:12:13 time: 0.3824 data_time: 0.0182 memory: 5383 loss: 0.4150 decode.loss_ce: 0.4150 decode.acc_seg: 76.9882 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11:00:12 time: 0.3786 data_time: 0.0150 memory: 5386 loss: 0.4626 decode.loss_ce: 0.4626 decode.acc_seg: 75.6145 +2024/10/27 23:07:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 23:07:25 - mmengine - INFO - Iter(train) [ 61000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:59:58 time: 0.3833 data_time: 0.0162 memory: 5385 loss: 0.4241 decode.loss_ce: 0.4241 decode.acc_seg: 89.5099 +2024/10/27 23:07:44 - mmengine - INFO - Iter(train) [ 61050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:59:36 time: 0.3794 data_time: 0.0152 memory: 5384 loss: 0.3944 decode.loss_ce: 0.3944 decode.acc_seg: 73.4346 +2024/10/27 23:08:04 - mmengine - INFO - Iter(train) [ 61100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:59:15 time: 0.3810 data_time: 0.0153 memory: 5383 loss: 0.3528 decode.loss_ce: 0.3528 decode.acc_seg: 85.4984 +2024/10/27 23:08:25 - mmengine - INFO - Iter(train) [ 61150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:58:58 time: 0.3748 data_time: 0.0164 memory: 5384 loss: 0.4694 decode.loss_ce: 0.4694 decode.acc_seg: 69.9923 +2024/10/27 23:08:44 - mmengine - INFO - Iter(train) [ 61200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:58:35 time: 0.3782 data_time: 0.0169 memory: 5384 loss: 0.3749 decode.loss_ce: 0.3749 decode.acc_seg: 81.3765 +2024/10/27 23:09:03 - mmengine - INFO - Iter(train) [ 61250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:58:13 time: 0.3867 data_time: 0.0179 memory: 5386 loss: 0.5293 decode.loss_ce: 0.5293 decode.acc_seg: 88.3767 +2024/10/27 23:09:25 - mmengine - INFO - Iter(train) [ 61300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:57:57 time: 0.3785 data_time: 0.0156 memory: 5384 loss: 0.4158 decode.loss_ce: 0.4158 decode.acc_seg: 92.5328 +2024/10/27 23:09:44 - mmengine - INFO - Iter(train) [ 61350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:57:35 time: 0.3750 data_time: 0.0152 memory: 5384 loss: 0.3500 decode.loss_ce: 0.3500 decode.acc_seg: 86.9502 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10:55:55 time: 0.3789 data_time: 0.0170 memory: 5384 loss: 0.3979 decode.loss_ce: 0.3979 decode.acc_seg: 83.6935 +2024/10/27 23:11:43 - mmengine - INFO - Iter(train) [ 61650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:55:33 time: 0.3798 data_time: 0.0162 memory: 5384 loss: 0.3644 decode.loss_ce: 0.3644 decode.acc_seg: 80.4269 +2024/10/27 23:12:02 - mmengine - INFO - Iter(train) [ 61700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:55:10 time: 0.3785 data_time: 0.0171 memory: 5384 loss: 0.3657 decode.loss_ce: 0.3657 decode.acc_seg: 79.5983 +2024/10/27 23:12:23 - mmengine - INFO - Iter(train) [ 61750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:54:53 time: 0.3752 data_time: 0.0170 memory: 5384 loss: 0.3657 decode.loss_ce: 0.3657 decode.acc_seg: 85.4673 +2024/10/27 23:12:42 - mmengine - INFO - Iter(train) [ 61800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:54:32 time: 0.3776 data_time: 0.0169 memory: 5384 loss: 0.3494 decode.loss_ce: 0.3494 decode.acc_seg: 91.0105 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+2024/10/27 23:14:24 - mmengine - INFO - Iter(train) [ 62050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:52:55 time: 0.3800 data_time: 0.0151 memory: 5384 loss: 0.3776 decode.loss_ce: 0.3776 decode.acc_seg: 89.2060 +2024/10/27 23:14:43 - mmengine - INFO - Iter(train) [ 62100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:52:33 time: 0.3761 data_time: 0.0164 memory: 5384 loss: 0.3965 decode.loss_ce: 0.3965 decode.acc_seg: 94.0702 +2024/10/27 23:15:03 - mmengine - INFO - Iter(train) [ 62150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:52:12 time: 0.3845 data_time: 0.0165 memory: 5385 loss: 0.3934 decode.loss_ce: 0.3934 decode.acc_seg: 89.0569 +2024/10/27 23:15:24 - mmengine - INFO - Iter(train) [ 62200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:51:56 time: 0.3773 data_time: 0.0154 memory: 5384 loss: 0.3834 decode.loss_ce: 0.3834 decode.acc_seg: 81.5846 +2024/10/27 23:15:44 - mmengine - INFO - Iter(train) [ 62250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:51:35 time: 0.3793 data_time: 0.0167 memory: 5384 loss: 0.3251 decode.loss_ce: 0.3251 decode.acc_seg: 93.5780 +2024/10/27 23:16:03 - mmengine - INFO - Iter(train) [ 62300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:51:13 time: 0.3791 data_time: 0.0161 memory: 5384 loss: 0.4170 decode.loss_ce: 0.4170 decode.acc_seg: 88.1238 +2024/10/27 23:16:25 - mmengine - INFO - Iter(train) [ 62350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:50:56 time: 0.3796 data_time: 0.0164 memory: 5383 loss: 0.3728 decode.loss_ce: 0.3728 decode.acc_seg: 85.2181 +2024/10/27 23:16:44 - mmengine - INFO - Iter(train) [ 62400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:50:34 time: 0.3792 data_time: 0.0166 memory: 5383 loss: 0.3858 decode.loss_ce: 0.3858 decode.acc_seg: 82.7084 +2024/10/27 23:17:03 - mmengine - INFO - Iter(train) [ 62450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:50:13 time: 0.3806 data_time: 0.0167 memory: 5384 loss: 0.3573 decode.loss_ce: 0.3573 decode.acc_seg: 84.5497 +2024/10/27 23:17:24 - mmengine - INFO - Iter(train) [ 62500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:49:55 time: 0.3788 data_time: 0.0159 memory: 5384 loss: 0.3600 decode.loss_ce: 0.3600 decode.acc_seg: 79.0315 +2024/10/27 23:17:43 - mmengine - INFO - Iter(train) [ 62550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:49:33 time: 0.3775 data_time: 0.0165 memory: 5382 loss: 0.3447 decode.loss_ce: 0.3447 decode.acc_seg: 87.8832 +2024/10/27 23:18:02 - mmengine - INFO - Iter(train) [ 62600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:49:11 time: 0.3889 data_time: 0.0162 memory: 5384 loss: 0.3594 decode.loss_ce: 0.3594 decode.acc_seg: 87.8537 +2024/10/27 23:18:25 - mmengine - INFO - Iter(train) [ 62650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:48:57 time: 0.3766 data_time: 0.0146 memory: 5383 loss: 0.2865 decode.loss_ce: 0.2865 decode.acc_seg: 91.8013 +2024/10/27 23:18:44 - mmengine - INFO - Iter(train) [ 62700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:48:34 time: 0.3781 data_time: 0.0155 memory: 5384 loss: 0.4541 decode.loss_ce: 0.4541 decode.acc_seg: 75.3450 +2024/10/27 23:19:03 - mmengine - INFO - Iter(train) [ 62750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:48:12 time: 0.3841 data_time: 0.0147 memory: 5383 loss: 0.3088 decode.loss_ce: 0.3088 decode.acc_seg: 89.0900 +2024/10/27 23:19:26 - mmengine - INFO - Iter(train) [ 62800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:47:58 time: 0.4037 data_time: 0.0142 memory: 5384 loss: 0.4100 decode.loss_ce: 0.4100 decode.acc_seg: 85.6693 +2024/10/27 23:19:46 - mmengine - INFO - Iter(train) [ 62850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:47:38 time: 0.3727 data_time: 0.0148 memory: 5384 loss: 0.4802 decode.loss_ce: 0.4802 decode.acc_seg: 82.1133 +2024/10/27 23:20:05 - mmengine - INFO - Iter(train) [ 62900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:47:16 time: 0.3891 data_time: 0.0155 memory: 5382 loss: 0.3461 decode.loss_ce: 0.3461 decode.acc_seg: 81.7414 +2024/10/27 23:20:24 - mmengine - INFO - Iter(train) [ 62950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:46:55 time: 0.3792 data_time: 0.0154 memory: 5386 loss: 0.3297 decode.loss_ce: 0.3297 decode.acc_seg: 88.2781 +2024/10/27 23:20:43 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 23:20:43 - mmengine - INFO - Iter(train) [ 63000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:46:33 time: 0.3773 data_time: 0.0139 memory: 5384 loss: 0.3380 decode.loss_ce: 0.3380 decode.acc_seg: 84.0162 +2024/10/27 23:21:03 - mmengine - INFO - Iter(train) [ 63050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:46:12 time: 0.4015 data_time: 0.0137 memory: 5382 loss: 0.3777 decode.loss_ce: 0.3777 decode.acc_seg: 87.2103 +2024/10/27 23:21:25 - mmengine - INFO - Iter(train) [ 63100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:45:56 time: 0.3771 data_time: 0.0144 memory: 5384 loss: 0.4036 decode.loss_ce: 0.4036 decode.acc_seg: 77.5895 +2024/10/27 23:21:45 - mmengine - INFO - Iter(train) [ 63150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:45:36 time: 0.4032 data_time: 0.0138 memory: 5384 loss: 0.3845 decode.loss_ce: 0.3845 decode.acc_seg: 86.3531 +2024/10/27 23:22:05 - mmengine - INFO - Iter(train) [ 63200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:45:15 time: 0.3885 data_time: 0.0163 memory: 5384 loss: 0.3542 decode.loss_ce: 0.3542 decode.acc_seg: 80.6318 +2024/10/27 23:22:25 - mmengine - INFO - Iter(train) [ 63250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:44:56 time: 0.3767 data_time: 0.0176 memory: 5385 loss: 0.3636 decode.loss_ce: 0.3636 decode.acc_seg: 81.5825 +2024/10/27 23:22:44 - mmengine - INFO - Iter(train) [ 63300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:44:33 time: 0.3784 data_time: 0.0167 memory: 5384 loss: 0.3839 decode.loss_ce: 0.3839 decode.acc_seg: 74.9115 +2024/10/27 23:23:03 - mmengine - INFO - Iter(train) [ 63350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:44:12 time: 0.3822 data_time: 0.0164 memory: 5385 loss: 0.3980 decode.loss_ce: 0.3980 decode.acc_seg: 81.8146 +2024/10/27 23:23:25 - mmengine - INFO - Iter(train) [ 63400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:43:55 time: 0.3808 data_time: 0.0165 memory: 5385 loss: 0.3696 decode.loss_ce: 0.3696 decode.acc_seg: 92.4664 +2024/10/27 23:23:43 - mmengine - INFO - Iter(train) [ 63450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:43:33 time: 0.3785 data_time: 0.0160 memory: 5385 loss: 0.3755 decode.loss_ce: 0.3755 decode.acc_seg: 91.2536 +2024/10/27 23:24:02 - mmengine - INFO - Iter(train) [ 63500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:43:11 time: 0.3791 data_time: 0.0163 memory: 5384 loss: 0.3422 decode.loss_ce: 0.3422 decode.acc_seg: 84.2905 +2024/10/27 23:24:25 - mmengine - INFO - Iter(train) [ 63550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:42:56 time: 0.3797 data_time: 0.0174 memory: 5384 loss: 0.3495 decode.loss_ce: 0.3495 decode.acc_seg: 83.8927 +2024/10/27 23:24:44 - mmengine - INFO - Iter(train) [ 63600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:42:35 time: 0.3823 data_time: 0.0157 memory: 5386 loss: 0.3870 decode.loss_ce: 0.3870 decode.acc_seg: 86.5269 +2024/10/27 23:25:03 - mmengine - INFO - Iter(train) [ 63650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:42:13 time: 0.3816 data_time: 0.0173 memory: 5384 loss: 0.3969 decode.loss_ce: 0.3969 decode.acc_seg: 80.5112 +2024/10/27 23:25:24 - mmengine - INFO - Iter(train) [ 63700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:41:55 time: 0.3841 data_time: 0.0182 memory: 5383 loss: 0.4372 decode.loss_ce: 0.4372 decode.acc_seg: 75.6442 +2024/10/27 23:25:44 - mmengine - INFO - Iter(train) [ 63750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:41:33 time: 0.3827 data_time: 0.0162 memory: 5384 loss: 0.4332 decode.loss_ce: 0.4332 decode.acc_seg: 82.6971 +2024/10/27 23:26:03 - mmengine - INFO - Iter(train) [ 63800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:41:12 time: 0.3875 data_time: 0.0172 memory: 5385 loss: 0.4468 decode.loss_ce: 0.4468 decode.acc_seg: 83.4222 +2024/10/27 23:26:25 - mmengine - INFO - Iter(train) [ 63850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:40:55 time: 0.3976 data_time: 0.0168 memory: 5383 loss: 0.4427 decode.loss_ce: 0.4427 decode.acc_seg: 79.6580 +2024/10/27 23:26:44 - mmengine - INFO - Iter(train) [ 63900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:40:35 time: 0.3828 data_time: 0.0182 memory: 5384 loss: 0.3622 decode.loss_ce: 0.3622 decode.acc_seg: 88.3758 +2024/10/27 23:27:04 - mmengine - INFO - Iter(train) [ 63950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:40:14 time: 0.3928 data_time: 0.0160 memory: 5385 loss: 0.3488 decode.loss_ce: 0.3488 decode.acc_seg: 88.5768 +2024/10/27 23:27:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 23:27:25 - mmengine - INFO - Iter(train) [ 64000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:39:56 time: 0.3939 data_time: 0.0165 memory: 5384 loss: 0.3713 decode.loss_ce: 0.3713 decode.acc_seg: 88.4607 +2024/10/27 23:27:25 - mmengine - INFO - Saving checkpoint at 64000 iterations +2024/10/27 23:27:29 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:00:15 time: 0.0331 data_time: 0.0016 memory: 980 +2024/10/27 23:27:31 - mmengine - INFO - Iter(val) [100/500] eta: 0:00:13 time: 0.0324 data_time: 0.0014 memory: 1050 +2024/10/27 23:27:33 - mmengine - INFO - Iter(val) [150/500] eta: 0:00:11 time: 0.0330 data_time: 0.0015 memory: 767 +2024/10/27 23:27:34 - mmengine - INFO - Iter(val) [200/500] eta: 0:00:09 time: 0.0330 data_time: 0.0015 memory: 800 +2024/10/27 23:27:36 - mmengine - INFO - Iter(val) [250/500] eta: 0:00:08 time: 0.0336 data_time: 0.0016 memory: 839 +2024/10/27 23:27:38 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:06 time: 0.0343 data_time: 0.0020 memory: 1961 +2024/10/27 23:27:39 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:04 time: 0.0348 data_time: 0.0018 memory: 765 +2024/10/27 23:27:41 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:03 time: 0.0338 data_time: 0.0015 memory: 837 +2024/10/27 23:27:43 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:01 time: 0.0333 data_time: 0.0014 memory: 772 +2024/10/27 23:27:44 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.0330 data_time: 0.0014 memory: 822 +2024/10/27 23:27:46 - mmengine - INFO - per class results: +2024/10/27 23:27:46 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 69.06 | 85.52 | +| building | 76.43 | 87.77 | +| sky | 92.23 | 96.56 | +| floor | 75.3 | 87.49 | +| tree | 68.95 | 87.39 | +| ceiling | 79.48 | 89.55 | +| road | 77.99 | 87.24 | +| bed | 81.85 | 90.51 | +| windowpane | 53.27 | 64.96 | +| grass | 65.63 | 79.28 | +| cabinet | 51.02 | 60.46 | +| sidewalk | 59.54 | 73.33 | +| person | 69.85 | 86.36 | +| earth | 29.32 | 47.66 | +| door | 34.89 | 54.51 | +| table | 48.06 | 66.94 | +| mountain | 46.61 | 55.56 | +| plant | 43.75 | 51.88 | +| curtain | 63.91 | 80.98 | +| chair | 46.2 | 60.87 | +| car | 76.16 | 87.77 | +| water | 37.14 | 45.75 | +| painting | 64.82 | 74.58 | +| sofa | 59.86 | 76.64 | +| shelf | 28.62 | 47.46 | +| house | 31.88 | 44.84 | +| sea | 40.81 | 73.32 | +| mirror | 49.46 | 55.06 | +| rug | 56.75 | 68.53 | +| field | 28.91 | 47.98 | +| armchair | 34.73 | 49.16 | +| seat | 51.57 | 76.28 | +| fence | 34.08 | 42.2 | +| desk | 29.34 | 37.22 | +| rock | 38.91 | 61.73 | +| wardrobe | 44.51 | 62.41 | +| lamp | 47.32 | 57.07 | +| bathtub | 60.15 | 68.52 | +| railing | 23.73 | 33.84 | +| cushion | 42.1 | 53.19 | +| base | 10.9 | 14.16 | +| box | 15.0 | 24.76 | +| column | 33.15 | 54.64 | +| signboard | 24.39 | 32.13 | +| chest of drawers | 34.62 | 55.28 | +| counter | 23.45 | 30.94 | +| sand | 25.79 | 56.33 | +| sink | 56.05 | 68.72 | +| skyscraper | 59.08 | 81.71 | +| fireplace | 58.1 | 84.07 | +| refrigerator | 66.67 | 83.76 | +| grandstand | 28.37 | 60.1 | +| path | 20.15 | 35.79 | +| stairs | 19.36 | 26.25 | +| runway | 64.55 | 78.12 | +| case | 43.28 | 64.99 | +| pool table | 46.43 | 48.05 | +| pillow | 44.18 | 52.45 | +| screen door | 46.87 | 51.62 | +| stairway | 24.61 | 35.42 | +| river | 6.06 | 17.24 | +| bridge | 42.48 | 51.55 | +| bookcase | 29.72 | 56.2 | +| blind | 36.27 | 48.39 | +| coffee table | 49.85 | 73.32 | +| toilet | 69.96 | 86.32 | +| flower | 31.83 | 43.64 | +| book | 35.8 | 45.21 | +| hill | 3.44 | 5.6 | +| bench | 28.28 | 41.3 | +| countertop | 47.75 | 59.51 | +| stove | 60.85 | 68.56 | +| palm | 34.85 | 41.23 | +| kitchen island | 24.67 | 36.81 | +| computer | 43.63 | 62.12 | +| swivel chair | 26.36 | 35.6 | +| boat | 36.85 | 42.2 | +| bar | 21.87 | 27.06 | +| arcade machine | 63.21 | 71.85 | +| hovel | 29.38 | 40.5 | +| bus | 51.78 | 68.08 | +| towel | 40.24 | 50.59 | +| light | 30.08 | 33.95 | +| truck | 16.4 | 24.37 | +| tower | 37.5 | 63.38 | +| chandelier | 48.75 | 58.95 | +| awning | 20.62 | 24.11 | +| streetlight | 9.92 | 12.79 | +| booth | 38.27 | 47.05 | +| television receiver | 56.51 | 71.95 | +| airplane | 47.68 | 51.15 | +| dirt track | 0.0 | 0.0 | +| apparel | 22.16 | 32.61 | +| pole | 12.05 | 15.04 | +| land | 13.94 | 19.33 | +| bannister | 2.81 | 3.45 | +| escalator | 18.43 | 20.44 | +| ottoman | 38.97 | 44.98 | +| bottle | 25.83 | 40.87 | +| buffet | 44.82 | 51.72 | +| poster | 21.78 | 33.0 | +| stage | 13.53 | 25.7 | +| van | 31.04 | 34.28 | +| ship | 65.86 | 95.21 | +| fountain | 19.84 | 20.86 | +| conveyer belt | 76.26 | 80.13 | +| canopy | 5.07 | 5.73 | +| washer | 60.88 | 65.65 | +| plaything | 11.52 | 25.84 | +| swimming pool | 26.21 | 26.89 | +| stool | 29.79 | 42.37 | +| barrel | 32.68 | 64.05 | +| basket | 16.18 | 19.7 | +| waterfall | 37.66 | 67.43 | +| tent | 65.25 | 80.94 | +| bag | 7.77 | 10.74 | +| minibike | 40.55 | 71.09 | +| cradle | 70.01 | 88.35 | +| oven | 39.56 | 51.46 | +| ball | 24.47 | 34.04 | +| food | 42.38 | 49.05 | +| step | 0.74 | 0.78 | +| tank | 31.58 | 35.86 | +| trade name | 22.28 | 26.32 | +| microwave | 36.94 | 41.7 | +| pot | 21.37 | 22.44 | +| animal | 31.89 | 33.97 | +| bicycle | 38.66 | 62.05 | +| lake | 44.44 | 79.55 | +| dishwasher | 33.81 | 38.82 | +| screen | 62.8 | 78.14 | +| blanket | 3.89 | 5.24 | +| sculpture | 23.15 | 36.34 | +| hood | 52.8 | 63.39 | +| sconce | 26.45 | 32.43 | +| vase | 20.35 | 24.45 | +| traffic light | 15.34 | 23.21 | +| tray | 0.95 | 1.36 | +| ashcan | 29.22 | 44.4 | +| fan | 37.71 | 49.38 | +| pier | 7.67 | 8.01 | +| crt screen | 1.1 | 3.04 | +| plate | 35.14 | 48.66 | +| monitor | 1.83 | 1.88 | +| bulletin board | 29.35 | 35.6 | +| shower | 0.22 | 1.1 | +| radiator | 36.03 | 42.6 | +| glass | 1.33 | 1.44 | +| clock | 19.16 | 35.45 | +| flag | 26.95 | 31.72 | ++---------------------+-------+-------+ +2024/10/27 23:27:46 - mmengine - INFO - Iter(val) [500/500] aAcc: 77.3200 mIoU: 37.2700 mAcc: 48.5900 data_time: 0.0016 time: 0.0334 +2024/10/27 23:28:06 - mmengine - INFO - Iter(train) [ 64050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:39:39 time: 0.3808 data_time: 0.0163 memory: 5384 loss: 0.3321 decode.loss_ce: 0.3321 decode.acc_seg: 86.5422 +2024/10/27 23:28:25 - mmengine - INFO - Iter(train) [ 64100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:39:17 time: 0.3778 data_time: 0.0166 memory: 5384 loss: 0.4353 decode.loss_ce: 0.4353 decode.acc_seg: 78.1608 +2024/10/27 23:28:44 - mmengine - INFO - Iter(train) [ 64150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:38:55 time: 0.3746 data_time: 0.0164 memory: 5384 loss: 0.4308 decode.loss_ce: 0.4308 decode.acc_seg: 88.3865 +2024/10/27 23:29:03 - mmengine - INFO - Iter(train) [ 64200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:38:33 time: 0.3817 data_time: 0.0157 memory: 5385 loss: 0.3661 decode.loss_ce: 0.3661 decode.acc_seg: 82.5281 +2024/10/27 23:29:24 - mmengine - INFO - Iter(train) [ 64250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:38:15 time: 0.3764 data_time: 0.0157 memory: 5384 loss: 0.4049 decode.loss_ce: 0.4049 decode.acc_seg: 91.7241 +2024/10/27 23:29:43 - mmengine - INFO - Iter(train) [ 64300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:37:53 time: 0.3821 data_time: 0.0153 memory: 5383 loss: 0.4007 decode.loss_ce: 0.4007 decode.acc_seg: 84.9073 +2024/10/27 23:30:03 - mmengine - INFO - Iter(train) [ 64350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:37:34 time: 0.3802 data_time: 0.0147 memory: 5383 loss: 0.3568 decode.loss_ce: 0.3568 decode.acc_seg: 89.7785 +2024/10/27 23:30:24 - mmengine - INFO - Iter(train) [ 64400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:37:15 time: 0.3781 data_time: 0.0152 memory: 5384 loss: 0.4269 decode.loss_ce: 0.4269 decode.acc_seg: 89.1700 +2024/10/27 23:30:43 - mmengine - INFO - Iter(train) [ 64450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:36:53 time: 0.3761 data_time: 0.0158 memory: 5385 loss: 0.3796 decode.loss_ce: 0.3796 decode.acc_seg: 78.6853 +2024/10/27 23:31:02 - mmengine - INFO - Iter(train) [ 64500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:36:31 time: 0.3779 data_time: 0.0155 memory: 5386 loss: 0.3353 decode.loss_ce: 0.3353 decode.acc_seg: 84.5369 +2024/10/27 23:31:23 - mmengine - INFO - Iter(train) [ 64550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:36:13 time: 0.3743 data_time: 0.0150 memory: 5384 loss: 0.3806 decode.loss_ce: 0.3806 decode.acc_seg: 84.1787 +2024/10/27 23:31:42 - mmengine - INFO - Iter(train) [ 64600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:35:52 time: 0.3756 data_time: 0.0155 memory: 5384 loss: 0.3000 decode.loss_ce: 0.3000 decode.acc_seg: 87.7202 +2024/10/27 23:32:01 - mmengine - INFO - Iter(train) [ 64650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:35:30 time: 0.3801 data_time: 0.0170 memory: 5383 loss: 0.4119 decode.loss_ce: 0.4119 decode.acc_seg: 89.4466 +2024/10/27 23:32:24 - mmengine - INFO - Iter(train) [ 64700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:35:16 time: 0.3790 data_time: 0.0170 memory: 5384 loss: 0.3788 decode.loss_ce: 0.3788 decode.acc_seg: 83.8819 +2024/10/27 23:32:43 - mmengine - INFO - Iter(train) [ 64750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:34:54 time: 0.3823 data_time: 0.0165 memory: 5384 loss: 0.3304 decode.loss_ce: 0.3304 decode.acc_seg: 90.7111 +2024/10/27 23:33:03 - mmengine - INFO - Iter(train) [ 64800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:34:32 time: 0.3866 data_time: 0.0156 memory: 5384 loss: 0.4298 decode.loss_ce: 0.4298 decode.acc_seg: 79.6635 +2024/10/27 23:33:24 - mmengine - INFO - Iter(train) [ 64850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:34:16 time: 0.3776 data_time: 0.0169 memory: 5387 loss: 0.3571 decode.loss_ce: 0.3571 decode.acc_seg: 74.7353 +2024/10/27 23:33:43 - mmengine - INFO - Iter(train) [ 64900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:33:54 time: 0.3753 data_time: 0.0167 memory: 5384 loss: 0.3798 decode.loss_ce: 0.3798 decode.acc_seg: 85.7745 +2024/10/27 23:34:03 - mmengine - INFO - Iter(train) [ 64950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:33:32 time: 0.3798 data_time: 0.0165 memory: 5384 loss: 0.3858 decode.loss_ce: 0.3858 decode.acc_seg: 82.1361 +2024/10/27 23:34:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 23:34:24 - mmengine - INFO - Iter(train) [ 65000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:33:14 time: 0.3773 data_time: 0.0174 memory: 5384 loss: 0.3782 decode.loss_ce: 0.3782 decode.acc_seg: 85.8859 +2024/10/27 23:34:43 - mmengine - INFO - Iter(train) [ 65050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:32:52 time: 0.3793 data_time: 0.0168 memory: 5384 loss: 0.3906 decode.loss_ce: 0.3906 decode.acc_seg: 80.7313 +2024/10/27 23:35:02 - mmengine - INFO - Iter(train) [ 65100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:32:31 time: 0.3763 data_time: 0.0173 memory: 5384 loss: 0.3475 decode.loss_ce: 0.3475 decode.acc_seg: 94.2578 +2024/10/27 23:35:24 - mmengine - INFO - Iter(train) [ 65150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:32:15 time: 0.3789 data_time: 0.0154 memory: 5384 loss: 0.3359 decode.loss_ce: 0.3359 decode.acc_seg: 78.5741 +2024/10/27 23:35:43 - mmengine - INFO - Iter(train) [ 65200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:31:53 time: 0.3758 data_time: 0.0154 memory: 5386 loss: 0.3796 decode.loss_ce: 0.3796 decode.acc_seg: 91.5560 +2024/10/27 23:36:02 - mmengine - INFO - Iter(train) [ 65250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:31:31 time: 0.3794 data_time: 0.0171 memory: 5384 loss: 0.3184 decode.loss_ce: 0.3184 decode.acc_seg: 83.0713 +2024/10/27 23:36:24 - mmengine - INFO - Iter(train) [ 65300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:31:15 time: 0.3776 data_time: 0.0166 memory: 5384 loss: 0.3371 decode.loss_ce: 0.3371 decode.acc_seg: 85.2791 +2024/10/27 23:36:43 - mmengine - INFO - Iter(train) [ 65350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:30:53 time: 0.3783 data_time: 0.0169 memory: 5382 loss: 0.2895 decode.loss_ce: 0.2895 decode.acc_seg: 93.3043 +2024/10/27 23:37:02 - mmengine - INFO - Iter(train) [ 65400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:30:32 time: 0.4066 data_time: 0.0176 memory: 5384 loss: 0.3488 decode.loss_ce: 0.3488 decode.acc_seg: 74.8529 +2024/10/27 23:37:24 - mmengine - INFO - Iter(train) [ 65450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:30:14 time: 0.3779 data_time: 0.0172 memory: 5384 loss: 0.3930 decode.loss_ce: 0.3930 decode.acc_seg: 84.9904 +2024/10/27 23:37:42 - mmengine - INFO - Iter(train) [ 65500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:29:51 time: 0.3740 data_time: 0.0166 memory: 5384 loss: 0.3798 decode.loss_ce: 0.3798 decode.acc_seg: 85.4454 +2024/10/27 23:38:01 - mmengine - INFO - Iter(train) [ 65550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:29:29 time: 0.3763 data_time: 0.0175 memory: 5384 loss: 0.3437 decode.loss_ce: 0.3437 decode.acc_seg: 90.0999 +2024/10/27 23:38:20 - mmengine - INFO - Iter(train) [ 65600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:29:07 time: 0.3753 data_time: 0.0170 memory: 5384 loss: 0.3876 decode.loss_ce: 0.3876 decode.acc_seg: 80.7884 +2024/10/27 23:38:39 - mmengine - INFO - Iter(train) [ 65650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:28:45 time: 0.3766 data_time: 0.0166 memory: 5384 loss: 0.4008 decode.loss_ce: 0.4008 decode.acc_seg: 87.9930 +2024/10/27 23:38:58 - mmengine - INFO - Iter(train) [ 65700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:28:23 time: 0.3777 data_time: 0.0158 memory: 5384 loss: 0.3556 decode.loss_ce: 0.3556 decode.acc_seg: 82.2291 +2024/10/27 23:39:17 - mmengine - INFO - Iter(train) [ 65750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:28:01 time: 0.3772 data_time: 0.0160 memory: 5384 loss: 0.3609 decode.loss_ce: 0.3609 decode.acc_seg: 78.6759 +2024/10/27 23:39:36 - mmengine - INFO - Iter(train) [ 65800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:27:39 time: 0.3722 data_time: 0.0148 memory: 5385 loss: 0.3625 decode.loss_ce: 0.3625 decode.acc_seg: 80.0181 +2024/10/27 23:39:55 - mmengine - INFO - Iter(train) [ 65850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:27:17 time: 0.3760 data_time: 0.0161 memory: 5384 loss: 0.3642 decode.loss_ce: 0.3642 decode.acc_seg: 76.6634 +2024/10/27 23:40:14 - mmengine - INFO - Iter(train) [ 65900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:26:55 time: 0.3760 data_time: 0.0169 memory: 5384 loss: 0.3665 decode.loss_ce: 0.3665 decode.acc_seg: 84.3198 +2024/10/27 23:40:33 - mmengine - INFO - Iter(train) [ 65950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:26:33 time: 0.3754 data_time: 0.0171 memory: 5384 loss: 0.3697 decode.loss_ce: 0.3697 decode.acc_seg: 87.9734 +2024/10/27 23:40:52 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 23:40:52 - mmengine - INFO - Iter(train) [ 66000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:26:11 time: 0.3752 data_time: 0.0174 memory: 5384 loss: 0.3619 decode.loss_ce: 0.3619 decode.acc_seg: 90.3530 +2024/10/27 23:41:11 - mmengine - INFO - Iter(train) [ 66050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:25:49 time: 0.3765 data_time: 0.0165 memory: 5384 loss: 0.4138 decode.loss_ce: 0.4138 decode.acc_seg: 85.9985 +2024/10/27 23:41:30 - mmengine - INFO - Iter(train) [ 66100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:25:27 time: 0.3725 data_time: 0.0156 memory: 5384 loss: 0.3922 decode.loss_ce: 0.3922 decode.acc_seg: 75.7199 +2024/10/27 23:41:48 - mmengine - INFO - Iter(train) [ 66150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:25:05 time: 0.3738 data_time: 0.0161 memory: 5384 loss: 0.3654 decode.loss_ce: 0.3654 decode.acc_seg: 86.7635 +2024/10/27 23:42:07 - mmengine - INFO - Iter(train) [ 66200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:24:43 time: 0.3808 data_time: 0.0151 memory: 5384 loss: 0.3476 decode.loss_ce: 0.3476 decode.acc_seg: 92.5806 +2024/10/27 23:42:26 - mmengine - INFO - Iter(train) [ 66250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:24:21 time: 0.3983 data_time: 0.0133 memory: 5384 loss: 0.3397 decode.loss_ce: 0.3397 decode.acc_seg: 87.2335 +2024/10/27 23:42:46 - mmengine - INFO - Iter(train) [ 66300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:24:01 time: 0.3983 data_time: 0.0134 memory: 5384 loss: 0.3754 decode.loss_ce: 0.3754 decode.acc_seg: 84.2719 +2024/10/27 23:43:06 - mmengine - INFO - Iter(train) [ 66350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:23:41 time: 0.3765 data_time: 0.0151 memory: 5386 loss: 0.4207 decode.loss_ce: 0.4207 decode.acc_seg: 70.0381 +2024/10/27 23:43:26 - mmengine - INFO - Iter(train) [ 66400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:23:20 time: 0.3743 data_time: 0.0158 memory: 5384 loss: 0.3852 decode.loss_ce: 0.3852 decode.acc_seg: 90.2857 +2024/10/27 23:43:45 - mmengine - INFO - Iter(train) [ 66450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:22:59 time: 0.3711 data_time: 0.0152 memory: 5384 loss: 0.3318 decode.loss_ce: 0.3318 decode.acc_seg: 90.1999 +2024/10/27 23:44:04 - mmengine - INFO - Iter(train) [ 66500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:22:36 time: 0.3785 data_time: 0.0154 memory: 5384 loss: 0.3296 decode.loss_ce: 0.3296 decode.acc_seg: 84.5563 +2024/10/27 23:44:25 - mmengine - INFO - Iter(train) [ 66550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:22:18 time: 0.3725 data_time: 0.0163 memory: 5384 loss: 0.4222 decode.loss_ce: 0.4222 decode.acc_seg: 74.7753 +2024/10/27 23:44:43 - mmengine - INFO - Iter(train) [ 66600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:21:56 time: 0.3778 data_time: 0.0166 memory: 5384 loss: 0.3913 decode.loss_ce: 0.3913 decode.acc_seg: 81.9840 +2024/10/27 23:45:02 - mmengine - INFO - Iter(train) [ 66650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:21:34 time: 0.3758 data_time: 0.0155 memory: 5385 loss: 0.3662 decode.loss_ce: 0.3662 decode.acc_seg: 88.3281 +2024/10/27 23:45:24 - mmengine - INFO 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memory: 5384 loss: 0.4048 decode.loss_ce: 0.4048 decode.acc_seg: 88.1336 +2024/10/27 23:47:03 - mmengine - INFO - Iter(train) [ 66950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:19:36 time: 0.3788 data_time: 0.0141 memory: 5386 loss: 0.3528 decode.loss_ce: 0.3528 decode.acc_seg: 78.3266 +2024/10/27 23:47:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/27 23:47:25 - mmengine - INFO - Iter(train) [ 67000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:19:19 time: 0.3737 data_time: 0.0143 memory: 5384 loss: 0.3595 decode.loss_ce: 0.3595 decode.acc_seg: 94.3818 +2024/10/27 23:47:43 - mmengine - INFO - Iter(train) [ 67050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:18:56 time: 0.3774 data_time: 0.0137 memory: 5384 loss: 0.3947 decode.loss_ce: 0.3947 decode.acc_seg: 84.4472 +2024/10/27 23:48:02 - mmengine - INFO - Iter(train) [ 67100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:18:35 time: 0.3803 data_time: 0.0145 memory: 5384 loss: 0.3076 decode.loss_ce: 0.3076 decode.acc_seg: 90.1361 +2024/10/27 23:48:25 - mmengine - INFO - Iter(train) [ 67150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:18:19 time: 0.3763 data_time: 0.0151 memory: 5384 loss: 0.3191 decode.loss_ce: 0.3191 decode.acc_seg: 92.4794 +2024/10/27 23:48:45 - mmengine - INFO - Iter(train) [ 67200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:17:59 time: 0.3992 data_time: 0.0136 memory: 5384 loss: 0.3708 decode.loss_ce: 0.3708 decode.acc_seg: 88.4661 +2024/10/27 23:49:05 - mmengine - INFO - Iter(train) [ 67250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:17:39 time: 0.4022 data_time: 0.0134 memory: 5384 loss: 0.3845 decode.loss_ce: 0.3845 decode.acc_seg: 81.4822 +2024/10/27 23:49:25 - mmengine - INFO - Iter(train) [ 67300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:17:19 time: 0.3787 data_time: 0.0141 memory: 5386 loss: 0.3428 decode.loss_ce: 0.3428 decode.acc_seg: 87.4469 +2024/10/27 23:49:44 - mmengine - INFO 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memory: 5383 loss: 0.3922 decode.loss_ce: 0.3922 decode.acc_seg: 85.4521 +2024/10/27 23:51:25 - mmengine - INFO - Iter(train) [ 67600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:15:19 time: 0.3781 data_time: 0.0185 memory: 5385 loss: 0.3577 decode.loss_ce: 0.3577 decode.acc_seg: 90.3430 +2024/10/27 23:51:44 - mmengine - INFO - Iter(train) [ 67650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:14:57 time: 0.3800 data_time: 0.0182 memory: 5383 loss: 0.3450 decode.loss_ce: 0.3450 decode.acc_seg: 86.1263 +2024/10/27 23:52:03 - mmengine - INFO - Iter(train) [ 67700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:14:36 time: 0.3809 data_time: 0.0175 memory: 5384 loss: 0.3625 decode.loss_ce: 0.3625 decode.acc_seg: 88.2484 +2024/10/27 23:52:24 - mmengine - INFO - Iter(train) [ 67750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:14:19 time: 0.3757 data_time: 0.0176 memory: 5384 loss: 0.3244 decode.loss_ce: 0.3244 decode.acc_seg: 81.1319 +2024/10/27 23:52:43 - mmengine - INFO 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memory: 5384 loss: 0.4342 decode.loss_ce: 0.4342 decode.acc_seg: 83.9969 +2024/10/27 23:55:44 - mmengine - INFO - Iter(train) [ 68250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:10:59 time: 0.3761 data_time: 0.0168 memory: 5386 loss: 0.3265 decode.loss_ce: 0.3265 decode.acc_seg: 83.5725 +2024/10/27 23:56:03 - mmengine - INFO - Iter(train) [ 68300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:10:37 time: 0.3761 data_time: 0.0159 memory: 5384 loss: 0.4200 decode.loss_ce: 0.4200 decode.acc_seg: 87.5807 +2024/10/27 23:56:25 - mmengine - INFO - Iter(train) [ 68350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:10:20 time: 0.3818 data_time: 0.0157 memory: 5384 loss: 0.3550 decode.loss_ce: 0.3550 decode.acc_seg: 90.2011 +2024/10/27 23:56:45 - mmengine - INFO - Iter(train) [ 68400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:10:00 time: 0.3968 data_time: 0.0131 memory: 5384 loss: 0.3855 decode.loss_ce: 0.3855 decode.acc_seg: 91.0545 +2024/10/27 23:57:05 - mmengine - INFO 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memory: 5384 loss: 0.3653 decode.loss_ce: 0.3653 decode.acc_seg: 92.1619 +2024/10/27 23:58:46 - mmengine - INFO - Iter(train) [ 68700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:08:02 time: 0.3997 data_time: 0.0129 memory: 5384 loss: 0.3933 decode.loss_ce: 0.3933 decode.acc_seg: 85.5920 +2024/10/27 23:59:06 - mmengine - INFO - Iter(train) [ 68750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:07:41 time: 0.3785 data_time: 0.0168 memory: 5384 loss: 0.3828 decode.loss_ce: 0.3828 decode.acc_seg: 81.3392 +2024/10/27 23:59:25 - mmengine - INFO - Iter(train) [ 68800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:07:21 time: 0.3848 data_time: 0.0172 memory: 5384 loss: 0.3911 decode.loss_ce: 0.3911 decode.acc_seg: 89.6880 +2024/10/27 23:59:44 - mmengine - INFO - Iter(train) [ 68850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:06:59 time: 0.3764 data_time: 0.0164 memory: 5384 loss: 0.3536 decode.loss_ce: 0.3536 decode.acc_seg: 81.3400 +2024/10/28 00:00:03 - mmengine - INFO 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Iter(train) [ 69100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:05:20 time: 0.3725 data_time: 0.0146 memory: 5385 loss: 0.3376 decode.loss_ce: 0.3376 decode.acc_seg: 85.0345 +2024/10/28 00:01:43 - mmengine - INFO - Iter(train) [ 69150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:04:57 time: 0.3726 data_time: 0.0144 memory: 5384 loss: 0.4053 decode.loss_ce: 0.4053 decode.acc_seg: 89.2242 +2024/10/28 00:02:02 - mmengine - INFO - Iter(train) [ 69200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:04:36 time: 0.3778 data_time: 0.0157 memory: 5384 loss: 0.3710 decode.loss_ce: 0.3710 decode.acc_seg: 89.7282 +2024/10/28 00:02:25 - mmengine - INFO - Iter(train) [ 69250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:04:21 time: 0.3946 data_time: 0.0158 memory: 5385 loss: 0.4234 decode.loss_ce: 0.4234 decode.acc_seg: 86.0751 +2024/10/28 00:02:44 - mmengine - INFO - Iter(train) [ 69300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:03:59 time: 0.3757 data_time: 0.0160 memory: 5383 loss: 0.3384 decode.loss_ce: 0.3384 decode.acc_seg: 92.2174 +2024/10/28 00:03:03 - mmengine - INFO - Iter(train) [ 69350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:03:38 time: 0.3832 data_time: 0.0152 memory: 5384 loss: 0.3451 decode.loss_ce: 0.3451 decode.acc_seg: 86.2398 +2024/10/28 00:03:25 - mmengine - INFO - Iter(train) [ 69400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:03:20 time: 0.3759 data_time: 0.0155 memory: 5384 loss: 0.4097 decode.loss_ce: 0.4097 decode.acc_seg: 90.4703 +2024/10/28 00:03:43 - mmengine - INFO - Iter(train) [ 69450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:02:58 time: 0.3758 data_time: 0.0144 memory: 5384 loss: 0.3795 decode.loss_ce: 0.3795 decode.acc_seg: 86.0274 +2024/10/28 00:04:03 - mmengine - INFO - Iter(train) [ 69500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:02:37 time: 0.3871 data_time: 0.0149 memory: 5384 loss: 0.3308 decode.loss_ce: 0.3308 decode.acc_seg: 89.0776 +2024/10/28 00:04:26 - mmengine - INFO 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memory: 5384 loss: 0.3502 decode.loss_ce: 0.3502 decode.acc_seg: 90.4085 +2024/10/28 00:06:03 - mmengine - INFO - Iter(train) [ 69800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:00:37 time: 0.3914 data_time: 0.0130 memory: 5384 loss: 0.3590 decode.loss_ce: 0.3590 decode.acc_seg: 84.4366 +2024/10/28 00:06:25 - mmengine - INFO - Iter(train) [ 69850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 10:00:20 time: 0.3764 data_time: 0.0144 memory: 5386 loss: 0.3786 decode.loss_ce: 0.3786 decode.acc_seg: 88.5935 +2024/10/28 00:06:44 - mmengine - INFO - Iter(train) [ 69900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:59:59 time: 0.3790 data_time: 0.0156 memory: 5385 loss: 0.3874 decode.loss_ce: 0.3874 decode.acc_seg: 87.0453 +2024/10/28 00:07:03 - mmengine - INFO - Iter(train) [ 69950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:59:37 time: 0.3817 data_time: 0.0151 memory: 5384 loss: 0.3692 decode.loss_ce: 0.3692 decode.acc_seg: 82.8809 +2024/10/28 00:07:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 00:07:25 - mmengine - INFO - Iter(train) [ 70000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:59:21 time: 0.3727 data_time: 0.0145 memory: 5384 loss: 0.3728 decode.loss_ce: 0.3728 decode.acc_seg: 86.2391 +2024/10/28 00:07:44 - mmengine - INFO - Iter(train) [ 70050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:58:59 time: 0.3753 data_time: 0.0149 memory: 5385 loss: 0.3855 decode.loss_ce: 0.3855 decode.acc_seg: 86.2682 +2024/10/28 00:08:03 - mmengine - INFO - Iter(train) [ 70100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:58:37 time: 0.3792 data_time: 0.0147 memory: 5384 loss: 0.3876 decode.loss_ce: 0.3876 decode.acc_seg: 80.4826 +2024/10/28 00:08:26 - mmengine - INFO - Iter(train) [ 70150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:58:22 time: 0.3801 data_time: 0.0141 memory: 5384 loss: 0.3032 decode.loss_ce: 0.3032 decode.acc_seg: 84.3211 +2024/10/28 00:08:44 - mmengine - INFO - Iter(train) [ 70200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:58:00 time: 0.3765 data_time: 0.0154 memory: 5384 loss: 0.3110 decode.loss_ce: 0.3110 decode.acc_seg: 85.6003 +2024/10/28 00:09:03 - mmengine - INFO - Iter(train) [ 70250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:57:38 time: 0.3790 data_time: 0.0156 memory: 5384 loss: 0.3840 decode.loss_ce: 0.3840 decode.acc_seg: 88.3202 +2024/10/28 00:09:26 - mmengine - INFO - Iter(train) [ 70300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:57:22 time: 0.3784 data_time: 0.0161 memory: 5384 loss: 0.3359 decode.loss_ce: 0.3359 decode.acc_seg: 87.1844 +2024/10/28 00:09:44 - mmengine - INFO - Iter(train) [ 70350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:57:00 time: 0.3760 data_time: 0.0151 memory: 5384 loss: 0.3408 decode.loss_ce: 0.3408 decode.acc_seg: 87.7273 +2024/10/28 00:10:03 - mmengine - INFO - Iter(train) [ 70400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:56:39 time: 0.4005 data_time: 0.0146 memory: 5384 loss: 0.4235 decode.loss_ce: 0.4235 decode.acc_seg: 86.8260 +2024/10/28 00:10:25 - mmengine - INFO - Iter(train) [ 70450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:56:22 time: 0.3764 data_time: 0.0162 memory: 5385 loss: 0.3326 decode.loss_ce: 0.3326 decode.acc_seg: 87.5028 +2024/10/28 00:10:44 - mmengine - INFO - Iter(train) [ 70500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:56:00 time: 0.3770 data_time: 0.0167 memory: 5384 loss: 0.3947 decode.loss_ce: 0.3947 decode.acc_seg: 84.7643 +2024/10/28 00:11:03 - mmengine - INFO - Iter(train) [ 70550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:55:39 time: 0.3777 data_time: 0.0171 memory: 5385 loss: 0.3812 decode.loss_ce: 0.3812 decode.acc_seg: 85.9404 +2024/10/28 00:11:24 - mmengine - INFO - Iter(train) [ 70600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:55:20 time: 0.3781 data_time: 0.0169 memory: 5384 loss: 0.3539 decode.loss_ce: 0.3539 decode.acc_seg: 86.5325 +2024/10/28 00:11:44 - mmengine - INFO - Iter(train) [ 70650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:54:59 time: 0.3798 data_time: 0.0168 memory: 5384 loss: 0.3304 decode.loss_ce: 0.3304 decode.acc_seg: 87.5051 +2024/10/28 00:12:03 - mmengine - INFO - Iter(train) [ 70700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:54:38 time: 0.3837 data_time: 0.0174 memory: 5384 loss: 0.5150 decode.loss_ce: 0.5150 decode.acc_seg: 73.8261 +2024/10/28 00:12:24 - mmengine - INFO - Iter(train) [ 70750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:54:20 time: 0.3764 data_time: 0.0165 memory: 5384 loss: 0.3580 decode.loss_ce: 0.3580 decode.acc_seg: 87.1792 +2024/10/28 00:12:43 - mmengine - INFO - Iter(train) [ 70800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:53:58 time: 0.3794 data_time: 0.0160 memory: 5384 loss: 0.3838 decode.loss_ce: 0.3838 decode.acc_seg: 90.6667 +2024/10/28 00:13:02 - mmengine - INFO - Iter(train) [ 70850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:53:36 time: 0.3807 data_time: 0.0162 memory: 5384 loss: 0.3554 decode.loss_ce: 0.3554 decode.acc_seg: 90.9658 +2024/10/28 00:13:26 - mmengine - INFO - Iter(train) [ 70900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:53:23 time: 0.3986 data_time: 0.0134 memory: 5384 loss: 0.3620 decode.loss_ce: 0.3620 decode.acc_seg: 88.1464 +2024/10/28 00:13:45 - mmengine - INFO - Iter(train) [ 70950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:53:01 time: 0.3736 data_time: 0.0158 memory: 5384 loss: 0.4086 decode.loss_ce: 0.4086 decode.acc_seg: 84.5416 +2024/10/28 00:14:04 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 00:14:04 - mmengine - INFO - Iter(train) [ 71000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:52:39 time: 0.3787 data_time: 0.0163 memory: 5385 loss: 0.3665 decode.loss_ce: 0.3665 decode.acc_seg: 90.1348 +2024/10/28 00:14:24 - mmengine - INFO - Iter(train) [ 71050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:52:20 time: 0.3771 data_time: 0.0150 memory: 5384 loss: 0.5161 decode.loss_ce: 0.5161 decode.acc_seg: 83.5218 +2024/10/28 00:14:43 - mmengine - INFO - Iter(train) [ 71100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:51:58 time: 0.3768 data_time: 0.0155 memory: 5386 loss: 0.3922 decode.loss_ce: 0.3922 decode.acc_seg: 90.4019 +2024/10/28 00:15:02 - mmengine - INFO - Iter(train) [ 71150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:51:36 time: 0.3733 data_time: 0.0160 memory: 5384 loss: 0.3945 decode.loss_ce: 0.3945 decode.acc_seg: 81.9636 +2024/10/28 00:15:25 - mmengine - INFO - Iter(train) [ 71200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:51:22 time: 0.3773 data_time: 0.0154 memory: 5384 loss: 0.3842 decode.loss_ce: 0.3842 decode.acc_seg: 87.9212 +2024/10/28 00:15:45 - mmengine - INFO - Iter(train) [ 71250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:51:02 time: 0.3994 data_time: 0.0136 memory: 5384 loss: 0.3254 decode.loss_ce: 0.3254 decode.acc_seg: 89.9691 +2024/10/28 00:16:04 - mmengine - INFO - Iter(train) [ 71300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:50:40 time: 0.3790 data_time: 0.0155 memory: 5384 loss: 0.3685 decode.loss_ce: 0.3685 decode.acc_seg: 89.8706 +2024/10/28 00:16:25 - mmengine - INFO - Iter(train) [ 71350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:50:22 time: 0.3782 data_time: 0.0158 memory: 5385 loss: 0.3674 decode.loss_ce: 0.3674 decode.acc_seg: 81.5027 +2024/10/28 00:16:44 - mmengine - INFO - Iter(train) [ 71400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:50:01 time: 0.3785 data_time: 0.0177 memory: 5385 loss: 0.3602 decode.loss_ce: 0.3602 decode.acc_seg: 87.5864 +2024/10/28 00:17:03 - mmengine - INFO - Iter(train) [ 71450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:49:39 time: 0.3892 data_time: 0.0162 memory: 5384 loss: 0.4279 decode.loss_ce: 0.4279 decode.acc_seg: 84.7403 +2024/10/28 00:17:26 - mmengine - INFO - Iter(train) [ 71500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:49:23 time: 0.4000 data_time: 0.0156 memory: 5384 loss: 0.3576 decode.loss_ce: 0.3576 decode.acc_seg: 90.5216 +2024/10/28 00:17:46 - mmengine - INFO - Iter(train) [ 71550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:49:04 time: 0.4008 data_time: 0.0166 memory: 5384 loss: 0.3410 decode.loss_ce: 0.3410 decode.acc_seg: 90.4127 +2024/10/28 00:18:05 - mmengine - INFO - Iter(train) [ 71600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:48:42 time: 0.3878 data_time: 0.0187 memory: 5383 loss: 0.3611 decode.loss_ce: 0.3611 decode.acc_seg: 81.9705 +2024/10/28 00:18:25 - mmengine - INFO - Iter(train) [ 71650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:48:22 time: 0.3764 data_time: 0.0146 memory: 5383 loss: 0.4142 decode.loss_ce: 0.4142 decode.acc_seg: 89.1083 +2024/10/28 00:18:45 - mmengine - INFO - Iter(train) [ 71700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:48:02 time: 0.3985 data_time: 0.0137 memory: 5384 loss: 0.3517 decode.loss_ce: 0.3517 decode.acc_seg: 85.3038 +2024/10/28 00:19:05 - mmengine - INFO - Iter(train) [ 71750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:47:42 time: 0.4041 data_time: 0.0152 memory: 5384 loss: 0.4089 decode.loss_ce: 0.4089 decode.acc_seg: 78.5944 +2024/10/28 00:19:27 - mmengine - INFO - Iter(train) [ 71800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:47:26 time: 0.4028 data_time: 0.0154 memory: 5384 loss: 0.3219 decode.loss_ce: 0.3219 decode.acc_seg: 90.3948 +2024/10/28 00:19:48 - mmengine - INFO - Iter(train) [ 71850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:47:06 time: 0.4045 data_time: 0.0154 memory: 5384 loss: 0.4456 decode.loss_ce: 0.4456 decode.acc_seg: 90.6313 +2024/10/28 00:20:07 - mmengine - INFO - Iter(train) [ 71900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:46:46 time: 0.3761 data_time: 0.0164 memory: 5386 loss: 0.4061 decode.loss_ce: 0.4061 decode.acc_seg: 83.1333 +2024/10/28 00:20:27 - mmengine - INFO - Iter(train) [ 71950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:46:25 time: 0.3993 data_time: 0.0134 memory: 5383 loss: 0.3972 decode.loss_ce: 0.3972 decode.acc_seg: 83.1722 +2024/10/28 00:20:47 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 00:20:47 - mmengine - INFO - Iter(train) [ 72000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:46:05 time: 0.3968 data_time: 0.0130 memory: 5384 loss: 0.3805 decode.loss_ce: 0.3805 decode.acc_seg: 82.7601 +2024/10/28 00:21:06 - mmengine - INFO - Iter(train) [ 72050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:45:44 time: 0.4015 data_time: 0.0154 memory: 5384 loss: 0.2973 decode.loss_ce: 0.2973 decode.acc_seg: 91.0540 +2024/10/28 00:21:26 - mmengine - INFO - Iter(train) [ 72100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:45:24 time: 0.4011 data_time: 0.0155 memory: 5386 loss: 0.3892 decode.loss_ce: 0.3892 decode.acc_seg: 87.3842 +2024/10/28 00:21:47 - mmengine - INFO - Iter(train) [ 72150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:45:05 time: 0.4022 data_time: 0.0150 memory: 5386 loss: 0.3253 decode.loss_ce: 0.3253 decode.acc_seg: 89.0452 +2024/10/28 00:22:07 - mmengine - INFO - Iter(train) [ 72200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:44:45 time: 0.3798 data_time: 0.0160 memory: 5383 loss: 0.3658 decode.loss_ce: 0.3658 decode.acc_seg: 85.5273 +2024/10/28 00:22:26 - mmengine - INFO - Iter(train) [ 72250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:44:24 time: 0.4037 data_time: 0.0158 memory: 5384 loss: 0.3583 decode.loss_ce: 0.3583 decode.acc_seg: 82.6465 +2024/10/28 00:22:45 - mmengine - INFO - Iter(train) [ 72300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:44:03 time: 0.3769 data_time: 0.0174 memory: 5384 loss: 0.3270 decode.loss_ce: 0.3270 decode.acc_seg: 85.0503 +2024/10/28 00:23:04 - mmengine - INFO - Iter(train) [ 72350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:43:41 time: 0.3791 data_time: 0.0178 memory: 5384 loss: 0.3421 decode.loss_ce: 0.3421 decode.acc_seg: 89.6112 +2024/10/28 00:23:25 - mmengine - INFO - Iter(train) [ 72400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:43:22 time: 0.3782 data_time: 0.0171 memory: 5383 loss: 0.2673 decode.loss_ce: 0.2673 decode.acc_seg: 92.7171 +2024/10/28 00:23:44 - mmengine - INFO - Iter(train) [ 72450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:43:00 time: 0.3818 data_time: 0.0173 memory: 5384 loss: 0.3592 decode.loss_ce: 0.3592 decode.acc_seg: 87.2087 +2024/10/28 00:24:03 - mmengine - INFO - Iter(train) [ 72500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:42:39 time: 0.3826 data_time: 0.0167 memory: 5384 loss: 0.3098 decode.loss_ce: 0.3098 decode.acc_seg: 92.3255 +2024/10/28 00:24:25 - mmengine - INFO - Iter(train) [ 72550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:42:23 time: 0.3776 data_time: 0.0158 memory: 5384 loss: 0.3142 decode.loss_ce: 0.3142 decode.acc_seg: 83.9393 +2024/10/28 00:24:44 - mmengine - INFO - Iter(train) [ 72600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:42:01 time: 0.3760 data_time: 0.0155 memory: 5384 loss: 0.3536 decode.loss_ce: 0.3536 decode.acc_seg: 90.0461 +2024/10/28 00:25:03 - mmengine - INFO - Iter(train) [ 72650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:41:39 time: 0.3733 data_time: 0.0162 memory: 5384 loss: 0.3919 decode.loss_ce: 0.3919 decode.acc_seg: 87.5009 +2024/10/28 00:25:25 - mmengine - INFO - Iter(train) [ 72700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:41:22 time: 0.3829 data_time: 0.0172 memory: 5383 loss: 0.3282 decode.loss_ce: 0.3282 decode.acc_seg: 87.0092 +2024/10/28 00:25:44 - mmengine - INFO - Iter(train) [ 72750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:41:01 time: 0.3781 data_time: 0.0176 memory: 5385 loss: 0.3299 decode.loss_ce: 0.3299 decode.acc_seg: 85.2469 +2024/10/28 00:26:03 - mmengine - INFO - Iter(train) [ 72800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:40:40 time: 0.3825 data_time: 0.0166 memory: 5384 loss: 0.3538 decode.loss_ce: 0.3538 decode.acc_seg: 83.2156 +2024/10/28 00:26:25 - mmengine - INFO - Iter(train) [ 72850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:40:23 time: 0.3954 data_time: 0.0160 memory: 5385 loss: 0.3724 decode.loss_ce: 0.3724 decode.acc_seg: 83.5898 +2024/10/28 00:26:45 - mmengine - INFO - Iter(train) [ 72900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:40:02 time: 0.3784 data_time: 0.0174 memory: 5385 loss: 0.3659 decode.loss_ce: 0.3659 decode.acc_seg: 86.2680 +2024/10/28 00:27:04 - mmengine - INFO - Iter(train) [ 72950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:39:41 time: 0.3849 data_time: 0.0170 memory: 5385 loss: 0.3947 decode.loss_ce: 0.3947 decode.acc_seg: 89.9518 +2024/10/28 00:27:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 00:27:25 - mmengine - INFO - Iter(train) [ 73000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:39:23 time: 0.3800 data_time: 0.0166 memory: 5386 loss: 0.3880 decode.loss_ce: 0.3880 decode.acc_seg: 88.2792 +2024/10/28 00:27:44 - mmengine - INFO - Iter(train) [ 73050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:39:02 time: 0.3753 data_time: 0.0171 memory: 5384 loss: 0.3347 decode.loss_ce: 0.3347 decode.acc_seg: 84.0196 +2024/10/28 00:28:04 - mmengine - INFO - Iter(train) [ 73100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:38:40 time: 0.3820 data_time: 0.0169 memory: 5384 loss: 0.3204 decode.loss_ce: 0.3204 decode.acc_seg: 91.5099 +2024/10/28 00:28:26 - mmengine - INFO - Iter(train) [ 73150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:38:24 time: 0.3778 data_time: 0.0177 memory: 5384 loss: 0.4116 decode.loss_ce: 0.4116 decode.acc_seg: 78.4558 +2024/10/28 00:28:45 - mmengine - INFO - Iter(train) [ 73200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:38:02 time: 0.3788 data_time: 0.0180 memory: 5384 loss: 0.3730 decode.loss_ce: 0.3730 decode.acc_seg: 79.3777 +2024/10/28 00:29:04 - mmengine - INFO - Iter(train) [ 73250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:37:41 time: 0.3785 data_time: 0.0174 memory: 5385 loss: 0.3631 decode.loss_ce: 0.3631 decode.acc_seg: 84.8559 +2024/10/28 00:29:25 - mmengine - INFO - Iter(train) [ 73300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:37:23 time: 0.3771 data_time: 0.0156 memory: 5384 loss: 0.3347 decode.loss_ce: 0.3347 decode.acc_seg: 89.5430 +2024/10/28 00:29:44 - mmengine - INFO - Iter(train) [ 73350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:37:01 time: 0.3942 data_time: 0.0158 memory: 5384 loss: 0.3718 decode.loss_ce: 0.3718 decode.acc_seg: 87.7053 +2024/10/28 00:30:04 - mmengine - INFO - Iter(train) [ 73400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:36:41 time: 0.3987 data_time: 0.0149 memory: 5386 loss: 0.3263 decode.loss_ce: 0.3263 decode.acc_seg: 88.3367 +2024/10/28 00:30:26 - mmengine - INFO - Iter(train) [ 73450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:36:24 time: 0.3995 data_time: 0.0147 memory: 5384 loss: 0.3808 decode.loss_ce: 0.3808 decode.acc_seg: 88.1707 +2024/10/28 00:30:46 - mmengine - INFO - Iter(train) [ 73500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:36:04 time: 0.3805 data_time: 0.0163 memory: 5384 loss: 0.4166 decode.loss_ce: 0.4166 decode.acc_seg: 83.2456 +2024/10/28 00:31:05 - mmengine - INFO - Iter(train) [ 73550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:35:43 time: 0.3807 data_time: 0.0174 memory: 5383 loss: 0.4389 decode.loss_ce: 0.4389 decode.acc_seg: 84.0017 +2024/10/28 00:31:25 - mmengine - INFO - Iter(train) [ 73600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:35:23 time: 0.3735 data_time: 0.0163 memory: 5384 loss: 0.3392 decode.loss_ce: 0.3392 decode.acc_seg: 76.4491 +2024/10/28 00:31:45 - mmengine - INFO - Iter(train) [ 73650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:35:02 time: 0.3776 data_time: 0.0164 memory: 5384 loss: 0.4075 decode.loss_ce: 0.4075 decode.acc_seg: 84.3320 +2024/10/28 00:32:04 - mmengine - INFO - Iter(train) [ 73700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:34:41 time: 0.3799 data_time: 0.0162 memory: 5384 loss: 0.3948 decode.loss_ce: 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lr: 1.2000e-04 eta: 9:31:40 time: 0.3786 data_time: 0.0164 memory: 5384 loss: 0.3311 decode.loss_ce: 0.3311 decode.acc_seg: 86.0659 +2024/10/28 00:35:25 - mmengine - INFO - Iter(train) [ 74200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:31:23 time: 0.3780 data_time: 0.0167 memory: 5384 loss: 0.3513 decode.loss_ce: 0.3513 decode.acc_seg: 87.5898 +2024/10/28 00:35:44 - mmengine - INFO - Iter(train) [ 74250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:31:02 time: 0.3818 data_time: 0.0169 memory: 5385 loss: 0.4304 decode.loss_ce: 0.4304 decode.acc_seg: 75.5872 +2024/10/28 00:36:03 - mmengine - INFO - Iter(train) [ 74300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:30:40 time: 0.3824 data_time: 0.0160 memory: 5384 loss: 0.4300 decode.loss_ce: 0.4300 decode.acc_seg: 78.2273 +2024/10/28 00:36:25 - mmengine - INFO - Iter(train) [ 74350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:30:24 time: 0.3770 data_time: 0.0160 memory: 5384 loss: 0.3416 decode.loss_ce: 0.3416 decode.acc_seg: 87.8251 +2024/10/28 00:36:44 - mmengine - INFO - Iter(train) [ 74400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:30:02 time: 0.3761 data_time: 0.0154 memory: 5383 loss: 0.3640 decode.loss_ce: 0.3640 decode.acc_seg: 88.6844 +2024/10/28 00:37:03 - mmengine - INFO - Iter(train) [ 74450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:29:41 time: 0.3822 data_time: 0.0165 memory: 5384 loss: 0.3967 decode.loss_ce: 0.3967 decode.acc_seg: 85.1993 +2024/10/28 00:37:25 - mmengine - INFO - Iter(train) [ 74500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:29:23 time: 0.3740 data_time: 0.0166 memory: 5384 loss: 0.3367 decode.loss_ce: 0.3367 decode.acc_seg: 89.5830 +2024/10/28 00:37:44 - mmengine - INFO - Iter(train) [ 74550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:29:01 time: 0.3761 data_time: 0.0165 memory: 5383 loss: 0.3362 decode.loss_ce: 0.3362 decode.acc_seg: 89.7050 +2024/10/28 00:38:02 - mmengine - INFO - Iter(train) [ 74600/160000] base_lr: 1.2000e-04 lr: 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decode.acc_seg: 88.4552 +2024/10/28 00:39:44 - mmengine - INFO - Iter(train) [ 74850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:27:02 time: 0.3786 data_time: 0.0159 memory: 5384 loss: 0.3200 decode.loss_ce: 0.3200 decode.acc_seg: 91.2444 +2024/10/28 00:40:03 - mmengine - INFO - Iter(train) [ 74900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:26:41 time: 0.3881 data_time: 0.0163 memory: 5384 loss: 0.4368 decode.loss_ce: 0.4368 decode.acc_seg: 86.7008 +2024/10/28 00:40:24 - mmengine - INFO - Iter(train) [ 74950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:26:21 time: 0.3723 data_time: 0.0146 memory: 5384 loss: 0.3731 decode.loss_ce: 0.3731 decode.acc_seg: 76.3320 +2024/10/28 00:40:42 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 00:40:42 - mmengine - INFO - Iter(train) [ 75000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:26:00 time: 0.3777 data_time: 0.0162 memory: 5384 loss: 0.3609 decode.loss_ce: 0.3609 decode.acc_seg: 89.2794 +2024/10/28 00:41:01 - mmengine - INFO - Iter(train) [ 75050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:25:38 time: 0.3787 data_time: 0.0166 memory: 5384 loss: 0.4360 decode.loss_ce: 0.4360 decode.acc_seg: 85.0531 +2024/10/28 00:41:20 - mmengine - INFO - Iter(train) [ 75100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:25:17 time: 0.3813 data_time: 0.0156 memory: 5384 loss: 0.3615 decode.loss_ce: 0.3615 decode.acc_seg: 88.3389 +2024/10/28 00:41:39 - mmengine - INFO - Iter(train) [ 75150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:24:55 time: 0.3740 data_time: 0.0159 memory: 5384 loss: 0.4032 decode.loss_ce: 0.4032 decode.acc_seg: 82.0827 +2024/10/28 00:41:58 - mmengine - INFO - Iter(train) [ 75200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:24:34 time: 0.3763 data_time: 0.0152 memory: 5384 loss: 0.3337 decode.loss_ce: 0.3337 decode.acc_seg: 82.0117 +2024/10/28 00:42:17 - mmengine - INFO - Iter(train) [ 75250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:24:13 time: 0.3791 data_time: 0.0166 memory: 5384 loss: 0.3273 decode.loss_ce: 0.3273 decode.acc_seg: 90.2039 +2024/10/28 00:42:37 - mmengine - INFO - Iter(train) [ 75300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:23:52 time: 0.3941 data_time: 0.0143 memory: 5384 loss: 0.3963 decode.loss_ce: 0.3963 decode.acc_seg: 88.1579 +2024/10/28 00:42:56 - mmengine - INFO - Iter(train) [ 75350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:23:30 time: 0.3740 data_time: 0.0160 memory: 5383 loss: 0.4030 decode.loss_ce: 0.4030 decode.acc_seg: 89.6269 +2024/10/28 00:43:15 - mmengine - INFO - Iter(train) [ 75400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:23:09 time: 0.3758 data_time: 0.0168 memory: 5384 loss: 0.3215 decode.loss_ce: 0.3215 decode.acc_seg: 88.9823 +2024/10/28 00:43:34 - mmengine - INFO - Iter(train) [ 75450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:22:48 time: 0.3804 data_time: 0.0166 memory: 5384 loss: 0.3405 decode.loss_ce: 0.3405 decode.acc_seg: 85.9072 +2024/10/28 00:43:54 - mmengine - INFO - Iter(train) [ 75500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:22:28 time: 0.4030 data_time: 0.0161 memory: 5384 loss: 0.3484 decode.loss_ce: 0.3484 decode.acc_seg: 86.5849 +2024/10/28 00:44:13 - mmengine - INFO - Iter(train) [ 75550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:22:07 time: 0.3736 data_time: 0.0165 memory: 5383 loss: 0.3564 decode.loss_ce: 0.3564 decode.acc_seg: 83.3093 +2024/10/28 00:44:32 - mmengine - INFO - Iter(train) [ 75600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:21:46 time: 0.3792 data_time: 0.0165 memory: 5384 loss: 0.3927 decode.loss_ce: 0.3927 decode.acc_seg: 84.1203 +2024/10/28 00:44:51 - mmengine - INFO - Iter(train) [ 75650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:21:24 time: 0.3824 data_time: 0.0170 memory: 5384 loss: 0.3894 decode.loss_ce: 0.3894 decode.acc_seg: 90.4091 +2024/10/28 00:45:11 - mmengine - INFO - Iter(train) [ 75700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:21:04 time: 0.4039 data_time: 0.0154 memory: 5385 loss: 0.4001 decode.loss_ce: 0.4001 decode.acc_seg: 86.7176 +2024/10/28 00:45:30 - mmengine - INFO - Iter(train) [ 75750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:20:43 time: 0.3771 data_time: 0.0164 memory: 5384 loss: 0.3256 decode.loss_ce: 0.3256 decode.acc_seg: 89.9335 +2024/10/28 00:45:50 - mmengine - INFO - Iter(train) [ 75800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:20:22 time: 0.4038 data_time: 0.0150 memory: 5384 loss: 0.3212 decode.loss_ce: 0.3212 decode.acc_seg: 84.4804 +2024/10/28 00:46:09 - mmengine - INFO - Iter(train) [ 75850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:20:01 time: 0.3803 data_time: 0.0165 memory: 5384 loss: 0.3821 decode.loss_ce: 0.3821 decode.acc_seg: 86.1701 +2024/10/28 00:46:28 - mmengine - INFO - Iter(train) [ 75900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:19:40 time: 0.3774 data_time: 0.0184 memory: 5384 loss: 0.3291 decode.loss_ce: 0.3291 decode.acc_seg: 81.0200 +2024/10/28 00:46:47 - mmengine - INFO - Iter(train) [ 75950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:19:18 time: 0.3777 data_time: 0.0176 memory: 5385 loss: 0.4206 decode.loss_ce: 0.4206 decode.acc_seg: 81.4315 +2024/10/28 00:47:06 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 00:47:06 - mmengine - INFO - Iter(train) [ 76000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:18:57 time: 0.3829 data_time: 0.0179 memory: 5384 loss: 0.3491 decode.loss_ce: 0.3491 decode.acc_seg: 88.0625 +2024/10/28 00:47:26 - mmengine - INFO - Iter(train) [ 76050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:18:37 time: 0.3784 data_time: 0.0181 memory: 5385 loss: 0.3239 decode.loss_ce: 0.3239 decode.acc_seg: 87.6681 +2024/10/28 00:47:45 - mmengine - INFO - Iter(train) [ 76100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:18:16 time: 0.3873 data_time: 0.0176 memory: 5384 loss: 0.3552 decode.loss_ce: 0.3552 decode.acc_seg: 82.0392 +2024/10/28 00:48:04 - mmengine - INFO - Iter(train) [ 76150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:17:55 time: 0.3813 data_time: 0.0183 memory: 5384 loss: 0.3370 decode.loss_ce: 0.3370 decode.acc_seg: 90.9447 +2024/10/28 00:48:26 - mmengine - INFO - Iter(train) [ 76200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:17:38 time: 0.4052 data_time: 0.0158 memory: 5386 loss: 0.3342 decode.loss_ce: 0.3342 decode.acc_seg: 82.3141 +2024/10/28 00:48:46 - mmengine - INFO - Iter(train) [ 76250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:17:18 time: 0.3881 data_time: 0.0158 memory: 5384 loss: 0.3498 decode.loss_ce: 0.3498 decode.acc_seg: 90.8614 +2024/10/28 00:49:05 - mmengine - INFO - Iter(train) [ 76300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:16:56 time: 0.3796 data_time: 0.0163 memory: 5384 loss: 0.3827 decode.loss_ce: 0.3827 decode.acc_seg: 86.3030 +2024/10/28 00:49:25 - mmengine - INFO - Iter(train) [ 76350/160000] base_lr: 1.2000e-04 lr: 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decode.acc_seg: 86.9643 +2024/10/28 00:51:03 - mmengine - INFO - Iter(train) [ 76600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:14:54 time: 0.3827 data_time: 0.0163 memory: 5384 loss: 0.3690 decode.loss_ce: 0.3690 decode.acc_seg: 84.3819 +2024/10/28 00:51:24 - mmengine - INFO - Iter(train) [ 76650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:14:36 time: 0.3807 data_time: 0.0171 memory: 5384 loss: 0.3573 decode.loss_ce: 0.3573 decode.acc_seg: 91.5789 +2024/10/28 00:51:43 - mmengine - INFO - Iter(train) [ 76700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:14:15 time: 0.3766 data_time: 0.0175 memory: 5384 loss: 0.4066 decode.loss_ce: 0.4066 decode.acc_seg: 82.4012 +2024/10/28 00:52:02 - mmengine - INFO - Iter(train) [ 76750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:13:54 time: 0.3817 data_time: 0.0178 memory: 5384 loss: 0.4182 decode.loss_ce: 0.4182 decode.acc_seg: 86.2429 +2024/10/28 00:52:24 - mmengine - INFO - Iter(train) [ 76800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:13:36 time: 0.3816 data_time: 0.0170 memory: 5384 loss: 0.4036 decode.loss_ce: 0.4036 decode.acc_seg: 86.6851 +2024/10/28 00:52:44 - mmengine - INFO - Iter(train) [ 76850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:13:15 time: 0.3792 data_time: 0.0174 memory: 5386 loss: 0.3479 decode.loss_ce: 0.3479 decode.acc_seg: 84.9898 +2024/10/28 00:53:03 - mmengine - INFO - Iter(train) [ 76900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:12:54 time: 0.3812 data_time: 0.0160 memory: 5384 loss: 0.3334 decode.loss_ce: 0.3334 decode.acc_seg: 89.7693 +2024/10/28 00:53:26 - mmengine - INFO - Iter(train) [ 76950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:12:38 time: 0.3812 data_time: 0.0167 memory: 5385 loss: 0.4502 decode.loss_ce: 0.4502 decode.acc_seg: 76.6089 +2024/10/28 00:53:45 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 00:53:45 - mmengine - INFO - Iter(train) [ 77000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:12:17 time: 0.3790 data_time: 0.0162 memory: 5384 loss: 0.3776 decode.loss_ce: 0.3776 decode.acc_seg: 76.2305 +2024/10/28 00:54:04 - mmengine - INFO - Iter(train) [ 77050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:11:56 time: 0.3822 data_time: 0.0171 memory: 5384 loss: 0.3411 decode.loss_ce: 0.3411 decode.acc_seg: 83.6126 +2024/10/28 00:54:25 - mmengine - INFO - Iter(train) [ 77100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:11:37 time: 0.3760 data_time: 0.0173 memory: 5384 loss: 0.2961 decode.loss_ce: 0.2961 decode.acc_seg: 90.3188 +2024/10/28 00:54:44 - mmengine - INFO - Iter(train) [ 77150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:11:16 time: 0.3783 data_time: 0.0162 memory: 5384 loss: 0.3352 decode.loss_ce: 0.3352 decode.acc_seg: 92.1154 +2024/10/28 00:55:03 - mmengine - INFO - Iter(train) [ 77200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:10:55 time: 0.3851 data_time: 0.0164 memory: 5384 loss: 0.3247 decode.loss_ce: 0.3247 decode.acc_seg: 88.3900 +2024/10/28 00:55:26 - mmengine - INFO - Iter(train) [ 77250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:10:38 time: 0.3773 data_time: 0.0182 memory: 5386 loss: 0.3448 decode.loss_ce: 0.3448 decode.acc_seg: 87.8010 +2024/10/28 00:55:46 - mmengine - INFO - Iter(train) [ 77300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:10:19 time: 0.4059 data_time: 0.0167 memory: 5384 loss: 0.3233 decode.loss_ce: 0.3233 decode.acc_seg: 89.1352 +2024/10/28 00:56:06 - mmengine - INFO - Iter(train) [ 77350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:09:59 time: 0.3962 data_time: 0.0158 memory: 5384 loss: 0.3445 decode.loss_ce: 0.3445 decode.acc_seg: 86.5622 +2024/10/28 00:56:25 - mmengine - INFO - Iter(train) [ 77400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:09:38 time: 0.3776 data_time: 0.0181 memory: 5385 loss: 0.3197 decode.loss_ce: 0.3197 decode.acc_seg: 91.5693 +2024/10/28 00:56:44 - mmengine - INFO - Iter(train) [ 77450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:09:17 time: 0.3759 data_time: 0.0168 memory: 5384 loss: 0.4587 decode.loss_ce: 0.4587 decode.acc_seg: 89.8756 +2024/10/28 00:57:03 - mmengine - INFO - Iter(train) [ 77500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:08:56 time: 0.3838 data_time: 0.0167 memory: 5384 loss: 0.3560 decode.loss_ce: 0.3560 decode.acc_seg: 87.8147 +2024/10/28 00:57:24 - mmengine - INFO - Iter(train) [ 77550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:08:37 time: 0.3766 data_time: 0.0176 memory: 5384 loss: 0.3977 decode.loss_ce: 0.3977 decode.acc_seg: 89.2510 +2024/10/28 00:57:44 - mmengine - INFO - Iter(train) [ 77600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:08:16 time: 0.3768 data_time: 0.0171 memory: 5384 loss: 0.3673 decode.loss_ce: 0.3673 decode.acc_seg: 90.2020 +2024/10/28 00:58:02 - mmengine - INFO - Iter(train) [ 77650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:07:55 time: 0.3764 data_time: 0.0167 memory: 5386 loss: 0.4444 decode.loss_ce: 0.4444 decode.acc_seg: 89.1487 +2024/10/28 00:58:25 - mmengine - INFO - Iter(train) [ 77700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:07:38 time: 0.3772 data_time: 0.0168 memory: 5384 loss: 0.3289 decode.loss_ce: 0.3289 decode.acc_seg: 86.9985 +2024/10/28 00:58:44 - mmengine - INFO - Iter(train) [ 77750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:07:16 time: 0.3809 data_time: 0.0164 memory: 5384 loss: 0.3956 decode.loss_ce: 0.3956 decode.acc_seg: 90.4305 +2024/10/28 00:59:03 - mmengine - INFO - Iter(train) [ 77800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:06:55 time: 0.3794 data_time: 0.0178 memory: 5384 loss: 0.3422 decode.loss_ce: 0.3422 decode.acc_seg: 89.7698 +2024/10/28 00:59:25 - mmengine - INFO - Iter(train) [ 77850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:06:38 time: 0.3761 data_time: 0.0177 memory: 5386 loss: 0.3538 decode.loss_ce: 0.3538 decode.acc_seg: 88.9349 +2024/10/28 00:59:44 - mmengine - INFO - Iter(train) [ 77900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:06:17 time: 0.3778 data_time: 0.0178 memory: 5384 loss: 0.4342 decode.loss_ce: 0.4342 decode.acc_seg: 86.0138 +2024/10/28 01:00:03 - mmengine - INFO - Iter(train) [ 77950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:05:55 time: 0.3813 data_time: 0.0163 memory: 5384 loss: 0.3788 decode.loss_ce: 0.3788 decode.acc_seg: 87.6573 +2024/10/28 01:00:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 01:00:24 - mmengine - INFO - Iter(train) [ 78000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:05:37 time: 0.3746 data_time: 0.0168 memory: 5384 loss: 0.4068 decode.loss_ce: 0.4068 decode.acc_seg: 82.7977 +2024/10/28 01:00:43 - mmengine - INFO - Iter(train) [ 78050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:05:16 time: 0.3782 data_time: 0.0167 memory: 5383 loss: 0.3680 decode.loss_ce: 0.3680 decode.acc_seg: 82.6007 +2024/10/28 01:01:03 - mmengine - INFO - Iter(train) [ 78100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:04:56 time: 0.4082 data_time: 0.0150 memory: 5384 loss: 0.3367 decode.loss_ce: 0.3367 decode.acc_seg: 85.0596 +2024/10/28 01:01:25 - mmengine - INFO - Iter(train) [ 78150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:04:39 time: 0.3843 data_time: 0.0164 memory: 5385 loss: 0.4051 decode.loss_ce: 0.4051 decode.acc_seg: 76.6026 +2024/10/28 01:01:44 - mmengine - INFO - Iter(train) [ 78200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:04:18 time: 0.3748 data_time: 0.0152 memory: 5384 loss: 0.3638 decode.loss_ce: 0.3638 decode.acc_seg: 88.3945 +2024/10/28 01:02:03 - mmengine - INFO - Iter(train) [ 78250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:03:57 time: 0.3801 data_time: 0.0166 memory: 5384 loss: 0.3052 decode.loss_ce: 0.3052 decode.acc_seg: 84.0106 +2024/10/28 01:02:25 - mmengine - INFO - Iter(train) [ 78300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:03:39 time: 0.3737 data_time: 0.0149 memory: 5384 loss: 0.4124 decode.loss_ce: 0.4124 decode.acc_seg: 71.9134 +2024/10/28 01:02:44 - mmengine - INFO - Iter(train) [ 78350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:03:17 time: 0.3758 data_time: 0.0149 memory: 5384 loss: 0.3695 decode.loss_ce: 0.3695 decode.acc_seg: 85.1780 +2024/10/28 01:03:03 - mmengine - INFO - Iter(train) [ 78400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:02:56 time: 0.3790 data_time: 0.0170 memory: 5384 loss: 0.3745 decode.loss_ce: 0.3745 decode.acc_seg: 83.9533 +2024/10/28 01:03:25 - mmengine - INFO - Iter(train) [ 78450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:02:38 time: 0.3724 data_time: 0.0151 memory: 5384 loss: 0.3749 decode.loss_ce: 0.3749 decode.acc_seg: 84.9231 +2024/10/28 01:03:43 - mmengine - INFO - Iter(train) [ 78500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:02:17 time: 0.3738 data_time: 0.0151 memory: 5384 loss: 0.3861 decode.loss_ce: 0.3861 decode.acc_seg: 86.5910 +2024/10/28 01:04:03 - mmengine - INFO - Iter(train) [ 78550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:01:56 time: 0.3744 data_time: 0.0146 memory: 5384 loss: 0.3057 decode.loss_ce: 0.3057 decode.acc_seg: 88.7194 +2024/10/28 01:04:25 - mmengine - INFO - Iter(train) [ 78600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:01:39 time: 0.3810 data_time: 0.0166 memory: 5384 loss: 0.3998 decode.loss_ce: 0.3998 decode.acc_seg: 87.5200 +2024/10/28 01:04:43 - mmengine - INFO - Iter(train) [ 78650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:01:17 time: 0.3755 data_time: 0.0174 memory: 5383 loss: 0.3356 decode.loss_ce: 0.3356 decode.acc_seg: 85.2868 +2024/10/28 01:05:02 - mmengine - INFO - Iter(train) [ 78700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:00:56 time: 0.3785 data_time: 0.0170 memory: 5382 loss: 0.3916 decode.loss_ce: 0.3916 decode.acc_seg: 86.7427 +2024/10/28 01:05:25 - mmengine - INFO - Iter(train) [ 78750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:00:40 time: 0.3733 data_time: 0.0176 memory: 5383 loss: 0.3976 decode.loss_ce: 0.3976 decode.acc_seg: 91.3938 +2024/10/28 01:05:44 - mmengine - INFO - Iter(train) [ 78800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 9:00:19 time: 0.3744 data_time: 0.0162 memory: 5385 loss: 0.3944 decode.loss_ce: 0.3944 decode.acc_seg: 88.2658 +2024/10/28 01:06:03 - mmengine - INFO - Iter(train) [ 78850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:59:58 time: 0.3903 data_time: 0.0170 memory: 5384 loss: 0.3539 decode.loss_ce: 0.3539 decode.acc_seg: 85.4432 +2024/10/28 01:06:26 - mmengine - INFO - Iter(train) [ 78900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:59:41 time: 0.4032 data_time: 0.0160 memory: 5383 loss: 0.3338 decode.loss_ce: 0.3338 decode.acc_seg: 85.9827 +2024/10/28 01:06:45 - mmengine - INFO - Iter(train) [ 78950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:59:20 time: 0.3763 data_time: 0.0171 memory: 5384 loss: 0.3419 decode.loss_ce: 0.3419 decode.acc_seg: 85.1783 +2024/10/28 01:07:04 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 01:07:04 - mmengine - INFO - Iter(train) [ 79000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:58:59 time: 0.3832 data_time: 0.0153 memory: 5384 loss: 0.3297 decode.loss_ce: 0.3297 decode.acc_seg: 88.3005 +2024/10/28 01:07:25 - mmengine - INFO - Iter(train) [ 79050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:58:40 time: 0.3804 data_time: 0.0167 memory: 5384 loss: 0.2707 decode.loss_ce: 0.2707 decode.acc_seg: 85.4663 +2024/10/28 01:07:45 - mmengine - INFO - Iter(train) [ 79100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:58:19 time: 0.3960 data_time: 0.0172 memory: 5384 loss: 0.3081 decode.loss_ce: 0.3081 decode.acc_seg: 90.7900 +2024/10/28 01:08:04 - mmengine - INFO - Iter(train) [ 79150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:57:58 time: 0.3794 data_time: 0.0177 memory: 5384 loss: 0.3247 decode.loss_ce: 0.3247 decode.acc_seg: 83.3112 +2024/10/28 01:08:25 - mmengine - INFO - Iter(train) [ 79200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:57:39 time: 0.3811 data_time: 0.0178 memory: 5386 loss: 0.3561 decode.loss_ce: 0.3561 decode.acc_seg: 88.8430 +2024/10/28 01:08:44 - mmengine - INFO - Iter(train) [ 79250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:57:18 time: 0.3797 data_time: 0.0177 memory: 5384 loss: 0.3646 decode.loss_ce: 0.3646 decode.acc_seg: 90.8111 +2024/10/28 01:09:03 - mmengine - INFO - Iter(train) [ 79300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:56:57 time: 0.3795 data_time: 0.0154 memory: 5383 loss: 0.3640 decode.loss_ce: 0.3640 decode.acc_seg: 79.2681 +2024/10/28 01:09:25 - mmengine - INFO - Iter(train) [ 79350/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:56:40 time: 0.3836 data_time: 0.0180 memory: 5384 loss: 0.4033 decode.loss_ce: 0.4033 decode.acc_seg: 84.0028 +2024/10/28 01:09:44 - mmengine - INFO - Iter(train) [ 79400/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:56:19 time: 0.3855 data_time: 0.0171 memory: 5382 loss: 0.3842 decode.loss_ce: 0.3842 decode.acc_seg: 86.2739 +2024/10/28 01:10:03 - mmengine - INFO - Iter(train) [ 79450/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:55:58 time: 0.3873 data_time: 0.0180 memory: 5384 loss: 0.2879 decode.loss_ce: 0.2879 decode.acc_seg: 91.1600 +2024/10/28 01:10:25 - mmengine - INFO - Iter(train) [ 79500/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:55:41 time: 0.3769 data_time: 0.0161 memory: 5384 loss: 0.3433 decode.loss_ce: 0.3433 decode.acc_seg: 87.9562 +2024/10/28 01:10:45 - mmengine - INFO - Iter(train) [ 79550/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:55:20 time: 0.3840 data_time: 0.0155 memory: 5384 loss: 0.3833 decode.loss_ce: 0.3833 decode.acc_seg: 86.8081 +2024/10/28 01:11:04 - mmengine - INFO - Iter(train) [ 79600/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:54:59 time: 0.3813 data_time: 0.0162 memory: 5385 loss: 0.3426 decode.loss_ce: 0.3426 decode.acc_seg: 85.7610 +2024/10/28 01:11:25 - mmengine - INFO - Iter(train) [ 79650/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:54:40 time: 0.3817 data_time: 0.0159 memory: 5384 loss: 0.2981 decode.loss_ce: 0.2981 decode.acc_seg: 94.3186 +2024/10/28 01:11:44 - mmengine - INFO - Iter(train) [ 79700/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:54:19 time: 0.3768 data_time: 0.0167 memory: 5384 loss: 0.3296 decode.loss_ce: 0.3296 decode.acc_seg: 83.6890 +2024/10/28 01:12:04 - mmengine - INFO - Iter(train) [ 79750/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:53:59 time: 0.3985 data_time: 0.0157 memory: 5384 loss: 0.3637 decode.loss_ce: 0.3637 decode.acc_seg: 81.9716 +2024/10/28 01:12:25 - mmengine - INFO - Iter(train) [ 79800/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:53:41 time: 0.4038 data_time: 0.0142 memory: 5386 loss: 0.3505 decode.loss_ce: 0.3505 decode.acc_seg: 85.6484 +2024/10/28 01:12:46 - mmengine - INFO - Iter(train) [ 79850/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:53:22 time: 0.4003 data_time: 0.0150 memory: 5383 loss: 0.3518 decode.loss_ce: 0.3518 decode.acc_seg: 84.7756 +2024/10/28 01:13:06 - mmengine - INFO - Iter(train) [ 79900/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:53:02 time: 0.4032 data_time: 0.0157 memory: 5384 loss: 0.3639 decode.loss_ce: 0.3639 decode.acc_seg: 85.4507 +2024/10/28 01:13:25 - mmengine - INFO - Iter(train) [ 79950/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:52:41 time: 0.3792 data_time: 0.0168 memory: 5384 loss: 0.3386 decode.loss_ce: 0.3386 decode.acc_seg: 86.0283 +2024/10/28 01:13:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 01:13:44 - mmengine - INFO - Iter(train) [ 80000/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:52:20 time: 0.3802 data_time: 0.0161 memory: 5383 loss: 0.3778 decode.loss_ce: 0.3778 decode.acc_seg: 80.3733 +2024/10/28 01:13:44 - mmengine - INFO - Saving checkpoint at 80000 iterations +2024/10/28 01:13:49 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:00:15 time: 0.0334 data_time: 0.0016 memory: 980 +2024/10/28 01:13:50 - mmengine - INFO - Iter(val) [100/500] eta: 0:00:13 time: 0.0343 data_time: 0.0016 memory: 1050 +2024/10/28 01:13:52 - mmengine - INFO - Iter(val) [150/500] eta: 0:00:11 time: 0.0332 data_time: 0.0015 memory: 767 +2024/10/28 01:13:54 - mmengine - INFO - Iter(val) [200/500] eta: 0:00:10 time: 0.0342 data_time: 0.0016 memory: 800 +2024/10/28 01:13:55 - mmengine - INFO - Iter(val) [250/500] eta: 0:00:08 time: 0.0337 data_time: 0.0016 memory: 839 +2024/10/28 01:13:57 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:06 time: 0.0340 data_time: 0.0019 memory: 1961 +2024/10/28 01:13:59 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:05 time: 0.0326 data_time: 0.0014 memory: 765 +2024/10/28 01:14:00 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:03 time: 0.0327 data_time: 0.0015 memory: 837 +2024/10/28 01:14:02 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:01 time: 0.0332 data_time: 0.0015 memory: 772 +2024/10/28 01:14:04 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.0326 data_time: 0.0013 memory: 822 +2024/10/28 01:14:08 - mmengine - INFO - per class results: +2024/10/28 01:14:08 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 70.05 | 84.4 | +| building | 77.62 | 88.95 | +| sky | 92.35 | 96.1 | +| floor | 74.3 | 86.47 | +| tree | 68.21 | 85.72 | +| ceiling | 79.24 | 90.75 | +| road | 78.86 | 87.99 | +| bed | 84.2 | 92.22 | +| windowpane | 54.27 | 68.17 | +| grass | 58.79 | 77.0 | +| cabinet | 49.0 | 62.92 | +| sidewalk | 45.3 | 56.41 | +| person | 69.33 | 88.25 | +| earth | 30.16 | 42.16 | +| door | 35.74 | 57.99 | +| table | 49.31 | 63.57 | +| mountain | 48.36 | 65.54 | +| plant | 43.22 | 51.39 | +| curtain | 64.41 | 75.43 | +| chair | 46.81 | 62.15 | +| car | 77.17 | 87.32 | +| water | 43.63 | 55.05 | +| painting | 64.07 | 76.69 | +| sofa | 59.11 | 74.8 | +| shelf | 31.13 | 48.0 | +| house | 42.17 | 57.52 | +| sea | 37.35 | 60.96 | +| mirror | 54.02 | 63.88 | +| rug | 56.34 | 73.23 | +| field | 22.38 | 39.29 | +| armchair | 41.91 | 63.34 | +| seat | 51.18 | 72.0 | +| fence | 30.73 | 40.45 | +| desk | 39.02 | 55.87 | +| rock | 28.88 | 40.97 | +| wardrobe | 40.41 | 69.02 | +| lamp | 45.72 | 63.51 | +| bathtub | 68.28 | 75.09 | +| railing | 27.29 | 47.4 | +| cushion | 44.41 | 55.3 | +| base | 11.89 | 15.46 | +| box | 15.51 | 23.84 | +| column | 28.83 | 39.79 | +| signboard | 24.08 | 41.03 | +| chest of drawers | 29.28 | 49.32 | +| counter | 23.42 | 27.34 | +| sand | 33.66 | 53.11 | +| sink | 55.64 | 62.47 | +| skyscraper | 45.11 | 55.8 | +| fireplace | 61.56 | 88.09 | +| refrigerator | 61.45 | 73.16 | +| grandstand | 31.55 | 60.67 | +| path | 7.2 | 27.37 | +| stairs | 22.63 | 28.27 | +| runway | 62.42 | 80.36 | +| case | 27.92 | 30.94 | +| pool table | 59.78 | 62.42 | +| pillow | 46.03 | 55.21 | +| screen door | 29.08 | 33.01 | +| stairway | 25.67 | 33.78 | +| river | 11.01 | 24.46 | +| bridge | 59.95 | 77.68 | +| bookcase | 33.85 | 49.43 | +| blind | 21.68 | 25.01 | +| coffee table | 50.15 | 70.52 | +| toilet | 70.89 | 84.62 | +| flower | 31.59 | 48.24 | +| book | 36.6 | 52.88 | +| hill | 2.64 | 5.85 | +| bench | 23.34 | 31.08 | +| countertop | 47.98 | 73.08 | +| stove | 61.84 | 74.08 | +| palm | 42.79 | 56.19 | +| kitchen island | 26.81 | 55.87 | +| computer | 43.42 | 55.5 | +| swivel chair | 33.51 | 56.99 | +| boat | 51.57 | 62.76 | +| bar | 25.1 | 37.09 | +| arcade machine | 27.56 | 32.99 | +| hovel | 14.96 | 20.85 | +| bus | 64.99 | 72.66 | +| towel | 45.72 | 54.44 | +| light | 26.46 | 28.58 | +| truck | 19.44 | 37.14 | +| tower | 34.75 | 58.74 | +| chandelier | 50.18 | 63.23 | +| awning | 21.86 | 23.82 | +| streetlight | 11.74 | 17.01 | +| booth | 52.37 | 61.87 | +| television receiver | 57.2 | 71.29 | +| airplane | 44.83 | 55.36 | +| dirt track | 4.58 | 31.17 | +| apparel | 11.0 | 12.86 | +| pole | 10.95 | 15.71 | +| land | 2.97 | 6.64 | +| bannister | 2.49 | 3.09 | +| escalator | 16.23 | 20.23 | +| ottoman | 38.46 | 48.88 | +| bottle | 27.56 | 43.33 | +| buffet | 44.27 | 63.06 | +| poster | 16.99 | 20.62 | +| stage | 14.86 | 32.04 | +| van | 33.8 | 50.3 | +| ship | 26.67 | 54.76 | +| fountain | 20.04 | 20.36 | +| conveyer belt | 44.94 | 63.42 | +| canopy | 19.19 | 26.27 | +| washer | 63.32 | 75.81 | +| plaything | 17.0 | 34.13 | +| swimming pool | 58.6 | 73.62 | +| stool | 30.93 | 50.67 | +| barrel | 5.71 | 64.33 | +| basket | 16.84 | 19.6 | +| waterfall | 53.28 | 65.23 | +| tent | 92.47 | 97.3 | +| bag | 5.71 | 7.49 | +| minibike | 41.04 | 52.99 | +| cradle | 62.66 | 82.02 | +| oven | 40.15 | 49.28 | +| ball | 24.22 | 27.15 | +| food | 43.81 | 61.47 | +| step | 16.57 | 22.39 | +| tank | 29.87 | 34.69 | +| trade name | 14.01 | 15.71 | +| microwave | 32.75 | 35.92 | +| pot | 24.18 | 28.32 | +| animal | 39.96 | 41.44 | +| bicycle | 37.51 | 56.87 | +| lake | 52.4 | 61.58 | +| dishwasher | 41.24 | 47.51 | +| screen | 51.38 | 81.09 | +| blanket | 11.33 | 14.72 | +| sculpture | 29.76 | 32.88 | +| hood | 49.63 | 62.49 | +| sconce | 26.0 | 29.08 | +| vase | 23.74 | 31.73 | +| traffic light | 17.73 | 23.77 | +| tray | 6.27 | 9.72 | +| ashcan | 33.22 | 43.81 | +| fan | 22.91 | 25.18 | +| pier | 31.55 | 41.5 | +| crt screen | 0.9 | 2.01 | +| plate | 31.01 | 34.35 | +| monitor | 10.92 | 16.14 | +| bulletin board | 28.28 | 35.13 | +| shower | 0.0 | 0.0 | +| radiator | 41.21 | 47.95 | +| glass | 3.35 | 3.57 | +| clock | 10.27 | 12.92 | +| flag | 33.19 | 39.87 | ++---------------------+-------+-------+ +2024/10/28 01:14:08 - mmengine - INFO - Iter(val) [500/500] aAcc: 77.1300 mIoU: 37.4400 mAcc: 49.2700 data_time: 0.0016 time: 0.0335 +2024/10/28 01:14:27 - mmengine - INFO - Iter(train) [ 80050/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:52:04 time: 0.3835 data_time: 0.0162 memory: 5384 loss: 0.3107 decode.loss_ce: 0.3107 decode.acc_seg: 84.6533 +2024/10/28 01:14:46 - mmengine - INFO - Iter(train) [ 80100/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:51:44 time: 0.3754 data_time: 0.0177 memory: 5384 loss: 0.3696 decode.loss_ce: 0.3696 decode.acc_seg: 93.0553 +2024/10/28 01:15:05 - mmengine - INFO - Iter(train) [ 80150/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:51:22 time: 0.3777 data_time: 0.0176 memory: 5384 loss: 0.3444 decode.loss_ce: 0.3444 decode.acc_seg: 89.0836 +2024/10/28 01:15:25 - mmengine - INFO - Iter(train) [ 80200/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:51:02 time: 0.3774 data_time: 0.0172 memory: 5385 loss: 0.3747 decode.loss_ce: 0.3747 decode.acc_seg: 90.0187 +2024/10/28 01:15:44 - mmengine - INFO - Iter(train) [ 80250/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:50:40 time: 0.3790 data_time: 0.0159 memory: 5384 loss: 0.3434 decode.loss_ce: 0.3434 decode.acc_seg: 83.1954 +2024/10/28 01:16:03 - mmengine - INFO - Iter(train) [ 80300/160000] base_lr: 1.2000e-04 lr: 1.2000e-04 eta: 8:50:19 time: 0.3781 data_time: 0.0168 memory: 5386 loss: 0.4013 decode.loss_ce: 0.4013 decode.acc_seg: 78.3315 +2024/10/28 01:16:25 - mmengine - INFO - Iter(train) [ 80350/160000] base_lr: 1.1999e-04 lr: 1.1999e-04 eta: 8:50:02 time: 0.3765 data_time: 0.0173 memory: 5384 loss: 0.3439 decode.loss_ce: 0.3439 decode.acc_seg: 87.0487 +2024/10/28 01:16:44 - mmengine - INFO - Iter(train) [ 80400/160000] base_lr: 1.1999e-04 lr: 1.1999e-04 eta: 8:49:41 time: 0.3790 data_time: 0.0187 memory: 5384 loss: 0.3870 decode.loss_ce: 0.3870 decode.acc_seg: 91.4670 +2024/10/28 01:17:03 - mmengine - INFO - Iter(train) [ 80450/160000] base_lr: 1.1999e-04 lr: 1.1999e-04 eta: 8:49:20 time: 0.3798 data_time: 0.0163 memory: 5384 loss: 0.4072 decode.loss_ce: 0.4072 decode.acc_seg: 76.8974 +2024/10/28 01:17:25 - mmengine - INFO - Iter(train) [ 80500/160000] base_lr: 1.1999e-04 lr: 1.1999e-04 eta: 8:49:02 time: 0.3801 data_time: 0.0183 memory: 5385 loss: 0.4109 decode.loss_ce: 0.4109 decode.acc_seg: 89.0442 +2024/10/28 01:17:44 - mmengine - INFO - Iter(train) [ 80550/160000] base_lr: 1.1999e-04 lr: 1.1999e-04 eta: 8:48:41 time: 0.3765 data_time: 0.0155 memory: 5384 loss: 0.3268 decode.loss_ce: 0.3268 decode.acc_seg: 86.3242 +2024/10/28 01:18:03 - mmengine - INFO - Iter(train) [ 80600/160000] base_lr: 1.1998e-04 lr: 1.1998e-04 eta: 8:48:19 time: 0.3794 data_time: 0.0160 memory: 5384 loss: 0.3068 decode.loss_ce: 0.3068 decode.acc_seg: 85.7426 +2024/10/28 01:18:25 - mmengine - INFO - Iter(train) [ 80650/160000] base_lr: 1.1998e-04 lr: 1.1998e-04 eta: 8:48:03 time: 0.4055 data_time: 0.0165 memory: 5383 loss: 0.3667 decode.loss_ce: 0.3667 decode.acc_seg: 87.8926 +2024/10/28 01:18:45 - mmengine - INFO - Iter(train) [ 80700/160000] base_lr: 1.1998e-04 lr: 1.1998e-04 eta: 8:47:43 time: 0.4032 data_time: 0.0156 memory: 5384 loss: 0.3498 decode.loss_ce: 0.3498 decode.acc_seg: 83.7987 +2024/10/28 01:19:06 - mmengine - INFO - Iter(train) [ 80750/160000] base_lr: 1.1997e-04 lr: 1.1997e-04 eta: 8:47:23 time: 0.4031 data_time: 0.0150 memory: 5384 loss: 0.2852 decode.loss_ce: 0.2852 decode.acc_seg: 92.4740 +2024/10/28 01:19:26 - mmengine - INFO - Iter(train) [ 80800/160000] base_lr: 1.1997e-04 lr: 1.1997e-04 eta: 8:47:04 time: 0.4037 data_time: 0.0150 memory: 5383 loss: 0.3156 decode.loss_ce: 0.3156 decode.acc_seg: 86.3936 +2024/10/28 01:19:47 - mmengine - INFO - Iter(train) [ 80850/160000] base_lr: 1.1997e-04 lr: 1.1997e-04 eta: 8:46:44 time: 0.4042 data_time: 0.0157 memory: 5384 loss: 0.3443 decode.loss_ce: 0.3443 decode.acc_seg: 77.5273 +2024/10/28 01:20:06 - mmengine - INFO - Iter(train) [ 80900/160000] base_lr: 1.1996e-04 lr: 1.1996e-04 eta: 8:46:24 time: 0.3814 data_time: 0.0176 memory: 5384 loss: 0.3821 decode.loss_ce: 0.3821 decode.acc_seg: 87.8611 +2024/10/28 01:20:25 - mmengine - INFO - Iter(train) [ 80950/160000] base_lr: 1.1996e-04 lr: 1.1996e-04 eta: 8:46:03 time: 0.3814 data_time: 0.0170 memory: 5384 loss: 0.3493 decode.loss_ce: 0.3493 decode.acc_seg: 82.5292 +2024/10/28 01:20:45 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 01:20:45 - mmengine - INFO - Iter(train) [ 81000/160000] base_lr: 1.1995e-04 lr: 1.1995e-04 eta: 8:45:42 time: 0.3842 data_time: 0.0167 memory: 5384 loss: 0.3244 decode.loss_ce: 0.3244 decode.acc_seg: 92.1788 +2024/10/28 01:21:04 - mmengine - INFO - Iter(train) [ 81050/160000] base_lr: 1.1995e-04 lr: 1.1995e-04 eta: 8:45:21 time: 0.3851 data_time: 0.0184 memory: 5384 loss: 0.3568 decode.loss_ce: 0.3568 decode.acc_seg: 84.0188 +2024/10/28 01:21:24 - mmengine - INFO - Iter(train) [ 81100/160000] base_lr: 1.1994e-04 lr: 1.1994e-04 eta: 8:45:02 time: 0.3819 data_time: 0.0186 memory: 5384 loss: 0.4590 decode.loss_ce: 0.4590 decode.acc_seg: 88.7526 +2024/10/28 01:21:44 - mmengine - INFO - Iter(train) [ 81150/160000] base_lr: 1.1994e-04 lr: 1.1994e-04 eta: 8:44:41 time: 0.3803 data_time: 0.0173 memory: 5383 loss: 0.3399 decode.loss_ce: 0.3399 decode.acc_seg: 93.0195 +2024/10/28 01:22:03 - mmengine - INFO - Iter(train) [ 81200/160000] base_lr: 1.1993e-04 lr: 1.1993e-04 eta: 8:44:20 time: 0.3803 data_time: 0.0171 memory: 5384 loss: 0.3187 decode.loss_ce: 0.3187 decode.acc_seg: 85.8958 +2024/10/28 01:22:25 - mmengine - INFO - Iter(train) [ 81250/160000] base_lr: 1.1993e-04 lr: 1.1993e-04 eta: 8:44:03 time: 0.3812 data_time: 0.0171 memory: 5384 loss: 0.3731 decode.loss_ce: 0.3731 decode.acc_seg: 87.0889 +2024/10/28 01:22:44 - mmengine - INFO - Iter(train) [ 81300/160000] base_lr: 1.1992e-04 lr: 1.1992e-04 eta: 8:43:42 time: 0.3793 data_time: 0.0168 memory: 5384 loss: 0.4011 decode.loss_ce: 0.4011 decode.acc_seg: 82.3758 +2024/10/28 01:23:03 - mmengine - INFO - Iter(train) [ 81350/160000] base_lr: 1.1992e-04 lr: 1.1992e-04 eta: 8:43:20 time: 0.3817 data_time: 0.0161 memory: 5384 loss: 0.3592 decode.loss_ce: 0.3592 decode.acc_seg: 78.9293 +2024/10/28 01:23:25 - mmengine - INFO - Iter(train) [ 81400/160000] base_lr: 1.1991e-04 lr: 1.1991e-04 eta: 8:43:03 time: 0.3829 data_time: 0.0162 memory: 5384 loss: 0.3694 decode.loss_ce: 0.3694 decode.acc_seg: 82.1932 +2024/10/28 01:23:44 - mmengine - INFO - Iter(train) [ 81450/160000] base_lr: 1.1990e-04 lr: 1.1990e-04 eta: 8:42:42 time: 0.3848 data_time: 0.0170 memory: 5384 loss: 0.3937 decode.loss_ce: 0.3937 decode.acc_seg: 89.9534 +2024/10/28 01:24:04 - mmengine - INFO - Iter(train) [ 81500/160000] base_lr: 1.1990e-04 lr: 1.1990e-04 eta: 8:42:22 time: 0.4061 data_time: 0.0142 memory: 5383 loss: 0.3082 decode.loss_ce: 0.3082 decode.acc_seg: 87.1137 +2024/10/28 01:24:25 - mmengine - INFO - Iter(train) [ 81550/160000] base_lr: 1.1989e-04 lr: 1.1989e-04 eta: 8:42:04 time: 0.3808 data_time: 0.0166 memory: 5384 loss: 0.3435 decode.loss_ce: 0.3435 decode.acc_seg: 93.5772 +2024/10/28 01:24:45 - mmengine - INFO - Iter(train) [ 81600/160000] base_lr: 1.1988e-04 lr: 1.1988e-04 eta: 8:41:43 time: 0.3802 data_time: 0.0161 memory: 5384 loss: 0.2969 decode.loss_ce: 0.2969 decode.acc_seg: 88.5942 +2024/10/28 01:25:04 - mmengine - INFO - Iter(train) [ 81650/160000] base_lr: 1.1987e-04 lr: 1.1987e-04 eta: 8:41:22 time: 0.3815 data_time: 0.0165 memory: 5384 loss: 0.3639 decode.loss_ce: 0.3639 decode.acc_seg: 81.9476 +2024/10/28 01:25:25 - mmengine - INFO - Iter(train) [ 81700/160000] base_lr: 1.1987e-04 lr: 1.1987e-04 eta: 8:41:04 time: 0.3732 data_time: 0.0155 memory: 5384 loss: 0.3879 decode.loss_ce: 0.3879 decode.acc_seg: 85.3805 +2024/10/28 01:25:44 - mmengine - INFO - Iter(train) [ 81750/160000] base_lr: 1.1986e-04 lr: 1.1986e-04 eta: 8:40:42 time: 0.3777 data_time: 0.0160 memory: 5384 loss: 0.3311 decode.loss_ce: 0.3311 decode.acc_seg: 83.6987 +2024/10/28 01:26:03 - mmengine - INFO - Iter(train) [ 81800/160000] base_lr: 1.1985e-04 lr: 1.1985e-04 eta: 8:40:21 time: 0.3772 data_time: 0.0170 memory: 5386 loss: 0.3545 decode.loss_ce: 0.3545 decode.acc_seg: 90.7117 +2024/10/28 01:26:25 - mmengine - INFO - Iter(train) [ 81850/160000] base_lr: 1.1984e-04 lr: 1.1984e-04 eta: 8:40:04 time: 0.3758 data_time: 0.0170 memory: 5384 loss: 0.4222 decode.loss_ce: 0.4222 decode.acc_seg: 90.0131 +2024/10/28 01:26:44 - mmengine - INFO - Iter(train) [ 81900/160000] base_lr: 1.1983e-04 lr: 1.1983e-04 eta: 8:39:43 time: 0.3747 data_time: 0.0168 memory: 5384 loss: 0.3177 decode.loss_ce: 0.3177 decode.acc_seg: 92.1937 +2024/10/28 01:27:03 - mmengine - INFO - Iter(train) [ 81950/160000] base_lr: 1.1982e-04 lr: 1.1982e-04 eta: 8:39:21 time: 0.3798 data_time: 0.0172 memory: 5384 loss: 0.2970 decode.loss_ce: 0.2970 decode.acc_seg: 91.5005 +2024/10/28 01:27:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 01:27:24 - mmengine - INFO - Iter(train) [ 82000/160000] base_lr: 1.1982e-04 lr: 1.1982e-04 eta: 8:39:03 time: 0.3761 data_time: 0.0153 memory: 5384 loss: 0.3305 decode.loss_ce: 0.3305 decode.acc_seg: 88.9825 +2024/10/28 01:27:43 - mmengine - INFO - Iter(train) [ 82050/160000] base_lr: 1.1981e-04 lr: 1.1981e-04 eta: 8:38:42 time: 0.3711 data_time: 0.0154 memory: 5384 loss: 0.3538 decode.loss_ce: 0.3538 decode.acc_seg: 84.3425 +2024/10/28 01:28:02 - mmengine - INFO - Iter(train) [ 82100/160000] base_lr: 1.1980e-04 lr: 1.1980e-04 eta: 8:38:20 time: 0.3732 data_time: 0.0152 memory: 5384 loss: 0.4005 decode.loss_ce: 0.4005 decode.acc_seg: 87.6716 +2024/10/28 01:28:25 - mmengine - INFO - Iter(train) [ 82150/160000] base_lr: 1.1979e-04 lr: 1.1979e-04 eta: 8:38:04 time: 0.3764 data_time: 0.0152 memory: 5384 loss: 0.3446 decode.loss_ce: 0.3446 decode.acc_seg: 86.6807 +2024/10/28 01:28:44 - mmengine - INFO - Iter(train) [ 82200/160000] base_lr: 1.1978e-04 lr: 1.1978e-04 eta: 8:37:43 time: 0.3726 data_time: 0.0152 memory: 5384 loss: 0.3543 decode.loss_ce: 0.3543 decode.acc_seg: 90.4319 +2024/10/28 01:29:03 - mmengine - INFO - Iter(train) [ 82250/160000] base_lr: 1.1977e-04 lr: 1.1977e-04 eta: 8:37:22 time: 0.3747 data_time: 0.0150 memory: 5384 loss: 0.3547 decode.loss_ce: 0.3547 decode.acc_seg: 83.8003 +2024/10/28 01:29:25 - mmengine - INFO - Iter(train) [ 82300/160000] base_lr: 1.1976e-04 lr: 1.1976e-04 eta: 8:37:04 time: 0.3780 data_time: 0.0156 memory: 5383 loss: 0.3596 decode.loss_ce: 0.3596 decode.acc_seg: 82.3274 +2024/10/28 01:29:44 - mmengine - INFO - Iter(train) [ 82350/160000] base_lr: 1.1974e-04 lr: 1.1974e-04 eta: 8:36:43 time: 0.3729 data_time: 0.0150 memory: 5386 loss: 0.3015 decode.loss_ce: 0.3015 decode.acc_seg: 85.6990 +2024/10/28 01:30:03 - mmengine - INFO - Iter(train) [ 82400/160000] base_lr: 1.1973e-04 lr: 1.1973e-04 eta: 8:36:22 time: 0.3815 data_time: 0.0157 memory: 5385 loss: 0.4483 decode.loss_ce: 0.4483 decode.acc_seg: 76.4358 +2024/10/28 01:30:24 - mmengine - INFO - Iter(train) [ 82450/160000] base_lr: 1.1972e-04 lr: 1.1972e-04 eta: 8:36:03 time: 0.3734 data_time: 0.0165 memory: 5386 loss: 0.3614 decode.loss_ce: 0.3614 decode.acc_seg: 86.5117 +2024/10/28 01:30:43 - mmengine - INFO - Iter(train) [ 82500/160000] base_lr: 1.1971e-04 lr: 1.1971e-04 eta: 8:35:42 time: 0.3755 data_time: 0.0150 memory: 5384 loss: 0.3316 decode.loss_ce: 0.3316 decode.acc_seg: 88.8774 +2024/10/28 01:31:02 - mmengine - INFO - Iter(train) [ 82550/160000] base_lr: 1.1970e-04 lr: 1.1970e-04 eta: 8:35:20 time: 0.3755 data_time: 0.0156 memory: 5383 loss: 0.3510 decode.loss_ce: 0.3510 decode.acc_seg: 88.0297 +2024/10/28 01:31:20 - mmengine - INFO - Iter(train) [ 82600/160000] base_lr: 1.1969e-04 lr: 1.1969e-04 eta: 8:34:59 time: 0.3750 data_time: 0.0153 memory: 5384 loss: 0.3256 decode.loss_ce: 0.3256 decode.acc_seg: 89.4005 +2024/10/28 01:31:39 - mmengine - INFO - Iter(train) [ 82650/160000] base_lr: 1.1968e-04 lr: 1.1968e-04 eta: 8:34:38 time: 0.3726 data_time: 0.0147 memory: 5384 loss: 0.3375 decode.loss_ce: 0.3375 decode.acc_seg: 83.4836 +2024/10/28 01:31:59 - mmengine - INFO - Iter(train) [ 82700/160000] base_lr: 1.1966e-04 lr: 1.1966e-04 eta: 8:34:17 time: 0.3739 data_time: 0.0163 memory: 5384 loss: 0.3336 decode.loss_ce: 0.3336 decode.acc_seg: 87.6179 +2024/10/28 01:32:18 - mmengine - INFO - Iter(train) [ 82750/160000] base_lr: 1.1965e-04 lr: 1.1965e-04 eta: 8:33:56 time: 0.3760 data_time: 0.0165 memory: 5384 loss: 0.3142 decode.loss_ce: 0.3142 decode.acc_seg: 86.5505 +2024/10/28 01:32:38 - mmengine - INFO - Iter(train) [ 82800/160000] base_lr: 1.1964e-04 lr: 1.1964e-04 eta: 8:33:36 time: 0.4017 data_time: 0.0157 memory: 5384 loss: 0.3166 decode.loss_ce: 0.3166 decode.acc_seg: 89.3868 +2024/10/28 01:32:58 - mmengine - INFO - Iter(train) [ 82850/160000] base_lr: 1.1962e-04 lr: 1.1962e-04 eta: 8:33:17 time: 0.4013 data_time: 0.0150 memory: 5384 loss: 0.3609 decode.loss_ce: 0.3609 decode.acc_seg: 83.4029 +2024/10/28 01:33:17 - mmengine - INFO - Iter(train) [ 82900/160000] base_lr: 1.1961e-04 lr: 1.1961e-04 eta: 8:32:56 time: 0.3780 data_time: 0.0160 memory: 5384 loss: 0.3139 decode.loss_ce: 0.3139 decode.acc_seg: 88.8943 +2024/10/28 01:33:36 - mmengine - INFO - Iter(train) [ 82950/160000] base_lr: 1.1960e-04 lr: 1.1960e-04 eta: 8:32:35 time: 0.3769 data_time: 0.0163 memory: 5384 loss: 0.3350 decode.loss_ce: 0.3350 decode.acc_seg: 87.2746 +2024/10/28 01:33:55 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 01:33:55 - mmengine - INFO - Iter(train) [ 83000/160000] base_lr: 1.1958e-04 lr: 1.1958e-04 eta: 8:32:13 time: 0.3776 data_time: 0.0150 memory: 5384 loss: 0.3758 decode.loss_ce: 0.3758 decode.acc_seg: 88.5930 +2024/10/28 01:34:14 - mmengine - INFO - Iter(train) [ 83050/160000] base_lr: 1.1957e-04 lr: 1.1957e-04 eta: 8:31:52 time: 0.3774 data_time: 0.0150 memory: 5384 loss: 0.3741 decode.loss_ce: 0.3741 decode.acc_seg: 82.4635 +2024/10/28 01:34:33 - mmengine - INFO - Iter(train) [ 83100/160000] base_lr: 1.1956e-04 lr: 1.1956e-04 eta: 8:31:32 time: 0.3813 data_time: 0.0190 memory: 5383 loss: 0.3144 decode.loss_ce: 0.3144 decode.acc_seg: 76.9252 +2024/10/28 01:34:53 - mmengine - INFO - Iter(train) [ 83150/160000] base_lr: 1.1954e-04 lr: 1.1954e-04 eta: 8:31:11 time: 0.3800 data_time: 0.0190 memory: 5385 loss: 0.3474 decode.loss_ce: 0.3474 decode.acc_seg: 91.1657 +2024/10/28 01:35:12 - mmengine - INFO - Iter(train) [ 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decode.loss_ce: 0.3323 decode.acc_seg: 88.9933 +2024/10/28 01:41:03 - mmengine - INFO - Iter(train) [ 84100/160000] base_lr: 1.1922e-04 lr: 1.1922e-04 eta: 8:24:41 time: 0.3760 data_time: 0.0163 memory: 5383 loss: 0.3419 decode.loss_ce: 0.3419 decode.acc_seg: 86.9004 +2024/10/28 01:41:25 - mmengine - INFO - Iter(train) [ 84150/160000] base_lr: 1.1921e-04 lr: 1.1921e-04 eta: 8:24:23 time: 0.3739 data_time: 0.0164 memory: 5386 loss: 0.4018 decode.loss_ce: 0.4018 decode.acc_seg: 85.7788 +2024/10/28 01:41:43 - mmengine - INFO - Iter(train) [ 84200/160000] base_lr: 1.1919e-04 lr: 1.1919e-04 eta: 8:24:02 time: 0.3757 data_time: 0.0166 memory: 5384 loss: 0.3441 decode.loss_ce: 0.3441 decode.acc_seg: 89.0809 +2024/10/28 01:42:02 - mmengine - INFO - Iter(train) [ 84250/160000] base_lr: 1.1917e-04 lr: 1.1917e-04 eta: 8:23:41 time: 0.3820 data_time: 0.0183 memory: 5383 loss: 0.3217 decode.loss_ce: 0.3217 decode.acc_seg: 86.8832 +2024/10/28 01:42:24 - mmengine - INFO - Iter(train) [ 84300/160000] base_lr: 1.1915e-04 lr: 1.1915e-04 eta: 8:23:23 time: 0.3773 data_time: 0.0168 memory: 5383 loss: 0.3211 decode.loss_ce: 0.3211 decode.acc_seg: 90.3117 +2024/10/28 01:42:44 - mmengine - INFO - Iter(train) [ 84350/160000] base_lr: 1.1913e-04 lr: 1.1913e-04 eta: 8:23:02 time: 0.3928 data_time: 0.0154 memory: 5384 loss: 0.3292 decode.loss_ce: 0.3292 decode.acc_seg: 91.0159 +2024/10/28 01:43:03 - mmengine - INFO - Iter(train) [ 84400/160000] base_lr: 1.1911e-04 lr: 1.1911e-04 eta: 8:22:42 time: 0.3993 data_time: 0.0158 memory: 5384 loss: 0.3343 decode.loss_ce: 0.3343 decode.acc_seg: 83.0443 +2024/10/28 01:43:25 - mmengine - INFO - Iter(train) [ 84450/160000] base_lr: 1.1909e-04 lr: 1.1909e-04 eta: 8:22:24 time: 0.3974 data_time: 0.0168 memory: 5384 loss: 0.3228 decode.loss_ce: 0.3228 decode.acc_seg: 86.6226 +2024/10/28 01:43:44 - mmengine - INFO - Iter(train) [ 84500/160000] base_lr: 1.1907e-04 lr: 1.1907e-04 eta: 8:22:04 time: 0.3811 data_time: 0.0180 memory: 5384 loss: 0.3301 decode.loss_ce: 0.3301 decode.acc_seg: 85.2846 +2024/10/28 01:44:03 - mmengine - INFO - Iter(train) [ 84550/160000] base_lr: 1.1905e-04 lr: 1.1905e-04 eta: 8:21:43 time: 0.3752 data_time: 0.0173 memory: 5384 loss: 0.3284 decode.loss_ce: 0.3284 decode.acc_seg: 90.5308 +2024/10/28 01:44:25 - mmengine - INFO - Iter(train) [ 84600/160000] base_lr: 1.1902e-04 lr: 1.1902e-04 eta: 8:21:25 time: 0.3813 data_time: 0.0165 memory: 5384 loss: 0.3185 decode.loss_ce: 0.3185 decode.acc_seg: 86.6960 +2024/10/28 01:44:44 - mmengine - INFO - Iter(train) [ 84650/160000] base_lr: 1.1900e-04 lr: 1.1900e-04 eta: 8:21:04 time: 0.3804 data_time: 0.0181 memory: 5384 loss: 0.3642 decode.loss_ce: 0.3642 decode.acc_seg: 84.3371 +2024/10/28 01:45:03 - mmengine - INFO - Iter(train) [ 84700/160000] base_lr: 1.1898e-04 lr: 1.1898e-04 eta: 8:20:43 time: 0.3909 data_time: 0.0162 memory: 5384 loss: 0.3999 decode.loss_ce: 0.3999 decode.acc_seg: 85.2595 +2024/10/28 01:45:25 - mmengine - INFO - Iter(train) [ 84750/160000] base_lr: 1.1896e-04 lr: 1.1896e-04 eta: 8:20:25 time: 0.3815 data_time: 0.0164 memory: 5384 loss: 0.3279 decode.loss_ce: 0.3279 decode.acc_seg: 90.5107 +2024/10/28 01:45:44 - mmengine - INFO - Iter(train) [ 84800/160000] base_lr: 1.1894e-04 lr: 1.1894e-04 eta: 8:20:04 time: 0.3814 data_time: 0.0169 memory: 5384 loss: 0.4001 decode.loss_ce: 0.4001 decode.acc_seg: 86.6743 +2024/10/28 01:46:03 - mmengine - INFO - Iter(train) [ 84850/160000] base_lr: 1.1892e-04 lr: 1.1892e-04 eta: 8:19:43 time: 0.3838 data_time: 0.0173 memory: 5384 loss: 0.3545 decode.loss_ce: 0.3545 decode.acc_seg: 80.9996 +2024/10/28 01:46:25 - mmengine - INFO - Iter(train) [ 84900/160000] base_lr: 1.1889e-04 lr: 1.1889e-04 eta: 8:19:25 time: 0.3778 data_time: 0.0166 memory: 5384 loss: 0.3772 decode.loss_ce: 0.3772 decode.acc_seg: 91.4942 +2024/10/28 01:46:44 - mmengine - INFO - Iter(train) [ 84950/160000] base_lr: 1.1887e-04 lr: 1.1887e-04 eta: 8:19:04 time: 0.3831 data_time: 0.0159 memory: 5384 loss: 0.2972 decode.loss_ce: 0.2972 decode.acc_seg: 83.6781 +2024/10/28 01:47:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 01:47:03 - mmengine - INFO - Iter(train) [ 85000/160000] base_lr: 1.1885e-04 lr: 1.1885e-04 eta: 8:18:43 time: 0.3803 data_time: 0.0161 memory: 5384 loss: 0.4126 decode.loss_ce: 0.4126 decode.acc_seg: 87.1689 +2024/10/28 01:47:25 - mmengine - INFO - Iter(train) [ 85050/160000] base_lr: 1.1882e-04 lr: 1.1882e-04 eta: 8:18:25 time: 0.3766 data_time: 0.0180 memory: 5384 loss: 0.3743 decode.loss_ce: 0.3743 decode.acc_seg: 87.4573 +2024/10/28 01:47:44 - mmengine - INFO - Iter(train) [ 85100/160000] base_lr: 1.1880e-04 lr: 1.1880e-04 eta: 8:18:04 time: 0.3757 data_time: 0.0171 memory: 5384 loss: 0.2922 decode.loss_ce: 0.2922 decode.acc_seg: 87.0590 +2024/10/28 01:48:03 - mmengine - INFO - Iter(train) [ 85150/160000] base_lr: 1.1878e-04 lr: 1.1878e-04 eta: 8:17:43 time: 0.3809 data_time: 0.0176 memory: 5385 loss: 0.3136 decode.loss_ce: 0.3136 decode.acc_seg: 88.2856 +2024/10/28 01:48:25 - mmengine - INFO - Iter(train) [ 85200/160000] base_lr: 1.1875e-04 lr: 1.1875e-04 eta: 8:17:26 time: 0.3752 data_time: 0.0185 memory: 5385 loss: 0.3361 decode.loss_ce: 0.3361 decode.acc_seg: 85.9629 +2024/10/28 01:48:44 - mmengine - INFO - Iter(train) [ 85250/160000] base_lr: 1.1873e-04 lr: 1.1873e-04 eta: 8:17:05 time: 0.3792 data_time: 0.0189 memory: 5384 loss: 0.3168 decode.loss_ce: 0.3168 decode.acc_seg: 88.1494 +2024/10/28 01:49:03 - mmengine - INFO - Iter(train) [ 85300/160000] base_lr: 1.1871e-04 lr: 1.1871e-04 eta: 8:16:44 time: 0.3833 data_time: 0.0184 memory: 5384 loss: 0.3133 decode.loss_ce: 0.3133 decode.acc_seg: 88.2836 +2024/10/28 01:49:24 - mmengine - INFO - Iter(train) [ 85350/160000] base_lr: 1.1868e-04 lr: 1.1868e-04 eta: 8:16:25 time: 0.3802 data_time: 0.0160 memory: 5386 loss: 0.3618 decode.loss_ce: 0.3618 decode.acc_seg: 91.2977 +2024/10/28 01:49:43 - mmengine - INFO - Iter(train) [ 85400/160000] 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decode.loss_ce: 0.3604 decode.acc_seg: 84.4530 +2024/10/28 01:51:24 - mmengine - INFO - Iter(train) [ 85650/160000] base_lr: 1.1853e-04 lr: 1.1853e-04 eta: 8:14:26 time: 0.3739 data_time: 0.0175 memory: 5384 loss: 0.3398 decode.loss_ce: 0.3398 decode.acc_seg: 88.0666 +2024/10/28 01:51:44 - mmengine - INFO - Iter(train) [ 85700/160000] base_lr: 1.1850e-04 lr: 1.1850e-04 eta: 8:14:05 time: 0.3902 data_time: 0.0177 memory: 5386 loss: 0.3153 decode.loss_ce: 0.3153 decode.acc_seg: 91.7248 +2024/10/28 01:52:03 - mmengine - INFO - Iter(train) [ 85750/160000] base_lr: 1.1848e-04 lr: 1.1848e-04 eta: 8:13:44 time: 0.3827 data_time: 0.0168 memory: 5384 loss: 0.3288 decode.loss_ce: 0.3288 decode.acc_seg: 82.1380 +2024/10/28 01:52:24 - mmengine - INFO - Iter(train) [ 85800/160000] base_lr: 1.1845e-04 lr: 1.1845e-04 eta: 8:13:25 time: 0.3773 data_time: 0.0182 memory: 5384 loss: 0.2884 decode.loss_ce: 0.2884 decode.acc_seg: 86.8533 +2024/10/28 01:52:43 - mmengine - INFO - Iter(train) [ 85850/160000] base_lr: 1.1842e-04 lr: 1.1842e-04 eta: 8:13:04 time: 0.3747 data_time: 0.0179 memory: 5384 loss: 0.2968 decode.loss_ce: 0.2968 decode.acc_seg: 86.8296 +2024/10/28 01:53:02 - mmengine - INFO - Iter(train) [ 85900/160000] base_lr: 1.1840e-04 lr: 1.1840e-04 eta: 8:12:44 time: 0.3843 data_time: 0.0154 memory: 5383 loss: 0.3718 decode.loss_ce: 0.3718 decode.acc_seg: 91.2789 +2024/10/28 01:53:25 - mmengine - INFO - Iter(train) [ 85950/160000] base_lr: 1.1837e-04 lr: 1.1837e-04 eta: 8:12:26 time: 0.3724 data_time: 0.0152 memory: 5384 loss: 0.3944 decode.loss_ce: 0.3944 decode.acc_seg: 81.3008 +2024/10/28 01:53:43 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 01:53:43 - mmengine - INFO - Iter(train) [ 86000/160000] base_lr: 1.1834e-04 lr: 1.1834e-04 eta: 8:12:05 time: 0.3765 data_time: 0.0152 memory: 5384 loss: 0.3225 decode.loss_ce: 0.3225 decode.acc_seg: 90.5770 +2024/10/28 01:54:02 - mmengine - INFO - Iter(train) [ 86050/160000] base_lr: 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0.3919 decode.acc_seg: 88.4154 +2024/10/28 01:55:46 - mmengine - INFO - Iter(train) [ 86300/160000] base_lr: 1.1817e-04 lr: 1.1817e-04 eta: 8:10:08 time: 0.4039 data_time: 0.0170 memory: 5384 loss: 0.3631 decode.loss_ce: 0.3631 decode.acc_seg: 85.3261 +2024/10/28 01:56:06 - mmengine - INFO - Iter(train) [ 86350/160000] base_lr: 1.1814e-04 lr: 1.1814e-04 eta: 8:09:48 time: 0.3777 data_time: 0.0168 memory: 5386 loss: 0.3304 decode.loss_ce: 0.3304 decode.acc_seg: 89.1440 +2024/10/28 01:56:25 - mmengine - INFO - Iter(train) [ 86400/160000] base_lr: 1.1812e-04 lr: 1.1812e-04 eta: 8:09:27 time: 0.3738 data_time: 0.0165 memory: 5385 loss: 0.2743 decode.loss_ce: 0.2743 decode.acc_seg: 86.5002 +2024/10/28 01:56:44 - mmengine - INFO - Iter(train) [ 86450/160000] base_lr: 1.1809e-04 lr: 1.1809e-04 eta: 8:09:06 time: 0.3778 data_time: 0.0166 memory: 5386 loss: 0.3346 decode.loss_ce: 0.3346 decode.acc_seg: 89.6124 +2024/10/28 01:57:03 - mmengine - INFO - Iter(train) [ 86500/160000] base_lr: 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0.3172 decode.acc_seg: 89.4709 +2024/10/28 01:58:46 - mmengine - INFO - Iter(train) [ 86750/160000] base_lr: 1.1791e-04 lr: 1.1791e-04 eta: 8:07:09 time: 0.4009 data_time: 0.0161 memory: 5384 loss: 0.3134 decode.loss_ce: 0.3134 decode.acc_seg: 94.0334 +2024/10/28 01:59:06 - mmengine - INFO - Iter(train) [ 86800/160000] base_lr: 1.1787e-04 lr: 1.1787e-04 eta: 8:06:49 time: 0.3821 data_time: 0.0161 memory: 5383 loss: 0.3339 decode.loss_ce: 0.3339 decode.acc_seg: 87.6181 +2024/10/28 01:59:25 - mmengine - INFO - Iter(train) [ 86850/160000] base_lr: 1.1784e-04 lr: 1.1784e-04 eta: 8:06:28 time: 0.3785 data_time: 0.0169 memory: 5385 loss: 0.3563 decode.loss_ce: 0.3563 decode.acc_seg: 87.3006 +2024/10/28 01:59:44 - mmengine - INFO - Iter(train) [ 86900/160000] base_lr: 1.1781e-04 lr: 1.1781e-04 eta: 8:06:07 time: 0.3778 data_time: 0.0164 memory: 5384 loss: 0.3090 decode.loss_ce: 0.3090 decode.acc_seg: 90.5712 +2024/10/28 02:00:03 - mmengine - INFO - Iter(train) [ 86950/160000] base_lr: 1.1778e-04 lr: 1.1778e-04 eta: 8:05:46 time: 0.3766 data_time: 0.0159 memory: 5384 loss: 0.3082 decode.loss_ce: 0.3082 decode.acc_seg: 85.3038 +2024/10/28 02:00:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 02:00:25 - mmengine - INFO - Iter(train) [ 87000/160000] base_lr: 1.1775e-04 lr: 1.1775e-04 eta: 8:05:29 time: 0.3784 data_time: 0.0161 memory: 5384 loss: 0.3752 decode.loss_ce: 0.3752 decode.acc_seg: 83.3309 +2024/10/28 02:00:44 - mmengine - INFO - Iter(train) [ 87050/160000] base_lr: 1.1772e-04 lr: 1.1772e-04 eta: 8:05:08 time: 0.3780 data_time: 0.0169 memory: 5383 loss: 0.3519 decode.loss_ce: 0.3519 decode.acc_seg: 82.5450 +2024/10/28 02:01:03 - mmengine - INFO - Iter(train) [ 87100/160000] base_lr: 1.1768e-04 lr: 1.1768e-04 eta: 8:04:47 time: 0.3843 data_time: 0.0161 memory: 5385 loss: 0.3315 decode.loss_ce: 0.3315 decode.acc_seg: 89.6100 +2024/10/28 02:01:25 - mmengine - INFO - Iter(train) [ 87150/160000] base_lr: 1.1765e-04 lr: 1.1765e-04 eta: 8:04:28 time: 0.3925 data_time: 0.0173 memory: 5384 loss: 0.3847 decode.loss_ce: 0.3847 decode.acc_seg: 86.9331 +2024/10/28 02:01:44 - mmengine - INFO - Iter(train) [ 87200/160000] base_lr: 1.1762e-04 lr: 1.1762e-04 eta: 8:04:07 time: 0.3778 data_time: 0.0168 memory: 5384 loss: 0.3623 decode.loss_ce: 0.3623 decode.acc_seg: 88.0632 +2024/10/28 02:02:03 - mmengine - INFO - Iter(train) [ 87250/160000] base_lr: 1.1759e-04 lr: 1.1759e-04 eta: 8:03:47 time: 0.3809 data_time: 0.0161 memory: 5383 loss: 0.3910 decode.loss_ce: 0.3910 decode.acc_seg: 86.1564 +2024/10/28 02:02:25 - mmengine - INFO - Iter(train) [ 87300/160000] base_lr: 1.1755e-04 lr: 1.1755e-04 eta: 8:03:29 time: 0.3823 data_time: 0.0171 memory: 5384 loss: 0.3149 decode.loss_ce: 0.3149 decode.acc_seg: 89.1709 +2024/10/28 02:02:44 - mmengine - INFO - Iter(train) [ 87350/160000] base_lr: 1.1752e-04 lr: 1.1752e-04 eta: 8:03:08 time: 0.3768 data_time: 0.0163 memory: 5384 loss: 0.2988 decode.loss_ce: 0.2988 decode.acc_seg: 92.3244 +2024/10/28 02:03:03 - mmengine - INFO - Iter(train) [ 87400/160000] base_lr: 1.1749e-04 lr: 1.1749e-04 eta: 8:02:47 time: 0.3808 data_time: 0.0164 memory: 5384 loss: 0.3540 decode.loss_ce: 0.3540 decode.acc_seg: 89.5188 +2024/10/28 02:03:24 - mmengine - INFO - Iter(train) [ 87450/160000] base_lr: 1.1745e-04 lr: 1.1745e-04 eta: 8:02:28 time: 0.3795 data_time: 0.0164 memory: 5384 loss: 0.3328 decode.loss_ce: 0.3328 decode.acc_seg: 81.7590 +2024/10/28 02:03:43 - mmengine - INFO - Iter(train) [ 87500/160000] base_lr: 1.1742e-04 lr: 1.1742e-04 eta: 8:02:07 time: 0.3763 data_time: 0.0170 memory: 5384 loss: 0.3197 decode.loss_ce: 0.3197 decode.acc_seg: 86.7431 +2024/10/28 02:04:02 - mmengine - INFO - Iter(train) [ 87550/160000] base_lr: 1.1738e-04 lr: 1.1738e-04 eta: 8:01:46 time: 0.3738 data_time: 0.0163 memory: 5384 loss: 0.3018 decode.loss_ce: 0.3018 decode.acc_seg: 91.5962 +2024/10/28 02:04:25 - mmengine - INFO - Iter(train) [ 87600/160000] base_lr: 1.1735e-04 lr: 1.1735e-04 eta: 8:01:29 time: 0.3784 data_time: 0.0169 memory: 5384 loss: 0.4213 decode.loss_ce: 0.4213 decode.acc_seg: 89.5698 +2024/10/28 02:04:43 - mmengine - INFO - Iter(train) [ 87650/160000] base_lr: 1.1731e-04 lr: 1.1731e-04 eta: 8:01:08 time: 0.3746 data_time: 0.0166 memory: 5384 loss: 0.3423 decode.loss_ce: 0.3423 decode.acc_seg: 75.0638 +2024/10/28 02:05:02 - mmengine - INFO - Iter(train) [ 87700/160000] base_lr: 1.1728e-04 lr: 1.1728e-04 eta: 8:00:46 time: 0.3745 data_time: 0.0164 memory: 5384 loss: 0.3482 decode.loss_ce: 0.3482 decode.acc_seg: 86.9960 +2024/10/28 02:05:25 - mmengine - INFO - Iter(train) [ 87750/160000] base_lr: 1.1724e-04 lr: 1.1724e-04 eta: 8:00:29 time: 0.3782 data_time: 0.0167 memory: 5384 loss: 0.3886 decode.loss_ce: 0.3886 decode.acc_seg: 91.2394 +2024/10/28 02:05:44 - mmengine - INFO - Iter(train) [ 87800/160000] base_lr: 1.1721e-04 lr: 1.1721e-04 eta: 8:00:08 time: 0.3769 data_time: 0.0164 memory: 5384 loss: 0.3141 decode.loss_ce: 0.3141 decode.acc_seg: 92.2185 +2024/10/28 02:06:03 - mmengine - INFO - Iter(train) [ 87850/160000] base_lr: 1.1717e-04 lr: 1.1717e-04 eta: 7:59:47 time: 0.3837 data_time: 0.0173 memory: 5384 loss: 0.2710 decode.loss_ce: 0.2710 decode.acc_seg: 91.0024 +2024/10/28 02:06:25 - mmengine - INFO - Iter(train) [ 87900/160000] base_lr: 1.1714e-04 lr: 1.1714e-04 eta: 7:59:30 time: 0.3745 data_time: 0.0174 memory: 5384 loss: 0.3694 decode.loss_ce: 0.3694 decode.acc_seg: 83.2775 +2024/10/28 02:06:45 - mmengine - INFO - Iter(train) [ 87950/160000] base_lr: 1.1710e-04 lr: 1.1710e-04 eta: 7:59:10 time: 0.3970 data_time: 0.0165 memory: 5384 loss: 0.3492 decode.loss_ce: 0.3492 decode.acc_seg: 90.8317 +2024/10/28 02:07:05 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 02:07:05 - mmengine - INFO - Iter(train) [ 88000/160000] base_lr: 1.1706e-04 lr: 1.1706e-04 eta: 7:58:50 time: 0.3867 data_time: 0.0166 memory: 5384 loss: 0.4002 decode.loss_ce: 0.4002 decode.acc_seg: 89.1211 +2024/10/28 02:07:25 - mmengine - INFO - Iter(train) [ 88050/160000] base_lr: 1.1703e-04 lr: 1.1703e-04 eta: 7:58:30 time: 0.3791 data_time: 0.0168 memory: 5385 loss: 0.3042 decode.loss_ce: 0.3042 decode.acc_seg: 86.8190 +2024/10/28 02:07:45 - mmengine - INFO - Iter(train) [ 88100/160000] base_lr: 1.1699e-04 lr: 1.1699e-04 eta: 7:58:10 time: 0.3995 data_time: 0.0142 memory: 5382 loss: 0.3529 decode.loss_ce: 0.3529 decode.acc_seg: 85.8950 +2024/10/28 02:08:05 - mmengine - INFO - Iter(train) [ 88150/160000] base_lr: 1.1695e-04 lr: 1.1695e-04 eta: 7:57:50 time: 0.3826 data_time: 0.0163 memory: 5384 loss: 0.3353 decode.loss_ce: 0.3353 decode.acc_seg: 90.1375 +2024/10/28 02:08:25 - mmengine - INFO - Iter(train) [ 88200/160000] base_lr: 1.1692e-04 lr: 1.1692e-04 eta: 7:57:30 time: 0.3787 data_time: 0.0167 memory: 5384 loss: 0.3433 decode.loss_ce: 0.3433 decode.acc_seg: 88.2532 +2024/10/28 02:08:44 - mmengine - INFO - Iter(train) [ 88250/160000] base_lr: 1.1688e-04 lr: 1.1688e-04 eta: 7:57:09 time: 0.3782 data_time: 0.0163 memory: 5384 loss: 0.3232 decode.loss_ce: 0.3232 decode.acc_seg: 92.2712 +2024/10/28 02:09:03 - mmengine - INFO - Iter(train) [ 88300/160000] base_lr: 1.1684e-04 lr: 1.1684e-04 eta: 7:56:48 time: 0.3811 data_time: 0.0169 memory: 5386 loss: 0.3364 decode.loss_ce: 0.3364 decode.acc_seg: 89.9851 +2024/10/28 02:09:26 - mmengine - INFO - Iter(train) [ 88350/160000] base_lr: 1.1680e-04 lr: 1.1680e-04 eta: 7:56:31 time: 0.3792 data_time: 0.0173 memory: 5384 loss: 0.3643 decode.loss_ce: 0.3643 decode.acc_seg: 85.0709 +2024/10/28 02:09:45 - mmengine - INFO - Iter(train) [ 88400/160000] base_lr: 1.1677e-04 lr: 1.1677e-04 eta: 7:56:10 time: 0.3780 data_time: 0.0176 memory: 5384 loss: 0.3514 decode.loss_ce: 0.3514 decode.acc_seg: 83.7060 +2024/10/28 02:10:04 - mmengine - INFO - Iter(train) [ 88450/160000] base_lr: 1.1673e-04 lr: 1.1673e-04 eta: 7:55:49 time: 0.3768 data_time: 0.0165 memory: 5385 loss: 0.3496 decode.loss_ce: 0.3496 decode.acc_seg: 87.0719 +2024/10/28 02:10:24 - mmengine - INFO - Iter(train) [ 88500/160000] base_lr: 1.1669e-04 lr: 1.1669e-04 eta: 7:55:30 time: 0.3766 data_time: 0.0163 memory: 5384 loss: 0.3287 decode.loss_ce: 0.3287 decode.acc_seg: 87.0066 +2024/10/28 02:10:44 - mmengine - INFO - Iter(train) [ 88550/160000] base_lr: 1.1665e-04 lr: 1.1665e-04 eta: 7:55:09 time: 0.3786 data_time: 0.0162 memory: 5384 loss: 0.3688 decode.loss_ce: 0.3688 decode.acc_seg: 89.2003 +2024/10/28 02:11:03 - mmengine - INFO - Iter(train) [ 88600/160000] base_lr: 1.1661e-04 lr: 1.1661e-04 eta: 7:54:48 time: 0.3824 data_time: 0.0171 memory: 5384 loss: 0.2963 decode.loss_ce: 0.2963 decode.acc_seg: 88.3792 +2024/10/28 02:11:25 - mmengine - INFO - Iter(train) [ 88650/160000] base_lr: 1.1657e-04 lr: 1.1657e-04 eta: 7:54:31 time: 0.3752 data_time: 0.0173 memory: 5384 loss: 0.3617 decode.loss_ce: 0.3617 decode.acc_seg: 86.6138 +2024/10/28 02:11:44 - mmengine - INFO - Iter(train) [ 88700/160000] base_lr: 1.1653e-04 lr: 1.1653e-04 eta: 7:54:10 time: 0.3832 data_time: 0.0179 memory: 5384 loss: 0.3252 decode.loss_ce: 0.3252 decode.acc_seg: 89.7300 +2024/10/28 02:12:03 - mmengine - INFO - Iter(train) [ 88750/160000] base_lr: 1.1649e-04 lr: 1.1649e-04 eta: 7:53:49 time: 0.3826 data_time: 0.0173 memory: 5385 loss: 0.2913 decode.loss_ce: 0.2913 decode.acc_seg: 89.1795 +2024/10/28 02:12:26 - mmengine - INFO - Iter(train) [ 88800/160000] base_lr: 1.1645e-04 lr: 1.1645e-04 eta: 7:53:32 time: 0.3791 data_time: 0.0172 memory: 5384 loss: 0.3595 decode.loss_ce: 0.3595 decode.acc_seg: 84.1003 +2024/10/28 02:12:45 - mmengine - INFO - Iter(train) [ 88850/160000] base_lr: 1.1641e-04 lr: 1.1641e-04 eta: 7:53:11 time: 0.3771 data_time: 0.0166 memory: 5384 loss: 0.3136 decode.loss_ce: 0.3136 decode.acc_seg: 91.1092 +2024/10/28 02:13:04 - mmengine - INFO - Iter(train) [ 88900/160000] base_lr: 1.1637e-04 lr: 1.1637e-04 eta: 7:52:50 time: 0.3839 data_time: 0.0169 memory: 5384 loss: 0.3370 decode.loss_ce: 0.3370 decode.acc_seg: 89.4420 +2024/10/28 02:13:25 - mmengine - INFO - Iter(train) [ 88950/160000] base_lr: 1.1633e-04 lr: 1.1633e-04 eta: 7:52:31 time: 0.3811 data_time: 0.0168 memory: 5384 loss: 0.3388 decode.loss_ce: 0.3388 decode.acc_seg: 91.2312 +2024/10/28 02:13:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 02:13:44 - mmengine - INFO - Iter(train) [ 89000/160000] base_lr: 1.1629e-04 lr: 1.1629e-04 eta: 7:52:11 time: 0.3745 data_time: 0.0167 memory: 5384 loss: 0.2872 decode.loss_ce: 0.2872 decode.acc_seg: 92.2330 +2024/10/28 02:14:03 - mmengine - INFO - Iter(train) [ 89050/160000] base_lr: 1.1625e-04 lr: 1.1625e-04 eta: 7:51:49 time: 0.3777 data_time: 0.0175 memory: 5383 loss: 0.3379 decode.loss_ce: 0.3379 decode.acc_seg: 88.9019 +2024/10/28 02:14:25 - mmengine - INFO - Iter(train) [ 89100/160000] base_lr: 1.1621e-04 lr: 1.1621e-04 eta: 7:51:32 time: 0.3787 data_time: 0.0168 memory: 5384 loss: 0.2802 decode.loss_ce: 0.2802 decode.acc_seg: 82.2355 +2024/10/28 02:14:44 - mmengine - INFO - Iter(train) [ 89150/160000] base_lr: 1.1617e-04 lr: 1.1617e-04 eta: 7:51:11 time: 0.3774 data_time: 0.0160 memory: 5384 loss: 0.3519 decode.loss_ce: 0.3519 decode.acc_seg: 82.0465 +2024/10/28 02:15:03 - mmengine - INFO - Iter(train) [ 89200/160000] base_lr: 1.1613e-04 lr: 1.1613e-04 eta: 7:50:50 time: 0.3805 data_time: 0.0158 memory: 5385 loss: 0.4340 decode.loss_ce: 0.4340 decode.acc_seg: 88.3934 +2024/10/28 02:15:25 - mmengine - INFO - Iter(train) [ 89250/160000] base_lr: 1.1609e-04 lr: 1.1609e-04 eta: 7:50:32 time: 0.3782 data_time: 0.0168 memory: 5384 loss: 0.3262 decode.loss_ce: 0.3262 decode.acc_seg: 88.0326 +2024/10/28 02:15:45 - mmengine - INFO - Iter(train) [ 89300/160000] base_lr: 1.1604e-04 lr: 1.1604e-04 eta: 7:50:11 time: 0.3796 data_time: 0.0179 memory: 5384 loss: 0.3054 decode.loss_ce: 0.3054 decode.acc_seg: 89.2391 +2024/10/28 02:16:04 - mmengine - INFO - Iter(train) [ 89350/160000] base_lr: 1.1600e-04 lr: 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decode.acc_seg: 88.5017 +2024/10/28 02:17:46 - mmengine - INFO - Iter(train) [ 89600/160000] base_lr: 1.1579e-04 lr: 1.1579e-04 eta: 7:48:13 time: 0.3803 data_time: 0.0163 memory: 5385 loss: 0.3407 decode.loss_ce: 0.3407 decode.acc_seg: 82.8655 +2024/10/28 02:18:05 - mmengine - INFO - Iter(train) [ 89650/160000] base_lr: 1.1574e-04 lr: 1.1574e-04 eta: 7:47:52 time: 0.3816 data_time: 0.0165 memory: 5383 loss: 0.3101 decode.loss_ce: 0.3101 decode.acc_seg: 90.3067 +2024/10/28 02:18:24 - mmengine - INFO - Iter(train) [ 89700/160000] base_lr: 1.1570e-04 lr: 1.1570e-04 eta: 7:47:32 time: 0.3759 data_time: 0.0169 memory: 5383 loss: 0.3833 decode.loss_ce: 0.3833 decode.acc_seg: 77.3805 +2024/10/28 02:18:43 - mmengine - INFO - Iter(train) [ 89750/160000] base_lr: 1.1566e-04 lr: 1.1566e-04 eta: 7:47:11 time: 0.3737 data_time: 0.0163 memory: 5384 loss: 0.3394 decode.loss_ce: 0.3394 decode.acc_seg: 84.7580 +2024/10/28 02:19:02 - mmengine - INFO - Iter(train) [ 89800/160000] base_lr: 1.1561e-04 lr: 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eta: 7:45:33 time: 0.3783 data_time: 0.0184 memory: 5384 loss: 0.2980 decode.loss_ce: 0.2980 decode.acc_seg: 81.9498 +2024/10/28 02:20:44 - mmengine - INFO - Iter(train) [ 90050/160000] base_lr: 1.1539e-04 lr: 1.1539e-04 eta: 7:45:12 time: 0.3785 data_time: 0.0186 memory: 5384 loss: 0.3208 decode.loss_ce: 0.3208 decode.acc_seg: 90.3327 +2024/10/28 02:21:03 - mmengine - INFO - Iter(train) [ 90100/160000] base_lr: 1.1534e-04 lr: 1.1534e-04 eta: 7:44:51 time: 0.3867 data_time: 0.0177 memory: 5385 loss: 0.3339 decode.loss_ce: 0.3339 decode.acc_seg: 86.8359 +2024/10/28 02:21:26 - mmengine - INFO - Iter(train) [ 90150/160000] base_lr: 1.1530e-04 lr: 1.1530e-04 eta: 7:44:34 time: 0.4001 data_time: 0.0163 memory: 5384 loss: 0.3633 decode.loss_ce: 0.3633 decode.acc_seg: 93.3063 +2024/10/28 02:21:45 - mmengine - INFO - Iter(train) [ 90200/160000] base_lr: 1.1525e-04 lr: 1.1525e-04 eta: 7:44:13 time: 0.3802 data_time: 0.0183 memory: 5384 loss: 0.3277 decode.loss_ce: 0.3277 decode.acc_seg: 90.0102 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time: 0.3806 data_time: 0.0177 memory: 5384 loss: 0.3229 decode.loss_ce: 0.3229 decode.acc_seg: 86.8015 +2024/10/28 02:23:44 - mmengine - INFO - Iter(train) [ 90500/160000] base_lr: 1.1497e-04 lr: 1.1497e-04 eta: 7:42:13 time: 0.3748 data_time: 0.0178 memory: 5384 loss: 0.3706 decode.loss_ce: 0.3706 decode.acc_seg: 82.1180 +2024/10/28 02:24:03 - mmengine - INFO - Iter(train) [ 90550/160000] base_lr: 1.1492e-04 lr: 1.1492e-04 eta: 7:41:52 time: 0.3822 data_time: 0.0171 memory: 5384 loss: 0.3509 decode.loss_ce: 0.3509 decode.acc_seg: 88.7533 +2024/10/28 02:24:25 - mmengine - INFO - Iter(train) [ 90600/160000] base_lr: 1.1488e-04 lr: 1.1488e-04 eta: 7:41:34 time: 0.4042 data_time: 0.0157 memory: 5384 loss: 0.3448 decode.loss_ce: 0.3448 decode.acc_seg: 90.6289 +2024/10/28 02:24:45 - mmengine - INFO - Iter(train) [ 90650/160000] base_lr: 1.1483e-04 lr: 1.1483e-04 eta: 7:41:14 time: 0.3777 data_time: 0.0179 memory: 5386 loss: 0.3239 decode.loss_ce: 0.3239 decode.acc_seg: 92.0567 +2024/10/28 02:25:04 - mmengine - INFO - Iter(train) [ 90700/160000] base_lr: 1.1478e-04 lr: 1.1478e-04 eta: 7:40:53 time: 0.3829 data_time: 0.0175 memory: 5383 loss: 0.3303 decode.loss_ce: 0.3303 decode.acc_seg: 86.7645 +2024/10/28 02:25:25 - mmengine - INFO - Iter(train) [ 90750/160000] base_lr: 1.1473e-04 lr: 1.1473e-04 eta: 7:40:34 time: 0.3784 data_time: 0.0187 memory: 5384 loss: 0.4097 decode.loss_ce: 0.4097 decode.acc_seg: 90.4690 +2024/10/28 02:25:44 - mmengine - INFO - Iter(train) [ 90800/160000] base_lr: 1.1469e-04 lr: 1.1469e-04 eta: 7:40:13 time: 0.3799 data_time: 0.0180 memory: 5383 loss: 0.3179 decode.loss_ce: 0.3179 decode.acc_seg: 91.7334 +2024/10/28 02:26:03 - mmengine - INFO - Iter(train) [ 90850/160000] base_lr: 1.1464e-04 lr: 1.1464e-04 eta: 7:39:52 time: 0.3819 data_time: 0.0164 memory: 5384 loss: 0.3545 decode.loss_ce: 0.3545 decode.acc_seg: 79.3053 +2024/10/28 02:26:25 - mmengine - INFO - Iter(train) [ 90900/160000] base_lr: 1.1459e-04 lr: 1.1459e-04 eta: 7:39:34 time: 0.3789 data_time: 0.0165 memory: 5382 loss: 0.3271 decode.loss_ce: 0.3271 decode.acc_seg: 85.3094 +2024/10/28 02:26:45 - mmengine - INFO - Iter(train) [ 90950/160000] base_lr: 1.1454e-04 lr: 1.1454e-04 eta: 7:39:14 time: 0.3792 data_time: 0.0160 memory: 5385 loss: 0.3489 decode.loss_ce: 0.3489 decode.acc_seg: 90.9920 +2024/10/28 02:27:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 02:27:03 - mmengine - INFO - Iter(train) [ 91000/160000] base_lr: 1.1449e-04 lr: 1.1449e-04 eta: 7:38:53 time: 0.3755 data_time: 0.0155 memory: 5386 loss: 0.3261 decode.loss_ce: 0.3261 decode.acc_seg: 93.1722 +2024/10/28 02:27:24 - mmengine - INFO - Iter(train) [ 91050/160000] base_lr: 1.1444e-04 lr: 1.1444e-04 eta: 7:38:34 time: 0.3716 data_time: 0.0148 memory: 5384 loss: 0.3276 decode.loss_ce: 0.3276 decode.acc_seg: 84.2610 +2024/10/28 02:27:43 - mmengine - INFO - Iter(train) [ 91100/160000] base_lr: 1.1439e-04 lr: 1.1439e-04 eta: 7:38:13 time: 0.3746 data_time: 0.0153 memory: 5384 loss: 0.3767 decode.loss_ce: 0.3767 decode.acc_seg: 93.1837 +2024/10/28 02:28:02 - mmengine - INFO - Iter(train) [ 91150/160000] base_lr: 1.1434e-04 lr: 1.1434e-04 eta: 7:37:52 time: 0.3751 data_time: 0.0158 memory: 5385 loss: 0.3303 decode.loss_ce: 0.3303 decode.acc_seg: 80.3419 +2024/10/28 02:28:24 - mmengine - INFO - Iter(train) [ 91200/160000] base_lr: 1.1429e-04 lr: 1.1429e-04 eta: 7:37:34 time: 0.3795 data_time: 0.0148 memory: 5384 loss: 0.4054 decode.loss_ce: 0.4054 decode.acc_seg: 79.9872 +2024/10/28 02:28:43 - mmengine - INFO - Iter(train) [ 91250/160000] base_lr: 1.1424e-04 lr: 1.1424e-04 eta: 7:37:13 time: 0.3715 data_time: 0.0151 memory: 5384 loss: 0.3420 decode.loss_ce: 0.3420 decode.acc_seg: 84.3566 +2024/10/28 02:29:02 - mmengine - INFO - Iter(train) [ 91300/160000] base_lr: 1.1419e-04 lr: 1.1419e-04 eta: 7:36:52 time: 0.3730 data_time: 0.0153 memory: 5384 loss: 0.3754 decode.loss_ce: 0.3754 decode.acc_seg: 87.1606 +2024/10/28 02:29:24 - 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data_time: 0.0160 memory: 5384 loss: 0.2864 decode.loss_ce: 0.2864 decode.acc_seg: 90.1983 +2024/10/28 02:31:05 - mmengine - INFO - Iter(train) [ 91600/160000] base_lr: 1.1388e-04 lr: 1.1388e-04 eta: 7:34:55 time: 0.3869 data_time: 0.0165 memory: 5384 loss: 0.3013 decode.loss_ce: 0.3013 decode.acc_seg: 87.0659 +2024/10/28 02:31:25 - mmengine - INFO - Iter(train) [ 91650/160000] base_lr: 1.1383e-04 lr: 1.1383e-04 eta: 7:34:35 time: 0.3804 data_time: 0.0164 memory: 5383 loss: 0.3304 decode.loss_ce: 0.3304 decode.acc_seg: 84.5221 +2024/10/28 02:31:44 - mmengine - INFO - Iter(train) [ 91700/160000] base_lr: 1.1378e-04 lr: 1.1378e-04 eta: 7:34:14 time: 0.3776 data_time: 0.0164 memory: 5385 loss: 0.3142 decode.loss_ce: 0.3142 decode.acc_seg: 86.6958 +2024/10/28 02:32:03 - mmengine - INFO - Iter(train) [ 91750/160000] base_lr: 1.1373e-04 lr: 1.1373e-04 eta: 7:33:53 time: 0.3809 data_time: 0.0163 memory: 5384 loss: 0.3146 decode.loss_ce: 0.3146 decode.acc_seg: 92.0672 +2024/10/28 02:32:25 - mmengine - INFO - Iter(train) [ 91800/160000] base_lr: 1.1367e-04 lr: 1.1367e-04 eta: 7:33:35 time: 0.3757 data_time: 0.0178 memory: 5384 loss: 0.3409 decode.loss_ce: 0.3409 decode.acc_seg: 89.1839 +2024/10/28 02:32:44 - mmengine - INFO - Iter(train) [ 91850/160000] base_lr: 1.1362e-04 lr: 1.1362e-04 eta: 7:33:15 time: 0.3790 data_time: 0.0179 memory: 5385 loss: 0.3971 decode.loss_ce: 0.3971 decode.acc_seg: 86.8317 +2024/10/28 02:33:03 - mmengine - INFO - Iter(train) [ 91900/160000] base_lr: 1.1357e-04 lr: 1.1357e-04 eta: 7:32:54 time: 0.3820 data_time: 0.0183 memory: 5384 loss: 0.3498 decode.loss_ce: 0.3498 decode.acc_seg: 93.2793 +2024/10/28 02:33:25 - mmengine - INFO - Iter(train) [ 91950/160000] base_lr: 1.1351e-04 lr: 1.1351e-04 eta: 7:32:36 time: 0.3835 data_time: 0.0191 memory: 5384 loss: 0.3604 decode.loss_ce: 0.3604 decode.acc_seg: 88.1995 +2024/10/28 02:33:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 02:33:44 - mmengine - 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memory: 5384 loss: 0.3694 decode.loss_ce: 0.3694 decode.acc_seg: 92.6521 +2024/10/28 02:35:25 - mmengine - INFO - Iter(train) [ 92250/160000] base_lr: 1.1319e-04 lr: 1.1319e-04 eta: 7:30:36 time: 0.3783 data_time: 0.0173 memory: 5384 loss: 0.3208 decode.loss_ce: 0.3208 decode.acc_seg: 94.2982 +2024/10/28 02:35:44 - mmengine - INFO - Iter(train) [ 92300/160000] base_lr: 1.1314e-04 lr: 1.1314e-04 eta: 7:30:15 time: 0.3795 data_time: 0.0173 memory: 5384 loss: 0.3450 decode.loss_ce: 0.3450 decode.acc_seg: 87.5010 +2024/10/28 02:36:03 - mmengine - INFO - Iter(train) [ 92350/160000] base_lr: 1.1308e-04 lr: 1.1308e-04 eta: 7:29:54 time: 0.3778 data_time: 0.0175 memory: 5384 loss: 0.3287 decode.loss_ce: 0.3287 decode.acc_seg: 87.3948 +2024/10/28 02:36:25 - mmengine - INFO - Iter(train) [ 92400/160000] base_lr: 1.1303e-04 lr: 1.1303e-04 eta: 7:29:36 time: 0.3815 data_time: 0.0168 memory: 5384 loss: 0.3211 decode.loss_ce: 0.3211 decode.acc_seg: 91.0096 +2024/10/28 02:36:44 - mmengine - INFO - 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fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 02:47:03 - mmengine - INFO - Iter(train) [ 94000/160000] base_lr: 1.1116e-04 lr: 1.1116e-04 eta: 7:18:56 time: 0.3794 data_time: 0.0165 memory: 5384 loss: 0.3323 decode.loss_ce: 0.3323 decode.acc_seg: 85.9656 +2024/10/28 02:47:25 - mmengine - INFO - Iter(train) [ 94050/160000] base_lr: 1.1110e-04 lr: 1.1110e-04 eta: 7:18:38 time: 0.3744 data_time: 0.0176 memory: 5382 loss: 0.3530 decode.loss_ce: 0.3530 decode.acc_seg: 89.4952 +2024/10/28 02:47:44 - mmengine - INFO - Iter(train) [ 94100/160000] base_lr: 1.1104e-04 lr: 1.1104e-04 eta: 7:18:17 time: 0.3765 data_time: 0.0170 memory: 5383 loss: 0.3516 decode.loss_ce: 0.3516 decode.acc_seg: 79.5896 +2024/10/28 02:48:03 - mmengine - INFO - Iter(train) [ 94150/160000] base_lr: 1.1097e-04 lr: 1.1097e-04 eta: 7:17:57 time: 0.3818 data_time: 0.0170 memory: 5384 loss: 0.3584 decode.loss_ce: 0.3584 decode.acc_seg: 87.6478 +2024/10/28 02:48:25 - mmengine - INFO - Iter(train) [ 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decode.loss_ce: 0.3311 decode.acc_seg: 88.7743 +2024/10/28 02:54:25 - mmengine - INFO - Iter(train) [ 95100/160000] base_lr: 1.0976e-04 lr: 1.0976e-04 eta: 7:11:40 time: 0.3735 data_time: 0.0161 memory: 5385 loss: 0.3413 decode.loss_ce: 0.3413 decode.acc_seg: 87.0479 +2024/10/28 02:54:44 - mmengine - INFO - Iter(train) [ 95150/160000] base_lr: 1.0969e-04 lr: 1.0969e-04 eta: 7:11:19 time: 0.3722 data_time: 0.0163 memory: 5383 loss: 0.2882 decode.loss_ce: 0.2882 decode.acc_seg: 90.6256 +2024/10/28 02:55:02 - mmengine - INFO - Iter(train) [ 95200/160000] base_lr: 1.0963e-04 lr: 1.0963e-04 eta: 7:10:58 time: 0.3797 data_time: 0.0168 memory: 5385 loss: 0.3137 decode.loss_ce: 0.3137 decode.acc_seg: 90.3086 +2024/10/28 02:55:24 - mmengine - INFO - Iter(train) [ 95250/160000] base_lr: 1.0956e-04 lr: 1.0956e-04 eta: 7:10:39 time: 0.3760 data_time: 0.0175 memory: 5384 loss: 0.3540 decode.loss_ce: 0.3540 decode.acc_seg: 88.4604 +2024/10/28 02:55:43 - mmengine - INFO - Iter(train) [ 95300/160000] 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decode.loss_ce: 0.3110 decode.acc_seg: 90.5691 +2024/10/28 02:57:25 - mmengine - INFO - Iter(train) [ 95550/160000] base_lr: 1.0916e-04 lr: 1.0916e-04 eta: 7:08:40 time: 0.3738 data_time: 0.0162 memory: 5384 loss: 0.3456 decode.loss_ce: 0.3456 decode.acc_seg: 82.3732 +2024/10/28 02:57:44 - mmengine - INFO - Iter(train) [ 95600/160000] base_lr: 1.0909e-04 lr: 1.0909e-04 eta: 7:08:19 time: 0.3759 data_time: 0.0166 memory: 5383 loss: 0.3092 decode.loss_ce: 0.3092 decode.acc_seg: 80.9351 +2024/10/28 02:58:03 - mmengine - INFO - Iter(train) [ 95650/160000] base_lr: 1.0902e-04 lr: 1.0902e-04 eta: 7:07:59 time: 0.3773 data_time: 0.0165 memory: 5383 loss: 0.2996 decode.loss_ce: 0.2996 decode.acc_seg: 86.7555 +2024/10/28 02:58:24 - mmengine - INFO - Iter(train) [ 95700/160000] base_lr: 1.0895e-04 lr: 1.0895e-04 eta: 7:07:40 time: 0.3734 data_time: 0.0155 memory: 5384 loss: 0.4356 decode.loss_ce: 0.4356 decode.acc_seg: 80.7847 +2024/10/28 02:58:43 - mmengine - INFO - Iter(train) [ 95750/160000] base_lr: 1.0889e-04 lr: 1.0889e-04 eta: 7:07:19 time: 0.3751 data_time: 0.0157 memory: 5385 loss: 0.2850 decode.loss_ce: 0.2850 decode.acc_seg: 87.2534 +2024/10/28 02:59:02 - mmengine - INFO - Iter(train) [ 95800/160000] base_lr: 1.0882e-04 lr: 1.0882e-04 eta: 7:06:58 time: 0.3721 data_time: 0.0155 memory: 5385 loss: 0.3069 decode.loss_ce: 0.3069 decode.acc_seg: 87.6069 +2024/10/28 02:59:24 - mmengine - INFO - Iter(train) [ 95850/160000] base_lr: 1.0875e-04 lr: 1.0875e-04 eta: 7:06:40 time: 0.3722 data_time: 0.0153 memory: 5384 loss: 0.2993 decode.loss_ce: 0.2993 decode.acc_seg: 89.2333 +2024/10/28 02:59:43 - mmengine - INFO - Iter(train) [ 95900/160000] base_lr: 1.0868e-04 lr: 1.0868e-04 eta: 7:06:19 time: 0.3729 data_time: 0.0155 memory: 5384 loss: 0.3331 decode.loss_ce: 0.3331 decode.acc_seg: 90.8531 +2024/10/28 03:00:02 - mmengine - INFO - Iter(train) [ 95950/160000] base_lr: 1.0861e-04 lr: 1.0861e-04 eta: 7:05:58 time: 0.3754 data_time: 0.0155 memory: 5383 loss: 0.3303 decode.loss_ce: 0.3303 decode.acc_seg: 92.3855 +2024/10/28 03:00:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 03:00:25 - mmengine - INFO - Iter(train) [ 96000/160000] base_lr: 1.0854e-04 lr: 1.0854e-04 eta: 7:05:41 time: 0.3764 data_time: 0.0156 memory: 5384 loss: 0.3224 decode.loss_ce: 0.3224 decode.acc_seg: 89.6474 +2024/10/28 03:00:25 - mmengine - INFO - Saving checkpoint at 96000 iterations +2024/10/28 03:00:30 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:00:15 time: 0.0338 data_time: 0.0015 memory: 980 +2024/10/28 03:00:31 - mmengine - INFO - Iter(val) [100/500] eta: 0:00:13 time: 0.0326 data_time: 0.0013 memory: 1050 +2024/10/28 03:00:33 - mmengine - INFO - Iter(val) [150/500] eta: 0:00:11 time: 0.0330 data_time: 0.0014 memory: 767 +2024/10/28 03:00:35 - mmengine - INFO - Iter(val) [200/500] eta: 0:00:10 time: 0.0338 data_time: 0.0015 memory: 800 +2024/10/28 03:00:36 - mmengine - INFO - Iter(val) [250/500] eta: 0:00:08 time: 0.0333 data_time: 0.0015 memory: 839 +2024/10/28 03:00:38 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:06 time: 0.0338 data_time: 0.0017 memory: 1961 +2024/10/28 03:00:40 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:05 time: 0.0331 data_time: 0.0014 memory: 765 +2024/10/28 03:00:41 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:03 time: 0.0335 data_time: 0.0014 memory: 837 +2024/10/28 03:00:43 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:01 time: 0.0333 data_time: 0.0014 memory: 772 +2024/10/28 03:00:45 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.0336 data_time: 0.0014 memory: 822 +2024/10/28 03:00:46 - mmengine - INFO - per class results: +2024/10/28 03:00:46 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 71.31 | 85.28 | +| building | 76.78 | 88.31 | +| sky | 92.69 | 96.53 | +| floor | 75.42 | 88.0 | +| tree | 68.89 | 85.27 | +| ceiling | 78.85 | 88.39 | +| road | 77.89 | 84.93 | +| bed | 84.04 | 92.33 | +| windowpane | 54.25 | 64.16 | +| grass | 61.3 | 73.46 | +| cabinet | 53.43 | 66.09 | +| sidewalk | 57.4 | 77.12 | +| person | 69.86 | 84.72 | +| earth | 31.24 | 46.23 | +| door | 37.16 | 63.2 | +| table | 50.68 | 63.15 | +| mountain | 47.09 | 58.61 | +| plant | 45.95 | 54.56 | +| curtain | 66.32 | 78.6 | +| chair | 45.83 | 60.52 | +| car | 75.96 | 89.72 | +| water | 40.18 | 52.18 | +| painting | 62.35 | 77.34 | +| sofa | 58.03 | 71.45 | +| shelf | 34.76 | 50.17 | +| house | 33.56 | 61.64 | +| sea | 45.51 | 71.45 | +| mirror | 54.62 | 66.82 | +| rug | 55.3 | 64.99 | +| field | 29.16 | 53.42 | +| armchair | 36.8 | 65.14 | +| seat | 51.39 | 80.76 | +| fence | 25.8 | 37.57 | +| desk | 42.69 | 59.51 | +| rock | 31.74 | 57.84 | +| wardrobe | 37.69 | 59.4 | +| lamp | 49.4 | 62.6 | +| bathtub | 58.58 | 92.14 | +| railing | 27.08 | 41.14 | +| cushion | 43.01 | 54.73 | +| base | 10.81 | 12.76 | +| box | 14.24 | 21.25 | +| column | 33.99 | 51.41 | +| signboard | 25.85 | 32.78 | +| chest of drawers | 32.6 | 51.39 | +| counter | 25.92 | 39.08 | +| sand | 38.26 | 60.93 | +| sink | 47.67 | 56.86 | +| skyscraper | 39.16 | 62.39 | +| fireplace | 63.66 | 84.26 | +| refrigerator | 68.51 | 84.38 | +| grandstand | 29.78 | 61.66 | +| path | 15.87 | 25.78 | +| stairs | 28.42 | 36.42 | +| runway | 60.92 | 91.43 | +| case | 36.64 | 50.99 | +| pool table | 86.87 | 90.09 | +| pillow | 46.71 | 56.18 | +| screen door | 40.26 | 45.14 | +| stairway | 21.48 | 31.57 | +| river | 9.91 | 18.14 | +| bridge | 36.42 | 45.36 | +| bookcase | 29.86 | 55.51 | +| blind | 35.11 | 41.48 | +| coffee table | 55.41 | 75.09 | +| toilet | 74.02 | 80.29 | +| flower | 28.53 | 45.35 | +| book | 36.93 | 48.73 | +| hill | 4.34 | 8.47 | +| bench | 31.27 | 47.23 | +| countertop | 45.48 | 57.43 | +| stove | 62.04 | 72.02 | +| palm | 38.99 | 54.67 | +| kitchen island | 30.22 | 69.91 | +| computer | 43.3 | 59.49 | +| swivel chair | 25.16 | 33.77 | +| boat | 50.71 | 69.02 | +| bar | 24.33 | 27.75 | +| arcade machine | 42.64 | 43.71 | +| hovel | 12.58 | 13.48 | +| bus | 71.97 | 80.68 | +| towel | 49.17 | 59.66 | +| light | 25.28 | 27.17 | +| truck | 5.82 | 7.63 | +| tower | 18.92 | 29.86 | +| chandelier | 57.68 | 77.29 | +| awning | 15.14 | 16.96 | +| streetlight | 8.21 | 9.76 | +| booth | 62.4 | 91.17 | +| television receiver | 55.63 | 76.5 | +| airplane | 53.22 | 66.09 | +| dirt track | 12.86 | 20.04 | +| apparel | 22.81 | 38.98 | +| pole | 14.28 | 23.82 | +| land | 4.27 | 7.37 | +| bannister | 2.33 | 4.2 | +| escalator | 18.07 | 20.82 | +| ottoman | 30.65 | 46.59 | +| bottle | 23.44 | 41.3 | +| buffet | 44.93 | 51.98 | +| poster | 0.91 | 0.92 | +| stage | 12.02 | 21.07 | +| van | 30.45 | 35.41 | +| ship | 71.03 | 74.07 | +| fountain | 19.51 | 20.4 | +| conveyer belt | 55.43 | 58.63 | +| canopy | 9.23 | 12.99 | +| washer | 65.48 | 71.4 | +| plaything | 20.73 | 25.51 | +| swimming pool | 52.06 | 65.49 | +| stool | 33.06 | 37.98 | +| barrel | 37.61 | 63.83 | +| basket | 18.69 | 26.96 | +| waterfall | 55.87 | 84.05 | +| tent | 86.82 | 92.02 | +| bag | 5.63 | 8.7 | +| minibike | 42.5 | 78.95 | +| cradle | 70.64 | 89.95 | +| oven | 38.29 | 59.45 | +| ball | 36.94 | 42.09 | +| food | 22.12 | 24.36 | +| step | 5.38 | 7.09 | +| tank | 30.6 | 36.86 | +| trade name | 14.43 | 16.05 | +| microwave | 35.46 | 39.72 | +| pot | 30.11 | 34.58 | +| animal | 43.95 | 45.28 | +| bicycle | 43.9 | 66.13 | +| lake | 48.49 | 78.0 | +| dishwasher | 41.49 | 44.26 | +| screen | 50.72 | 56.16 | +| blanket | 9.78 | 12.97 | +| sculpture | 36.23 | 57.85 | +| hood | 50.34 | 58.37 | +| sconce | 20.67 | 22.75 | +| vase | 21.93 | 29.48 | +| traffic light | 16.19 | 20.24 | +| tray | 4.71 | 15.97 | +| ashcan | 27.63 | 35.03 | +| fan | 42.75 | 61.43 | +| pier | 15.5 | 21.11 | +| crt screen | 1.65 | 3.36 | +| plate | 29.28 | 43.73 | +| monitor | 18.72 | 20.45 | +| bulletin board | 32.92 | 35.19 | +| shower | 0.0 | 0.0 | +| radiator | 44.33 | 52.46 | +| glass | 2.55 | 2.7 | +| clock | 6.92 | 11.83 | +| flag | 38.16 | 47.19 | ++---------------------+-------+-------+ +2024/10/28 03:00:46 - mmengine - INFO - Iter(val) [500/500] aAcc: 77.7500 mIoU: 38.4800 mAcc: 50.3400 data_time: 0.0015 time: 0.0338 +2024/10/28 03:01:05 - mmengine - INFO - Iter(train) [ 96050/160000] base_lr: 1.0847e-04 lr: 1.0847e-04 eta: 7:05:21 time: 0.3741 data_time: 0.0152 memory: 5384 loss: 0.3173 decode.loss_ce: 0.3173 decode.acc_seg: 89.6745 +2024/10/28 03:01:24 - mmengine - INFO - Iter(train) [ 96100/160000] base_lr: 1.0840e-04 lr: 1.0840e-04 eta: 7:05:00 time: 0.3758 data_time: 0.0155 memory: 5386 loss: 0.3788 decode.loss_ce: 0.3788 decode.acc_seg: 81.8376 +2024/10/28 03:01:43 - mmengine - INFO - Iter(train) [ 96150/160000] base_lr: 1.0833e-04 lr: 1.0833e-04 eta: 7:04:40 time: 0.3746 data_time: 0.0160 memory: 5384 loss: 0.3547 decode.loss_ce: 0.3547 decode.acc_seg: 77.7335 +2024/10/28 03:02:02 - mmengine - INFO - Iter(train) [ 96200/160000] base_lr: 1.0826e-04 lr: 1.0826e-04 eta: 7:04:19 time: 0.3810 data_time: 0.0157 memory: 5384 loss: 0.3484 decode.loss_ce: 0.3484 decode.acc_seg: 77.5865 +2024/10/28 03:02:25 - mmengine - INFO - Iter(train) [ 96250/160000] base_lr: 1.0819e-04 lr: 1.0819e-04 eta: 7:04:01 time: 0.3731 data_time: 0.0155 memory: 5385 loss: 0.3018 decode.loss_ce: 0.3018 decode.acc_seg: 83.1128 +2024/10/28 03:02:43 - mmengine - INFO - Iter(train) [ 96300/160000] base_lr: 1.0812e-04 lr: 1.0812e-04 eta: 7:03:40 time: 0.3772 data_time: 0.0157 memory: 5384 loss: 0.3425 decode.loss_ce: 0.3425 decode.acc_seg: 86.9871 +2024/10/28 03:03:02 - mmengine - INFO - Iter(train) [ 96350/160000] base_lr: 1.0805e-04 lr: 1.0805e-04 eta: 7:03:19 time: 0.3803 data_time: 0.0171 memory: 5386 loss: 0.2874 decode.loss_ce: 0.2874 decode.acc_seg: 87.8673 +2024/10/28 03:03:25 - mmengine - INFO - Iter(train) [ 96400/160000] base_lr: 1.0798e-04 lr: 1.0798e-04 eta: 7:03:01 time: 0.3733 data_time: 0.0172 memory: 5385 loss: 0.3128 decode.loss_ce: 0.3128 decode.acc_seg: 87.8716 +2024/10/28 03:03:44 - mmengine - INFO - Iter(train) [ 96450/160000] base_lr: 1.0791e-04 lr: 1.0791e-04 eta: 7:02:40 time: 0.3757 data_time: 0.0185 memory: 5384 loss: 0.2553 decode.loss_ce: 0.2553 decode.acc_seg: 88.9497 +2024/10/28 03:04:03 - mmengine - INFO - Iter(train) [ 96500/160000] base_lr: 1.0784e-04 lr: 1.0784e-04 eta: 7:02:20 time: 0.3822 data_time: 0.0190 memory: 5386 loss: 0.2901 decode.loss_ce: 0.2901 decode.acc_seg: 87.9049 +2024/10/28 03:04:25 - mmengine - INFO - Iter(train) [ 96550/160000] base_lr: 1.0777e-04 lr: 1.0777e-04 eta: 7:02:01 time: 0.3850 data_time: 0.0193 memory: 5384 loss: 0.4531 decode.loss_ce: 0.4531 decode.acc_seg: 93.1822 +2024/10/28 03:04:44 - mmengine - INFO - Iter(train) [ 96600/160000] base_lr: 1.0770e-04 lr: 1.0770e-04 eta: 7:01:40 time: 0.3765 data_time: 0.0194 memory: 5384 loss: 0.4010 decode.loss_ce: 0.4010 decode.acc_seg: 88.7414 +2024/10/28 03:05:03 - mmengine - INFO - Iter(train) [ 96650/160000] base_lr: 1.0763e-04 lr: 1.0763e-04 eta: 7:01:20 time: 0.3811 data_time: 0.0184 memory: 5384 loss: 0.3358 decode.loss_ce: 0.3358 decode.acc_seg: 87.6402 +2024/10/28 03:05:25 - mmengine - INFO - Iter(train) [ 96700/160000] base_lr: 1.0755e-04 lr: 1.0755e-04 eta: 7:01:02 time: 0.3786 data_time: 0.0181 memory: 5385 loss: 0.3250 decode.loss_ce: 0.3250 decode.acc_seg: 89.2799 +2024/10/28 03:05:44 - mmengine - INFO - Iter(train) [ 96750/160000] base_lr: 1.0748e-04 lr: 1.0748e-04 eta: 7:00:41 time: 0.3793 data_time: 0.0181 memory: 5384 loss: 0.3448 decode.loss_ce: 0.3448 decode.acc_seg: 90.6077 +2024/10/28 03:06:03 - mmengine - INFO - Iter(train) [ 96800/160000] base_lr: 1.0741e-04 lr: 1.0741e-04 eta: 7:00:20 time: 0.3776 data_time: 0.0184 memory: 5384 loss: 0.2825 decode.loss_ce: 0.2825 decode.acc_seg: 89.5833 +2024/10/28 03:06:25 - mmengine - INFO - Iter(train) [ 96850/160000] base_lr: 1.0734e-04 lr: 1.0734e-04 eta: 7:00:02 time: 0.3797 data_time: 0.0184 memory: 5384 loss: 0.3359 decode.loss_ce: 0.3359 decode.acc_seg: 88.7491 +2024/10/28 03:06:44 - mmengine - INFO - Iter(train) [ 96900/160000] base_lr: 1.0727e-04 lr: 1.0727e-04 eta: 6:59:41 time: 0.3825 data_time: 0.0188 memory: 5384 loss: 0.3151 decode.loss_ce: 0.3151 decode.acc_seg: 89.5970 +2024/10/28 03:07:03 - mmengine - INFO - Iter(train) [ 96950/160000] base_lr: 1.0719e-04 lr: 1.0719e-04 eta: 6:59:20 time: 0.3774 data_time: 0.0161 memory: 5384 loss: 0.3404 decode.loss_ce: 0.3404 decode.acc_seg: 81.9853 +2024/10/28 03:07:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 03:07:24 - mmengine - INFO - Iter(train) [ 97000/160000] base_lr: 1.0712e-04 lr: 1.0712e-04 eta: 6:59:01 time: 0.3754 data_time: 0.0166 memory: 5384 loss: 0.3380 decode.loss_ce: 0.3380 decode.acc_seg: 87.4940 +2024/10/28 03:07:43 - mmengine - INFO - Iter(train) [ 97050/160000] base_lr: 1.0705e-04 lr: 1.0705e-04 eta: 6:58:41 time: 0.3770 data_time: 0.0167 memory: 5384 loss: 0.3115 decode.loss_ce: 0.3115 decode.acc_seg: 81.6512 +2024/10/28 03:08:02 - mmengine - INFO - Iter(train) [ 97100/160000] base_lr: 1.0697e-04 lr: 1.0697e-04 eta: 6:58:20 time: 0.3822 data_time: 0.0162 memory: 5384 loss: 0.3736 decode.loss_ce: 0.3736 decode.acc_seg: 85.4745 +2024/10/28 03:08:25 - mmengine - INFO - Iter(train) [ 97150/160000] base_lr: 1.0690e-04 lr: 1.0690e-04 eta: 6:58:02 time: 0.3767 data_time: 0.0166 memory: 5384 loss: 0.3164 decode.loss_ce: 0.3164 decode.acc_seg: 89.5752 +2024/10/28 03:08:44 - mmengine - INFO - Iter(train) [ 97200/160000] base_lr: 1.0683e-04 lr: 1.0683e-04 eta: 6:57:41 time: 0.3812 data_time: 0.0170 memory: 5384 loss: 0.3022 decode.loss_ce: 0.3022 decode.acc_seg: 78.3114 +2024/10/28 03:09:03 - mmengine - INFO - Iter(train) [ 97250/160000] base_lr: 1.0675e-04 lr: 1.0675e-04 eta: 6:57:21 time: 0.3873 data_time: 0.0172 memory: 5384 loss: 0.3003 decode.loss_ce: 0.3003 decode.acc_seg: 85.4121 +2024/10/28 03:09:25 - mmengine - INFO - Iter(train) [ 97300/160000] base_lr: 1.0668e-04 lr: 1.0668e-04 eta: 6:57:02 time: 0.3772 data_time: 0.0166 memory: 5384 loss: 0.3133 decode.loss_ce: 0.3133 decode.acc_seg: 87.4262 +2024/10/28 03:09:44 - mmengine - INFO - Iter(train) [ 97350/160000] base_lr: 1.0661e-04 lr: 1.0661e-04 eta: 6:56:41 time: 0.3754 data_time: 0.0163 memory: 5386 loss: 0.3368 decode.loss_ce: 0.3368 decode.acc_seg: 84.2123 +2024/10/28 03:10:03 - mmengine - INFO - Iter(train) [ 97400/160000] base_lr: 1.0653e-04 lr: 1.0653e-04 eta: 6:56:21 time: 0.3859 data_time: 0.0160 memory: 5384 loss: 0.3405 decode.loss_ce: 0.3405 decode.acc_seg: 91.5951 +2024/10/28 03:10:25 - mmengine - INFO - Iter(train) [ 97450/160000] base_lr: 1.0646e-04 lr: 1.0646e-04 eta: 6:56:02 time: 0.3780 data_time: 0.0178 memory: 5385 loss: 0.3459 decode.loss_ce: 0.3459 decode.acc_seg: 86.4175 +2024/10/28 03:10:45 - mmengine - INFO - Iter(train) [ 97500/160000] base_lr: 1.0638e-04 lr: 1.0638e-04 eta: 6:55:43 time: 0.3934 data_time: 0.0167 memory: 5384 loss: 0.3000 decode.loss_ce: 0.3000 decode.acc_seg: 88.4026 +2024/10/28 03:11:04 - mmengine - INFO - Iter(train) [ 97550/160000] base_lr: 1.0631e-04 lr: 1.0631e-04 eta: 6:55:22 time: 0.3871 data_time: 0.0169 memory: 5384 loss: 0.2428 decode.loss_ce: 0.2428 decode.acc_seg: 90.8233 +2024/10/28 03:11:25 - mmengine - INFO - Iter(train) [ 97600/160000] base_lr: 1.0623e-04 lr: 1.0623e-04 eta: 6:55:03 time: 0.3811 data_time: 0.0183 memory: 5384 loss: 0.2919 decode.loss_ce: 0.2919 decode.acc_seg: 92.5997 +2024/10/28 03:11:44 - mmengine - INFO - Iter(train) [ 97650/160000] base_lr: 1.0616e-04 lr: 1.0616e-04 eta: 6:54:42 time: 0.3778 data_time: 0.0178 memory: 5384 loss: 0.3501 decode.loss_ce: 0.3501 decode.acc_seg: 87.3054 +2024/10/28 03:12:03 - mmengine - INFO - Iter(train) [ 97700/160000] base_lr: 1.0608e-04 lr: 1.0608e-04 eta: 6:54:22 time: 0.3834 data_time: 0.0176 memory: 5386 loss: 0.3459 decode.loss_ce: 0.3459 decode.acc_seg: 82.4496 +2024/10/28 03:12:25 - mmengine - INFO - Iter(train) [ 97750/160000] base_lr: 1.0601e-04 lr: 1.0601e-04 eta: 6:54:03 time: 0.3802 data_time: 0.0182 memory: 5384 loss: 0.3767 decode.loss_ce: 0.3767 decode.acc_seg: 90.1167 +2024/10/28 03:12:45 - mmengine - INFO - Iter(train) [ 97800/160000] base_lr: 1.0593e-04 lr: 1.0593e-04 eta: 6:53:43 time: 0.4063 data_time: 0.0162 memory: 5384 loss: 0.3070 decode.loss_ce: 0.3070 decode.acc_seg: 83.6642 +2024/10/28 03:13:06 - mmengine - INFO - Iter(train) [ 97850/160000] base_lr: 1.0585e-04 lr: 1.0585e-04 eta: 6:53:23 time: 0.3845 data_time: 0.0173 memory: 5385 loss: 0.3307 decode.loss_ce: 0.3307 decode.acc_seg: 84.4602 +2024/10/28 03:13:25 - mmengine - INFO - Iter(train) [ 97900/160000] base_lr: 1.0578e-04 lr: 1.0578e-04 eta: 6:53:03 time: 0.3753 data_time: 0.0167 memory: 5385 loss: 0.3117 decode.loss_ce: 0.3117 decode.acc_seg: 87.2794 +2024/10/28 03:13:44 - mmengine - INFO - Iter(train) [ 97950/160000] base_lr: 1.0570e-04 lr: 1.0570e-04 eta: 6:52:42 time: 0.3821 data_time: 0.0174 memory: 5384 loss: 0.3111 decode.loss_ce: 0.3111 decode.acc_seg: 84.7297 +2024/10/28 03:14:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 03:14:03 - mmengine - INFO - Iter(train) [ 98000/160000] base_lr: 1.0563e-04 lr: 1.0563e-04 eta: 6:52:22 time: 0.3823 data_time: 0.0172 memory: 5384 loss: 0.3571 decode.loss_ce: 0.3571 decode.acc_seg: 74.6478 +2024/10/28 03:14:25 - mmengine - INFO - Iter(train) [ 98050/160000] base_lr: 1.0555e-04 lr: 1.0555e-04 eta: 6:52:04 time: 0.3786 data_time: 0.0169 memory: 5386 loss: 0.2803 decode.loss_ce: 0.2803 decode.acc_seg: 78.1132 +2024/10/28 03:14:44 - mmengine - INFO - Iter(train) [ 98100/160000] base_lr: 1.0547e-04 lr: 1.0547e-04 eta: 6:51:43 time: 0.3745 data_time: 0.0160 memory: 5383 loss: 0.3151 decode.loss_ce: 0.3151 decode.acc_seg: 91.6107 +2024/10/28 03:15:03 - mmengine - INFO - Iter(train) [ 98150/160000] base_lr: 1.0540e-04 lr: 1.0540e-04 eta: 6:51:22 time: 0.3825 data_time: 0.0170 memory: 5384 loss: 0.2599 decode.loss_ce: 0.2599 decode.acc_seg: 91.7852 +2024/10/28 03:15:25 - mmengine - INFO - Iter(train) [ 98200/160000] base_lr: 1.0532e-04 lr: 1.0532e-04 eta: 6:51:03 time: 0.3814 data_time: 0.0167 memory: 5383 loss: 0.3264 decode.loss_ce: 0.3264 decode.acc_seg: 82.8843 +2024/10/28 03:15:44 - mmengine - INFO - Iter(train) [ 98250/160000] base_lr: 1.0524e-04 lr: 1.0524e-04 eta: 6:50:43 time: 0.3793 data_time: 0.0169 memory: 5384 loss: 0.3742 decode.loss_ce: 0.3742 decode.acc_seg: 94.4643 +2024/10/28 03:16:03 - mmengine - INFO - Iter(train) [ 98300/160000] base_lr: 1.0516e-04 lr: 1.0516e-04 eta: 6:50:22 time: 0.3775 data_time: 0.0161 memory: 5384 loss: 0.3322 decode.loss_ce: 0.3322 decode.acc_seg: 87.2930 +2024/10/28 03:16:24 - mmengine - INFO - Iter(train) [ 98350/160000] base_lr: 1.0509e-04 lr: 1.0509e-04 eta: 6:50:03 time: 0.3790 data_time: 0.0171 memory: 5385 loss: 0.3452 decode.loss_ce: 0.3452 decode.acc_seg: 86.3306 +2024/10/28 03:16:44 - mmengine - INFO - Iter(train) [ 98400/160000] base_lr: 1.0501e-04 lr: 1.0501e-04 eta: 6:49:43 time: 0.4060 data_time: 0.0163 memory: 5384 loss: 0.4124 decode.loss_ce: 0.4124 decode.acc_seg: 89.5676 +2024/10/28 03:17:05 - mmengine - INFO - Iter(train) [ 98450/160000] base_lr: 1.0493e-04 lr: 1.0493e-04 eta: 6:49:24 time: 0.4098 data_time: 0.0154 memory: 5383 loss: 0.2897 decode.loss_ce: 0.2897 decode.acc_seg: 86.4780 +2024/10/28 03:17:25 - mmengine - INFO - Iter(train) [ 98500/160000] base_lr: 1.0485e-04 lr: 1.0485e-04 eta: 6:49:04 time: 0.3820 data_time: 0.0167 memory: 5386 loss: 0.3752 decode.loss_ce: 0.3752 decode.acc_seg: 87.2247 +2024/10/28 03:17:44 - mmengine - INFO - Iter(train) [ 98550/160000] base_lr: 1.0477e-04 lr: 1.0477e-04 eta: 6:48:44 time: 0.3809 data_time: 0.0158 memory: 5384 loss: 0.3419 decode.loss_ce: 0.3419 decode.acc_seg: 86.9338 +2024/10/28 03:18:04 - mmengine - INFO - Iter(train) [ 98600/160000] base_lr: 1.0470e-04 lr: 1.0470e-04 eta: 6:48:23 time: 0.3871 data_time: 0.0169 memory: 5384 loss: 0.3086 decode.loss_ce: 0.3086 decode.acc_seg: 85.2588 +2024/10/28 03:18:25 - mmengine - INFO - 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5384 loss: 0.3484 decode.loss_ce: 0.3484 decode.acc_seg: 91.0638 +2024/10/28 03:20:03 - mmengine - INFO - Iter(train) [ 98900/160000] base_lr: 1.0422e-04 lr: 1.0422e-04 eta: 6:46:23 time: 0.3804 data_time: 0.0174 memory: 5384 loss: 0.3023 decode.loss_ce: 0.3023 decode.acc_seg: 84.7296 +2024/10/28 03:20:24 - mmengine - INFO - Iter(train) [ 98950/160000] base_lr: 1.0414e-04 lr: 1.0414e-04 eta: 6:46:04 time: 0.3772 data_time: 0.0168 memory: 5386 loss: 0.3242 decode.loss_ce: 0.3242 decode.acc_seg: 88.4725 +2024/10/28 03:20:43 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 03:20:43 - mmengine - INFO - Iter(train) [ 99000/160000] base_lr: 1.0406e-04 lr: 1.0406e-04 eta: 6:45:43 time: 0.3805 data_time: 0.0168 memory: 5383 loss: 0.3718 decode.loss_ce: 0.3718 decode.acc_seg: 89.0580 +2024/10/28 03:21:02 - mmengine - INFO - Iter(train) [ 99050/160000] base_lr: 1.0398e-04 lr: 1.0398e-04 eta: 6:45:23 time: 0.3756 data_time: 0.0162 memory: 5384 loss: 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0.2624 decode.loss_ce: 0.2624 decode.acc_seg: 94.7242 +2024/10/28 03:27:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 03:27:25 - mmengine - INFO - Iter(train) [100000/160000] base_lr: 1.0243e-04 lr: 1.0243e-04 eta: 6:39:06 time: 0.3854 data_time: 0.0171 memory: 5384 loss: 0.3030 decode.loss_ce: 0.3030 decode.acc_seg: 90.7185 +2024/10/28 03:27:44 - mmengine - INFO - Iter(train) [100050/160000] base_lr: 1.0234e-04 lr: 1.0234e-04 eta: 6:38:45 time: 0.3792 data_time: 0.0169 memory: 5384 loss: 0.3051 decode.loss_ce: 0.3051 decode.acc_seg: 92.7068 +2024/10/28 03:28:03 - mmengine - INFO - Iter(train) [100100/160000] base_lr: 1.0226e-04 lr: 1.0226e-04 eta: 6:38:25 time: 0.3797 data_time: 0.0163 memory: 5384 loss: 0.3378 decode.loss_ce: 0.3378 decode.acc_seg: 90.9109 +2024/10/28 03:28:25 - mmengine - INFO - Iter(train) [100150/160000] base_lr: 1.0218e-04 lr: 1.0218e-04 eta: 6:38:06 time: 0.3735 data_time: 0.0178 memory: 5385 loss: 0.2882 decode.loss_ce: 0.2882 decode.acc_seg: 89.5304 +2024/10/28 03:28:43 - mmengine - INFO - Iter(train) [100200/160000] base_lr: 1.0209e-04 lr: 1.0209e-04 eta: 6:37:45 time: 0.3782 data_time: 0.0177 memory: 5384 loss: 0.3324 decode.loss_ce: 0.3324 decode.acc_seg: 82.0224 +2024/10/28 03:29:02 - mmengine - INFO - Iter(train) [100250/160000] base_lr: 1.0201e-04 lr: 1.0201e-04 eta: 6:37:24 time: 0.3817 data_time: 0.0166 memory: 5386 loss: 0.3253 decode.loss_ce: 0.3253 decode.acc_seg: 88.7510 +2024/10/28 03:29:25 - mmengine - INFO - Iter(train) [100300/160000] base_lr: 1.0193e-04 lr: 1.0193e-04 eta: 6:37:06 time: 0.3766 data_time: 0.0169 memory: 5384 loss: 0.3573 decode.loss_ce: 0.3573 decode.acc_seg: 87.9926 +2024/10/28 03:29:44 - mmengine - INFO - Iter(train) [100350/160000] base_lr: 1.0184e-04 lr: 1.0184e-04 eta: 6:36:46 time: 0.3774 data_time: 0.0173 memory: 5386 loss: 0.3489 decode.loss_ce: 0.3489 decode.acc_seg: 87.7960 +2024/10/28 03:30:03 - mmengine - INFO - Iter(train) [100400/160000] 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decode.loss_ce: 0.3571 decode.acc_seg: 87.9483 +2024/10/28 03:31:45 - mmengine - INFO - Iter(train) [100650/160000] base_lr: 1.0133e-04 lr: 1.0133e-04 eta: 6:34:46 time: 0.3795 data_time: 0.0175 memory: 5384 loss: 0.3171 decode.loss_ce: 0.3171 decode.acc_seg: 87.6040 +2024/10/28 03:32:04 - mmengine - INFO - Iter(train) [100700/160000] base_lr: 1.0125e-04 lr: 1.0125e-04 eta: 6:34:26 time: 0.3783 data_time: 0.0163 memory: 5384 loss: 0.3091 decode.loss_ce: 0.3091 decode.acc_seg: 85.1617 +2024/10/28 03:32:25 - mmengine - INFO - Iter(train) [100750/160000] base_lr: 1.0116e-04 lr: 1.0116e-04 eta: 6:34:07 time: 0.3809 data_time: 0.0166 memory: 5384 loss: 0.2984 decode.loss_ce: 0.2984 decode.acc_seg: 91.6733 +2024/10/28 03:32:44 - mmengine - INFO - Iter(train) [100800/160000] base_lr: 1.0107e-04 lr: 1.0107e-04 eta: 6:33:46 time: 0.3742 data_time: 0.0155 memory: 5384 loss: 0.2986 decode.loss_ce: 0.2986 decode.acc_seg: 90.8634 +2024/10/28 03:33:03 - mmengine - INFO - Iter(train) [100850/160000] 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1.0064e-04 lr: 1.0064e-04 eta: 6:32:07 time: 0.3784 data_time: 0.0155 memory: 5384 loss: 0.2996 decode.loss_ce: 0.2996 decode.acc_seg: 93.7053 +2024/10/28 03:34:44 - mmengine - INFO - Iter(train) [101100/160000] base_lr: 1.0056e-04 lr: 1.0056e-04 eta: 6:31:46 time: 0.3731 data_time: 0.0150 memory: 5384 loss: 0.2472 decode.loss_ce: 0.2472 decode.acc_seg: 92.7996 +2024/10/28 03:35:02 - mmengine - INFO - Iter(train) [101150/160000] base_lr: 1.0047e-04 lr: 1.0047e-04 eta: 6:31:25 time: 0.3780 data_time: 0.0150 memory: 5384 loss: 0.2939 decode.loss_ce: 0.2939 decode.acc_seg: 87.2751 +2024/10/28 03:35:24 - mmengine - INFO - Iter(train) [101200/160000] base_lr: 1.0038e-04 lr: 1.0038e-04 eta: 6:31:07 time: 0.3749 data_time: 0.0154 memory: 5384 loss: 0.3074 decode.loss_ce: 0.3074 decode.acc_seg: 84.6327 +2024/10/28 03:35:43 - mmengine - INFO - Iter(train) [101250/160000] base_lr: 1.0030e-04 lr: 1.0030e-04 eta: 6:30:46 time: 0.3757 data_time: 0.0149 memory: 5384 loss: 0.3874 decode.loss_ce: 0.3874 decode.acc_seg: 82.1477 +2024/10/28 03:36:02 - mmengine - INFO - Iter(train) [101300/160000] base_lr: 1.0021e-04 lr: 1.0021e-04 eta: 6:30:25 time: 0.3805 data_time: 0.0151 memory: 5386 loss: 0.2861 decode.loss_ce: 0.2861 decode.acc_seg: 85.2590 +2024/10/28 03:36:25 - mmengine - INFO - Iter(train) [101350/160000] base_lr: 1.0012e-04 lr: 1.0012e-04 eta: 6:30:07 time: 0.3749 data_time: 0.0154 memory: 5384 loss: 0.3144 decode.loss_ce: 0.3144 decode.acc_seg: 86.5674 +2024/10/28 03:36:44 - mmengine - INFO - Iter(train) [101400/160000] base_lr: 1.0003e-04 lr: 1.0003e-04 eta: 6:29:47 time: 0.3806 data_time: 0.0149 memory: 5384 loss: 0.2893 decode.loss_ce: 0.2893 decode.acc_seg: 89.2027 +2024/10/28 03:37:04 - mmengine - INFO - Iter(train) [101450/160000] base_lr: 9.9945e-05 lr: 9.9945e-05 eta: 6:29:27 time: 0.3961 data_time: 0.0135 memory: 5384 loss: 0.3500 decode.loss_ce: 0.3500 decode.acc_seg: 91.9831 +2024/10/28 03:37:26 - mmengine - INFO - Iter(train) [101500/160000] base_lr: 9.9857e-05 lr: 9.9857e-05 eta: 6:29:08 time: 0.3771 data_time: 0.0173 memory: 5383 loss: 0.2813 decode.loss_ce: 0.2813 decode.acc_seg: 91.3532 +2024/10/28 03:37:45 - mmengine - INFO - Iter(train) [101550/160000] base_lr: 9.9769e-05 lr: 9.9769e-05 eta: 6:28:47 time: 0.3783 data_time: 0.0169 memory: 5384 loss: 0.3351 decode.loss_ce: 0.3351 decode.acc_seg: 86.2594 +2024/10/28 03:38:04 - mmengine - INFO - Iter(train) [101600/160000] base_lr: 9.9680e-05 lr: 9.9680e-05 eta: 6:28:27 time: 0.3811 data_time: 0.0163 memory: 5384 loss: 0.3348 decode.loss_ce: 0.3348 decode.acc_seg: 93.4050 +2024/10/28 03:38:25 - mmengine - INFO - Iter(train) [101650/160000] base_lr: 9.9592e-05 lr: 9.9592e-05 eta: 6:28:08 time: 0.3780 data_time: 0.0163 memory: 5384 loss: 0.2461 decode.loss_ce: 0.2461 decode.acc_seg: 89.5386 +2024/10/28 03:38:44 - mmengine - INFO - Iter(train) [101700/160000] base_lr: 9.9503e-05 lr: 9.9503e-05 eta: 6:27:47 time: 0.3776 data_time: 0.0164 memory: 5384 loss: 0.2690 decode.loss_ce: 0.2690 decode.acc_seg: 89.3623 +2024/10/28 03:39:03 - mmengine - INFO - Iter(train) [101750/160000] base_lr: 9.9415e-05 lr: 9.9415e-05 eta: 6:27:27 time: 0.3812 data_time: 0.0174 memory: 5385 loss: 0.2932 decode.loss_ce: 0.2932 decode.acc_seg: 91.7035 +2024/10/28 03:39:25 - mmengine - INFO - Iter(train) [101800/160000] base_lr: 9.9326e-05 lr: 9.9326e-05 eta: 6:27:08 time: 0.3759 data_time: 0.0165 memory: 5385 loss: 0.2855 decode.loss_ce: 0.2855 decode.acc_seg: 92.5024 +2024/10/28 03:39:44 - mmengine - INFO - Iter(train) [101850/160000] base_lr: 9.9237e-05 lr: 9.9237e-05 eta: 6:26:47 time: 0.4003 data_time: 0.0151 memory: 5384 loss: 0.3238 decode.loss_ce: 0.3238 decode.acc_seg: 87.2561 +2024/10/28 03:40:03 - mmengine - INFO - Iter(train) [101900/160000] base_lr: 9.9148e-05 lr: 9.9148e-05 eta: 6:26:27 time: 0.3817 data_time: 0.0167 memory: 5384 loss: 0.2807 decode.loss_ce: 0.2807 decode.acc_seg: 93.1221 +2024/10/28 03:40:25 - mmengine - INFO - Iter(train) [101950/160000] base_lr: 9.9058e-05 lr: 9.9058e-05 eta: 6:26:08 time: 0.3788 data_time: 0.0168 memory: 5384 loss: 0.3049 decode.loss_ce: 0.3049 decode.acc_seg: 88.7442 +2024/10/28 03:40:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 03:40:44 - mmengine - INFO - Iter(train) [102000/160000] base_lr: 9.8969e-05 lr: 9.8969e-05 eta: 6:25:47 time: 0.3731 data_time: 0.0157 memory: 5384 loss: 0.2867 decode.loss_ce: 0.2867 decode.acc_seg: 84.3245 +2024/10/28 03:41:03 - mmengine - INFO - Iter(train) [102050/160000] base_lr: 9.8879e-05 lr: 9.8879e-05 eta: 6:25:27 time: 0.3789 data_time: 0.0152 memory: 5384 loss: 0.3453 decode.loss_ce: 0.3453 decode.acc_seg: 89.7848 +2024/10/28 03:41:25 - mmengine - INFO - Iter(train) [102100/160000] base_lr: 9.8789e-05 lr: 9.8789e-05 eta: 6:25:08 time: 0.3809 data_time: 0.0150 memory: 5384 loss: 0.2373 decode.loss_ce: 0.2373 decode.acc_seg: 88.8076 +2024/10/28 03:41:43 - mmengine - INFO - Iter(train) [102150/160000] base_lr: 9.8699e-05 lr: 9.8699e-05 eta: 6:24:47 time: 0.3736 data_time: 0.0150 memory: 5384 loss: 0.2792 decode.loss_ce: 0.2792 decode.acc_seg: 84.5019 +2024/10/28 03:42:02 - mmengine - INFO - Iter(train) [102200/160000] base_lr: 9.8609e-05 lr: 9.8609e-05 eta: 6:24:26 time: 0.3774 data_time: 0.0150 memory: 5384 loss: 0.3010 decode.loss_ce: 0.3010 decode.acc_seg: 84.6459 +2024/10/28 03:42:25 - mmengine - INFO - Iter(train) [102250/160000] base_lr: 9.8519e-05 lr: 9.8519e-05 eta: 6:24:08 time: 0.3770 data_time: 0.0177 memory: 5384 loss: 0.3391 decode.loss_ce: 0.3391 decode.acc_seg: 84.6661 +2024/10/28 03:42:44 - mmengine - INFO - Iter(train) [102300/160000] base_lr: 9.8429e-05 lr: 9.8429e-05 eta: 6:23:48 time: 0.3802 data_time: 0.0184 memory: 5385 loss: 0.3005 decode.loss_ce: 0.3005 decode.acc_seg: 88.9443 +2024/10/28 03:43:03 - mmengine - INFO - Iter(train) [102350/160000] base_lr: 9.8338e-05 lr: 9.8338e-05 eta: 6:23:27 time: 0.3815 data_time: 0.0170 memory: 5384 loss: 0.2733 decode.loss_ce: 0.2733 decode.acc_seg: 90.4783 +2024/10/28 03:43:25 - mmengine - INFO - Iter(train) [102400/160000] base_lr: 9.8247e-05 lr: 9.8247e-05 eta: 6:23:09 time: 0.3767 data_time: 0.0175 memory: 5386 loss: 0.3438 decode.loss_ce: 0.3438 decode.acc_seg: 88.8219 +2024/10/28 03:43:44 - mmengine - INFO - Iter(train) [102450/160000] base_lr: 9.8156e-05 lr: 9.8156e-05 eta: 6:22:48 time: 0.3776 data_time: 0.0168 memory: 5384 loss: 0.2738 decode.loss_ce: 0.2738 decode.acc_seg: 85.8709 +2024/10/28 03:44:03 - mmengine - INFO - Iter(train) [102500/160000] base_lr: 9.8065e-05 lr: 9.8065e-05 eta: 6:22:27 time: 0.3821 data_time: 0.0165 memory: 5383 loss: 0.3074 decode.loss_ce: 0.3074 decode.acc_seg: 91.2415 +2024/10/28 03:44:25 - mmengine - INFO - Iter(train) [102550/160000] base_lr: 9.7974e-05 lr: 9.7974e-05 eta: 6:22:08 time: 0.3779 data_time: 0.0173 memory: 5384 loss: 0.2961 decode.loss_ce: 0.2961 decode.acc_seg: 84.7002 +2024/10/28 03:44:43 - mmengine - INFO - Iter(train) [102600/160000] base_lr: 9.7883e-05 lr: 9.7883e-05 eta: 6:21:48 time: 0.3775 data_time: 0.0169 memory: 5384 loss: 0.3186 decode.loss_ce: 0.3186 decode.acc_seg: 90.3554 +2024/10/28 03:45:03 - mmengine - INFO - Iter(train) [102650/160000] base_lr: 9.7792e-05 lr: 9.7792e-05 eta: 6:21:27 time: 0.3843 data_time: 0.0172 memory: 5384 loss: 0.2810 decode.loss_ce: 0.2810 decode.acc_seg: 92.0895 +2024/10/28 03:45:25 - mmengine - INFO - Iter(train) [102700/160000] base_lr: 9.7700e-05 lr: 9.7700e-05 eta: 6:21:09 time: 0.3781 data_time: 0.0158 memory: 5384 loss: 0.3235 decode.loss_ce: 0.3235 decode.acc_seg: 86.9275 +2024/10/28 03:45:44 - mmengine - INFO - Iter(train) [102750/160000] base_lr: 9.7608e-05 lr: 9.7608e-05 eta: 6:20:48 time: 0.3763 data_time: 0.0173 memory: 5384 loss: 0.2951 decode.loss_ce: 0.2951 decode.acc_seg: 89.0498 +2024/10/28 03:46:03 - mmengine - INFO - Iter(train) [102800/160000] base_lr: 9.7516e-05 lr: 9.7516e-05 eta: 6:20:28 time: 0.3833 data_time: 0.0172 memory: 5383 loss: 0.2790 decode.loss_ce: 0.2790 decode.acc_seg: 88.7817 +2024/10/28 03:46:25 - mmengine - INFO - Iter(train) [102850/160000] base_lr: 9.7424e-05 lr: 9.7424e-05 eta: 6:20:09 time: 0.3788 data_time: 0.0175 memory: 5384 loss: 0.2824 decode.loss_ce: 0.2824 decode.acc_seg: 87.5229 +2024/10/28 03:46:44 - mmengine - INFO - Iter(train) [102900/160000] base_lr: 9.7332e-05 lr: 9.7332e-05 eta: 6:19:49 time: 0.3815 data_time: 0.0176 memory: 5382 loss: 0.2736 decode.loss_ce: 0.2736 decode.acc_seg: 91.4749 +2024/10/28 03:47:04 - mmengine - INFO - Iter(train) [102950/160000] base_lr: 9.7240e-05 lr: 9.7240e-05 eta: 6:19:28 time: 0.3770 data_time: 0.0174 memory: 5384 loss: 0.2769 decode.loss_ce: 0.2769 decode.acc_seg: 90.6133 +2024/10/28 03:47:26 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 03:47:26 - mmengine - INFO - Iter(train) [103000/160000] base_lr: 9.7147e-05 lr: 9.7147e-05 eta: 6:19:10 time: 0.3795 data_time: 0.0176 memory: 5384 loss: 0.2666 decode.loss_ce: 0.2666 decode.acc_seg: 91.2507 +2024/10/28 03:47:45 - mmengine - INFO - Iter(train) [103050/160000] base_lr: 9.7055e-05 lr: 9.7055e-05 eta: 6:18:49 time: 0.3792 data_time: 0.0174 memory: 5384 loss: 0.3135 decode.loss_ce: 0.3135 decode.acc_seg: 87.7898 +2024/10/28 03:48:04 - mmengine - INFO - Iter(train) [103100/160000] base_lr: 9.6962e-05 lr: 9.6962e-05 eta: 6:18:29 time: 0.3903 data_time: 0.0165 memory: 5384 loss: 0.2617 decode.loss_ce: 0.2617 decode.acc_seg: 82.7332 +2024/10/28 03:48:25 - mmengine - INFO - Iter(train) [103150/160000] base_lr: 9.6869e-05 lr: 9.6869e-05 eta: 6:18:09 time: 0.3797 data_time: 0.0171 memory: 5384 loss: 0.2933 decode.loss_ce: 0.2933 decode.acc_seg: 85.8124 +2024/10/28 03:48:44 - mmengine - INFO - Iter(train) [103200/160000] base_lr: 9.6776e-05 lr: 9.6776e-05 eta: 6:17:49 time: 0.3793 data_time: 0.0171 memory: 5384 loss: 0.3011 decode.loss_ce: 0.3011 decode.acc_seg: 85.8353 +2024/10/28 03:49:03 - mmengine - INFO - Iter(train) [103250/160000] base_lr: 9.6683e-05 lr: 9.6683e-05 eta: 6:17:28 time: 0.3816 data_time: 0.0174 memory: 5384 loss: 0.3219 decode.loss_ce: 0.3219 decode.acc_seg: 90.7018 +2024/10/28 03:49:26 - mmengine - INFO - Iter(train) [103300/160000] base_lr: 9.6590e-05 lr: 9.6590e-05 eta: 6:17:10 time: 0.3765 data_time: 0.0176 memory: 5384 loss: 0.3703 decode.loss_ce: 0.3703 decode.acc_seg: 82.1856 +2024/10/28 03:49:45 - mmengine - INFO - Iter(train) [103350/160000] base_lr: 9.6496e-05 lr: 9.6496e-05 eta: 6:16:50 time: 0.3784 data_time: 0.0172 memory: 5383 loss: 0.3398 decode.loss_ce: 0.3398 decode.acc_seg: 75.8210 +2024/10/28 03:50:04 - mmengine - INFO - Iter(train) [103400/160000] base_lr: 9.6403e-05 lr: 9.6403e-05 eta: 6:16:29 time: 0.3820 data_time: 0.0171 memory: 5384 loss: 0.3465 decode.loss_ce: 0.3465 decode.acc_seg: 85.4482 +2024/10/28 03:50:25 - mmengine - INFO - Iter(train) [103450/160000] base_lr: 9.6309e-05 lr: 9.6309e-05 eta: 6:16:10 time: 0.3782 data_time: 0.0171 memory: 5384 loss: 0.3532 decode.loss_ce: 0.3532 decode.acc_seg: 85.4227 +2024/10/28 03:50:44 - mmengine - INFO - Iter(train) [103500/160000] base_lr: 9.6215e-05 lr: 9.6215e-05 eta: 6:15:49 time: 0.3763 data_time: 0.0184 memory: 5384 loss: 0.3131 decode.loss_ce: 0.3131 decode.acc_seg: 85.2825 +2024/10/28 03:51:03 - mmengine - INFO - Iter(train) [103550/160000] base_lr: 9.6121e-05 lr: 9.6121e-05 eta: 6:15:28 time: 0.3840 data_time: 0.0173 memory: 5384 loss: 0.3120 decode.loss_ce: 0.3120 decode.acc_seg: 89.5978 +2024/10/28 03:51:26 - mmengine - INFO - Iter(train) [103600/160000] base_lr: 9.6027e-05 lr: 9.6027e-05 eta: 6:15:11 time: 0.4030 data_time: 0.0151 memory: 5384 loss: 0.3117 decode.loss_ce: 0.3117 decode.acc_seg: 84.2953 +2024/10/28 03:51:46 - mmengine - INFO - Iter(train) [103650/160000] base_lr: 9.5933e-05 lr: 9.5933e-05 eta: 6:14:50 time: 0.3809 data_time: 0.0161 memory: 5384 loss: 0.3451 decode.loss_ce: 0.3451 decode.acc_seg: 87.8235 +2024/10/28 03:52:05 - mmengine - INFO - Iter(train) [103700/160000] base_lr: 9.5838e-05 lr: 9.5838e-05 eta: 6:14:30 time: 0.3828 data_time: 0.0171 memory: 5384 loss: 0.2812 decode.loss_ce: 0.2812 decode.acc_seg: 94.0282 +2024/10/28 03:52:25 - mmengine - INFO - Iter(train) [103750/160000] base_lr: 9.5744e-05 lr: 9.5744e-05 eta: 6:14:10 time: 0.3819 data_time: 0.0171 memory: 5384 loss: 0.3012 decode.loss_ce: 0.3012 decode.acc_seg: 88.8167 +2024/10/28 03:52:44 - mmengine - INFO - Iter(train) [103800/160000] base_lr: 9.5649e-05 lr: 9.5649e-05 eta: 6:13:50 time: 0.3835 data_time: 0.0165 memory: 5385 loss: 0.3404 decode.loss_ce: 0.3404 decode.acc_seg: 92.8917 +2024/10/28 03:53:04 - mmengine - INFO - Iter(train) [103850/160000] base_lr: 9.5554e-05 lr: 9.5554e-05 eta: 6:13:30 time: 0.3874 data_time: 0.0168 memory: 5384 loss: 0.3595 decode.loss_ce: 0.3595 decode.acc_seg: 91.8573 +2024/10/28 03:53:25 - mmengine - INFO - Iter(train) [103900/160000] base_lr: 9.5459e-05 lr: 9.5459e-05 eta: 6:13:10 time: 0.3792 data_time: 0.0177 memory: 5384 loss: 0.3208 decode.loss_ce: 0.3208 decode.acc_seg: 88.0139 +2024/10/28 03:53:44 - mmengine - INFO - Iter(train) [103950/160000] base_lr: 9.5364e-05 lr: 9.5364e-05 eta: 6:12:49 time: 0.3794 data_time: 0.0167 memory: 5385 loss: 0.2364 decode.loss_ce: 0.2364 decode.acc_seg: 92.9218 +2024/10/28 03:54:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 03:54:03 - mmengine - INFO - Iter(train) [104000/160000] base_lr: 9.5269e-05 lr: 9.5269e-05 eta: 6:12:29 time: 0.3926 data_time: 0.0155 memory: 5384 loss: 0.3501 decode.loss_ce: 0.3501 decode.acc_seg: 90.0904 +2024/10/28 03:54:25 - mmengine - INFO - Iter(train) [104050/160000] base_lr: 9.5174e-05 lr: 9.5174e-05 eta: 6:12:10 time: 0.3801 data_time: 0.0155 memory: 5384 loss: 0.2815 decode.loss_ce: 0.2815 decode.acc_seg: 91.6240 +2024/10/28 03:54:44 - mmengine - INFO - Iter(train) [104100/160000] base_lr: 9.5078e-05 lr: 9.5078e-05 eta: 6:11:50 time: 0.4019 data_time: 0.0159 memory: 5384 loss: 0.3171 decode.loss_ce: 0.3171 decode.acc_seg: 83.9598 +2024/10/28 03:55:04 - mmengine - INFO - Iter(train) [104150/160000] base_lr: 9.4982e-05 lr: 9.4982e-05 eta: 6:11:30 time: 0.4083 data_time: 0.0152 memory: 5385 loss: 0.3295 decode.loss_ce: 0.3295 decode.acc_seg: 90.7285 +2024/10/28 03:55:25 - mmengine - INFO - Iter(train) [104200/160000] base_lr: 9.4887e-05 lr: 9.4887e-05 eta: 6:11:11 time: 0.4046 data_time: 0.0159 memory: 5384 loss: 0.3047 decode.loss_ce: 0.3047 decode.acc_seg: 87.1292 +2024/10/28 03:55:44 - mmengine - INFO - Iter(train) [104250/160000] base_lr: 9.4791e-05 lr: 9.4791e-05 eta: 6:10:50 time: 0.3771 data_time: 0.0187 memory: 5386 loss: 0.2733 decode.loss_ce: 0.2733 decode.acc_seg: 88.1731 +2024/10/28 03:56:03 - mmengine - INFO - Iter(train) [104300/160000] base_lr: 9.4695e-05 lr: 9.4695e-05 eta: 6:10:30 time: 0.3812 data_time: 0.0169 memory: 5384 loss: 0.2533 decode.loss_ce: 0.2533 decode.acc_seg: 92.4823 +2024/10/28 03:56:24 - mmengine - INFO - Iter(train) [104350/160000] base_lr: 9.4599e-05 lr: 9.4599e-05 eta: 6:10:10 time: 0.3798 data_time: 0.0178 memory: 5386 loss: 0.3134 decode.loss_ce: 0.3134 decode.acc_seg: 85.5240 +2024/10/28 03:56:44 - mmengine - INFO - Iter(train) [104400/160000] base_lr: 9.4502e-05 lr: 9.4502e-05 eta: 6:09:50 time: 0.3808 data_time: 0.0176 memory: 5385 loss: 0.2949 decode.loss_ce: 0.2949 decode.acc_seg: 89.1041 +2024/10/28 03:57:03 - mmengine - INFO - Iter(train) [104450/160000] base_lr: 9.4406e-05 lr: 9.4406e-05 eta: 6:09:29 time: 0.3837 data_time: 0.0173 memory: 5384 loss: 0.3061 decode.loss_ce: 0.3061 decode.acc_seg: 83.0175 +2024/10/28 03:57:25 - mmengine - INFO - Iter(train) [104500/160000] base_lr: 9.4309e-05 lr: 9.4309e-05 eta: 6:09:11 time: 0.3792 data_time: 0.0174 memory: 5384 loss: 0.3016 decode.loss_ce: 0.3016 decode.acc_seg: 92.1821 +2024/10/28 03:57:44 - mmengine - INFO - Iter(train) [104550/160000] base_lr: 9.4212e-05 lr: 9.4212e-05 eta: 6:08:50 time: 0.3989 data_time: 0.0183 memory: 5384 loss: 0.2637 decode.loss_ce: 0.2637 decode.acc_seg: 89.3819 +2024/10/28 03:58:03 - mmengine - INFO - Iter(train) [104600/160000] base_lr: 9.4116e-05 lr: 9.4116e-05 eta: 6:08:30 time: 0.3854 data_time: 0.0160 memory: 5384 loss: 0.2454 decode.loss_ce: 0.2454 decode.acc_seg: 90.3263 +2024/10/28 03:58:25 - mmengine - INFO - Iter(train) [104650/160000] base_lr: 9.4019e-05 lr: 9.4019e-05 eta: 6:08:11 time: 0.3782 data_time: 0.0174 memory: 5384 loss: 0.3009 decode.loss_ce: 0.3009 decode.acc_seg: 86.6449 +2024/10/28 03:58:44 - mmengine - INFO - Iter(train) [104700/160000] base_lr: 9.3922e-05 lr: 9.3922e-05 eta: 6:07:51 time: 0.3862 data_time: 0.0170 memory: 5385 loss: 0.2780 decode.loss_ce: 0.2780 decode.acc_seg: 90.3453 +2024/10/28 03:59:04 - mmengine - INFO - Iter(train) [104750/160000] base_lr: 9.3824e-05 lr: 9.3824e-05 eta: 6:07:30 time: 0.3831 data_time: 0.0160 memory: 5384 loss: 0.2946 decode.loss_ce: 0.2946 decode.acc_seg: 92.0621 +2024/10/28 03:59:24 - mmengine - INFO - Iter(train) [104800/160000] base_lr: 9.3727e-05 lr: 9.3727e-05 eta: 6:07:11 time: 0.3759 data_time: 0.0163 memory: 5385 loss: 0.2956 decode.loss_ce: 0.2956 decode.acc_seg: 88.8958 +2024/10/28 03:59:43 - mmengine - INFO - Iter(train) [104850/160000] base_lr: 9.3629e-05 lr: 9.3629e-05 eta: 6:06:50 time: 0.3784 data_time: 0.0163 memory: 5385 loss: 0.3125 decode.loss_ce: 0.3125 decode.acc_seg: 93.1122 +2024/10/28 04:00:02 - mmengine - INFO - Iter(train) [104900/160000] base_lr: 9.3532e-05 lr: 9.3532e-05 eta: 6:06:30 time: 0.3829 data_time: 0.0152 memory: 5384 loss: 0.3574 decode.loss_ce: 0.3574 decode.acc_seg: 80.9707 +2024/10/28 04:00:25 - mmengine - INFO - Iter(train) [104950/160000] base_lr: 9.3434e-05 lr: 9.3434e-05 eta: 6:06:11 time: 0.3808 data_time: 0.0174 memory: 5384 loss: 0.3837 decode.loss_ce: 0.3837 decode.acc_seg: 79.4166 +2024/10/28 04:00:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:00:44 - mmengine - INFO - Iter(train) [105000/160000] base_lr: 9.3336e-05 lr: 9.3336e-05 eta: 6:05:51 time: 0.3761 data_time: 0.0161 memory: 5384 loss: 0.2660 decode.loss_ce: 0.2660 decode.acc_seg: 92.1399 +2024/10/28 04:01:03 - mmengine - INFO - Iter(train) [105050/160000] base_lr: 9.3238e-05 lr: 9.3238e-05 eta: 6:05:30 time: 0.3856 data_time: 0.0169 memory: 5384 loss: 0.2454 decode.loss_ce: 0.2454 decode.acc_seg: 89.6157 +2024/10/28 04:01:24 - mmengine - INFO - Iter(train) [105100/160000] base_lr: 9.3140e-05 lr: 9.3140e-05 eta: 6:05:11 time: 0.3800 data_time: 0.0175 memory: 5384 loss: 0.2960 decode.loss_ce: 0.2960 decode.acc_seg: 87.5809 +2024/10/28 04:01:43 - mmengine - INFO - Iter(train) [105150/160000] base_lr: 9.3042e-05 lr: 9.3042e-05 eta: 6:04:50 time: 0.3786 data_time: 0.0165 memory: 5385 loss: 0.2940 decode.loss_ce: 0.2940 decode.acc_seg: 92.6771 +2024/10/28 04:02:02 - mmengine - INFO - Iter(train) [105200/160000] base_lr: 9.2943e-05 lr: 9.2943e-05 eta: 6:04:30 time: 0.3776 data_time: 0.0163 memory: 5383 loss: 0.3541 decode.loss_ce: 0.3541 decode.acc_seg: 79.2823 +2024/10/28 04:02:25 - mmengine - INFO - Iter(train) [105250/160000] base_lr: 9.2845e-05 lr: 9.2845e-05 eta: 6:04:11 time: 0.3778 data_time: 0.0174 memory: 5384 loss: 0.2889 decode.loss_ce: 0.2889 decode.acc_seg: 93.0410 +2024/10/28 04:02:44 - mmengine - INFO - Iter(train) [105300/160000] base_lr: 9.2746e-05 lr: 9.2746e-05 eta: 6:03:51 time: 0.4045 data_time: 0.0160 memory: 5386 loss: 0.2488 decode.loss_ce: 0.2488 decode.acc_seg: 89.1721 +2024/10/28 04:03:04 - mmengine - INFO - Iter(train) [105350/160000] base_lr: 9.2647e-05 lr: 9.2647e-05 eta: 6:03:31 time: 0.3895 data_time: 0.0166 memory: 5384 loss: 0.3061 decode.loss_ce: 0.3061 decode.acc_seg: 89.7450 +2024/10/28 04:03:25 - mmengine - INFO - Iter(train) [105400/160000] base_lr: 9.2548e-05 lr: 9.2548e-05 eta: 6:03:12 time: 0.3763 data_time: 0.0170 memory: 5384 loss: 0.2616 decode.loss_ce: 0.2616 decode.acc_seg: 84.5151 +2024/10/28 04:03:44 - mmengine - INFO - Iter(train) [105450/160000] base_lr: 9.2449e-05 lr: 9.2449e-05 eta: 6:02:51 time: 0.3795 data_time: 0.0173 memory: 5384 loss: 0.3006 decode.loss_ce: 0.3006 decode.acc_seg: 86.7991 +2024/10/28 04:04:03 - mmengine - INFO - Iter(train) [105500/160000] base_lr: 9.2350e-05 lr: 9.2350e-05 eta: 6:02:31 time: 0.3783 data_time: 0.0172 memory: 5384 loss: 0.3156 decode.loss_ce: 0.3156 decode.acc_seg: 91.0411 +2024/10/28 04:04:24 - mmengine - INFO - Iter(train) [105550/160000] base_lr: 9.2251e-05 lr: 9.2251e-05 eta: 6:02:12 time: 0.3766 data_time: 0.0175 memory: 5384 loss: 0.2869 decode.loss_ce: 0.2869 decode.acc_seg: 82.6491 +2024/10/28 04:04:43 - mmengine - INFO - Iter(train) [105600/160000] base_lr: 9.2152e-05 lr: 9.2152e-05 eta: 6:01:51 time: 0.3778 data_time: 0.0181 memory: 5382 loss: 0.2862 decode.loss_ce: 0.2862 decode.acc_seg: 90.2001 +2024/10/28 04:05:02 - mmengine - INFO - Iter(train) [105650/160000] base_lr: 9.2052e-05 lr: 9.2052e-05 eta: 6:01:30 time: 0.3818 data_time: 0.0172 memory: 5384 loss: 0.3032 decode.loss_ce: 0.3032 decode.acc_seg: 87.2316 +2024/10/28 04:05:25 - mmengine - INFO - Iter(train) [105700/160000] base_lr: 9.1952e-05 lr: 9.1952e-05 eta: 6:01:12 time: 0.3778 data_time: 0.0173 memory: 5385 loss: 0.3141 decode.loss_ce: 0.3141 decode.acc_seg: 89.5827 +2024/10/28 04:05:44 - mmengine - INFO - Iter(train) [105750/160000] base_lr: 9.1853e-05 lr: 9.1853e-05 eta: 6:00:51 time: 0.3753 data_time: 0.0173 memory: 5384 loss: 0.3360 decode.loss_ce: 0.3360 decode.acc_seg: 89.0989 +2024/10/28 04:06:03 - mmengine - INFO - Iter(train) [105800/160000] base_lr: 9.1753e-05 lr: 9.1753e-05 eta: 6:00:31 time: 0.3814 data_time: 0.0177 memory: 5384 loss: 0.3142 decode.loss_ce: 0.3142 decode.acc_seg: 89.0184 +2024/10/28 04:06:25 - mmengine - INFO - Iter(train) [105850/160000] base_lr: 9.1653e-05 lr: 9.1653e-05 eta: 6:00:12 time: 0.3790 data_time: 0.0181 memory: 5384 loss: 0.3548 decode.loss_ce: 0.3548 decode.acc_seg: 88.6294 +2024/10/28 04:06:44 - mmengine - INFO - Iter(train) [105900/160000] base_lr: 9.1553e-05 lr: 9.1553e-05 eta: 5:59:52 time: 0.3780 data_time: 0.0160 memory: 5384 loss: 0.2784 decode.loss_ce: 0.2784 decode.acc_seg: 88.0809 +2024/10/28 04:07:02 - mmengine - INFO - Iter(train) [105950/160000] base_lr: 9.1452e-05 lr: 9.1452e-05 eta: 5:59:31 time: 0.3732 data_time: 0.0158 memory: 5384 loss: 0.3142 decode.loss_ce: 0.3142 decode.acc_seg: 87.4499 +2024/10/28 04:07:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:07:24 - mmengine - INFO - Iter(train) [106000/160000] base_lr: 9.1352e-05 lr: 9.1352e-05 eta: 5:59:12 time: 0.3744 data_time: 0.0153 memory: 5386 loss: 0.2903 decode.loss_ce: 0.2903 decode.acc_seg: 88.6219 +2024/10/28 04:07:43 - mmengine - INFO - Iter(train) [106050/160000] base_lr: 9.1251e-05 lr: 9.1251e-05 eta: 5:58:52 time: 0.3745 data_time: 0.0154 memory: 5384 loss: 0.3411 decode.loss_ce: 0.3411 decode.acc_seg: 92.3495 +2024/10/28 04:08:02 - mmengine - INFO - Iter(train) [106100/160000] base_lr: 9.1151e-05 lr: 9.1151e-05 eta: 5:58:31 time: 0.3773 data_time: 0.0153 memory: 5384 loss: 0.3523 decode.loss_ce: 0.3523 decode.acc_seg: 85.5230 +2024/10/28 04:08:24 - mmengine - INFO - Iter(train) [106150/160000] base_lr: 9.1050e-05 lr: 9.1050e-05 eta: 5:58:12 time: 0.3723 data_time: 0.0158 memory: 5385 loss: 0.3292 decode.loss_ce: 0.3292 decode.acc_seg: 85.1068 +2024/10/28 04:08:43 - mmengine - INFO - Iter(train) [106200/160000] base_lr: 9.0949e-05 lr: 9.0949e-05 eta: 5:57:52 time: 0.3767 data_time: 0.0154 memory: 5383 loss: 0.2436 decode.loss_ce: 0.2436 decode.acc_seg: 86.7596 +2024/10/28 04:09:02 - mmengine - INFO - Iter(train) [106250/160000] base_lr: 9.0848e-05 lr: 9.0848e-05 eta: 5:57:31 time: 0.3795 data_time: 0.0156 memory: 5384 loss: 0.3435 decode.loss_ce: 0.3435 decode.acc_seg: 86.4556 +2024/10/28 04:09:25 - mmengine - INFO - Iter(train) [106300/160000] base_lr: 9.0747e-05 lr: 9.0747e-05 eta: 5:57:13 time: 0.3748 data_time: 0.0157 memory: 5384 loss: 0.3158 decode.loss_ce: 0.3158 decode.acc_seg: 86.2951 +2024/10/28 04:09:44 - mmengine - INFO - Iter(train) [106350/160000] base_lr: 9.0646e-05 lr: 9.0646e-05 eta: 5:56:52 time: 0.3734 data_time: 0.0155 memory: 5384 loss: 0.2364 decode.loss_ce: 0.2364 decode.acc_seg: 89.9607 +2024/10/28 04:10:03 - mmengine - INFO - Iter(train) [106400/160000] base_lr: 9.0545e-05 lr: 9.0545e-05 eta: 5:56:32 time: 0.3817 data_time: 0.0153 memory: 5383 loss: 0.2454 decode.loss_ce: 0.2454 decode.acc_seg: 92.8913 +2024/10/28 04:10:24 - mmengine - INFO - Iter(train) [106450/160000] base_lr: 9.0443e-05 lr: 9.0443e-05 eta: 5:56:13 time: 0.3759 data_time: 0.0156 memory: 5385 loss: 0.3212 decode.loss_ce: 0.3212 decode.acc_seg: 90.9876 +2024/10/28 04:10:43 - mmengine - INFO - Iter(train) [106500/160000] base_lr: 9.0341e-05 lr: 9.0341e-05 eta: 5:55:52 time: 0.3730 data_time: 0.0158 memory: 5384 loss: 0.2827 decode.loss_ce: 0.2827 decode.acc_seg: 88.9306 +2024/10/28 04:11:02 - mmengine - INFO - Iter(train) [106550/160000] base_lr: 9.0240e-05 lr: 9.0240e-05 eta: 5:55:31 time: 0.3774 data_time: 0.0161 memory: 5385 loss: 0.3029 decode.loss_ce: 0.3029 decode.acc_seg: 87.6007 +2024/10/28 04:11:24 - mmengine - INFO - Iter(train) [106600/160000] base_lr: 9.0138e-05 lr: 9.0138e-05 eta: 5:55:13 time: 0.3751 data_time: 0.0157 memory: 5384 loss: 0.2918 decode.loss_ce: 0.2918 decode.acc_seg: 85.9069 +2024/10/28 04:11:43 - mmengine - INFO - Iter(train) [106650/160000] base_lr: 9.0036e-05 lr: 9.0036e-05 eta: 5:54:52 time: 0.3788 data_time: 0.0155 memory: 5384 loss: 0.3302 decode.loss_ce: 0.3302 decode.acc_seg: 91.6697 +2024/10/28 04:12:02 - mmengine - INFO - Iter(train) [106700/160000] base_lr: 8.9934e-05 lr: 8.9934e-05 eta: 5:54:32 time: 0.3742 data_time: 0.0158 memory: 5385 loss: 0.2943 decode.loss_ce: 0.2943 decode.acc_seg: 87.1803 +2024/10/28 04:12:25 - mmengine - INFO - Iter(train) [106750/160000] base_lr: 8.9832e-05 lr: 8.9832e-05 eta: 5:54:13 time: 0.3763 data_time: 0.0153 memory: 5384 loss: 0.3166 decode.loss_ce: 0.3166 decode.acc_seg: 77.2380 +2024/10/28 04:12:43 - mmengine - INFO - Iter(train) [106800/160000] base_lr: 8.9730e-05 lr: 8.9730e-05 eta: 5:53:53 time: 0.3763 data_time: 0.0154 memory: 5384 loss: 0.2626 decode.loss_ce: 0.2626 decode.acc_seg: 86.8751 +2024/10/28 04:13:03 - mmengine - INFO - Iter(train) [106850/160000] base_lr: 8.9627e-05 lr: 8.9627e-05 eta: 5:53:32 time: 0.3793 data_time: 0.0160 memory: 5383 loss: 0.2607 decode.loss_ce: 0.2607 decode.acc_seg: 84.5497 +2024/10/28 04:13:24 - mmengine - INFO - Iter(train) [106900/160000] base_lr: 8.9525e-05 lr: 8.9525e-05 eta: 5:53:13 time: 0.3798 data_time: 0.0168 memory: 5384 loss: 0.3137 decode.loss_ce: 0.3137 decode.acc_seg: 86.4805 +2024/10/28 04:13:43 - mmengine - INFO - Iter(train) [106950/160000] base_lr: 8.9422e-05 lr: 8.9422e-05 eta: 5:52:53 time: 0.3810 data_time: 0.0161 memory: 5385 loss: 0.3023 decode.loss_ce: 0.3023 decode.acc_seg: 87.4756 +2024/10/28 04:14:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:14:03 - mmengine - INFO - Iter(train) [107000/160000] base_lr: 8.9319e-05 lr: 8.9319e-05 eta: 5:52:32 time: 0.3832 data_time: 0.0170 memory: 5384 loss: 0.2902 decode.loss_ce: 0.2902 decode.acc_seg: 87.9576 +2024/10/28 04:14:24 - mmengine - INFO - Iter(train) [107050/160000] base_lr: 8.9216e-05 lr: 8.9216e-05 eta: 5:52:13 time: 0.3735 data_time: 0.0150 memory: 5385 loss: 0.3391 decode.loss_ce: 0.3391 decode.acc_seg: 90.8935 +2024/10/28 04:14:43 - mmengine - INFO - Iter(train) [107100/160000] base_lr: 8.9114e-05 lr: 8.9114e-05 eta: 5:51:53 time: 0.3768 data_time: 0.0159 memory: 5384 loss: 0.3088 decode.loss_ce: 0.3088 decode.acc_seg: 92.4886 +2024/10/28 04:15:02 - mmengine - INFO - Iter(train) [107150/160000] base_lr: 8.9010e-05 lr: 8.9010e-05 eta: 5:51:32 time: 0.3785 data_time: 0.0162 memory: 5385 loss: 0.3232 decode.loss_ce: 0.3232 decode.acc_seg: 88.9887 +2024/10/28 04:15:24 - mmengine - INFO - Iter(train) [107200/160000] base_lr: 8.8907e-05 lr: 8.8907e-05 eta: 5:51:13 time: 0.3772 data_time: 0.0160 memory: 5384 loss: 0.3659 decode.loss_ce: 0.3659 decode.acc_seg: 87.7111 +2024/10/28 04:15:43 - mmengine - INFO - Iter(train) [107250/160000] base_lr: 8.8804e-05 lr: 8.8804e-05 eta: 5:50:53 time: 0.3779 data_time: 0.0154 memory: 5384 loss: 0.2792 decode.loss_ce: 0.2792 decode.acc_seg: 88.4328 +2024/10/28 04:16:02 - mmengine - INFO - Iter(train) [107300/160000] base_lr: 8.8701e-05 lr: 8.8701e-05 eta: 5:50:32 time: 0.3735 data_time: 0.0158 memory: 5384 loss: 0.3055 decode.loss_ce: 0.3055 decode.acc_seg: 89.7715 +2024/10/28 04:16:24 - mmengine - INFO - Iter(train) [107350/160000] base_lr: 8.8597e-05 lr: 8.8597e-05 eta: 5:50:14 time: 0.3755 data_time: 0.0160 memory: 5383 loss: 0.3005 decode.loss_ce: 0.3005 decode.acc_seg: 90.7425 +2024/10/28 04:16:44 - mmengine - INFO - Iter(train) [107400/160000] base_lr: 8.8493e-05 lr: 8.8493e-05 eta: 5:49:53 time: 0.4009 data_time: 0.0142 memory: 5384 loss: 0.2944 decode.loss_ce: 0.2944 decode.acc_seg: 86.2534 +2024/10/28 04:17:04 - mmengine - INFO - Iter(train) [107450/160000] base_lr: 8.8390e-05 lr: 8.8390e-05 eta: 5:49:33 time: 0.3992 data_time: 0.0142 memory: 5384 loss: 0.2606 decode.loss_ce: 0.2606 decode.acc_seg: 91.8148 +2024/10/28 04:17:24 - mmengine - INFO - Iter(train) [107500/160000] base_lr: 8.8286e-05 lr: 8.8286e-05 eta: 5:49:14 time: 0.3942 data_time: 0.0176 memory: 5385 loss: 0.2673 decode.loss_ce: 0.2673 decode.acc_seg: 89.9308 +2024/10/28 04:17:45 - mmengine - INFO - Iter(train) [107550/160000] base_lr: 8.8182e-05 lr: 8.8182e-05 eta: 5:48:54 time: 0.4027 data_time: 0.0173 memory: 5384 loss: 0.2913 decode.loss_ce: 0.2913 decode.acc_seg: 88.9864 +2024/10/28 04:18:04 - mmengine - INFO - Iter(train) [107600/160000] base_lr: 8.8078e-05 lr: 8.8078e-05 eta: 5:48:34 time: 0.3793 data_time: 0.0171 memory: 5383 loss: 0.2734 decode.loss_ce: 0.2734 decode.acc_seg: 89.3168 +2024/10/28 04:18:26 - mmengine - INFO - Iter(train) [107650/160000] base_lr: 8.7974e-05 lr: 8.7974e-05 eta: 5:48:15 time: 0.4241 data_time: 0.0171 memory: 5384 loss: 0.2706 decode.loss_ce: 0.2706 decode.acc_seg: 91.3475 +2024/10/28 04:18:46 - mmengine - INFO - Iter(train) [107700/160000] base_lr: 8.7869e-05 lr: 8.7869e-05 eta: 5:47:55 time: 0.4007 data_time: 0.0170 memory: 5384 loss: 0.3236 decode.loss_ce: 0.3236 decode.acc_seg: 87.5936 +2024/10/28 04:19:05 - mmengine - INFO - Iter(train) [107750/160000] base_lr: 8.7765e-05 lr: 8.7765e-05 eta: 5:47:34 time: 0.3770 data_time: 0.0160 memory: 5384 loss: 0.2639 decode.loss_ce: 0.2639 decode.acc_seg: 91.4670 +2024/10/28 04:19:24 - mmengine - INFO - Iter(train) [107800/160000] base_lr: 8.7661e-05 lr: 8.7661e-05 eta: 5:47:14 time: 0.3731 data_time: 0.0162 memory: 5384 loss: 0.2660 decode.loss_ce: 0.2660 decode.acc_seg: 90.3539 +2024/10/28 04:19:43 - mmengine - INFO - Iter(train) [107850/160000] base_lr: 8.7556e-05 lr: 8.7556e-05 eta: 5:46:53 time: 0.3744 data_time: 0.0161 memory: 5383 loss: 0.3311 decode.loss_ce: 0.3311 decode.acc_seg: 89.6765 +2024/10/28 04:20:01 - mmengine - INFO - Iter(train) [107900/160000] base_lr: 8.7451e-05 lr: 8.7451e-05 eta: 5:46:33 time: 0.3751 data_time: 0.0154 memory: 5384 loss: 0.3064 decode.loss_ce: 0.3064 decode.acc_seg: 84.7647 +2024/10/28 04:20:25 - mmengine - INFO - Iter(train) [107950/160000] base_lr: 8.7346e-05 lr: 8.7346e-05 eta: 5:46:15 time: 0.4016 data_time: 0.0147 memory: 5384 loss: 0.3062 decode.loss_ce: 0.3062 decode.acc_seg: 89.2951 +2024/10/28 04:20:45 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:20:45 - mmengine - INFO - Iter(train) [108000/160000] base_lr: 8.7242e-05 lr: 8.7242e-05 eta: 5:45:55 time: 0.4051 data_time: 0.0160 memory: 5384 loss: 0.2973 decode.loss_ce: 0.2973 decode.acc_seg: 90.8561 +2024/10/28 04:21:05 - mmengine - INFO - Iter(train) [108050/160000] base_lr: 8.7137e-05 lr: 8.7137e-05 eta: 5:45:35 time: 0.3849 data_time: 0.0161 memory: 5384 loss: 0.2731 decode.loss_ce: 0.2731 decode.acc_seg: 90.1810 +2024/10/28 04:21:24 - mmengine - INFO - Iter(train) [108100/160000] base_lr: 8.7031e-05 lr: 8.7031e-05 eta: 5:45:15 time: 0.4035 data_time: 0.0154 memory: 5383 loss: 0.2714 decode.loss_ce: 0.2714 decode.acc_seg: 92.5792 +2024/10/28 04:21:45 - mmengine - INFO - Iter(train) [108150/160000] base_lr: 8.6926e-05 lr: 8.6926e-05 eta: 5:44:55 time: 0.3972 data_time: 0.0140 memory: 5383 loss: 0.2668 decode.loss_ce: 0.2668 decode.acc_seg: 80.8180 +2024/10/28 04:22:04 - mmengine - INFO - Iter(train) [108200/160000] base_lr: 8.6821e-05 lr: 8.6821e-05 eta: 5:44:34 time: 0.3822 data_time: 0.0163 memory: 5384 loss: 0.2414 decode.loss_ce: 0.2414 decode.acc_seg: 91.7598 +2024/10/28 04:22:24 - mmengine - INFO - Iter(train) [108250/160000] base_lr: 8.6715e-05 lr: 8.6715e-05 eta: 5:44:15 time: 0.4056 data_time: 0.0162 memory: 5384 loss: 0.3713 decode.loss_ce: 0.3713 decode.acc_seg: 85.3270 +2024/10/28 04:22:45 - mmengine - INFO - Iter(train) [108300/160000] base_lr: 8.6610e-05 lr: 8.6610e-05 eta: 5:43:55 time: 0.4066 data_time: 0.0160 memory: 5385 loss: 0.2748 decode.loss_ce: 0.2748 decode.acc_seg: 92.2746 +2024/10/28 04:23:05 - mmengine - INFO - Iter(train) [108350/160000] base_lr: 8.6504e-05 lr: 8.6504e-05 eta: 5:43:35 time: 0.4189 data_time: 0.0158 memory: 5386 loss: 0.3205 decode.loss_ce: 0.3205 decode.acc_seg: 89.1434 +2024/10/28 04:23:26 - mmengine - INFO - Iter(train) [108400/160000] base_lr: 8.6398e-05 lr: 8.6398e-05 eta: 5:43:16 time: 0.3793 data_time: 0.0168 memory: 5384 loss: 0.2970 decode.loss_ce: 0.2970 decode.acc_seg: 91.8211 +2024/10/28 04:23:45 - mmengine - INFO - Iter(train) [108450/160000] base_lr: 8.6293e-05 lr: 8.6293e-05 eta: 5:42:55 time: 0.3839 data_time: 0.0164 memory: 5384 loss: 0.2737 decode.loss_ce: 0.2737 decode.acc_seg: 77.7083 +2024/10/28 04:24:04 - mmengine - INFO - Iter(train) [108500/160000] base_lr: 8.6187e-05 lr: 8.6187e-05 eta: 5:42:35 time: 0.3838 data_time: 0.0162 memory: 5384 loss: 0.2727 decode.loss_ce: 0.2727 decode.acc_seg: 88.0109 +2024/10/28 04:24:25 - mmengine - INFO - Iter(train) [108550/160000] base_lr: 8.6081e-05 lr: 8.6081e-05 eta: 5:42:16 time: 0.3764 data_time: 0.0167 memory: 5385 loss: 0.2877 decode.loss_ce: 0.2877 decode.acc_seg: 85.2370 +2024/10/28 04:24:44 - mmengine - INFO - Iter(train) [108600/160000] base_lr: 8.5974e-05 lr: 8.5974e-05 eta: 5:41:55 time: 0.3791 data_time: 0.0161 memory: 5384 loss: 0.3129 decode.loss_ce: 0.3129 decode.acc_seg: 87.5054 +2024/10/28 04:25:03 - mmengine - INFO - Iter(train) [108650/160000] base_lr: 8.5868e-05 lr: 8.5868e-05 eta: 5:41:35 time: 0.3817 data_time: 0.0154 memory: 5384 loss: 0.3067 decode.loss_ce: 0.3067 decode.acc_seg: 85.3168 +2024/10/28 04:25:25 - mmengine - INFO - Iter(train) [108700/160000] base_lr: 8.5762e-05 lr: 8.5762e-05 eta: 5:41:16 time: 0.4036 data_time: 0.0153 memory: 5384 loss: 0.2624 decode.loss_ce: 0.2624 decode.acc_seg: 87.4096 +2024/10/28 04:25:45 - mmengine - INFO - Iter(train) [108750/160000] base_lr: 8.5655e-05 lr: 8.5655e-05 eta: 5:40:56 time: 0.4067 data_time: 0.0156 memory: 5383 loss: 0.2992 decode.loss_ce: 0.2992 decode.acc_seg: 88.4213 +2024/10/28 04:26:05 - mmengine - INFO - Iter(train) [108800/160000] base_lr: 8.5549e-05 lr: 8.5549e-05 eta: 5:40:36 time: 0.3824 data_time: 0.0171 memory: 5386 loss: 0.2645 decode.loss_ce: 0.2645 decode.acc_seg: 91.4108 +2024/10/28 04:26:25 - mmengine - INFO - Iter(train) [108850/160000] base_lr: 8.5442e-05 lr: 8.5442e-05 eta: 5:40:16 time: 0.3801 data_time: 0.0177 memory: 5384 loss: 0.2973 decode.loss_ce: 0.2973 decode.acc_seg: 93.3093 +2024/10/28 04:26:44 - mmengine - INFO - Iter(train) [108900/160000] base_lr: 8.5336e-05 lr: 8.5336e-05 eta: 5:39:55 time: 0.3768 data_time: 0.0175 memory: 5384 loss: 0.2703 decode.loss_ce: 0.2703 decode.acc_seg: 91.1535 +2024/10/28 04:27:03 - mmengine - INFO - Iter(train) [108950/160000] base_lr: 8.5229e-05 lr: 8.5229e-05 eta: 5:39:35 time: 0.3850 data_time: 0.0169 memory: 5384 loss: 0.2559 decode.loss_ce: 0.2559 decode.acc_seg: 89.4718 +2024/10/28 04:27:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:27:24 - mmengine - INFO - Iter(train) [109000/160000] base_lr: 8.5122e-05 lr: 8.5122e-05 eta: 5:39:15 time: 0.3795 data_time: 0.0174 memory: 5384 loss: 0.3023 decode.loss_ce: 0.3023 decode.acc_seg: 89.1412 +2024/10/28 04:27:43 - mmengine - INFO - Iter(train) [109050/160000] base_lr: 8.5015e-05 lr: 8.5015e-05 eta: 5:38:55 time: 0.3818 data_time: 0.0178 memory: 5384 loss: 0.3101 decode.loss_ce: 0.3101 decode.acc_seg: 90.2976 +2024/10/28 04:28:02 - mmengine - INFO - Iter(train) [109100/160000] base_lr: 8.4908e-05 lr: 8.4908e-05 eta: 5:38:34 time: 0.3788 data_time: 0.0172 memory: 5385 loss: 0.2827 decode.loss_ce: 0.2827 decode.acc_seg: 90.0707 +2024/10/28 04:28:24 - mmengine - INFO - Iter(train) [109150/160000] base_lr: 8.4800e-05 lr: 8.4800e-05 eta: 5:38:16 time: 0.3792 data_time: 0.0176 memory: 5384 loss: 0.3082 decode.loss_ce: 0.3082 decode.acc_seg: 77.8696 +2024/10/28 04:28:43 - mmengine - INFO - Iter(train) [109200/160000] base_lr: 8.4693e-05 lr: 8.4693e-05 eta: 5:37:55 time: 0.3806 data_time: 0.0193 memory: 5383 loss: 0.3256 decode.loss_ce: 0.3256 decode.acc_seg: 88.5537 +2024/10/28 04:29:02 - mmengine - INFO - Iter(train) [109250/160000] base_lr: 8.4586e-05 lr: 8.4586e-05 eta: 5:37:35 time: 0.3809 data_time: 0.0165 memory: 5384 loss: 0.2769 decode.loss_ce: 0.2769 decode.acc_seg: 85.8753 +2024/10/28 04:29:25 - mmengine - INFO - Iter(train) [109300/160000] base_lr: 8.4478e-05 lr: 8.4478e-05 eta: 5:37:16 time: 0.3783 data_time: 0.0178 memory: 5384 loss: 0.2723 decode.loss_ce: 0.2723 decode.acc_seg: 89.4802 +2024/10/28 04:29:44 - mmengine - INFO - Iter(train) [109350/160000] base_lr: 8.4370e-05 lr: 8.4370e-05 eta: 5:36:56 time: 0.3789 data_time: 0.0183 memory: 5383 loss: 0.2777 decode.loss_ce: 0.2777 decode.acc_seg: 88.0759 +2024/10/28 04:30:03 - mmengine - INFO - Iter(train) [109400/160000] base_lr: 8.4263e-05 lr: 8.4263e-05 eta: 5:36:35 time: 0.3925 data_time: 0.0173 memory: 5384 loss: 0.3004 decode.loss_ce: 0.3004 decode.acc_seg: 89.0262 +2024/10/28 04:30:24 - mmengine - INFO - Iter(train) [109450/160000] base_lr: 8.4155e-05 lr: 8.4155e-05 eta: 5:36:16 time: 0.3758 data_time: 0.0179 memory: 5384 loss: 0.2770 decode.loss_ce: 0.2770 decode.acc_seg: 88.9206 +2024/10/28 04:30:43 - mmengine - INFO - Iter(train) [109500/160000] base_lr: 8.4047e-05 lr: 8.4047e-05 eta: 5:35:55 time: 0.3779 data_time: 0.0176 memory: 5383 loss: 0.2819 decode.loss_ce: 0.2819 decode.acc_seg: 84.5927 +2024/10/28 04:31:02 - mmengine - INFO - Iter(train) [109550/160000] base_lr: 8.3939e-05 lr: 8.3939e-05 eta: 5:35:35 time: 0.3815 data_time: 0.0191 memory: 5384 loss: 0.3067 decode.loss_ce: 0.3067 decode.acc_seg: 88.5373 +2024/10/28 04:31:25 - mmengine - INFO - Iter(train) [109600/160000] base_lr: 8.3831e-05 lr: 8.3831e-05 eta: 5:35:16 time: 0.3798 data_time: 0.0172 memory: 5384 loss: 0.2677 decode.loss_ce: 0.2677 decode.acc_seg: 87.6436 +2024/10/28 04:31:44 - mmengine - INFO - Iter(train) [109650/160000] base_lr: 8.3723e-05 lr: 8.3723e-05 eta: 5:34:56 time: 0.3755 data_time: 0.0177 memory: 5384 loss: 0.2716 decode.loss_ce: 0.2716 decode.acc_seg: 91.1826 +2024/10/28 04:32:03 - mmengine - INFO - Iter(train) [109700/160000] base_lr: 8.3615e-05 lr: 8.3615e-05 eta: 5:34:35 time: 0.3797 data_time: 0.0167 memory: 5384 loss: 0.2849 decode.loss_ce: 0.2849 decode.acc_seg: 89.7572 +2024/10/28 04:32:25 - mmengine - INFO - Iter(train) [109750/160000] base_lr: 8.3506e-05 lr: 8.3506e-05 eta: 5:34:16 time: 0.3800 data_time: 0.0180 memory: 5385 loss: 0.2369 decode.loss_ce: 0.2369 decode.acc_seg: 91.7091 +2024/10/28 04:32:44 - mmengine - INFO - Iter(train) [109800/160000] base_lr: 8.3398e-05 lr: 8.3398e-05 eta: 5:33:56 time: 0.3760 data_time: 0.0158 memory: 5384 loss: 0.3200 decode.loss_ce: 0.3200 decode.acc_seg: 89.5089 +2024/10/28 04:33:03 - mmengine - INFO - Iter(train) [109850/160000] base_lr: 8.3289e-05 lr: 8.3289e-05 eta: 5:33:35 time: 0.3763 data_time: 0.0156 memory: 5384 loss: 0.2514 decode.loss_ce: 0.2514 decode.acc_seg: 88.2630 +2024/10/28 04:33:24 - mmengine - INFO - Iter(train) [109900/160000] base_lr: 8.3181e-05 lr: 8.3181e-05 eta: 5:33:16 time: 0.3732 data_time: 0.0162 memory: 5385 loss: 0.2719 decode.loss_ce: 0.2719 decode.acc_seg: 86.1603 +2024/10/28 04:33:43 - mmengine - INFO - Iter(train) [109950/160000] base_lr: 8.3072e-05 lr: 8.3072e-05 eta: 5:32:56 time: 0.3762 data_time: 0.0161 memory: 5384 loss: 0.2456 decode.loss_ce: 0.2456 decode.acc_seg: 91.2628 +2024/10/28 04:34:02 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:34:02 - mmengine - INFO - Iter(train) [110000/160000] base_lr: 8.2963e-05 lr: 8.2963e-05 eta: 5:32:35 time: 0.3775 data_time: 0.0164 memory: 5384 loss: 0.3063 decode.loss_ce: 0.3063 decode.acc_seg: 86.1963 +2024/10/28 04:34:25 - mmengine - INFO - Iter(train) [110050/160000] base_lr: 8.2854e-05 lr: 8.2854e-05 eta: 5:32:17 time: 0.4022 data_time: 0.0150 memory: 5384 loss: 0.2954 decode.loss_ce: 0.2954 decode.acc_seg: 92.8031 +2024/10/28 04:34:45 - mmengine - INFO - Iter(train) [110100/160000] base_lr: 8.2745e-05 lr: 8.2745e-05 eta: 5:31:57 time: 0.3810 data_time: 0.0168 memory: 5384 loss: 0.3411 decode.loss_ce: 0.3411 decode.acc_seg: 82.4714 +2024/10/28 04:35:04 - mmengine - INFO - Iter(train) [110150/160000] base_lr: 8.2636e-05 lr: 8.2636e-05 eta: 5:31:36 time: 0.3823 data_time: 0.0171 memory: 5383 loss: 0.2306 decode.loss_ce: 0.2306 decode.acc_seg: 91.2781 +2024/10/28 04:35:25 - mmengine - INFO - Iter(train) [110200/160000] base_lr: 8.2527e-05 lr: 8.2527e-05 eta: 5:31:17 time: 0.3756 data_time: 0.0172 memory: 5384 loss: 0.2635 decode.loss_ce: 0.2635 decode.acc_seg: 90.5625 +2024/10/28 04:35:44 - mmengine - INFO - Iter(train) [110250/160000] base_lr: 8.2418e-05 lr: 8.2418e-05 eta: 5:30:56 time: 0.3775 data_time: 0.0174 memory: 5385 loss: 0.2665 decode.loss_ce: 0.2665 decode.acc_seg: 89.5553 +2024/10/28 04:36:03 - mmengine - INFO - Iter(train) [110300/160000] base_lr: 8.2309e-05 lr: 8.2309e-05 eta: 5:30:36 time: 0.3949 data_time: 0.0153 memory: 5384 loss: 0.2807 decode.loss_ce: 0.2807 decode.acc_seg: 88.5715 +2024/10/28 04:36:25 - mmengine - INFO - Iter(train) [110350/160000] base_lr: 8.2199e-05 lr: 8.2199e-05 eta: 5:30:17 time: 0.3799 data_time: 0.0164 memory: 5383 loss: 0.2723 decode.loss_ce: 0.2723 decode.acc_seg: 85.2353 +2024/10/28 04:36:44 - mmengine - INFO - Iter(train) [110400/160000] base_lr: 8.2090e-05 lr: 8.2090e-05 eta: 5:29:57 time: 0.3754 data_time: 0.0167 memory: 5384 loss: 0.2475 decode.loss_ce: 0.2475 decode.acc_seg: 92.8900 +2024/10/28 04:37:03 - mmengine - INFO - Iter(train) [110450/160000] base_lr: 8.1980e-05 lr: 8.1980e-05 eta: 5:29:36 time: 0.3821 data_time: 0.0171 memory: 5384 loss: 0.2619 decode.loss_ce: 0.2619 decode.acc_seg: 88.8214 +2024/10/28 04:37:25 - mmengine - INFO - Iter(train) [110500/160000] base_lr: 8.1870e-05 lr: 8.1870e-05 eta: 5:29:17 time: 0.3780 data_time: 0.0166 memory: 5382 loss: 0.2765 decode.loss_ce: 0.2765 decode.acc_seg: 90.2263 +2024/10/28 04:37:45 - mmengine - INFO - Iter(train) [110550/160000] base_lr: 8.1761e-05 lr: 8.1761e-05 eta: 5:28:57 time: 0.4037 data_time: 0.0159 memory: 5384 loss: 0.2637 decode.loss_ce: 0.2637 decode.acc_seg: 90.7270 +2024/10/28 04:38:04 - mmengine - INFO - Iter(train) [110600/160000] base_lr: 8.1651e-05 lr: 8.1651e-05 eta: 5:28:37 time: 0.3802 data_time: 0.0166 memory: 5384 loss: 0.3935 decode.loss_ce: 0.3935 decode.acc_seg: 78.1648 +2024/10/28 04:38:26 - mmengine - INFO - Iter(train) [110650/160000] base_lr: 8.1541e-05 lr: 8.1541e-05 eta: 5:28:18 time: 0.4011 data_time: 0.0143 memory: 5385 loss: 0.2798 decode.loss_ce: 0.2798 decode.acc_seg: 87.2956 +2024/10/28 04:38:46 - mmengine - INFO - Iter(train) [110700/160000] base_lr: 8.1431e-05 lr: 8.1431e-05 eta: 5:27:58 time: 0.3808 data_time: 0.0167 memory: 5385 loss: 0.2387 decode.loss_ce: 0.2387 decode.acc_seg: 89.8326 +2024/10/28 04:39:05 - mmengine - INFO - Iter(train) [110750/160000] base_lr: 8.1321e-05 lr: 8.1321e-05 eta: 5:27:38 time: 0.3924 data_time: 0.0173 memory: 5384 loss: 0.2821 decode.loss_ce: 0.2821 decode.acc_seg: 90.2369 +2024/10/28 04:39:25 - mmengine - INFO - Iter(train) [110800/160000] base_lr: 8.1211e-05 lr: 8.1211e-05 eta: 5:27:18 time: 0.3818 data_time: 0.0163 memory: 5383 loss: 0.3152 decode.loss_ce: 0.3152 decode.acc_seg: 84.3378 +2024/10/28 04:39:45 - mmengine - INFO - Iter(train) [110850/160000] base_lr: 8.1100e-05 lr: 8.1100e-05 eta: 5:26:58 time: 0.3820 data_time: 0.0167 memory: 5384 loss: 0.2878 decode.loss_ce: 0.2878 decode.acc_seg: 96.7510 +2024/10/28 04:40:04 - mmengine - INFO - Iter(train) [110900/160000] base_lr: 8.0990e-05 lr: 8.0990e-05 eta: 5:26:37 time: 0.3820 data_time: 0.0167 memory: 5384 loss: 0.2736 decode.loss_ce: 0.2736 decode.acc_seg: 84.6454 +2024/10/28 04:40:25 - mmengine - INFO - Iter(train) [110950/160000] base_lr: 8.0880e-05 lr: 8.0880e-05 eta: 5:26:18 time: 0.3767 data_time: 0.0153 memory: 5384 loss: 0.2418 decode.loss_ce: 0.2418 decode.acc_seg: 92.9380 +2024/10/28 04:40:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:40:44 - mmengine - INFO - Iter(train) [111000/160000] base_lr: 8.0769e-05 lr: 8.0769e-05 eta: 5:25:57 time: 0.3824 data_time: 0.0187 memory: 5385 loss: 0.2547 decode.loss_ce: 0.2547 decode.acc_seg: 89.2628 +2024/10/28 04:41:03 - mmengine - INFO - Iter(train) [111050/160000] base_lr: 8.0659e-05 lr: 8.0659e-05 eta: 5:25:37 time: 0.3842 data_time: 0.0191 memory: 5384 loss: 0.2905 decode.loss_ce: 0.2905 decode.acc_seg: 85.2775 +2024/10/28 04:41:25 - mmengine - INFO - Iter(train) [111100/160000] base_lr: 8.0548e-05 lr: 8.0548e-05 eta: 5:25:18 time: 0.3798 data_time: 0.0180 memory: 5384 loss: 0.2714 decode.loss_ce: 0.2714 decode.acc_seg: 91.9287 +2024/10/28 04:41:44 - mmengine - INFO - Iter(train) [111150/160000] base_lr: 8.0437e-05 lr: 8.0437e-05 eta: 5:24:57 time: 0.3775 data_time: 0.0188 memory: 5384 loss: 0.3104 decode.loss_ce: 0.3104 decode.acc_seg: 86.2440 +2024/10/28 04:42:03 - mmengine - INFO - Iter(train) [111200/160000] base_lr: 8.0326e-05 lr: 8.0326e-05 eta: 5:24:37 time: 0.3855 data_time: 0.0190 memory: 5384 loss: 0.2923 decode.loss_ce: 0.2923 decode.acc_seg: 87.3907 +2024/10/28 04:42:25 - mmengine - INFO - Iter(train) [111250/160000] base_lr: 8.0216e-05 lr: 8.0216e-05 eta: 5:24:18 time: 0.3838 data_time: 0.0172 memory: 5384 loss: 0.2471 decode.loss_ce: 0.2471 decode.acc_seg: 89.2839 +2024/10/28 04:42:44 - mmengine - INFO - Iter(train) [111300/160000] base_lr: 8.0105e-05 lr: 8.0105e-05 eta: 5:23:58 time: 0.3831 data_time: 0.0179 memory: 5384 loss: 0.2509 decode.loss_ce: 0.2509 decode.acc_seg: 88.6401 +2024/10/28 04:43:03 - mmengine - INFO - Iter(train) [111350/160000] base_lr: 7.9994e-05 lr: 7.9994e-05 eta: 5:23:37 time: 0.3850 data_time: 0.0184 memory: 5383 loss: 0.3502 decode.loss_ce: 0.3502 decode.acc_seg: 82.9689 +2024/10/28 04:43:25 - mmengine - INFO - Iter(train) [111400/160000] base_lr: 7.9882e-05 lr: 7.9882e-05 eta: 5:23:18 time: 0.3807 data_time: 0.0182 memory: 5384 loss: 0.3109 decode.loss_ce: 0.3109 decode.acc_seg: 93.1873 +2024/10/28 04:43:44 - mmengine - INFO - Iter(train) [111450/160000] base_lr: 7.9771e-05 lr: 7.9771e-05 eta: 5:22:58 time: 0.3798 data_time: 0.0171 memory: 5384 loss: 0.2716 decode.loss_ce: 0.2716 decode.acc_seg: 91.2004 +2024/10/28 04:44:03 - mmengine - INFO - Iter(train) [111500/160000] base_lr: 7.9660e-05 lr: 7.9660e-05 eta: 5:22:38 time: 0.3797 data_time: 0.0164 memory: 5384 loss: 0.2532 decode.loss_ce: 0.2532 decode.acc_seg: 92.0677 +2024/10/28 04:44:25 - mmengine - INFO - Iter(train) [111550/160000] base_lr: 7.9549e-05 lr: 7.9549e-05 eta: 5:22:19 time: 0.3777 data_time: 0.0171 memory: 5384 loss: 0.2684 decode.loss_ce: 0.2684 decode.acc_seg: 85.1528 +2024/10/28 04:44:44 - mmengine - INFO - Iter(train) [111600/160000] base_lr: 7.9437e-05 lr: 7.9437e-05 eta: 5:21:58 time: 0.3778 data_time: 0.0154 memory: 5384 loss: 0.2663 decode.loss_ce: 0.2663 decode.acc_seg: 84.3960 +2024/10/28 04:45:03 - mmengine - INFO - Iter(train) [111650/160000] base_lr: 7.9326e-05 lr: 7.9326e-05 eta: 5:21:38 time: 0.3841 data_time: 0.0182 memory: 5384 loss: 0.2721 decode.loss_ce: 0.2721 decode.acc_seg: 89.0849 +2024/10/28 04:45:25 - mmengine - INFO - Iter(train) [111700/160000] base_lr: 7.9214e-05 lr: 7.9214e-05 eta: 5:21:19 time: 0.3750 data_time: 0.0172 memory: 5384 loss: 0.2465 decode.loss_ce: 0.2465 decode.acc_seg: 93.8483 +2024/10/28 04:45:44 - mmengine - INFO - Iter(train) [111750/160000] base_lr: 7.9103e-05 lr: 7.9103e-05 eta: 5:20:58 time: 0.3808 data_time: 0.0190 memory: 5385 loss: 0.3079 decode.loss_ce: 0.3079 decode.acc_seg: 92.4497 +2024/10/28 04:46:03 - mmengine - INFO - Iter(train) [111800/160000] base_lr: 7.8991e-05 lr: 7.8991e-05 eta: 5:20:38 time: 0.3822 data_time: 0.0185 memory: 5384 loss: 0.2758 decode.loss_ce: 0.2758 decode.acc_seg: 91.4866 +2024/10/28 04:46:25 - mmengine - INFO - Iter(train) [111850/160000] base_lr: 7.8879e-05 lr: 7.8879e-05 eta: 5:20:19 time: 0.3769 data_time: 0.0166 memory: 5384 loss: 0.2410 decode.loss_ce: 0.2410 decode.acc_seg: 92.7067 +2024/10/28 04:46:44 - mmengine - INFO - Iter(train) [111900/160000] base_lr: 7.8767e-05 lr: 7.8767e-05 eta: 5:19:58 time: 0.3745 data_time: 0.0161 memory: 5384 loss: 0.3098 decode.loss_ce: 0.3098 decode.acc_seg: 83.5753 +2024/10/28 04:47:03 - mmengine - INFO - Iter(train) [111950/160000] base_lr: 7.8655e-05 lr: 7.8655e-05 eta: 5:19:38 time: 0.3802 data_time: 0.0176 memory: 5384 loss: 0.2708 decode.loss_ce: 0.2708 decode.acc_seg: 93.4931 +2024/10/28 04:47:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:47:25 - mmengine - INFO - Iter(train) [112000/160000] base_lr: 7.8543e-05 lr: 7.8543e-05 eta: 5:19:19 time: 0.3795 data_time: 0.0177 memory: 5385 loss: 0.2406 decode.loss_ce: 0.2406 decode.acc_seg: 89.8942 +2024/10/28 04:47:25 - mmengine - INFO - Saving checkpoint at 112000 iterations +2024/10/28 04:47:29 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:00:15 time: 0.0329 data_time: 0.0014 memory: 980 +2024/10/28 04:47:31 - mmengine - INFO - Iter(val) [100/500] eta: 0:00:13 time: 0.0350 data_time: 0.0017 memory: 1050 +2024/10/28 04:47:32 - mmengine - INFO - Iter(val) [150/500] eta: 0:00:11 time: 0.0336 data_time: 0.0015 memory: 767 +2024/10/28 04:47:34 - mmengine - INFO - Iter(val) [200/500] eta: 0:00:10 time: 0.0332 data_time: 0.0014 memory: 800 +2024/10/28 04:47:36 - mmengine - INFO - Iter(val) [250/500] eta: 0:00:08 time: 0.0336 data_time: 0.0015 memory: 839 +2024/10/28 04:47:37 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:06 time: 0.0344 data_time: 0.0018 memory: 1961 +2024/10/28 04:47:39 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:05 time: 0.0338 data_time: 0.0014 memory: 765 +2024/10/28 04:47:41 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:03 time: 0.0342 data_time: 0.0015 memory: 837 +2024/10/28 04:47:43 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:01 time: 0.0334 data_time: 0.0013 memory: 772 +2024/10/28 04:47:44 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.0329 data_time: 0.0013 memory: 822 +2024/10/28 04:47:46 - mmengine - INFO - per class results: +2024/10/28 04:47:46 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 70.27 | 84.29 | +| building | 77.06 | 88.08 | +| sky | 92.5 | 97.26 | +| floor | 75.47 | 88.34 | +| tree | 69.22 | 87.38 | +| ceiling | 80.46 | 91.85 | +| road | 79.63 | 85.71 | +| bed | 83.58 | 92.26 | +| windowpane | 56.3 | 73.06 | +| grass | 66.15 | 81.71 | +| cabinet | 51.75 | 65.46 | +| sidewalk | 60.67 | 81.85 | +| person | 69.84 | 84.32 | +| earth | 23.19 | 33.86 | +| door | 37.85 | 53.2 | +| table | 49.15 | 67.47 | +| mountain | 48.81 | 64.97 | +| plant | 45.65 | 56.46 | +| curtain | 65.84 | 80.03 | +| chair | 48.72 | 61.95 | +| car | 78.72 | 88.8 | +| water | 49.37 | 59.72 | +| painting | 60.79 | 78.89 | +| sofa | 61.28 | 77.45 | +| shelf | 29.88 | 39.49 | +| house | 23.57 | 30.0 | +| sea | 51.82 | 80.02 | +| mirror | 60.52 | 69.48 | +| rug | 60.32 | 68.91 | +| field | 31.97 | 50.02 | +| armchair | 38.69 | 52.46 | +| seat | 46.78 | 73.08 | +| fence | 33.88 | 44.99 | +| desk | 41.17 | 57.62 | +| rock | 30.41 | 40.58 | +| wardrobe | 43.09 | 67.39 | +| lamp | 47.76 | 59.62 | +| bathtub | 60.84 | 66.47 | +| railing | 32.44 | 42.12 | +| cushion | 47.8 | 58.25 | +| base | 12.16 | 18.96 | +| box | 14.76 | 23.46 | +| column | 31.81 | 50.85 | +| signboard | 26.47 | 38.68 | +| chest of drawers | 28.55 | 37.54 | +| counter | 29.27 | 43.07 | +| sand | 26.95 | 71.85 | +| sink | 57.67 | 71.68 | +| skyscraper | 60.6 | 85.19 | +| fireplace | 63.96 | 88.26 | +| refrigerator | 64.74 | 87.81 | +| grandstand | 30.03 | 66.13 | +| path | 11.24 | 20.76 | +| stairs | 26.95 | 36.76 | +| runway | 70.28 | 87.08 | +| case | 45.22 | 69.86 | +| pool table | 77.32 | 81.92 | +| pillow | 50.52 | 59.85 | +| screen door | 57.45 | 82.5 | +| stairway | 18.96 | 31.8 | +| river | 8.02 | 16.15 | +| bridge | 33.96 | 38.83 | +| bookcase | 30.64 | 45.37 | +| blind | 39.35 | 46.2 | +| coffee table | 54.77 | 75.13 | +| toilet | 75.06 | 80.09 | +| flower | 30.59 | 43.35 | +| book | 35.4 | 54.83 | +| hill | 3.54 | 5.53 | +| bench | 37.32 | 45.33 | +| countertop | 47.04 | 72.22 | +| stove | 64.07 | 71.37 | +| palm | 42.92 | 64.99 | +| kitchen island | 32.41 | 65.16 | +| computer | 44.51 | 49.19 | +| swivel chair | 29.81 | 44.68 | +| boat | 55.29 | 80.38 | +| bar | 28.84 | 33.49 | +| arcade machine | 27.58 | 30.41 | +| hovel | 32.13 | 35.63 | +| bus | 72.91 | 86.24 | +| towel | 45.11 | 48.51 | +| light | 32.93 | 35.82 | +| truck | 13.99 | 18.18 | +| tower | 34.24 | 66.23 | +| chandelier | 54.4 | 70.35 | +| awning | 18.61 | 21.91 | +| streetlight | 12.75 | 17.92 | +| booth | 53.49 | 64.73 | +| television receiver | 63.08 | 77.54 | +| airplane | 46.92 | 54.69 | +| dirt track | 3.94 | 17.35 | +| apparel | 22.05 | 34.66 | +| pole | 9.63 | 11.16 | +| land | 4.44 | 7.66 | +| bannister | 7.69 | 10.49 | +| escalator | 7.37 | 7.76 | +| ottoman | 42.21 | 53.29 | +| bottle | 14.14 | 39.17 | +| buffet | 38.99 | 48.8 | +| poster | 19.27 | 26.36 | +| stage | 10.19 | 23.34 | +| van | 34.86 | 50.14 | +| ship | 46.4 | 51.45 | +| fountain | 18.53 | 20.98 | +| conveyer belt | 62.57 | 70.53 | +| canopy | 8.43 | 9.76 | +| washer | 65.77 | 67.74 | +| plaything | 11.84 | 20.98 | +| swimming pool | 36.79 | 63.21 | +| stool | 29.74 | 43.57 | +| barrel | 20.78 | 64.02 | +| basket | 18.5 | 19.89 | +| waterfall | 43.16 | 62.56 | +| tent | 81.17 | 93.28 | +| bag | 6.97 | 10.2 | +| minibike | 35.71 | 49.33 | +| cradle | 66.21 | 80.12 | +| oven | 44.46 | 70.51 | +| ball | 38.64 | 45.02 | +| food | 21.16 | 24.34 | +| step | 17.83 | 20.22 | +| tank | 43.89 | 55.21 | +| trade name | 14.24 | 15.37 | +| microwave | 33.81 | 35.91 | +| pot | 29.54 | 35.75 | +| animal | 44.92 | 55.2 | +| bicycle | 41.1 | 50.13 | +| lake | 57.52 | 62.36 | +| dishwasher | 64.1 | 70.82 | +| screen | 66.35 | 88.01 | +| blanket | 4.07 | 4.5 | +| sculpture | 26.45 | 42.57 | +| hood | 51.77 | 55.28 | +| sconce | 25.75 | 28.77 | +| vase | 23.12 | 33.77 | +| traffic light | 20.41 | 38.93 | +| tray | 4.07 | 5.46 | +| ashcan | 28.28 | 37.09 | +| fan | 32.56 | 37.27 | +| pier | 20.87 | 23.19 | +| crt screen | 10.81 | 12.98 | +| plate | 31.08 | 43.55 | +| monitor | 55.05 | 69.64 | +| bulletin board | 27.1 | 37.51 | +| shower | 0.0 | 0.0 | +| radiator | 42.71 | 47.82 | +| glass | 3.67 | 4.28 | +| clock | 21.56 | 25.1 | +| flag | 37.63 | 42.03 | ++---------------------+-------+-------+ +2024/10/28 04:47:46 - mmengine - INFO - Iter(val) [500/500] aAcc: 78.2400 mIoU: 39.6100 mAcc: 51.4400 data_time: 0.0015 time: 0.0338 +2024/10/28 04:48:05 - mmengine - INFO - Iter(train) [112050/160000] base_lr: 7.8431e-05 lr: 7.8431e-05 eta: 5:18:59 time: 0.3811 data_time: 0.0170 memory: 5384 loss: 0.2733 decode.loss_ce: 0.2733 decode.acc_seg: 88.7080 +2024/10/28 04:48:25 - mmengine - INFO - Iter(train) [112100/160000] base_lr: 7.8319e-05 lr: 7.8319e-05 eta: 5:18:39 time: 0.3798 data_time: 0.0169 memory: 5384 loss: 0.3247 decode.loss_ce: 0.3247 decode.acc_seg: 90.9651 +2024/10/28 04:48:44 - mmengine - INFO - Iter(train) [112150/160000] base_lr: 7.8207e-05 lr: 7.8207e-05 eta: 5:18:19 time: 0.3808 data_time: 0.0166 memory: 5384 loss: 0.3008 decode.loss_ce: 0.3008 decode.acc_seg: 86.1439 +2024/10/28 04:49:03 - mmengine - INFO - Iter(train) [112200/160000] base_lr: 7.8095e-05 lr: 7.8095e-05 eta: 5:17:58 time: 0.3764 data_time: 0.0162 memory: 5384 loss: 0.3101 decode.loss_ce: 0.3101 decode.acc_seg: 72.4093 +2024/10/28 04:49:26 - mmengine - INFO - Iter(train) [112250/160000] base_lr: 7.7982e-05 lr: 7.7982e-05 eta: 5:17:40 time: 0.3823 data_time: 0.0194 memory: 5383 loss: 0.2648 decode.loss_ce: 0.2648 decode.acc_seg: 87.2959 +2024/10/28 04:49:45 - mmengine - INFO - Iter(train) [112300/160000] base_lr: 7.7870e-05 lr: 7.7870e-05 eta: 5:17:19 time: 0.3766 data_time: 0.0183 memory: 5383 loss: 0.2892 decode.loss_ce: 0.2892 decode.acc_seg: 90.3021 +2024/10/28 04:50:04 - mmengine - INFO - Iter(train) [112350/160000] base_lr: 7.7757e-05 lr: 7.7757e-05 eta: 5:16:59 time: 0.3787 data_time: 0.0146 memory: 5384 loss: 0.2853 decode.loss_ce: 0.2853 decode.acc_seg: 92.3468 +2024/10/28 04:50:25 - mmengine - INFO - Iter(train) [112400/160000] base_lr: 7.7645e-05 lr: 7.7645e-05 eta: 5:16:40 time: 0.3875 data_time: 0.0143 memory: 5384 loss: 0.3151 decode.loss_ce: 0.3151 decode.acc_seg: 93.1901 +2024/10/28 04:50:44 - mmengine - INFO - Iter(train) [112450/160000] base_lr: 7.7532e-05 lr: 7.7532e-05 eta: 5:16:19 time: 0.3808 data_time: 0.0146 memory: 5384 loss: 0.3321 decode.loss_ce: 0.3321 decode.acc_seg: 82.8458 +2024/10/28 04:51:03 - mmengine - INFO - Iter(train) [112500/160000] base_lr: 7.7419e-05 lr: 7.7419e-05 eta: 5:15:59 time: 0.3827 data_time: 0.0169 memory: 5386 loss: 0.2676 decode.loss_ce: 0.2676 decode.acc_seg: 90.4351 +2024/10/28 04:51:25 - mmengine - INFO - Iter(train) [112550/160000] base_lr: 7.7307e-05 lr: 7.7307e-05 eta: 5:15:40 time: 0.4030 data_time: 0.0171 memory: 5384 loss: 0.3074 decode.loss_ce: 0.3074 decode.acc_seg: 94.0479 +2024/10/28 04:51:45 - mmengine - INFO - Iter(train) [112600/160000] base_lr: 7.7194e-05 lr: 7.7194e-05 eta: 5:15:20 time: 0.4055 data_time: 0.0169 memory: 5384 loss: 0.2945 decode.loss_ce: 0.2945 decode.acc_seg: 90.7003 +2024/10/28 04:52:05 - mmengine - INFO - Iter(train) [112650/160000] base_lr: 7.7081e-05 lr: 7.7081e-05 eta: 5:15:00 time: 0.3852 data_time: 0.0179 memory: 5383 loss: 0.2512 decode.loss_ce: 0.2512 decode.acc_seg: 90.8192 +2024/10/28 04:52:25 - mmengine - INFO - Iter(train) [112700/160000] base_lr: 7.6968e-05 lr: 7.6968e-05 eta: 5:14:40 time: 0.3801 data_time: 0.0182 memory: 5384 loss: 0.2790 decode.loss_ce: 0.2790 decode.acc_seg: 83.7398 +2024/10/28 04:52:44 - mmengine - INFO - Iter(train) [112750/160000] base_lr: 7.6855e-05 lr: 7.6855e-05 eta: 5:14:20 time: 0.3761 data_time: 0.0184 memory: 5386 loss: 0.2585 decode.loss_ce: 0.2585 decode.acc_seg: 88.5026 +2024/10/28 04:53:04 - mmengine - INFO - Iter(train) [112800/160000] base_lr: 7.6742e-05 lr: 7.6742e-05 eta: 5:13:59 time: 0.3917 data_time: 0.0170 memory: 5384 loss: 0.2688 decode.loss_ce: 0.2688 decode.acc_seg: 92.7398 +2024/10/28 04:53:25 - mmengine - INFO - Iter(train) [112850/160000] base_lr: 7.6629e-05 lr: 7.6629e-05 eta: 5:13:40 time: 0.3789 data_time: 0.0190 memory: 5384 loss: 0.2352 decode.loss_ce: 0.2352 decode.acc_seg: 89.7716 +2024/10/28 04:53:44 - mmengine - INFO - Iter(train) [112900/160000] base_lr: 7.6515e-05 lr: 7.6515e-05 eta: 5:13:20 time: 0.3746 data_time: 0.0161 memory: 5386 loss: 0.2607 decode.loss_ce: 0.2607 decode.acc_seg: 89.2204 +2024/10/28 04:54:03 - mmengine - INFO - Iter(train) [112950/160000] base_lr: 7.6402e-05 lr: 7.6402e-05 eta: 5:12:59 time: 0.3775 data_time: 0.0158 memory: 5385 loss: 0.2752 decode.loss_ce: 0.2752 decode.acc_seg: 85.7100 +2024/10/28 04:54:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 04:54:25 - mmengine - INFO - Iter(train) [113000/160000] base_lr: 7.6289e-05 lr: 7.6289e-05 eta: 5:12:40 time: 0.3820 data_time: 0.0189 memory: 5385 loss: 0.2750 decode.loss_ce: 0.2750 decode.acc_seg: 88.2037 +2024/10/28 04:54:44 - mmengine - INFO - Iter(train) [113050/160000] base_lr: 7.6175e-05 lr: 7.6175e-05 eta: 5:12:20 time: 0.3845 data_time: 0.0194 memory: 5386 loss: 0.2969 decode.loss_ce: 0.2969 decode.acc_seg: 89.9231 +2024/10/28 04:55:03 - mmengine - INFO - Iter(train) [113100/160000] base_lr: 7.6062e-05 lr: 7.6062e-05 eta: 5:12:00 time: 0.3839 data_time: 0.0182 memory: 5384 loss: 0.2626 decode.loss_ce: 0.2626 decode.acc_seg: 91.3599 +2024/10/28 04:55:24 - mmengine - INFO - Iter(train) [113150/160000] base_lr: 7.5948e-05 lr: 7.5948e-05 eta: 5:11:40 time: 0.3798 data_time: 0.0188 memory: 5384 loss: 0.2446 decode.loss_ce: 0.2446 decode.acc_seg: 94.9809 +2024/10/28 04:55:43 - mmengine - INFO - Iter(train) [113200/160000] base_lr: 7.5835e-05 lr: 7.5835e-05 eta: 5:11:20 time: 0.3840 data_time: 0.0185 memory: 5384 loss: 0.3082 decode.loss_ce: 0.3082 decode.acc_seg: 76.2495 +2024/10/28 04:56:03 - mmengine - INFO - Iter(train) [113250/160000] base_lr: 7.5721e-05 lr: 7.5721e-05 eta: 5:10:59 time: 0.3856 data_time: 0.0175 memory: 5384 loss: 0.2805 decode.loss_ce: 0.2805 decode.acc_seg: 87.4916 +2024/10/28 04:56:25 - mmengine - INFO - Iter(train) [113300/160000] base_lr: 7.5607e-05 lr: 7.5607e-05 eta: 5:10:41 time: 0.3984 data_time: 0.0136 memory: 5384 loss: 0.2668 decode.loss_ce: 0.2668 decode.acc_seg: 90.5000 +2024/10/28 04:56:45 - mmengine - INFO - Iter(train) [113350/160000] base_lr: 7.5493e-05 lr: 7.5493e-05 eta: 5:10:20 time: 0.3823 data_time: 0.0167 memory: 5384 loss: 0.3151 decode.loss_ce: 0.3151 decode.acc_seg: 79.3246 +2024/10/28 04:57:04 - mmengine - INFO - Iter(train) [113400/160000] base_lr: 7.5380e-05 lr: 7.5380e-05 eta: 5:10:00 time: 0.3876 data_time: 0.0173 memory: 5384 loss: 0.3180 decode.loss_ce: 0.3180 decode.acc_seg: 87.5807 +2024/10/28 04:57:25 - mmengine - INFO - Iter(train) [113450/160000] base_lr: 7.5266e-05 lr: 7.5266e-05 eta: 5:09:41 time: 0.3805 data_time: 0.0181 memory: 5384 loss: 0.2909 decode.loss_ce: 0.2909 decode.acc_seg: 85.6791 +2024/10/28 04:57:44 - mmengine - INFO - Iter(train) [113500/160000] base_lr: 7.5152e-05 lr: 7.5152e-05 eta: 5:09:20 time: 0.3797 data_time: 0.0185 memory: 5384 loss: 0.2785 decode.loss_ce: 0.2785 decode.acc_seg: 91.8030 +2024/10/28 04:58:03 - mmengine - INFO - Iter(train) [113550/160000] base_lr: 7.5038e-05 lr: 7.5038e-05 eta: 5:09:00 time: 0.3824 data_time: 0.0196 memory: 5384 loss: 0.2559 decode.loss_ce: 0.2559 decode.acc_seg: 93.9247 +2024/10/28 04:58:26 - mmengine - INFO - Iter(train) [113600/160000] base_lr: 7.4924e-05 lr: 7.4924e-05 eta: 5:08:41 time: 0.3824 data_time: 0.0174 memory: 5384 loss: 0.2943 decode.loss_ce: 0.2943 decode.acc_seg: 90.5652 +2024/10/28 04:58:45 - mmengine - INFO - Iter(train) [113650/160000] base_lr: 7.4810e-05 lr: 7.4810e-05 eta: 5:08:21 time: 0.3771 data_time: 0.0179 memory: 5383 loss: 0.2415 decode.loss_ce: 0.2415 decode.acc_seg: 91.3091 +2024/10/28 04:59:04 - mmengine - INFO - Iter(train) [113700/160000] base_lr: 7.4695e-05 lr: 7.4695e-05 eta: 5:08:00 time: 0.3817 data_time: 0.0167 memory: 5384 loss: 0.2924 decode.loss_ce: 0.2924 decode.acc_seg: 87.7043 +2024/10/28 04:59:25 - mmengine - INFO - Iter(train) [113750/160000] base_lr: 7.4581e-05 lr: 7.4581e-05 eta: 5:07:41 time: 0.3807 data_time: 0.0176 memory: 5384 loss: 0.2535 decode.loss_ce: 0.2535 decode.acc_seg: 88.3989 +2024/10/28 04:59:44 - mmengine - INFO - Iter(train) [113800/160000] base_lr: 7.4467e-05 lr: 7.4467e-05 eta: 5:07:21 time: 0.3792 data_time: 0.0176 memory: 5384 loss: 0.2450 decode.loss_ce: 0.2450 decode.acc_seg: 88.3156 +2024/10/28 05:00:03 - mmengine - INFO - Iter(train) [113850/160000] base_lr: 7.4352e-05 lr: 7.4352e-05 eta: 5:07:00 time: 0.3818 data_time: 0.0154 memory: 5384 loss: 0.2520 decode.loss_ce: 0.2520 decode.acc_seg: 86.2229 +2024/10/28 05:00:26 - mmengine - INFO - Iter(train) [113900/160000] base_lr: 7.4238e-05 lr: 7.4238e-05 eta: 5:06:42 time: 0.3794 data_time: 0.0173 memory: 5385 loss: 0.2535 decode.loss_ce: 0.2535 decode.acc_seg: 89.4313 +2024/10/28 05:00:45 - mmengine - INFO - Iter(train) [113950/160000] base_lr: 7.4124e-05 lr: 7.4124e-05 eta: 5:06:21 time: 0.3780 data_time: 0.0167 memory: 5384 loss: 0.2955 decode.loss_ce: 0.2955 decode.acc_seg: 79.7825 +2024/10/28 05:01:04 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:01:04 - mmengine - INFO - Iter(train) [114000/160000] base_lr: 7.4009e-05 lr: 7.4009e-05 eta: 5:06:01 time: 0.3803 data_time: 0.0163 memory: 5384 loss: 0.2447 decode.loss_ce: 0.2447 decode.acc_seg: 91.0262 +2024/10/28 05:01:26 - mmengine - INFO - Iter(train) [114050/160000] base_lr: 7.3894e-05 lr: 7.3894e-05 eta: 5:05:42 time: 0.3983 data_time: 0.0137 memory: 5385 loss: 0.2959 decode.loss_ce: 0.2959 decode.acc_seg: 88.3527 +2024/10/28 05:01:45 - mmengine - INFO - Iter(train) [114100/160000] base_lr: 7.3780e-05 lr: 7.3780e-05 eta: 5:05:21 time: 0.3799 data_time: 0.0165 memory: 5384 loss: 0.2410 decode.loss_ce: 0.2410 decode.acc_seg: 87.4153 +2024/10/28 05:02:05 - mmengine - INFO - Iter(train) [114150/160000] base_lr: 7.3665e-05 lr: 7.3665e-05 eta: 5:05:01 time: 0.3836 data_time: 0.0172 memory: 5383 loss: 0.2750 decode.loss_ce: 0.2750 decode.acc_seg: 88.8590 +2024/10/28 05:02:25 - mmengine - INFO - Iter(train) [114200/160000] base_lr: 7.3550e-05 lr: 7.3550e-05 eta: 5:04:41 time: 0.3840 data_time: 0.0179 memory: 5383 loss: 0.2797 decode.loss_ce: 0.2797 decode.acc_seg: 90.8685 +2024/10/28 05:02:44 - mmengine - INFO - Iter(train) [114250/160000] base_lr: 7.3436e-05 lr: 7.3436e-05 eta: 5:04:21 time: 0.3785 data_time: 0.0172 memory: 5383 loss: 0.3072 decode.loss_ce: 0.3072 decode.acc_seg: 89.2953 +2024/10/28 05:03:03 - mmengine - INFO - Iter(train) [114300/160000] base_lr: 7.3321e-05 lr: 7.3321e-05 eta: 5:04:00 time: 0.3811 data_time: 0.0182 memory: 5384 loss: 0.2891 decode.loss_ce: 0.2891 decode.acc_seg: 90.6979 +2024/10/28 05:03:25 - mmengine - INFO - Iter(train) [114350/160000] base_lr: 7.3206e-05 lr: 7.3206e-05 eta: 5:03:41 time: 0.3789 data_time: 0.0164 memory: 5384 loss: 0.2345 decode.loss_ce: 0.2345 decode.acc_seg: 90.0295 +2024/10/28 05:03:44 - mmengine - INFO - Iter(train) [114400/160000] base_lr: 7.3091e-05 lr: 7.3091e-05 eta: 5:03:21 time: 0.3770 data_time: 0.0172 memory: 5384 loss: 0.2328 decode.loss_ce: 0.2328 decode.acc_seg: 91.7392 +2024/10/28 05:04:03 - mmengine - INFO - Iter(train) [114450/160000] base_lr: 7.2976e-05 lr: 7.2976e-05 eta: 5:03:01 time: 0.3831 data_time: 0.0170 memory: 5386 loss: 0.2614 decode.loss_ce: 0.2614 decode.acc_seg: 88.7660 +2024/10/28 05:04:25 - mmengine - INFO - Iter(train) [114500/160000] base_lr: 7.2861e-05 lr: 7.2861e-05 eta: 5:02:42 time: 0.3807 data_time: 0.0171 memory: 5384 loss: 0.3039 decode.loss_ce: 0.3039 decode.acc_seg: 91.0105 +2024/10/28 05:04:44 - mmengine - INFO - Iter(train) [114550/160000] base_lr: 7.2746e-05 lr: 7.2746e-05 eta: 5:02:21 time: 0.3776 data_time: 0.0175 memory: 5385 loss: 0.2724 decode.loss_ce: 0.2724 decode.acc_seg: 90.1005 +2024/10/28 05:05:03 - mmengine - INFO - Iter(train) [114600/160000] base_lr: 7.2631e-05 lr: 7.2631e-05 eta: 5:02:01 time: 0.3789 data_time: 0.0175 memory: 5384 loss: 0.2731 decode.loss_ce: 0.2731 decode.acc_seg: 91.3165 +2024/10/28 05:05:25 - mmengine - INFO - Iter(train) [114650/160000] base_lr: 7.2515e-05 lr: 7.2515e-05 eta: 5:01:42 time: 0.3810 data_time: 0.0172 memory: 5384 loss: 0.2683 decode.loss_ce: 0.2683 decode.acc_seg: 90.1806 +2024/10/28 05:05:44 - mmengine - INFO - Iter(train) [114700/160000] base_lr: 7.2400e-05 lr: 7.2400e-05 eta: 5:01:21 time: 0.3764 data_time: 0.0166 memory: 5383 loss: 0.2991 decode.loss_ce: 0.2991 decode.acc_seg: 81.9651 +2024/10/28 05:06:03 - mmengine - INFO - Iter(train) [114750/160000] base_lr: 7.2285e-05 lr: 7.2285e-05 eta: 5:01:01 time: 0.3824 data_time: 0.0171 memory: 5384 loss: 0.3047 decode.loss_ce: 0.3047 decode.acc_seg: 85.5595 +2024/10/28 05:06:25 - mmengine - INFO - Iter(train) [114800/160000] base_lr: 7.2170e-05 lr: 7.2170e-05 eta: 5:00:42 time: 0.3779 data_time: 0.0175 memory: 5386 loss: 0.2687 decode.loss_ce: 0.2687 decode.acc_seg: 88.4796 +2024/10/28 05:06:44 - mmengine - INFO - Iter(train) [114850/160000] base_lr: 7.2054e-05 lr: 7.2054e-05 eta: 5:00:22 time: 0.3993 data_time: 0.0157 memory: 5384 loss: 0.2512 decode.loss_ce: 0.2512 decode.acc_seg: 85.9263 +2024/10/28 05:07:04 - mmengine - INFO - Iter(train) [114900/160000] base_lr: 7.1939e-05 lr: 7.1939e-05 eta: 5:00:02 time: 0.4004 data_time: 0.0163 memory: 5384 loss: 0.2478 decode.loss_ce: 0.2478 decode.acc_seg: 90.7893 +2024/10/28 05:07:26 - mmengine - INFO - Iter(train) [114950/160000] base_lr: 7.1823e-05 lr: 7.1823e-05 eta: 4:59:42 time: 0.3952 data_time: 0.0148 memory: 5385 loss: 0.2183 decode.loss_ce: 0.2183 decode.acc_seg: 88.4704 +2024/10/28 05:07:45 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:07:45 - mmengine - INFO - Iter(train) [115000/160000] base_lr: 7.1708e-05 lr: 7.1708e-05 eta: 4:59:22 time: 0.3763 data_time: 0.0169 memory: 5384 loss: 0.2875 decode.loss_ce: 0.2875 decode.acc_seg: 87.5521 +2024/10/28 05:08:04 - mmengine - INFO - Iter(train) [115050/160000] base_lr: 7.1592e-05 lr: 7.1592e-05 eta: 4:59:02 time: 0.3781 data_time: 0.0175 memory: 5384 loss: 0.2644 decode.loss_ce: 0.2644 decode.acc_seg: 92.3711 +2024/10/28 05:08:24 - mmengine - INFO - Iter(train) [115100/160000] base_lr: 7.1477e-05 lr: 7.1477e-05 eta: 4:58:42 time: 0.3754 data_time: 0.0173 memory: 5384 loss: 0.2484 decode.loss_ce: 0.2484 decode.acc_seg: 86.3643 +2024/10/28 05:08:43 - mmengine - INFO - Iter(train) [115150/160000] base_lr: 7.1361e-05 lr: 7.1361e-05 eta: 4:58:21 time: 0.3786 data_time: 0.0170 memory: 5384 loss: 0.2639 decode.loss_ce: 0.2639 decode.acc_seg: 89.1970 +2024/10/28 05:09:02 - mmengine - INFO - Iter(train) [115200/160000] base_lr: 7.1245e-05 lr: 7.1245e-05 eta: 4:58:01 time: 0.3774 data_time: 0.0165 memory: 5384 loss: 0.3038 decode.loss_ce: 0.3038 decode.acc_seg: 90.1231 +2024/10/28 05:09:25 - mmengine - INFO - Iter(train) [115250/160000] base_lr: 7.1129e-05 lr: 7.1129e-05 eta: 4:57:42 time: 0.3737 data_time: 0.0163 memory: 5383 loss: 0.2807 decode.loss_ce: 0.2807 decode.acc_seg: 85.6539 +2024/10/28 05:09:44 - mmengine - INFO - Iter(train) [115300/160000] base_lr: 7.1014e-05 lr: 7.1014e-05 eta: 4:57:22 time: 0.3910 data_time: 0.0152 memory: 5384 loss: 0.2974 decode.loss_ce: 0.2974 decode.acc_seg: 89.6199 +2024/10/28 05:10:04 - mmengine - INFO - Iter(train) [115350/160000] base_lr: 7.0898e-05 lr: 7.0898e-05 eta: 4:57:02 time: 0.4019 data_time: 0.0168 memory: 5384 loss: 0.2706 decode.loss_ce: 0.2706 decode.acc_seg: 92.3776 +2024/10/28 05:10:25 - mmengine - INFO - Iter(train) [115400/160000] base_lr: 7.0782e-05 lr: 7.0782e-05 eta: 4:56:43 time: 0.3805 data_time: 0.0174 memory: 5384 loss: 0.2878 decode.loss_ce: 0.2878 decode.acc_seg: 88.0432 +2024/10/28 05:10:44 - mmengine - INFO - Iter(train) [115450/160000] base_lr: 7.0666e-05 lr: 7.0666e-05 eta: 4:56:22 time: 0.3799 data_time: 0.0177 memory: 5383 loss: 0.2522 decode.loss_ce: 0.2522 decode.acc_seg: 90.1655 +2024/10/28 05:11:03 - mmengine - INFO - Iter(train) [115500/160000] base_lr: 7.0550e-05 lr: 7.0550e-05 eta: 4:56:02 time: 0.3815 data_time: 0.0173 memory: 5384 loss: 0.2751 decode.loss_ce: 0.2751 decode.acc_seg: 88.0774 +2024/10/28 05:11:25 - mmengine - INFO - Iter(train) [115550/160000] base_lr: 7.0434e-05 lr: 7.0434e-05 eta: 4:55:42 time: 0.3783 data_time: 0.0171 memory: 5384 loss: 0.2422 decode.loss_ce: 0.2422 decode.acc_seg: 94.3817 +2024/10/28 05:11:44 - mmengine - INFO - Iter(train) [115600/160000] base_lr: 7.0318e-05 lr: 7.0318e-05 eta: 4:55:22 time: 0.3788 data_time: 0.0177 memory: 5384 loss: 0.2854 decode.loss_ce: 0.2854 decode.acc_seg: 87.9405 +2024/10/28 05:12:03 - mmengine - INFO - Iter(train) [115650/160000] base_lr: 7.0202e-05 lr: 7.0202e-05 eta: 4:55:02 time: 0.3811 data_time: 0.0175 memory: 5384 loss: 0.2630 decode.loss_ce: 0.2630 decode.acc_seg: 85.5078 +2024/10/28 05:12:25 - mmengine - INFO - Iter(train) [115700/160000] base_lr: 7.0086e-05 lr: 7.0086e-05 eta: 4:54:43 time: 0.3768 data_time: 0.0157 memory: 5384 loss: 0.2727 decode.loss_ce: 0.2727 decode.acc_seg: 88.7081 +2024/10/28 05:12:44 - mmengine - INFO - Iter(train) [115750/160000] base_lr: 6.9970e-05 lr: 6.9970e-05 eta: 4:54:23 time: 0.3952 data_time: 0.0159 memory: 5384 loss: 0.2463 decode.loss_ce: 0.2463 decode.acc_seg: 90.7383 +2024/10/28 05:13:05 - mmengine - INFO - Iter(train) [115800/160000] base_lr: 6.9854e-05 lr: 6.9854e-05 eta: 4:54:03 time: 0.3893 data_time: 0.0168 memory: 5384 loss: 0.2834 decode.loss_ce: 0.2834 decode.acc_seg: 88.1251 +2024/10/28 05:13:24 - mmengine - INFO - Iter(train) [115850/160000] base_lr: 6.9737e-05 lr: 6.9737e-05 eta: 4:53:43 time: 0.3797 data_time: 0.0177 memory: 5384 loss: 0.2605 decode.loss_ce: 0.2605 decode.acc_seg: 86.7853 +2024/10/28 05:13:43 - mmengine - INFO - Iter(train) [115900/160000] base_lr: 6.9621e-05 lr: 6.9621e-05 eta: 4:53:22 time: 0.3737 data_time: 0.0171 memory: 5384 loss: 0.2361 decode.loss_ce: 0.2361 decode.acc_seg: 92.3701 +2024/10/28 05:14:02 - mmengine - INFO - Iter(train) [115950/160000] base_lr: 6.9505e-05 lr: 6.9505e-05 eta: 4:53:02 time: 0.3790 data_time: 0.0165 memory: 5385 loss: 0.2775 decode.loss_ce: 0.2775 decode.acc_seg: 90.1035 +2024/10/28 05:14:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:14:24 - mmengine - INFO - Iter(train) [116000/160000] base_lr: 6.9388e-05 lr: 6.9388e-05 eta: 4:52:43 time: 0.3781 data_time: 0.0180 memory: 5384 loss: 0.2557 decode.loss_ce: 0.2557 decode.acc_seg: 89.4698 +2024/10/28 05:14:43 - mmengine - INFO - Iter(train) [116050/160000] base_lr: 6.9272e-05 lr: 6.9272e-05 eta: 4:52:22 time: 0.3987 data_time: 0.0163 memory: 5383 loss: 0.2898 decode.loss_ce: 0.2898 decode.acc_seg: 93.0666 +2024/10/28 05:15:02 - mmengine - INFO - Iter(train) [116100/160000] base_lr: 6.9156e-05 lr: 6.9156e-05 eta: 4:52:02 time: 0.3802 data_time: 0.0163 memory: 5383 loss: 0.2991 decode.loss_ce: 0.2991 decode.acc_seg: 81.9305 +2024/10/28 05:15:25 - mmengine - INFO - Iter(train) [116150/160000] base_lr: 6.9039e-05 lr: 6.9039e-05 eta: 4:51:43 time: 0.3847 data_time: 0.0166 memory: 5384 loss: 0.2931 decode.loss_ce: 0.2931 decode.acc_seg: 90.9292 +2024/10/28 05:15:45 - mmengine - INFO - Iter(train) [116200/160000] base_lr: 6.8923e-05 lr: 6.8923e-05 eta: 4:51:23 time: 0.3823 data_time: 0.0172 memory: 5384 loss: 0.2864 decode.loss_ce: 0.2864 decode.acc_seg: 89.5689 +2024/10/28 05:16:04 - mmengine - INFO - Iter(train) [116250/160000] base_lr: 6.8806e-05 lr: 6.8806e-05 eta: 4:51:03 time: 0.3815 data_time: 0.0162 memory: 5384 loss: 0.3098 decode.loss_ce: 0.3098 decode.acc_seg: 90.2584 +2024/10/28 05:16:24 - mmengine - INFO - Iter(train) [116300/160000] base_lr: 6.8690e-05 lr: 6.8690e-05 eta: 4:50:43 time: 0.3760 data_time: 0.0165 memory: 5384 loss: 0.2510 decode.loss_ce: 0.2510 decode.acc_seg: 91.8587 +2024/10/28 05:16:43 - mmengine - INFO - Iter(train) [116350/160000] base_lr: 6.8573e-05 lr: 6.8573e-05 eta: 4:50:23 time: 0.3767 data_time: 0.0162 memory: 5385 loss: 0.2128 decode.loss_ce: 0.2128 decode.acc_seg: 89.9522 +2024/10/28 05:17:02 - mmengine - INFO - Iter(train) [116400/160000] base_lr: 6.8456e-05 lr: 6.8456e-05 eta: 4:50:02 time: 0.3791 data_time: 0.0162 memory: 5384 loss: 0.2453 decode.loss_ce: 0.2453 decode.acc_seg: 83.4162 +2024/10/28 05:17:26 - mmengine - INFO - Iter(train) [116450/160000] base_lr: 6.8340e-05 lr: 6.8340e-05 eta: 4:49:44 time: 0.3984 data_time: 0.0139 memory: 5384 loss: 0.2748 decode.loss_ce: 0.2748 decode.acc_seg: 91.5076 +2024/10/28 05:17:45 - mmengine - INFO - Iter(train) [116500/160000] base_lr: 6.8223e-05 lr: 6.8223e-05 eta: 4:49:24 time: 0.3814 data_time: 0.0157 memory: 5384 loss: 0.2820 decode.loss_ce: 0.2820 decode.acc_seg: 86.7564 +2024/10/28 05:18:05 - mmengine - INFO - Iter(train) [116550/160000] base_lr: 6.8106e-05 lr: 6.8106e-05 eta: 4:49:03 time: 0.3878 data_time: 0.0162 memory: 5384 loss: 0.2823 decode.loss_ce: 0.2823 decode.acc_seg: 84.5584 +2024/10/28 05:18:26 - mmengine - INFO - Iter(train) [116600/160000] base_lr: 6.7990e-05 lr: 6.7990e-05 eta: 4:48:44 time: 0.3790 data_time: 0.0187 memory: 5383 loss: 0.2937 decode.loss_ce: 0.2937 decode.acc_seg: 87.6974 +2024/10/28 05:18:44 - mmengine - INFO - Iter(train) [116650/160000] base_lr: 6.7873e-05 lr: 6.7873e-05 eta: 4:48:24 time: 0.3763 data_time: 0.0180 memory: 5383 loss: 0.2758 decode.loss_ce: 0.2758 decode.acc_seg: 89.7416 +2024/10/28 05:19:03 - mmengine - INFO - Iter(train) [116700/160000] base_lr: 6.7756e-05 lr: 6.7756e-05 eta: 4:48:03 time: 0.3789 data_time: 0.0160 memory: 5387 loss: 0.2213 decode.loss_ce: 0.2213 decode.acc_seg: 93.2508 +2024/10/28 05:19:25 - mmengine - INFO - Iter(train) [116750/160000] base_lr: 6.7639e-05 lr: 6.7639e-05 eta: 4:47:44 time: 0.4033 data_time: 0.0163 memory: 5385 loss: 0.2790 decode.loss_ce: 0.2790 decode.acc_seg: 86.3753 +2024/10/28 05:19:44 - mmengine - INFO - Iter(train) [116800/160000] base_lr: 6.7522e-05 lr: 6.7522e-05 eta: 4:47:23 time: 0.3786 data_time: 0.0173 memory: 5384 loss: 0.3017 decode.loss_ce: 0.3017 decode.acc_seg: 88.8805 +2024/10/28 05:20:03 - mmengine - INFO - Iter(train) [116850/160000] base_lr: 6.7405e-05 lr: 6.7405e-05 eta: 4:47:03 time: 0.3807 data_time: 0.0166 memory: 5384 loss: 0.2358 decode.loss_ce: 0.2358 decode.acc_seg: 85.3729 +2024/10/28 05:20:24 - mmengine - INFO - Iter(train) [116900/160000] base_lr: 6.7289e-05 lr: 6.7289e-05 eta: 4:46:44 time: 0.3793 data_time: 0.0173 memory: 5383 loss: 0.2766 decode.loss_ce: 0.2766 decode.acc_seg: 90.8829 +2024/10/28 05:20:43 - mmengine - INFO - Iter(train) [116950/160000] base_lr: 6.7172e-05 lr: 6.7172e-05 eta: 4:46:23 time: 0.3803 data_time: 0.0164 memory: 5384 loss: 0.2402 decode.loss_ce: 0.2402 decode.acc_seg: 91.3501 +2024/10/28 05:21:02 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:21:02 - mmengine - INFO - Iter(train) [117000/160000] base_lr: 6.7055e-05 lr: 6.7055e-05 eta: 4:46:03 time: 0.3761 data_time: 0.0166 memory: 5384 loss: 0.2426 decode.loss_ce: 0.2426 decode.acc_seg: 92.4325 +2024/10/28 05:21:24 - mmengine - INFO - Iter(train) [117050/160000] base_lr: 6.6938e-05 lr: 6.6938e-05 eta: 4:45:44 time: 0.3816 data_time: 0.0169 memory: 5384 loss: 0.2798 decode.loss_ce: 0.2798 decode.acc_seg: 88.8974 +2024/10/28 05:21:43 - mmengine - INFO - Iter(train) [117100/160000] base_lr: 6.6821e-05 lr: 6.6821e-05 eta: 4:45:24 time: 0.3826 data_time: 0.0163 memory: 5384 loss: 0.3243 decode.loss_ce: 0.3243 decode.acc_seg: 86.7000 +2024/10/28 05:22:03 - mmengine - INFO - Iter(train) [117150/160000] base_lr: 6.6703e-05 lr: 6.6703e-05 eta: 4:45:03 time: 0.3805 data_time: 0.0161 memory: 5384 loss: 0.2608 decode.loss_ce: 0.2608 decode.acc_seg: 91.4360 +2024/10/28 05:22:24 - mmengine - INFO - Iter(train) [117200/160000] base_lr: 6.6586e-05 lr: 6.6586e-05 eta: 4:44:44 time: 0.3758 data_time: 0.0179 memory: 5384 loss: 0.2699 decode.loss_ce: 0.2699 decode.acc_seg: 86.0454 +2024/10/28 05:22:44 - mmengine - INFO - Iter(train) [117250/160000] base_lr: 6.6469e-05 lr: 6.6469e-05 eta: 4:44:24 time: 0.3774 data_time: 0.0173 memory: 5383 loss: 0.2437 decode.loss_ce: 0.2437 decode.acc_seg: 85.2262 +2024/10/28 05:23:03 - mmengine - INFO - Iter(train) [117300/160000] base_lr: 6.6352e-05 lr: 6.6352e-05 eta: 4:44:03 time: 0.3813 data_time: 0.0172 memory: 5384 loss: 0.2902 decode.loss_ce: 0.2902 decode.acc_seg: 89.2432 +2024/10/28 05:23:25 - mmengine - INFO - Iter(train) [117350/160000] base_lr: 6.6235e-05 lr: 6.6235e-05 eta: 4:43:44 time: 0.3785 data_time: 0.0169 memory: 5385 loss: 0.2706 decode.loss_ce: 0.2706 decode.acc_seg: 84.7195 +2024/10/28 05:23:44 - mmengine - INFO - Iter(train) [117400/160000] base_lr: 6.6118e-05 lr: 6.6118e-05 eta: 4:43:24 time: 0.3758 data_time: 0.0168 memory: 5384 loss: 0.2674 decode.loss_ce: 0.2674 decode.acc_seg: 90.3593 +2024/10/28 05:24:03 - mmengine - INFO - Iter(train) [117450/160000] base_lr: 6.6001e-05 lr: 6.6001e-05 eta: 4:43:04 time: 0.3847 data_time: 0.0170 memory: 5385 loss: 0.2458 decode.loss_ce: 0.2458 decode.acc_seg: 90.9285 +2024/10/28 05:24:25 - mmengine - INFO - Iter(train) [117500/160000] base_lr: 6.5883e-05 lr: 6.5883e-05 eta: 4:42:45 time: 0.3810 data_time: 0.0166 memory: 5384 loss: 0.2873 decode.loss_ce: 0.2873 decode.acc_seg: 84.7421 +2024/10/28 05:24:45 - mmengine - INFO - Iter(train) [117550/160000] base_lr: 6.5766e-05 lr: 6.5766e-05 eta: 4:42:24 time: 0.3790 data_time: 0.0161 memory: 5386 loss: 0.2641 decode.loss_ce: 0.2641 decode.acc_seg: 92.3590 +2024/10/28 05:25:03 - mmengine - INFO - Iter(train) [117600/160000] base_lr: 6.5649e-05 lr: 6.5649e-05 eta: 4:42:04 time: 0.3813 data_time: 0.0161 memory: 5385 loss: 0.2564 decode.loss_ce: 0.2564 decode.acc_seg: 87.5941 +2024/10/28 05:25:25 - mmengine - INFO - Iter(train) [117650/160000] base_lr: 6.5532e-05 lr: 6.5532e-05 eta: 4:41:45 time: 0.3805 data_time: 0.0162 memory: 5385 loss: 0.2319 decode.loss_ce: 0.2319 decode.acc_seg: 92.4836 +2024/10/28 05:25:45 - mmengine - INFO - Iter(train) [117700/160000] base_lr: 6.5414e-05 lr: 6.5414e-05 eta: 4:41:25 time: 0.4046 data_time: 0.0162 memory: 5384 loss: 0.2672 decode.loss_ce: 0.2672 decode.acc_seg: 83.6290 +2024/10/28 05:26:05 - mmengine - INFO - Iter(train) [117750/160000] base_lr: 6.5297e-05 lr: 6.5297e-05 eta: 4:41:05 time: 0.4017 data_time: 0.0154 memory: 5383 loss: 0.2624 decode.loss_ce: 0.2624 decode.acc_seg: 87.7327 +2024/10/28 05:26:26 - mmengine - INFO - Iter(train) [117800/160000] base_lr: 6.5180e-05 lr: 6.5180e-05 eta: 4:40:45 time: 0.3837 data_time: 0.0163 memory: 5384 loss: 0.2365 decode.loss_ce: 0.2365 decode.acc_seg: 88.5119 +2024/10/28 05:26:45 - mmengine - INFO - Iter(train) [117850/160000] base_lr: 6.5062e-05 lr: 6.5062e-05 eta: 4:40:25 time: 0.3809 data_time: 0.0172 memory: 5384 loss: 0.2841 decode.loss_ce: 0.2841 decode.acc_seg: 90.0027 +2024/10/28 05:27:05 - mmengine - INFO - Iter(train) [117900/160000] base_lr: 6.4945e-05 lr: 6.4945e-05 eta: 4:40:05 time: 0.3817 data_time: 0.0177 memory: 5384 loss: 0.2355 decode.loss_ce: 0.2355 decode.acc_seg: 90.7040 +2024/10/28 05:27:24 - mmengine - INFO - Iter(train) [117950/160000] base_lr: 6.4827e-05 lr: 6.4827e-05 eta: 4:39:45 time: 0.3763 data_time: 0.0174 memory: 5384 loss: 0.2745 decode.loss_ce: 0.2745 decode.acc_seg: 87.7673 +2024/10/28 05:27:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:27:44 - mmengine - INFO - Iter(train) [118000/160000] base_lr: 6.4710e-05 lr: 6.4710e-05 eta: 4:39:25 time: 0.3841 data_time: 0.0183 memory: 5383 loss: 0.3061 decode.loss_ce: 0.3061 decode.acc_seg: 91.7727 +2024/10/28 05:28:03 - mmengine - INFO - Iter(train) [118050/160000] base_lr: 6.4592e-05 lr: 6.4592e-05 eta: 4:39:04 time: 0.3819 data_time: 0.0178 memory: 5385 loss: 0.2558 decode.loss_ce: 0.2558 decode.acc_seg: 88.0079 +2024/10/28 05:28:25 - mmengine - INFO - Iter(train) [118100/160000] base_lr: 6.4475e-05 lr: 6.4475e-05 eta: 4:38:45 time: 0.3797 data_time: 0.0164 memory: 5386 loss: 0.2945 decode.loss_ce: 0.2945 decode.acc_seg: 84.4866 +2024/10/28 05:28:44 - mmengine - INFO - Iter(train) [118150/160000] base_lr: 6.4357e-05 lr: 6.4357e-05 eta: 4:38:25 time: 0.3800 data_time: 0.0164 memory: 5384 loss: 0.2573 decode.loss_ce: 0.2573 decode.acc_seg: 85.0922 +2024/10/28 05:29:03 - mmengine - INFO - Iter(train) [118200/160000] base_lr: 6.4240e-05 lr: 6.4240e-05 eta: 4:38:04 time: 0.3816 data_time: 0.0170 memory: 5384 loss: 0.2687 decode.loss_ce: 0.2687 decode.acc_seg: 86.6134 +2024/10/28 05:29:26 - mmengine - INFO - Iter(train) [118250/160000] base_lr: 6.4122e-05 lr: 6.4122e-05 eta: 4:37:46 time: 0.3806 data_time: 0.0166 memory: 5384 loss: 0.2296 decode.loss_ce: 0.2296 decode.acc_seg: 92.8434 +2024/10/28 05:29:45 - mmengine - INFO - Iter(train) [118300/160000] base_lr: 6.4005e-05 lr: 6.4005e-05 eta: 4:37:25 time: 0.3764 data_time: 0.0150 memory: 5384 loss: 0.2821 decode.loss_ce: 0.2821 decode.acc_seg: 90.4524 +2024/10/28 05:30:04 - mmengine - INFO - Iter(train) [118350/160000] base_lr: 6.3887e-05 lr: 6.3887e-05 eta: 4:37:05 time: 0.3793 data_time: 0.0162 memory: 5384 loss: 0.2701 decode.loss_ce: 0.2701 decode.acc_seg: 88.6835 +2024/10/28 05:30:25 - mmengine - INFO - Iter(train) [118400/160000] base_lr: 6.3770e-05 lr: 6.3770e-05 eta: 4:36:46 time: 0.3786 data_time: 0.0170 memory: 5384 loss: 0.3125 decode.loss_ce: 0.3125 decode.acc_seg: 92.9409 +2024/10/28 05:30:44 - mmengine - INFO - Iter(train) [118450/160000] base_lr: 6.3652e-05 lr: 6.3652e-05 eta: 4:36:25 time: 0.3821 data_time: 0.0176 memory: 5384 loss: 0.2339 decode.loss_ce: 0.2339 decode.acc_seg: 91.7716 +2024/10/28 05:31:04 - mmengine - INFO - Iter(train) [118500/160000] base_lr: 6.3535e-05 lr: 6.3535e-05 eta: 4:36:05 time: 0.3804 data_time: 0.0171 memory: 5384 loss: 0.2710 decode.loss_ce: 0.2710 decode.acc_seg: 92.1244 +2024/10/28 05:31:26 - mmengine - INFO - Iter(train) [118550/160000] base_lr: 6.3417e-05 lr: 6.3417e-05 eta: 4:35:46 time: 0.3816 data_time: 0.0153 memory: 5384 loss: 0.2267 decode.loss_ce: 0.2267 decode.acc_seg: 88.7109 +2024/10/28 05:31:46 - mmengine - INFO - Iter(train) [118600/160000] base_lr: 6.3299e-05 lr: 6.3299e-05 eta: 4:35:26 time: 0.3816 data_time: 0.0165 memory: 5383 loss: 0.2628 decode.loss_ce: 0.2628 decode.acc_seg: 85.3664 +2024/10/28 05:32:05 - mmengine - INFO - Iter(train) [118650/160000] base_lr: 6.3182e-05 lr: 6.3182e-05 eta: 4:35:06 time: 0.3808 data_time: 0.0171 memory: 5384 loss: 0.3324 decode.loss_ce: 0.3324 decode.acc_seg: 87.0664 +2024/10/28 05:32:25 - mmengine - INFO - Iter(train) [118700/160000] base_lr: 6.3064e-05 lr: 6.3064e-05 eta: 4:34:46 time: 0.3824 data_time: 0.0169 memory: 5384 loss: 0.2540 decode.loss_ce: 0.2540 decode.acc_seg: 89.5404 +2024/10/28 05:32:44 - mmengine - INFO - Iter(train) [118750/160000] base_lr: 6.2946e-05 lr: 6.2946e-05 eta: 4:34:25 time: 0.3800 data_time: 0.0178 memory: 5383 loss: 0.2369 decode.loss_ce: 0.2369 decode.acc_seg: 92.7338 +2024/10/28 05:33:03 - mmengine - INFO - Iter(train) [118800/160000] base_lr: 6.2829e-05 lr: 6.2829e-05 eta: 4:34:05 time: 0.3861 data_time: 0.0184 memory: 5385 loss: 0.2398 decode.loss_ce: 0.2398 decode.acc_seg: 87.1950 +2024/10/28 05:33:25 - mmengine - INFO - Iter(train) [118850/160000] base_lr: 6.2711e-05 lr: 6.2711e-05 eta: 4:33:46 time: 0.3757 data_time: 0.0182 memory: 5384 loss: 0.2310 decode.loss_ce: 0.2310 decode.acc_seg: 89.3323 +2024/10/28 05:33:44 - mmengine - INFO - Iter(train) [118900/160000] base_lr: 6.2593e-05 lr: 6.2593e-05 eta: 4:33:25 time: 0.3798 data_time: 0.0184 memory: 5384 loss: 0.2675 decode.loss_ce: 0.2675 decode.acc_seg: 90.5900 +2024/10/28 05:34:03 - mmengine - INFO - Iter(train) [118950/160000] base_lr: 6.2476e-05 lr: 6.2476e-05 eta: 4:33:05 time: 0.3820 data_time: 0.0173 memory: 5384 loss: 0.2364 decode.loss_ce: 0.2364 decode.acc_seg: 91.8753 +2024/10/28 05:34:26 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:34:26 - mmengine - INFO - Iter(train) [119000/160000] base_lr: 6.2358e-05 lr: 6.2358e-05 eta: 4:32:46 time: 0.3797 data_time: 0.0186 memory: 5384 loss: 0.2834 decode.loss_ce: 0.2834 decode.acc_seg: 80.8846 +2024/10/28 05:34:44 - mmengine - INFO - Iter(train) [119050/160000] base_lr: 6.2240e-05 lr: 6.2240e-05 eta: 4:32:26 time: 0.3776 data_time: 0.0184 memory: 5384 loss: 0.2757 decode.loss_ce: 0.2757 decode.acc_seg: 93.6877 +2024/10/28 05:35:04 - mmengine - INFO - Iter(train) [119100/160000] base_lr: 6.2122e-05 lr: 6.2122e-05 eta: 4:32:05 time: 0.3829 data_time: 0.0174 memory: 5384 loss: 0.2656 decode.loss_ce: 0.2656 decode.acc_seg: 91.6425 +2024/10/28 05:35:26 - mmengine - INFO - Iter(train) [119150/160000] base_lr: 6.2005e-05 lr: 6.2005e-05 eta: 4:31:46 time: 0.3801 data_time: 0.0188 memory: 5384 loss: 0.2665 decode.loss_ce: 0.2665 decode.acc_seg: 92.8504 +2024/10/28 05:35:45 - mmengine - INFO - Iter(train) [119200/160000] base_lr: 6.1887e-05 lr: 6.1887e-05 eta: 4:31:26 time: 0.3779 data_time: 0.0186 memory: 5384 loss: 0.2613 decode.loss_ce: 0.2613 decode.acc_seg: 88.1436 +2024/10/28 05:36:04 - mmengine - INFO - Iter(train) [119250/160000] base_lr: 6.1769e-05 lr: 6.1769e-05 eta: 4:31:06 time: 0.3808 data_time: 0.0182 memory: 5384 loss: 0.2741 decode.loss_ce: 0.2741 decode.acc_seg: 86.0569 +2024/10/28 05:36:24 - mmengine - INFO - Iter(train) [119300/160000] base_lr: 6.1651e-05 lr: 6.1651e-05 eta: 4:30:46 time: 0.3803 data_time: 0.0172 memory: 5384 loss: 0.2408 decode.loss_ce: 0.2408 decode.acc_seg: 87.5306 +2024/10/28 05:36:43 - mmengine - INFO - Iter(train) [119350/160000] base_lr: 6.1534e-05 lr: 6.1534e-05 eta: 4:30:25 time: 0.3754 data_time: 0.0170 memory: 5384 loss: 0.2260 decode.loss_ce: 0.2260 decode.acc_seg: 88.1187 +2024/10/28 05:37:02 - mmengine - INFO - Iter(train) [119400/160000] base_lr: 6.1416e-05 lr: 6.1416e-05 eta: 4:30:05 time: 0.3733 data_time: 0.0169 memory: 5384 loss: 0.2211 decode.loss_ce: 0.2211 decode.acc_seg: 90.5592 +2024/10/28 05:37:20 - mmengine - INFO - Iter(train) [119450/160000] base_lr: 6.1298e-05 lr: 6.1298e-05 eta: 4:29:45 time: 0.3804 data_time: 0.0172 memory: 5384 loss: 0.2565 decode.loss_ce: 0.2565 decode.acc_seg: 91.4878 +2024/10/28 05:37:39 - mmengine - INFO - Iter(train) [119500/160000] base_lr: 6.1180e-05 lr: 6.1180e-05 eta: 4:29:24 time: 0.3762 data_time: 0.0171 memory: 5382 loss: 0.2590 decode.loss_ce: 0.2590 decode.acc_seg: 89.0406 +2024/10/28 05:37:58 - mmengine - INFO - Iter(train) [119550/160000] base_lr: 6.1063e-05 lr: 6.1063e-05 eta: 4:29:04 time: 0.3767 data_time: 0.0173 memory: 5382 loss: 0.2627 decode.loss_ce: 0.2627 decode.acc_seg: 90.8509 +2024/10/28 05:38:17 - mmengine - INFO - Iter(train) [119600/160000] base_lr: 6.0945e-05 lr: 6.0945e-05 eta: 4:28:44 time: 0.3755 data_time: 0.0180 memory: 5384 loss: 0.2592 decode.loss_ce: 0.2592 decode.acc_seg: 88.1560 +2024/10/28 05:38:37 - mmengine - INFO - Iter(train) [119650/160000] base_lr: 6.0827e-05 lr: 6.0827e-05 eta: 4:28:23 time: 0.3799 data_time: 0.0173 memory: 5384 loss: 0.2286 decode.loss_ce: 0.2286 decode.acc_seg: 94.7273 +2024/10/28 05:38:56 - mmengine - INFO - Iter(train) [119700/160000] base_lr: 6.0709e-05 lr: 6.0709e-05 eta: 4:28:03 time: 0.3798 data_time: 0.0174 memory: 5385 loss: 0.2314 decode.loss_ce: 0.2314 decode.acc_seg: 90.1525 +2024/10/28 05:39:15 - mmengine - INFO - Iter(train) [119750/160000] base_lr: 6.0591e-05 lr: 6.0591e-05 eta: 4:27:43 time: 0.3783 data_time: 0.0176 memory: 5384 loss: 0.2662 decode.loss_ce: 0.2662 decode.acc_seg: 91.7603 +2024/10/28 05:39:34 - mmengine - INFO - Iter(train) [119800/160000] base_lr: 6.0474e-05 lr: 6.0474e-05 eta: 4:27:22 time: 0.3755 data_time: 0.0168 memory: 5384 loss: 0.1860 decode.loss_ce: 0.1860 decode.acc_seg: 91.0058 +2024/10/28 05:39:53 - mmengine - INFO - Iter(train) [119850/160000] base_lr: 6.0356e-05 lr: 6.0356e-05 eta: 4:27:02 time: 0.3790 data_time: 0.0177 memory: 5384 loss: 0.2395 decode.loss_ce: 0.2395 decode.acc_seg: 89.1932 +2024/10/28 05:40:12 - mmengine - INFO - Iter(train) [119900/160000] base_lr: 6.0238e-05 lr: 6.0238e-05 eta: 4:26:42 time: 0.3771 data_time: 0.0176 memory: 5384 loss: 0.2314 decode.loss_ce: 0.2314 decode.acc_seg: 93.0229 +2024/10/28 05:40:31 - mmengine - INFO - Iter(train) [119950/160000] base_lr: 6.0120e-05 lr: 6.0120e-05 eta: 4:26:22 time: 0.3786 data_time: 0.0169 memory: 5383 loss: 0.2549 decode.loss_ce: 0.2549 decode.acc_seg: 86.4757 +2024/10/28 05:40:50 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:40:50 - mmengine - INFO - Iter(train) [120000/160000] base_lr: 6.0002e-05 lr: 6.0002e-05 eta: 4:26:01 time: 0.3767 data_time: 0.0170 memory: 5386 loss: 0.2729 decode.loss_ce: 0.2729 decode.acc_seg: 88.6575 +2024/10/28 05:41:09 - mmengine - INFO - Iter(train) [120050/160000] base_lr: 5.9885e-05 lr: 5.9885e-05 eta: 4:25:41 time: 0.3801 data_time: 0.0179 memory: 5384 loss: 0.2252 decode.loss_ce: 0.2252 decode.acc_seg: 90.5579 +2024/10/28 05:41:29 - mmengine - INFO - Iter(train) [120100/160000] base_lr: 5.9767e-05 lr: 5.9767e-05 eta: 4:25:21 time: 0.3788 data_time: 0.0168 memory: 5383 loss: 0.2506 decode.loss_ce: 0.2506 decode.acc_seg: 90.7205 +2024/10/28 05:41:47 - mmengine - INFO - Iter(train) [120150/160000] base_lr: 5.9649e-05 lr: 5.9649e-05 eta: 4:25:00 time: 0.3758 data_time: 0.0169 memory: 5384 loss: 0.2342 decode.loss_ce: 0.2342 decode.acc_seg: 90.1645 +2024/10/28 05:42:06 - mmengine - INFO - Iter(train) [120200/160000] base_lr: 5.9531e-05 lr: 5.9531e-05 eta: 4:24:40 time: 0.3791 data_time: 0.0174 memory: 5385 loss: 0.2869 decode.loss_ce: 0.2869 decode.acc_seg: 88.7302 +2024/10/28 05:42:25 - mmengine - INFO - Iter(train) [120250/160000] base_lr: 5.9413e-05 lr: 5.9413e-05 eta: 4:24:20 time: 0.3845 data_time: 0.0172 memory: 5384 loss: 0.2168 decode.loss_ce: 0.2168 decode.acc_seg: 90.8037 +2024/10/28 05:42:45 - mmengine - INFO - Iter(train) [120300/160000] base_lr: 5.9296e-05 lr: 5.9296e-05 eta: 4:23:59 time: 0.3781 data_time: 0.0175 memory: 5384 loss: 0.2421 decode.loss_ce: 0.2421 decode.acc_seg: 87.7398 +2024/10/28 05:43:04 - mmengine - INFO - Iter(train) [120350/160000] base_lr: 5.9178e-05 lr: 5.9178e-05 eta: 4:23:39 time: 0.3819 data_time: 0.0170 memory: 5386 loss: 0.2679 decode.loss_ce: 0.2679 decode.acc_seg: 93.9238 +2024/10/28 05:43:25 - mmengine - INFO - Iter(train) [120400/160000] base_lr: 5.9060e-05 lr: 5.9060e-05 eta: 4:23:20 time: 0.3791 data_time: 0.0164 memory: 5384 loss: 0.2361 decode.loss_ce: 0.2361 decode.acc_seg: 91.4345 +2024/10/28 05:43:44 - mmengine - INFO - Iter(train) [120450/160000] base_lr: 5.8942e-05 lr: 5.8942e-05 eta: 4:22:59 time: 0.3797 data_time: 0.0160 memory: 5384 loss: 0.2239 decode.loss_ce: 0.2239 decode.acc_seg: 90.0411 +2024/10/28 05:44:03 - mmengine - INFO - Iter(train) [120500/160000] base_lr: 5.8824e-05 lr: 5.8824e-05 eta: 4:22:39 time: 0.3813 data_time: 0.0162 memory: 5384 loss: 0.2548 decode.loss_ce: 0.2548 decode.acc_seg: 89.8137 +2024/10/28 05:44:25 - mmengine - INFO - Iter(train) [120550/160000] base_lr: 5.8707e-05 lr: 5.8707e-05 eta: 4:22:20 time: 0.3802 data_time: 0.0166 memory: 5384 loss: 0.2581 decode.loss_ce: 0.2581 decode.acc_seg: 76.8039 +2024/10/28 05:44:44 - mmengine - INFO - Iter(train) [120600/160000] base_lr: 5.8589e-05 lr: 5.8589e-05 eta: 4:22:00 time: 0.3795 data_time: 0.0173 memory: 5386 loss: 0.2829 decode.loss_ce: 0.2829 decode.acc_seg: 90.9026 +2024/10/28 05:45:03 - mmengine - INFO - Iter(train) [120650/160000] base_lr: 5.8471e-05 lr: 5.8471e-05 eta: 4:21:39 time: 0.3833 data_time: 0.0168 memory: 5384 loss: 0.2419 decode.loss_ce: 0.2419 decode.acc_seg: 93.1566 +2024/10/28 05:45:26 - mmengine - INFO - Iter(train) [120700/160000] base_lr: 5.8353e-05 lr: 5.8353e-05 eta: 4:21:20 time: 0.3790 data_time: 0.0168 memory: 5385 loss: 0.2733 decode.loss_ce: 0.2733 decode.acc_seg: 89.8205 +2024/10/28 05:45:45 - mmengine - INFO - Iter(train) [120750/160000] base_lr: 5.8235e-05 lr: 5.8235e-05 eta: 4:21:00 time: 0.3809 data_time: 0.0173 memory: 5384 loss: 0.2292 decode.loss_ce: 0.2292 decode.acc_seg: 89.8021 +2024/10/28 05:46:04 - mmengine - INFO - Iter(train) [120800/160000] base_lr: 5.8118e-05 lr: 5.8118e-05 eta: 4:20:40 time: 0.3827 data_time: 0.0158 memory: 5386 loss: 0.2500 decode.loss_ce: 0.2500 decode.acc_seg: 87.7157 +2024/10/28 05:46:25 - mmengine - INFO - Iter(train) [120850/160000] base_lr: 5.8000e-05 lr: 5.8000e-05 eta: 4:20:20 time: 0.3788 data_time: 0.0166 memory: 5383 loss: 0.2602 decode.loss_ce: 0.2602 decode.acc_seg: 88.3778 +2024/10/28 05:46:44 - mmengine - INFO - Iter(train) [120900/160000] base_lr: 5.7882e-05 lr: 5.7882e-05 eta: 4:20:00 time: 0.3766 data_time: 0.0165 memory: 5384 loss: 0.2396 decode.loss_ce: 0.2396 decode.acc_seg: 92.9749 +2024/10/28 05:47:03 - mmengine - INFO - Iter(train) [120950/160000] base_lr: 5.7764e-05 lr: 5.7764e-05 eta: 4:19:40 time: 0.3813 data_time: 0.0166 memory: 5384 loss: 0.2315 decode.loss_ce: 0.2315 decode.acc_seg: 91.7884 +2024/10/28 05:47:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:47:25 - mmengine - INFO - Iter(train) [121000/160000] base_lr: 5.7647e-05 lr: 5.7647e-05 eta: 4:19:20 time: 0.3844 data_time: 0.0174 memory: 5384 loss: 0.2627 decode.loss_ce: 0.2627 decode.acc_seg: 90.0376 +2024/10/28 05:47:44 - mmengine - INFO - Iter(train) [121050/160000] base_lr: 5.7529e-05 lr: 5.7529e-05 eta: 4:19:00 time: 0.3772 data_time: 0.0173 memory: 5384 loss: 0.2756 decode.loss_ce: 0.2756 decode.acc_seg: 82.0983 +2024/10/28 05:48:03 - mmengine - INFO - Iter(train) [121100/160000] base_lr: 5.7411e-05 lr: 5.7411e-05 eta: 4:18:40 time: 0.3785 data_time: 0.0158 memory: 5384 loss: 0.2547 decode.loss_ce: 0.2547 decode.acc_seg: 87.3722 +2024/10/28 05:48:25 - mmengine - INFO - Iter(train) [121150/160000] base_lr: 5.7294e-05 lr: 5.7294e-05 eta: 4:18:20 time: 0.3786 data_time: 0.0170 memory: 5384 loss: 0.2565 decode.loss_ce: 0.2565 decode.acc_seg: 93.9741 +2024/10/28 05:48:44 - mmengine - INFO - Iter(train) [121200/160000] base_lr: 5.7176e-05 lr: 5.7176e-05 eta: 4:18:00 time: 0.3804 data_time: 0.0162 memory: 5384 loss: 0.2425 decode.loss_ce: 0.2425 decode.acc_seg: 91.4610 +2024/10/28 05:49:04 - mmengine - INFO - Iter(train) [121250/160000] base_lr: 5.7058e-05 lr: 5.7058e-05 eta: 4:17:40 time: 0.4049 data_time: 0.0157 memory: 5383 loss: 0.2408 decode.loss_ce: 0.2408 decode.acc_seg: 93.9670 +2024/10/28 05:49:26 - mmengine - INFO - Iter(train) [121300/160000] base_lr: 5.6941e-05 lr: 5.6941e-05 eta: 4:17:21 time: 0.3749 data_time: 0.0177 memory: 5384 loss: 0.2432 decode.loss_ce: 0.2432 decode.acc_seg: 89.6820 +2024/10/28 05:49:45 - mmengine - INFO - Iter(train) [121350/160000] base_lr: 5.6823e-05 lr: 5.6823e-05 eta: 4:17:01 time: 0.3769 data_time: 0.0163 memory: 5384 loss: 0.3051 decode.loss_ce: 0.3051 decode.acc_seg: 86.1463 +2024/10/28 05:50:03 - mmengine - INFO - Iter(train) [121400/160000] base_lr: 5.6705e-05 lr: 5.6705e-05 eta: 4:16:40 time: 0.3776 data_time: 0.0173 memory: 5386 loss: 0.2221 decode.loss_ce: 0.2221 decode.acc_seg: 89.2359 +2024/10/28 05:50:25 - mmengine - INFO - Iter(train) [121450/160000] base_lr: 5.6588e-05 lr: 5.6588e-05 eta: 4:16:21 time: 0.3723 data_time: 0.0164 memory: 5386 loss: 0.2291 decode.loss_ce: 0.2291 decode.acc_seg: 94.6325 +2024/10/28 05:50:44 - mmengine - INFO - Iter(train) [121500/160000] base_lr: 5.6470e-05 lr: 5.6470e-05 eta: 4:16:01 time: 0.3738 data_time: 0.0174 memory: 5383 loss: 0.2291 decode.loss_ce: 0.2291 decode.acc_seg: 87.8976 +2024/10/28 05:51:03 - mmengine - INFO - Iter(train) [121550/160000] base_lr: 5.6353e-05 lr: 5.6353e-05 eta: 4:15:40 time: 0.3858 data_time: 0.0172 memory: 5384 loss: 0.2748 decode.loss_ce: 0.2748 decode.acc_seg: 92.0485 +2024/10/28 05:51:25 - mmengine - INFO - Iter(train) [121600/160000] base_lr: 5.6235e-05 lr: 5.6235e-05 eta: 4:15:21 time: 0.3783 data_time: 0.0156 memory: 5384 loss: 0.2900 decode.loss_ce: 0.2900 decode.acc_seg: 89.2706 +2024/10/28 05:51:44 - mmengine - INFO - Iter(train) [121650/160000] base_lr: 5.6117e-05 lr: 5.6117e-05 eta: 4:15:01 time: 0.3756 data_time: 0.0174 memory: 5384 loss: 0.2307 decode.loss_ce: 0.2307 decode.acc_seg: 92.6114 +2024/10/28 05:52:03 - mmengine - INFO - Iter(train) [121700/160000] base_lr: 5.6000e-05 lr: 5.6000e-05 eta: 4:14:41 time: 0.3860 data_time: 0.0166 memory: 5384 loss: 0.2255 decode.loss_ce: 0.2255 decode.acc_seg: 88.0203 +2024/10/28 05:52:26 - mmengine - INFO - Iter(train) [121750/160000] base_lr: 5.5882e-05 lr: 5.5882e-05 eta: 4:14:21 time: 0.4041 data_time: 0.0156 memory: 5386 loss: 0.2463 decode.loss_ce: 0.2463 decode.acc_seg: 94.5570 +2024/10/28 05:52:46 - mmengine - INFO - Iter(train) [121800/160000] base_lr: 5.5765e-05 lr: 5.5765e-05 eta: 4:14:02 time: 0.4056 data_time: 0.0159 memory: 5384 loss: 0.2349 decode.loss_ce: 0.2349 decode.acc_seg: 91.4132 +2024/10/28 05:53:06 - mmengine - INFO - Iter(train) [121850/160000] base_lr: 5.5647e-05 lr: 5.5647e-05 eta: 4:13:42 time: 0.3999 data_time: 0.0159 memory: 5382 loss: 0.2635 decode.loss_ce: 0.2635 decode.acc_seg: 90.4011 +2024/10/28 05:53:26 - mmengine - INFO - Iter(train) [121900/160000] base_lr: 5.5530e-05 lr: 5.5530e-05 eta: 4:13:22 time: 0.4030 data_time: 0.0167 memory: 5384 loss: 0.2250 decode.loss_ce: 0.2250 decode.acc_seg: 90.0008 +2024/10/28 05:53:47 - mmengine - INFO - Iter(train) [121950/160000] base_lr: 5.5412e-05 lr: 5.5412e-05 eta: 4:13:02 time: 0.4061 data_time: 0.0162 memory: 5385 loss: 0.2233 decode.loss_ce: 0.2233 decode.acc_seg: 92.1367 +2024/10/28 05:54:07 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 05:54:07 - mmengine - INFO - Iter(train) [122000/160000] base_lr: 5.5295e-05 lr: 5.5295e-05 eta: 4:12:42 time: 0.3860 data_time: 0.0158 memory: 5384 loss: 0.2552 decode.loss_ce: 0.2552 decode.acc_seg: 88.5303 +2024/10/28 05:54:26 - mmengine - INFO - Iter(train) [122050/160000] base_lr: 5.5177e-05 lr: 5.5177e-05 eta: 4:12:22 time: 0.3796 data_time: 0.0156 memory: 5383 loss: 0.2324 decode.loss_ce: 0.2324 decode.acc_seg: 91.2270 +2024/10/28 05:54:45 - mmengine - INFO - Iter(train) [122100/160000] base_lr: 5.5060e-05 lr: 5.5060e-05 eta: 4:12:02 time: 0.3793 data_time: 0.0172 memory: 5384 loss: 0.2889 decode.loss_ce: 0.2889 decode.acc_seg: 87.6769 +2024/10/28 05:55:04 - mmengine - INFO - Iter(train) [122150/160000] base_lr: 5.4943e-05 lr: 5.4943e-05 eta: 4:11:41 time: 0.3840 data_time: 0.0175 memory: 5386 loss: 0.2288 decode.loss_ce: 0.2288 decode.acc_seg: 90.4304 +2024/10/28 05:55:26 - mmengine - INFO - Iter(train) [122200/160000] base_lr: 5.4825e-05 lr: 5.4825e-05 eta: 4:11:22 time: 0.3747 data_time: 0.0173 memory: 5385 loss: 0.2413 decode.loss_ce: 0.2413 decode.acc_seg: 94.8558 +2024/10/28 05:55:45 - mmengine - INFO - Iter(train) [122250/160000] base_lr: 5.4708e-05 lr: 5.4708e-05 eta: 4:11:02 time: 0.3795 data_time: 0.0171 memory: 5383 loss: 0.2467 decode.loss_ce: 0.2467 decode.acc_seg: 87.8942 +2024/10/28 05:56:04 - mmengine - INFO - Iter(train) [122300/160000] base_lr: 5.4590e-05 lr: 5.4590e-05 eta: 4:10:42 time: 0.3813 data_time: 0.0171 memory: 5386 loss: 0.2306 decode.loss_ce: 0.2306 decode.acc_seg: 90.6412 +2024/10/28 05:56:26 - mmengine - INFO - Iter(train) [122350/160000] base_lr: 5.4473e-05 lr: 5.4473e-05 eta: 4:10:22 time: 0.3780 data_time: 0.0171 memory: 5384 loss: 0.2136 decode.loss_ce: 0.2136 decode.acc_seg: 93.3834 +2024/10/28 05:56:45 - mmengine - INFO - Iter(train) [122400/160000] base_lr: 5.4356e-05 lr: 5.4356e-05 eta: 4:10:02 time: 0.3750 data_time: 0.0163 memory: 5385 loss: 0.2135 decode.loss_ce: 0.2135 decode.acc_seg: 93.5478 +2024/10/28 05:57:04 - mmengine - INFO - Iter(train) [122450/160000] base_lr: 5.4239e-05 lr: 5.4239e-05 eta: 4:09:42 time: 0.3814 data_time: 0.0155 memory: 5384 loss: 0.2829 decode.loss_ce: 0.2829 decode.acc_seg: 86.8037 +2024/10/28 05:57:25 - mmengine - INFO - Iter(train) [122500/160000] base_lr: 5.4121e-05 lr: 5.4121e-05 eta: 4:09:22 time: 0.3794 data_time: 0.0159 memory: 5385 loss: 0.2791 decode.loss_ce: 0.2791 decode.acc_seg: 88.3290 +2024/10/28 05:57:44 - mmengine - INFO - Iter(train) [122550/160000] base_lr: 5.4004e-05 lr: 5.4004e-05 eta: 4:09:02 time: 0.3809 data_time: 0.0162 memory: 5384 loss: 0.2380 decode.loss_ce: 0.2380 decode.acc_seg: 90.4635 +2024/10/28 05:58:04 - mmengine - INFO - Iter(train) [122600/160000] base_lr: 5.3887e-05 lr: 5.3887e-05 eta: 4:08:42 time: 0.4031 data_time: 0.0147 memory: 5384 loss: 0.2280 decode.loss_ce: 0.2280 decode.acc_seg: 89.7548 +2024/10/28 05:58:25 - mmengine - INFO - Iter(train) [122650/160000] base_lr: 5.3770e-05 lr: 5.3770e-05 eta: 4:08:22 time: 0.3774 data_time: 0.0172 memory: 5384 loss: 0.2015 decode.loss_ce: 0.2015 decode.acc_seg: 90.7092 +2024/10/28 05:58:44 - mmengine - INFO - Iter(train) [122700/160000] base_lr: 5.3653e-05 lr: 5.3653e-05 eta: 4:08:02 time: 0.3798 data_time: 0.0168 memory: 5384 loss: 0.2536 decode.loss_ce: 0.2536 decode.acc_seg: 83.3303 +2024/10/28 05:59:03 - mmengine - INFO - Iter(train) [122750/160000] base_lr: 5.3535e-05 lr: 5.3535e-05 eta: 4:07:42 time: 0.3839 data_time: 0.0169 memory: 5386 loss: 0.2262 decode.loss_ce: 0.2262 decode.acc_seg: 89.3172 +2024/10/28 05:59:26 - mmengine - INFO - Iter(train) [122800/160000] base_lr: 5.3418e-05 lr: 5.3418e-05 eta: 4:07:23 time: 0.3821 data_time: 0.0172 memory: 5384 loss: 0.2636 decode.loss_ce: 0.2636 decode.acc_seg: 85.5373 +2024/10/28 05:59:45 - mmengine - INFO - Iter(train) [122850/160000] base_lr: 5.3301e-05 lr: 5.3301e-05 eta: 4:07:02 time: 0.3815 data_time: 0.0175 memory: 5384 loss: 0.3266 decode.loss_ce: 0.3266 decode.acc_seg: 89.4073 +2024/10/28 06:00:04 - mmengine - INFO - Iter(train) [122900/160000] base_lr: 5.3184e-05 lr: 5.3184e-05 eta: 4:06:42 time: 0.3842 data_time: 0.0177 memory: 5383 loss: 0.2295 decode.loss_ce: 0.2295 decode.acc_seg: 87.9692 +2024/10/28 06:00:25 - mmengine - INFO - Iter(train) [122950/160000] base_lr: 5.3067e-05 lr: 5.3067e-05 eta: 4:06:23 time: 0.3748 data_time: 0.0168 memory: 5384 loss: 0.2368 decode.loss_ce: 0.2368 decode.acc_seg: 92.6077 +2024/10/28 06:00:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:00:44 - mmengine - INFO - Iter(train) [123000/160000] base_lr: 5.2950e-05 lr: 5.2950e-05 eta: 4:06:02 time: 0.3770 data_time: 0.0170 memory: 5384 loss: 0.2359 decode.loss_ce: 0.2359 decode.acc_seg: 94.5575 +2024/10/28 06:01:03 - mmengine - INFO - Iter(train) [123050/160000] base_lr: 5.2833e-05 lr: 5.2833e-05 eta: 4:05:42 time: 0.3782 data_time: 0.0155 memory: 5384 loss: 0.2847 decode.loss_ce: 0.2847 decode.acc_seg: 90.3399 +2024/10/28 06:01:25 - mmengine - INFO - Iter(train) [123100/160000] base_lr: 5.2716e-05 lr: 5.2716e-05 eta: 4:05:23 time: 0.3766 data_time: 0.0161 memory: 5384 loss: 0.2237 decode.loss_ce: 0.2237 decode.acc_seg: 91.1975 +2024/10/28 06:01:45 - mmengine - INFO - Iter(train) [123150/160000] base_lr: 5.2599e-05 lr: 5.2599e-05 eta: 4:05:03 time: 0.3993 data_time: 0.0150 memory: 5383 loss: 0.2334 decode.loss_ce: 0.2334 decode.acc_seg: 91.6753 +2024/10/28 06:02:05 - mmengine - INFO - Iter(train) [123200/160000] base_lr: 5.2482e-05 lr: 5.2482e-05 eta: 4:04:43 time: 0.4059 data_time: 0.0152 memory: 5385 loss: 0.2286 decode.loss_ce: 0.2286 decode.acc_seg: 91.1072 +2024/10/28 06:02:26 - mmengine - INFO - Iter(train) [123250/160000] base_lr: 5.2365e-05 lr: 5.2365e-05 eta: 4:04:23 time: 0.3808 data_time: 0.0178 memory: 5384 loss: 0.2055 decode.loss_ce: 0.2055 decode.acc_seg: 89.5640 +2024/10/28 06:02:45 - mmengine - INFO - Iter(train) [123300/160000] base_lr: 5.2249e-05 lr: 5.2249e-05 eta: 4:04:03 time: 0.3781 data_time: 0.0175 memory: 5384 loss: 0.2577 decode.loss_ce: 0.2577 decode.acc_seg: 88.0100 +2024/10/28 06:03:04 - mmengine - INFO - Iter(train) [123350/160000] base_lr: 5.2132e-05 lr: 5.2132e-05 eta: 4:03:43 time: 0.3811 data_time: 0.0170 memory: 5384 loss: 0.1973 decode.loss_ce: 0.1973 decode.acc_seg: 93.1110 +2024/10/28 06:03:25 - mmengine - INFO - Iter(train) [123400/160000] base_lr: 5.2015e-05 lr: 5.2015e-05 eta: 4:03:23 time: 0.3781 data_time: 0.0159 memory: 5384 loss: 0.2320 decode.loss_ce: 0.2320 decode.acc_seg: 88.0306 +2024/10/28 06:03:44 - mmengine - INFO - Iter(train) [123450/160000] base_lr: 5.1898e-05 lr: 5.1898e-05 eta: 4:03:03 time: 0.3784 data_time: 0.0174 memory: 5383 loss: 0.2598 decode.loss_ce: 0.2598 decode.acc_seg: 87.8643 +2024/10/28 06:04:03 - mmengine - INFO - Iter(train) [123500/160000] base_lr: 5.1782e-05 lr: 5.1782e-05 eta: 4:02:43 time: 0.3794 data_time: 0.0171 memory: 5384 loss: 0.2205 decode.loss_ce: 0.2205 decode.acc_seg: 88.6311 +2024/10/28 06:04:26 - mmengine - INFO - Iter(train) [123550/160000] base_lr: 5.1665e-05 lr: 5.1665e-05 eta: 4:02:24 time: 0.3781 data_time: 0.0170 memory: 5384 loss: 0.2389 decode.loss_ce: 0.2389 decode.acc_seg: 89.5030 +2024/10/28 06:04:45 - mmengine - INFO - Iter(train) [123600/160000] base_lr: 5.1548e-05 lr: 5.1548e-05 eta: 4:02:03 time: 0.3774 data_time: 0.0166 memory: 5384 loss: 0.2088 decode.loss_ce: 0.2088 decode.acc_seg: 93.1915 +2024/10/28 06:05:04 - mmengine - INFO - Iter(train) [123650/160000] base_lr: 5.1432e-05 lr: 5.1432e-05 eta: 4:01:43 time: 0.3851 data_time: 0.0171 memory: 5384 loss: 0.2158 decode.loss_ce: 0.2158 decode.acc_seg: 90.9382 +2024/10/28 06:05:25 - mmengine - INFO - Iter(train) [123700/160000] base_lr: 5.1315e-05 lr: 5.1315e-05 eta: 4:01:24 time: 0.3747 data_time: 0.0173 memory: 5384 loss: 0.3238 decode.loss_ce: 0.3238 decode.acc_seg: 86.8214 +2024/10/28 06:05:44 - mmengine - INFO - Iter(train) [123750/160000] base_lr: 5.1199e-05 lr: 5.1199e-05 eta: 4:01:03 time: 0.3796 data_time: 0.0165 memory: 5382 loss: 0.2675 decode.loss_ce: 0.2675 decode.acc_seg: 89.2057 +2024/10/28 06:06:03 - mmengine - INFO - Iter(train) [123800/160000] base_lr: 5.1082e-05 lr: 5.1082e-05 eta: 4:00:43 time: 0.3808 data_time: 0.0165 memory: 5384 loss: 0.2967 decode.loss_ce: 0.2967 decode.acc_seg: 83.6024 +2024/10/28 06:06:25 - mmengine - INFO - Iter(train) [123850/160000] base_lr: 5.0966e-05 lr: 5.0966e-05 eta: 4:00:24 time: 0.3798 data_time: 0.0175 memory: 5384 loss: 0.2257 decode.loss_ce: 0.2257 decode.acc_seg: 90.3070 +2024/10/28 06:06:45 - mmengine - INFO - Iter(train) [123900/160000] base_lr: 5.0849e-05 lr: 5.0849e-05 eta: 4:00:04 time: 0.4041 data_time: 0.0168 memory: 5386 loss: 0.2234 decode.loss_ce: 0.2234 decode.acc_seg: 92.9447 +2024/10/28 06:07:05 - mmengine - INFO - Iter(train) [123950/160000] base_lr: 5.0733e-05 lr: 5.0733e-05 eta: 3:59:44 time: 0.4042 data_time: 0.0168 memory: 5384 loss: 0.2273 decode.loss_ce: 0.2273 decode.acc_seg: 93.8654 +2024/10/28 06:07:26 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:07:26 - mmengine - INFO - Iter(train) [124000/160000] base_lr: 5.0616e-05 lr: 5.0616e-05 eta: 3:59:24 time: 0.4024 data_time: 0.0169 memory: 5384 loss: 0.2179 decode.loss_ce: 0.2179 decode.acc_seg: 91.7039 +2024/10/28 06:07:46 - mmengine - INFO - Iter(train) [124050/160000] base_lr: 5.0500e-05 lr: 5.0500e-05 eta: 3:59:04 time: 0.3998 data_time: 0.0155 memory: 5384 loss: 0.2345 decode.loss_ce: 0.2345 decode.acc_seg: 86.4108 +2024/10/28 06:08:05 - mmengine - INFO - Iter(train) [124100/160000] base_lr: 5.0384e-05 lr: 5.0384e-05 eta: 3:58:44 time: 0.3846 data_time: 0.0160 memory: 5384 loss: 0.2634 decode.loss_ce: 0.2634 decode.acc_seg: 86.4870 +2024/10/28 06:08:25 - mmengine - INFO - Iter(train) [124150/160000] base_lr: 5.0267e-05 lr: 5.0267e-05 eta: 3:58:24 time: 0.4050 data_time: 0.0151 memory: 5383 loss: 0.2922 decode.loss_ce: 0.2922 decode.acc_seg: 90.0649 +2024/10/28 06:08:45 - mmengine - INFO - Iter(train) [124200/160000] base_lr: 5.0151e-05 lr: 5.0151e-05 eta: 3:58:04 time: 0.4010 data_time: 0.0144 memory: 5384 loss: 0.2264 decode.loss_ce: 0.2264 decode.acc_seg: 91.5911 +2024/10/28 06:09:06 - mmengine - INFO - Iter(train) [124250/160000] base_lr: 5.0035e-05 lr: 5.0035e-05 eta: 3:57:44 time: 0.4031 data_time: 0.0141 memory: 5384 loss: 0.2459 decode.loss_ce: 0.2459 decode.acc_seg: 90.6846 +2024/10/28 06:09:26 - mmengine - INFO - Iter(train) [124300/160000] base_lr: 4.9919e-05 lr: 4.9919e-05 eta: 3:57:24 time: 0.4238 data_time: 0.0142 memory: 5384 loss: 0.2105 decode.loss_ce: 0.2105 decode.acc_seg: 88.8876 +2024/10/28 06:09:46 - mmengine - INFO - Iter(train) [124350/160000] base_lr: 4.9803e-05 lr: 4.9803e-05 eta: 3:57:05 time: 0.4003 data_time: 0.0143 memory: 5384 loss: 0.2521 decode.loss_ce: 0.2521 decode.acc_seg: 88.0757 +2024/10/28 06:10:05 - mmengine - INFO - Iter(train) [124400/160000] base_lr: 4.9687e-05 lr: 4.9687e-05 eta: 3:56:44 time: 0.3817 data_time: 0.0171 memory: 5384 loss: 0.2150 decode.loss_ce: 0.2150 decode.acc_seg: 87.9182 +2024/10/28 06:10:25 - mmengine - INFO - Iter(train) [124450/160000] base_lr: 4.9571e-05 lr: 4.9571e-05 eta: 3:56:24 time: 0.3773 data_time: 0.0176 memory: 5384 loss: 0.2631 decode.loss_ce: 0.2631 decode.acc_seg: 93.3213 +2024/10/28 06:10:44 - mmengine - INFO - Iter(train) [124500/160000] base_lr: 4.9455e-05 lr: 4.9455e-05 eta: 3:56:04 time: 0.3800 data_time: 0.0178 memory: 5384 loss: 0.2411 decode.loss_ce: 0.2411 decode.acc_seg: 92.1722 +2024/10/28 06:11:03 - mmengine - INFO - Iter(train) [124550/160000] base_lr: 4.9339e-05 lr: 4.9339e-05 eta: 3:55:44 time: 0.3817 data_time: 0.0172 memory: 5384 loss: 0.2300 decode.loss_ce: 0.2300 decode.acc_seg: 91.4902 +2024/10/28 06:11:24 - mmengine - INFO - Iter(train) [124600/160000] base_lr: 4.9223e-05 lr: 4.9223e-05 eta: 3:55:24 time: 0.3841 data_time: 0.0169 memory: 5384 loss: 0.2249 decode.loss_ce: 0.2249 decode.acc_seg: 92.4198 +2024/10/28 06:11:43 - mmengine - INFO - Iter(train) [124650/160000] base_lr: 4.9107e-05 lr: 4.9107e-05 eta: 3:55:04 time: 0.3804 data_time: 0.0173 memory: 5385 loss: 0.2416 decode.loss_ce: 0.2416 decode.acc_seg: 88.4215 +2024/10/28 06:12:03 - mmengine - INFO - Iter(train) [124700/160000] base_lr: 4.8991e-05 lr: 4.8991e-05 eta: 3:54:44 time: 0.3903 data_time: 0.0163 memory: 5384 loss: 0.2538 decode.loss_ce: 0.2538 decode.acc_seg: 90.5868 +2024/10/28 06:12:26 - mmengine - INFO - Iter(train) [124750/160000] base_lr: 4.8875e-05 lr: 4.8875e-05 eta: 3:54:25 time: 0.3926 data_time: 0.0170 memory: 5384 loss: 0.2351 decode.loss_ce: 0.2351 decode.acc_seg: 90.2653 +2024/10/28 06:12:45 - mmengine - INFO - Iter(train) [124800/160000] base_lr: 4.8759e-05 lr: 4.8759e-05 eta: 3:54:05 time: 0.3755 data_time: 0.0168 memory: 5384 loss: 0.2364 decode.loss_ce: 0.2364 decode.acc_seg: 91.6367 +2024/10/28 06:13:04 - mmengine - INFO - Iter(train) [124850/160000] base_lr: 4.8644e-05 lr: 4.8644e-05 eta: 3:53:44 time: 0.3782 data_time: 0.0167 memory: 5384 loss: 0.2525 decode.loss_ce: 0.2525 decode.acc_seg: 90.1050 +2024/10/28 06:13:27 - mmengine - INFO - Iter(train) [124900/160000] base_lr: 4.8528e-05 lr: 4.8528e-05 eta: 3:53:26 time: 0.4055 data_time: 0.0160 memory: 5385 loss: 0.2262 decode.loss_ce: 0.2262 decode.acc_seg: 90.2156 +2024/10/28 06:13:47 - mmengine - INFO - Iter(train) [124950/160000] base_lr: 4.8412e-05 lr: 4.8412e-05 eta: 3:53:06 time: 0.3778 data_time: 0.0161 memory: 5385 loss: 0.2254 decode.loss_ce: 0.2254 decode.acc_seg: 93.5326 +2024/10/28 06:14:06 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:14:06 - mmengine - INFO - Iter(train) [125000/160000] base_lr: 4.8297e-05 lr: 4.8297e-05 eta: 3:52:45 time: 0.3785 data_time: 0.0168 memory: 5384 loss: 0.2302 decode.loss_ce: 0.2302 decode.acc_seg: 91.9996 +2024/10/28 06:14:25 - mmengine - INFO - Iter(train) [125050/160000] base_lr: 4.8181e-05 lr: 4.8181e-05 eta: 3:52:25 time: 0.3813 data_time: 0.0177 memory: 5384 loss: 0.2618 decode.loss_ce: 0.2618 decode.acc_seg: 88.3699 +2024/10/28 06:14:44 - mmengine - INFO - Iter(train) [125100/160000] base_lr: 4.8066e-05 lr: 4.8066e-05 eta: 3:52:05 time: 0.3818 data_time: 0.0178 memory: 5385 loss: 0.2441 decode.loss_ce: 0.2441 decode.acc_seg: 91.6652 +2024/10/28 06:15:04 - mmengine - INFO - Iter(train) [125150/160000] base_lr: 4.7950e-05 lr: 4.7950e-05 eta: 3:51:45 time: 0.3881 data_time: 0.0171 memory: 5384 loss: 0.2873 decode.loss_ce: 0.2873 decode.acc_seg: 90.1810 +2024/10/28 06:15:26 - mmengine - INFO - Iter(train) [125200/160000] base_lr: 4.7835e-05 lr: 4.7835e-05 eta: 3:51:25 time: 0.3805 data_time: 0.0176 memory: 5384 loss: 0.2641 decode.loss_ce: 0.2641 decode.acc_seg: 91.9626 +2024/10/28 06:15:45 - mmengine - INFO - Iter(train) [125250/160000] base_lr: 4.7720e-05 lr: 4.7720e-05 eta: 3:51:05 time: 0.3746 data_time: 0.0157 memory: 5385 loss: 0.2091 decode.loss_ce: 0.2091 decode.acc_seg: 91.9979 +2024/10/28 06:16:04 - mmengine - INFO - Iter(train) [125300/160000] base_lr: 4.7604e-05 lr: 4.7604e-05 eta: 3:50:45 time: 0.3852 data_time: 0.0164 memory: 5384 loss: 0.2289 decode.loss_ce: 0.2289 decode.acc_seg: 90.7962 +2024/10/28 06:16:25 - mmengine - INFO - Iter(train) [125350/160000] base_lr: 4.7489e-05 lr: 4.7489e-05 eta: 3:50:25 time: 0.3838 data_time: 0.0170 memory: 5384 loss: 0.2220 decode.loss_ce: 0.2220 decode.acc_seg: 91.7362 +2024/10/28 06:16:44 - mmengine - INFO - Iter(train) [125400/160000] base_lr: 4.7374e-05 lr: 4.7374e-05 eta: 3:50:05 time: 0.3793 data_time: 0.0167 memory: 5384 loss: 0.2521 decode.loss_ce: 0.2521 decode.acc_seg: 89.2070 +2024/10/28 06:17:03 - mmengine - INFO - Iter(train) [125450/160000] base_lr: 4.7259e-05 lr: 4.7259e-05 eta: 3:49:45 time: 0.3820 data_time: 0.0175 memory: 5385 loss: 0.2741 decode.loss_ce: 0.2741 decode.acc_seg: 95.4713 +2024/10/28 06:17:24 - mmengine - INFO - Iter(train) [125500/160000] base_lr: 4.7144e-05 lr: 4.7144e-05 eta: 3:49:25 time: 0.3808 data_time: 0.0178 memory: 5384 loss: 0.2390 decode.loss_ce: 0.2390 decode.acc_seg: 94.0899 +2024/10/28 06:17:43 - mmengine - INFO - Iter(train) [125550/160000] base_lr: 4.7029e-05 lr: 4.7029e-05 eta: 3:49:05 time: 0.3752 data_time: 0.0176 memory: 5384 loss: 0.2323 decode.loss_ce: 0.2323 decode.acc_seg: 90.7821 +2024/10/28 06:18:02 - mmengine - INFO - Iter(train) [125600/160000] base_lr: 4.6914e-05 lr: 4.6914e-05 eta: 3:48:45 time: 0.3797 data_time: 0.0173 memory: 5384 loss: 0.2141 decode.loss_ce: 0.2141 decode.acc_seg: 93.0363 +2024/10/28 06:18:25 - mmengine - INFO - Iter(train) [125650/160000] base_lr: 4.6799e-05 lr: 4.6799e-05 eta: 3:48:26 time: 0.3790 data_time: 0.0175 memory: 5383 loss: 0.2241 decode.loss_ce: 0.2241 decode.acc_seg: 89.3297 +2024/10/28 06:18:44 - mmengine - INFO - Iter(train) [125700/160000] base_lr: 4.6684e-05 lr: 4.6684e-05 eta: 3:48:05 time: 0.3812 data_time: 0.0173 memory: 5385 loss: 0.2436 decode.loss_ce: 0.2436 decode.acc_seg: 91.4665 +2024/10/28 06:19:03 - mmengine - INFO - Iter(train) [125750/160000] base_lr: 4.6569e-05 lr: 4.6569e-05 eta: 3:47:45 time: 0.3785 data_time: 0.0178 memory: 5384 loss: 0.2307 decode.loss_ce: 0.2307 decode.acc_seg: 88.1515 +2024/10/28 06:19:24 - mmengine - INFO - Iter(train) [125800/160000] base_lr: 4.6454e-05 lr: 4.6454e-05 eta: 3:47:26 time: 0.3826 data_time: 0.0177 memory: 5383 loss: 0.2233 decode.loss_ce: 0.2233 decode.acc_seg: 88.7968 +2024/10/28 06:19:43 - mmengine - INFO - Iter(train) [125850/160000] base_lr: 4.6339e-05 lr: 4.6339e-05 eta: 3:47:05 time: 0.3835 data_time: 0.0171 memory: 5383 loss: 0.2483 decode.loss_ce: 0.2483 decode.acc_seg: 90.7252 +2024/10/28 06:20:02 - mmengine - INFO - Iter(train) [125900/160000] base_lr: 4.6225e-05 lr: 4.6225e-05 eta: 3:46:45 time: 0.3796 data_time: 0.0172 memory: 5384 loss: 0.2690 decode.loss_ce: 0.2690 decode.acc_seg: 90.9144 +2024/10/28 06:20:24 - mmengine - INFO - Iter(train) [125950/160000] base_lr: 4.6110e-05 lr: 4.6110e-05 eta: 3:46:26 time: 0.3798 data_time: 0.0165 memory: 5384 loss: 0.2537 decode.loss_ce: 0.2537 decode.acc_seg: 87.1852 +2024/10/28 06:20:43 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:20:43 - mmengine - INFO - Iter(train) [126000/160000] base_lr: 4.5996e-05 lr: 4.5996e-05 eta: 3:46:05 time: 0.3770 data_time: 0.0171 memory: 5384 loss: 0.2414 decode.loss_ce: 0.2414 decode.acc_seg: 91.9326 +2024/10/28 06:21:02 - mmengine - INFO - Iter(train) [126050/160000] base_lr: 4.5881e-05 lr: 4.5881e-05 eta: 3:45:45 time: 0.3820 data_time: 0.0164 memory: 5384 loss: 0.1948 decode.loss_ce: 0.1948 decode.acc_seg: 92.1745 +2024/10/28 06:21:25 - mmengine - INFO - Iter(train) [126100/160000] base_lr: 4.5767e-05 lr: 4.5767e-05 eta: 3:45:26 time: 0.3757 data_time: 0.0173 memory: 5385 loss: 0.2337 decode.loss_ce: 0.2337 decode.acc_seg: 90.2836 +2024/10/28 06:21:44 - mmengine - INFO - Iter(train) [126150/160000] base_lr: 4.5652e-05 lr: 4.5652e-05 eta: 3:45:06 time: 0.3798 data_time: 0.0173 memory: 5384 loss: 0.2359 decode.loss_ce: 0.2359 decode.acc_seg: 91.1790 +2024/10/28 06:22:03 - mmengine - INFO - Iter(train) [126200/160000] base_lr: 4.5538e-05 lr: 4.5538e-05 eta: 3:44:46 time: 0.3807 data_time: 0.0171 memory: 5383 loss: 0.2440 decode.loss_ce: 0.2440 decode.acc_seg: 89.9513 +2024/10/28 06:22:26 - mmengine - INFO - Iter(train) [126250/160000] base_lr: 4.5423e-05 lr: 4.5423e-05 eta: 3:44:27 time: 0.3780 data_time: 0.0170 memory: 5384 loss: 0.2298 decode.loss_ce: 0.2298 decode.acc_seg: 90.8546 +2024/10/28 06:22:44 - mmengine - INFO - Iter(train) [126300/160000] base_lr: 4.5309e-05 lr: 4.5309e-05 eta: 3:44:06 time: 0.3727 data_time: 0.0152 memory: 5384 loss: 0.2298 decode.loss_ce: 0.2298 decode.acc_seg: 89.1437 +2024/10/28 06:23:03 - mmengine - INFO - Iter(train) [126350/160000] base_lr: 4.5195e-05 lr: 4.5195e-05 eta: 3:43:46 time: 0.3794 data_time: 0.0159 memory: 5384 loss: 0.2401 decode.loss_ce: 0.2401 decode.acc_seg: 90.4904 +2024/10/28 06:23:25 - mmengine - INFO - Iter(train) [126400/160000] base_lr: 4.5081e-05 lr: 4.5081e-05 eta: 3:43:27 time: 0.3766 data_time: 0.0185 memory: 5384 loss: 0.2381 decode.loss_ce: 0.2381 decode.acc_seg: 94.6586 +2024/10/28 06:23:44 - mmengine - INFO - Iter(train) [126450/160000] base_lr: 4.4967e-05 lr: 4.4967e-05 eta: 3:43:06 time: 0.3774 data_time: 0.0182 memory: 5384 loss: 0.2056 decode.loss_ce: 0.2056 decode.acc_seg: 91.1113 +2024/10/28 06:24:03 - mmengine - INFO - Iter(train) [126500/160000] base_lr: 4.4853e-05 lr: 4.4853e-05 eta: 3:42:46 time: 0.3801 data_time: 0.0175 memory: 5384 loss: 0.2184 decode.loss_ce: 0.2184 decode.acc_seg: 95.5694 +2024/10/28 06:24:25 - mmengine - INFO - Iter(train) [126550/160000] base_lr: 4.4739e-05 lr: 4.4739e-05 eta: 3:42:27 time: 0.3810 data_time: 0.0181 memory: 5382 loss: 0.2371 decode.loss_ce: 0.2371 decode.acc_seg: 90.5692 +2024/10/28 06:24:44 - mmengine - INFO - Iter(train) [126600/160000] base_lr: 4.4625e-05 lr: 4.4625e-05 eta: 3:42:07 time: 0.3853 data_time: 0.0180 memory: 5384 loss: 0.2309 decode.loss_ce: 0.2309 decode.acc_seg: 90.5192 +2024/10/28 06:25:04 - mmengine - INFO - Iter(train) [126650/160000] base_lr: 4.4511e-05 lr: 4.4511e-05 eta: 3:41:47 time: 0.4015 data_time: 0.0152 memory: 5385 loss: 0.2516 decode.loss_ce: 0.2516 decode.acc_seg: 91.4873 +2024/10/28 06:25:25 - mmengine - INFO - Iter(train) [126700/160000] base_lr: 4.4397e-05 lr: 4.4397e-05 eta: 3:41:27 time: 0.3790 data_time: 0.0164 memory: 5384 loss: 0.2140 decode.loss_ce: 0.2140 decode.acc_seg: 92.1307 +2024/10/28 06:25:44 - mmengine - INFO - Iter(train) [126750/160000] base_lr: 4.4284e-05 lr: 4.4284e-05 eta: 3:41:07 time: 0.3842 data_time: 0.0171 memory: 5384 loss: 0.2493 decode.loss_ce: 0.2493 decode.acc_seg: 92.1879 +2024/10/28 06:26:03 - mmengine - INFO - Iter(train) [126800/160000] base_lr: 4.4170e-05 lr: 4.4170e-05 eta: 3:40:46 time: 0.3810 data_time: 0.0168 memory: 5384 loss: 0.2137 decode.loss_ce: 0.2137 decode.acc_seg: 92.9445 +2024/10/28 06:26:24 - mmengine - INFO - Iter(train) [126850/160000] base_lr: 4.4056e-05 lr: 4.4056e-05 eta: 3:40:27 time: 0.3772 data_time: 0.0165 memory: 5385 loss: 0.2374 decode.loss_ce: 0.2374 decode.acc_seg: 88.5836 +2024/10/28 06:26:43 - mmengine - INFO - Iter(train) [126900/160000] base_lr: 4.3943e-05 lr: 4.3943e-05 eta: 3:40:07 time: 0.3789 data_time: 0.0162 memory: 5386 loss: 0.2553 decode.loss_ce: 0.2553 decode.acc_seg: 88.6610 +2024/10/28 06:27:02 - mmengine - INFO - Iter(train) [126950/160000] base_lr: 4.3829e-05 lr: 4.3829e-05 eta: 3:39:46 time: 0.3836 data_time: 0.0164 memory: 5383 loss: 0.2677 decode.loss_ce: 0.2677 decode.acc_seg: 94.2317 +2024/10/28 06:27:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:27:25 - mmengine - INFO - Iter(train) [127000/160000] base_lr: 4.3716e-05 lr: 4.3716e-05 eta: 3:39:27 time: 0.3901 data_time: 0.0163 memory: 5384 loss: 0.1905 decode.loss_ce: 0.1905 decode.acc_seg: 90.4646 +2024/10/28 06:27:45 - mmengine - INFO - Iter(train) [127050/160000] base_lr: 4.3602e-05 lr: 4.3602e-05 eta: 3:39:07 time: 0.3978 data_time: 0.0139 memory: 5385 loss: 0.2248 decode.loss_ce: 0.2248 decode.acc_seg: 90.2622 +2024/10/28 06:28:04 - mmengine - INFO - Iter(train) [127100/160000] base_lr: 4.3489e-05 lr: 4.3489e-05 eta: 3:38:47 time: 0.3831 data_time: 0.0168 memory: 5384 loss: 0.2518 decode.loss_ce: 0.2518 decode.acc_seg: 90.1328 +2024/10/28 06:28:25 - mmengine - INFO - Iter(train) [127150/160000] base_lr: 4.3376e-05 lr: 4.3376e-05 eta: 3:38:27 time: 0.3803 data_time: 0.0168 memory: 5384 loss: 0.2255 decode.loss_ce: 0.2255 decode.acc_seg: 92.7729 +2024/10/28 06:28:44 - mmengine - INFO - Iter(train) [127200/160000] base_lr: 4.3263e-05 lr: 4.3263e-05 eta: 3:38:07 time: 0.3754 data_time: 0.0168 memory: 5384 loss: 0.3019 decode.loss_ce: 0.3019 decode.acc_seg: 92.5148 +2024/10/28 06:29:03 - mmengine - INFO - Iter(train) [127250/160000] base_lr: 4.3150e-05 lr: 4.3150e-05 eta: 3:37:47 time: 0.3821 data_time: 0.0164 memory: 5385 loss: 0.2156 decode.loss_ce: 0.2156 decode.acc_seg: 91.8273 +2024/10/28 06:29:25 - mmengine - INFO - Iter(train) [127300/160000] base_lr: 4.3037e-05 lr: 4.3037e-05 eta: 3:37:28 time: 0.3776 data_time: 0.0170 memory: 5385 loss: 0.2491 decode.loss_ce: 0.2491 decode.acc_seg: 91.8179 +2024/10/28 06:29:44 - mmengine - INFO - Iter(train) [127350/160000] base_lr: 4.2924e-05 lr: 4.2924e-05 eta: 3:37:07 time: 0.3835 data_time: 0.0162 memory: 5385 loss: 0.2522 decode.loss_ce: 0.2522 decode.acc_seg: 91.0003 +2024/10/28 06:30:03 - mmengine - INFO - Iter(train) [127400/160000] base_lr: 4.2811e-05 lr: 4.2811e-05 eta: 3:36:47 time: 0.3780 data_time: 0.0166 memory: 5384 loss: 0.2492 decode.loss_ce: 0.2492 decode.acc_seg: 87.5053 +2024/10/28 06:30:25 - mmengine - INFO - Iter(train) [127450/160000] base_lr: 4.2698e-05 lr: 4.2698e-05 eta: 3:36:28 time: 0.3817 data_time: 0.0167 memory: 5384 loss: 0.2455 decode.loss_ce: 0.2455 decode.acc_seg: 88.8458 +2024/10/28 06:30:44 - mmengine - INFO - Iter(train) [127500/160000] base_lr: 4.2585e-05 lr: 4.2585e-05 eta: 3:36:07 time: 0.3800 data_time: 0.0165 memory: 5384 loss: 0.2224 decode.loss_ce: 0.2224 decode.acc_seg: 91.2313 +2024/10/28 06:31:03 - mmengine - INFO - Iter(train) [127550/160000] base_lr: 4.2472e-05 lr: 4.2472e-05 eta: 3:35:47 time: 0.3802 data_time: 0.0173 memory: 5383 loss: 0.2433 decode.loss_ce: 0.2433 decode.acc_seg: 91.6076 +2024/10/28 06:31:25 - mmengine - INFO - Iter(train) [127600/160000] base_lr: 4.2360e-05 lr: 4.2360e-05 eta: 3:35:28 time: 0.3780 data_time: 0.0161 memory: 5384 loss: 0.2422 decode.loss_ce: 0.2422 decode.acc_seg: 88.7981 +2024/10/28 06:31:44 - mmengine - INFO - Iter(train) [127650/160000] base_lr: 4.2247e-05 lr: 4.2247e-05 eta: 3:35:08 time: 0.3813 data_time: 0.0157 memory: 5384 loss: 0.2161 decode.loss_ce: 0.2161 decode.acc_seg: 90.1781 +2024/10/28 06:32:03 - mmengine - INFO - Iter(train) [127700/160000] base_lr: 4.2135e-05 lr: 4.2135e-05 eta: 3:34:47 time: 0.3820 data_time: 0.0159 memory: 5383 loss: 0.2901 decode.loss_ce: 0.2901 decode.acc_seg: 92.6133 +2024/10/28 06:32:25 - mmengine - INFO - Iter(train) [127750/160000] base_lr: 4.2022e-05 lr: 4.2022e-05 eta: 3:34:28 time: 0.3778 data_time: 0.0169 memory: 5385 loss: 0.2164 decode.loss_ce: 0.2164 decode.acc_seg: 89.5819 +2024/10/28 06:32:44 - mmengine - INFO - Iter(train) [127800/160000] base_lr: 4.1910e-05 lr: 4.1910e-05 eta: 3:34:08 time: 0.3790 data_time: 0.0169 memory: 5384 loss: 0.2287 decode.loss_ce: 0.2287 decode.acc_seg: 91.2913 +2024/10/28 06:33:04 - mmengine - INFO - Iter(train) [127850/160000] base_lr: 4.1798e-05 lr: 4.1798e-05 eta: 3:33:48 time: 0.4030 data_time: 0.0157 memory: 5384 loss: 0.2556 decode.loss_ce: 0.2556 decode.acc_seg: 84.2011 +2024/10/28 06:33:25 - mmengine - INFO - Iter(train) [127900/160000] base_lr: 4.1685e-05 lr: 4.1685e-05 eta: 3:33:28 time: 0.3821 data_time: 0.0165 memory: 5384 loss: 0.2435 decode.loss_ce: 0.2435 decode.acc_seg: 88.2033 +2024/10/28 06:33:44 - mmengine - INFO - Iter(train) [127950/160000] base_lr: 4.1573e-05 lr: 4.1573e-05 eta: 3:33:08 time: 0.3744 data_time: 0.0162 memory: 5384 loss: 0.2707 decode.loss_ce: 0.2707 decode.acc_seg: 91.1110 +2024/10/28 06:34:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:34:03 - mmengine - INFO - Iter(train) [128000/160000] base_lr: 4.1461e-05 lr: 4.1461e-05 eta: 3:32:48 time: 0.3810 data_time: 0.0171 memory: 5385 loss: 0.2302 decode.loss_ce: 0.2302 decode.acc_seg: 92.2765 +2024/10/28 06:34:05 - mmengine - INFO - Saving checkpoint at 128000 iterations +2024/10/28 06:34:10 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:00:15 time: 0.0337 data_time: 0.0015 memory: 980 +2024/10/28 06:34:11 - mmengine - INFO - Iter(val) [100/500] eta: 0:00:13 time: 0.0325 data_time: 0.0013 memory: 1050 +2024/10/28 06:34:13 - mmengine - INFO - Iter(val) [150/500] eta: 0:00:11 time: 0.0334 data_time: 0.0014 memory: 767 +2024/10/28 06:34:15 - mmengine - INFO - Iter(val) [200/500] eta: 0:00:10 time: 0.0383 data_time: 0.0014 memory: 800 +2024/10/28 06:34:17 - mmengine - INFO - Iter(val) [250/500] eta: 0:00:08 time: 0.0386 data_time: 0.0016 memory: 839 +2024/10/28 06:34:19 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:07 time: 0.0401 data_time: 0.0020 memory: 1961 +2024/10/28 06:34:21 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:05 time: 0.0372 data_time: 0.0014 memory: 765 +2024/10/28 06:34:22 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:03 time: 0.0374 data_time: 0.0014 memory: 837 +2024/10/28 06:34:24 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:01 time: 0.0382 data_time: 0.0014 memory: 772 +2024/10/28 06:34:26 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.0374 data_time: 0.0013 memory: 822 +2024/10/28 06:34:27 - mmengine - INFO - per class results: +2024/10/28 06:34:27 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 72.16 | 87.84 | +| building | 79.26 | 89.91 | +| sky | 92.62 | 96.72 | +| floor | 76.2 | 88.63 | +| tree | 69.87 | 86.72 | +| ceiling | 79.96 | 88.11 | +| road | 80.78 | 87.5 | +| bed | 85.85 | 92.42 | +| windowpane | 55.1 | 68.62 | +| grass | 63.54 | 82.45 | +| cabinet | 54.33 | 66.26 | +| sidewalk | 61.27 | 77.83 | +| person | 72.17 | 86.78 | +| earth | 32.88 | 44.26 | +| door | 39.55 | 54.25 | +| table | 51.79 | 69.92 | +| mountain | 50.67 | 63.3 | +| plant | 45.42 | 57.41 | +| curtain | 65.65 | 79.95 | +| chair | 48.64 | 64.46 | +| car | 77.24 | 88.0 | +| water | 46.86 | 60.96 | +| painting | 66.34 | 79.43 | +| sofa | 61.03 | 78.0 | +| shelf | 37.52 | 54.91 | +| house | 32.59 | 36.85 | +| sea | 56.22 | 81.95 | +| mirror | 58.25 | 70.88 | +| rug | 55.89 | 65.07 | +| field | 36.76 | 59.63 | +| armchair | 37.5 | 54.49 | +| seat | 51.46 | 74.06 | +| fence | 30.47 | 45.4 | +| desk | 42.58 | 60.94 | +| rock | 39.85 | 67.33 | +| wardrobe | 45.42 | 66.67 | +| lamp | 50.53 | 60.53 | +| bathtub | 64.65 | 71.24 | +| railing | 30.25 | 44.7 | +| cushion | 50.96 | 62.76 | +| base | 11.58 | 13.81 | +| box | 16.07 | 22.56 | +| column | 33.04 | 38.92 | +| signboard | 28.17 | 39.33 | +| chest of drawers | 32.23 | 48.18 | +| counter | 24.28 | 28.93 | +| sand | 39.51 | 61.84 | +| sink | 57.51 | 73.69 | +| skyscraper | 49.92 | 81.83 | +| fireplace | 66.55 | 81.73 | +| refrigerator | 61.38 | 89.14 | +| grandstand | 42.86 | 68.22 | +| path | 18.22 | 30.47 | +| stairs | 25.16 | 30.54 | +| runway | 69.56 | 91.68 | +| case | 31.44 | 50.72 | +| pool table | 85.81 | 90.83 | +| pillow | 52.13 | 64.02 | +| screen door | 54.17 | 61.22 | +| stairway | 24.5 | 33.83 | +| river | 6.49 | 13.77 | +| bridge | 56.97 | 74.46 | +| bookcase | 31.78 | 53.25 | +| blind | 25.79 | 27.04 | +| coffee table | 52.88 | 77.07 | +| toilet | 78.46 | 85.91 | +| flower | 29.1 | 45.36 | +| book | 40.06 | 62.56 | +| hill | 5.77 | 7.17 | +| bench | 39.77 | 46.17 | +| countertop | 52.6 | 66.21 | +| stove | 66.95 | 73.66 | +| palm | 44.13 | 57.79 | +| kitchen island | 36.66 | 67.55 | +| computer | 50.11 | 64.27 | +| swivel chair | 30.3 | 40.82 | +| boat | 48.32 | 76.86 | +| bar | 26.31 | 30.5 | +| arcade machine | 59.81 | 64.8 | +| hovel | 23.1 | 28.78 | +| bus | 79.47 | 91.09 | +| towel | 48.44 | 57.4 | +| light | 32.97 | 36.96 | +| truck | 8.63 | 10.28 | +| tower | 34.46 | 61.92 | +| chandelier | 52.72 | 65.09 | +| awning | 18.7 | 24.63 | +| streetlight | 13.72 | 18.5 | +| booth | 49.48 | 69.53 | +| television receiver | 62.49 | 74.42 | +| airplane | 46.26 | 58.84 | +| dirt track | 5.21 | 26.06 | +| apparel | 17.89 | 25.4 | +| pole | 17.37 | 25.82 | +| land | 3.7 | 5.91 | +| bannister | 3.82 | 5.69 | +| escalator | 14.87 | 16.55 | +| ottoman | 42.92 | 59.19 | +| bottle | 20.24 | 26.82 | +| buffet | 39.83 | 43.34 | +| poster | 26.76 | 37.55 | +| stage | 14.66 | 22.72 | +| van | 38.87 | 52.86 | +| ship | 15.41 | 16.62 | +| fountain | 1.27 | 1.3 | +| conveyer belt | 56.27 | 63.98 | +| canopy | 26.07 | 31.63 | +| washer | 52.39 | 61.49 | +| plaything | 17.76 | 38.15 | +| swimming pool | 34.72 | 51.81 | +| stool | 34.2 | 55.09 | +| barrel | 42.26 | 64.36 | +| basket | 19.44 | 25.26 | +| waterfall | 43.88 | 55.92 | +| tent | 83.75 | 95.58 | +| bag | 5.78 | 6.67 | +| minibike | 48.45 | 62.4 | +| cradle | 72.2 | 92.22 | +| oven | 36.55 | 44.8 | +| ball | 43.61 | 57.93 | +| food | 20.28 | 22.22 | +| step | 9.98 | 11.42 | +| tank | 31.39 | 38.13 | +| trade name | 17.27 | 18.83 | +| microwave | 35.29 | 39.53 | +| pot | 41.41 | 49.92 | +| animal | 48.34 | 51.26 | +| bicycle | 42.83 | 62.88 | +| lake | 49.57 | 63.49 | +| dishwasher | 55.68 | 59.87 | +| screen | 65.7 | 72.93 | +| blanket | 4.22 | 4.89 | +| sculpture | 37.42 | 50.87 | +| hood | 54.9 | 58.91 | +| sconce | 27.89 | 30.69 | +| vase | 24.88 | 34.39 | +| traffic light | 23.93 | 39.44 | +| tray | 6.35 | 13.93 | +| ashcan | 33.31 | 45.55 | +| fan | 41.79 | 55.67 | +| pier | 27.05 | 30.89 | +| crt screen | 12.58 | 31.05 | +| plate | 29.53 | 39.32 | +| monitor | 16.32 | 18.86 | +| bulletin board | 37.9 | 43.41 | +| shower | 1.87 | 8.25 | +| radiator | 40.29 | 44.38 | +| glass | 3.4 | 3.58 | +| clock | 8.96 | 28.13 | +| flag | 37.28 | 41.19 | ++---------------------+-------+-------+ +2024/10/28 06:34:27 - mmengine - INFO - Iter(val) [500/500] aAcc: 79.3200 mIoU: 40.6300 mAcc: 52.2600 data_time: 0.0015 time: 0.0368 +2024/10/28 06:34:47 - mmengine - INFO - Iter(train) [128050/160000] base_lr: 4.1349e-05 lr: 4.1349e-05 eta: 3:32:28 time: 0.3810 data_time: 0.0167 memory: 5386 loss: 0.2176 decode.loss_ce: 0.2176 decode.acc_seg: 93.1108 +2024/10/28 06:35:06 - mmengine - INFO - Iter(train) [128100/160000] base_lr: 4.1237e-05 lr: 4.1237e-05 eta: 3:32:08 time: 0.3810 data_time: 0.0156 memory: 5385 loss: 0.2086 decode.loss_ce: 0.2086 decode.acc_seg: 91.3527 +2024/10/28 06:35:25 - mmengine - INFO - Iter(train) [128150/160000] base_lr: 4.1125e-05 lr: 4.1125e-05 eta: 3:31:48 time: 0.3846 data_time: 0.0163 memory: 5384 loss: 0.1986 decode.loss_ce: 0.1986 decode.acc_seg: 88.8994 +2024/10/28 06:35:44 - mmengine - INFO - Iter(train) [128200/160000] base_lr: 4.1014e-05 lr: 4.1014e-05 eta: 3:31:27 time: 0.3791 data_time: 0.0161 memory: 5384 loss: 0.2251 decode.loss_ce: 0.2251 decode.acc_seg: 75.1594 +2024/10/28 06:36:03 - mmengine - INFO - Iter(train) [128250/160000] base_lr: 4.0902e-05 lr: 4.0902e-05 eta: 3:31:07 time: 0.3823 data_time: 0.0170 memory: 5384 loss: 0.2516 decode.loss_ce: 0.2516 decode.acc_seg: 90.9990 +2024/10/28 06:36:25 - mmengine - INFO - Iter(train) [128300/160000] base_lr: 4.0790e-05 lr: 4.0790e-05 eta: 3:30:48 time: 0.3840 data_time: 0.0170 memory: 5385 loss: 0.2299 decode.loss_ce: 0.2299 decode.acc_seg: 90.8119 +2024/10/28 06:36:44 - mmengine - INFO - Iter(train) [128350/160000] base_lr: 4.0679e-05 lr: 4.0679e-05 eta: 3:30:27 time: 0.3819 data_time: 0.0171 memory: 5384 loss: 0.2636 decode.loss_ce: 0.2636 decode.acc_seg: 92.1313 +2024/10/28 06:37:03 - mmengine - INFO - Iter(train) [128400/160000] base_lr: 4.0567e-05 lr: 4.0567e-05 eta: 3:30:07 time: 0.3862 data_time: 0.0176 memory: 5384 loss: 0.2640 decode.loss_ce: 0.2640 decode.acc_seg: 92.3226 +2024/10/28 06:37:25 - mmengine - INFO - Iter(train) [128450/160000] base_lr: 4.0456e-05 lr: 4.0456e-05 eta: 3:29:48 time: 0.3979 data_time: 0.0163 memory: 5384 loss: 0.2857 decode.loss_ce: 0.2857 decode.acc_seg: 94.8706 +2024/10/28 06:37:45 - mmengine - INFO - Iter(train) [128500/160000] base_lr: 4.0344e-05 lr: 4.0344e-05 eta: 3:29:28 time: 0.4008 data_time: 0.0155 memory: 5384 loss: 0.2150 decode.loss_ce: 0.2150 decode.acc_seg: 88.3903 +2024/10/28 06:38:05 - mmengine - INFO - Iter(train) [128550/160000] base_lr: 4.0233e-05 lr: 4.0233e-05 eta: 3:29:08 time: 0.3839 data_time: 0.0177 memory: 5383 loss: 0.2038 decode.loss_ce: 0.2038 decode.acc_seg: 91.0589 +2024/10/28 06:38:25 - mmengine - INFO - Iter(train) [128600/160000] base_lr: 4.0122e-05 lr: 4.0122e-05 eta: 3:28:48 time: 0.3792 data_time: 0.0177 memory: 5384 loss: 0.2058 decode.loss_ce: 0.2058 decode.acc_seg: 88.6867 +2024/10/28 06:38:44 - mmengine - INFO - Iter(train) [128650/160000] base_lr: 4.0011e-05 lr: 4.0011e-05 eta: 3:28:28 time: 0.3780 data_time: 0.0187 memory: 5386 loss: 0.2472 decode.loss_ce: 0.2472 decode.acc_seg: 90.9761 +2024/10/28 06:39:03 - mmengine - INFO - Iter(train) [128700/160000] base_lr: 3.9900e-05 lr: 3.9900e-05 eta: 3:28:08 time: 0.3820 data_time: 0.0193 memory: 5384 loss: 0.2193 decode.loss_ce: 0.2193 decode.acc_seg: 90.3365 +2024/10/28 06:39:24 - mmengine - INFO - Iter(train) [128750/160000] base_lr: 3.9789e-05 lr: 3.9789e-05 eta: 3:27:48 time: 0.4030 data_time: 0.0176 memory: 5384 loss: 0.1850 decode.loss_ce: 0.1850 decode.acc_seg: 92.6330 +2024/10/28 06:39:43 - mmengine - INFO - Iter(train) [128800/160000] base_lr: 3.9678e-05 lr: 3.9678e-05 eta: 3:27:28 time: 0.3790 data_time: 0.0174 memory: 5384 loss: 0.2330 decode.loss_ce: 0.2330 decode.acc_seg: 89.3601 +2024/10/28 06:40:02 - mmengine - INFO - Iter(train) [128850/160000] base_lr: 3.9567e-05 lr: 3.9567e-05 eta: 3:27:08 time: 0.3799 data_time: 0.0176 memory: 5384 loss: 0.2120 decode.loss_ce: 0.2120 decode.acc_seg: 90.1286 +2024/10/28 06:40:24 - mmengine - INFO - Iter(train) [128900/160000] base_lr: 3.9456e-05 lr: 3.9456e-05 eta: 3:26:48 time: 0.3748 data_time: 0.0163 memory: 5384 loss: 0.2539 decode.loss_ce: 0.2539 decode.acc_seg: 76.9471 +2024/10/28 06:40:43 - mmengine - INFO - Iter(train) [128950/160000] base_lr: 3.9346e-05 lr: 3.9346e-05 eta: 3:26:28 time: 0.3788 data_time: 0.0187 memory: 5384 loss: 0.2430 decode.loss_ce: 0.2430 decode.acc_seg: 91.2330 +2024/10/28 06:41:02 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:41:02 - mmengine - INFO - Iter(train) [129000/160000] base_lr: 3.9235e-05 lr: 3.9235e-05 eta: 3:26:08 time: 0.3847 data_time: 0.0192 memory: 5384 loss: 0.2523 decode.loss_ce: 0.2523 decode.acc_seg: 85.5224 +2024/10/28 06:41:26 - mmengine - INFO - Iter(train) [129050/160000] base_lr: 3.9125e-05 lr: 3.9125e-05 eta: 3:25:49 time: 0.3827 data_time: 0.0188 memory: 5384 loss: 0.2256 decode.loss_ce: 0.2256 decode.acc_seg: 89.4864 +2024/10/28 06:41:45 - mmengine - INFO - Iter(train) [129100/160000] base_lr: 3.9014e-05 lr: 3.9014e-05 eta: 3:25:29 time: 0.3820 data_time: 0.0189 memory: 5385 loss: 0.2177 decode.loss_ce: 0.2177 decode.acc_seg: 90.9857 +2024/10/28 06:42:04 - mmengine - INFO - Iter(train) [129150/160000] base_lr: 3.8904e-05 lr: 3.8904e-05 eta: 3:25:08 time: 0.3846 data_time: 0.0155 memory: 5385 loss: 0.2554 decode.loss_ce: 0.2554 decode.acc_seg: 83.8509 +2024/10/28 06:42:26 - mmengine - INFO - Iter(train) [129200/160000] base_lr: 3.8794e-05 lr: 3.8794e-05 eta: 3:24:49 time: 0.3774 data_time: 0.0173 memory: 5384 loss: 0.2389 decode.loss_ce: 0.2389 decode.acc_seg: 90.7444 +2024/10/28 06:42:45 - mmengine - INFO - Iter(train) [129250/160000] base_lr: 3.8684e-05 lr: 3.8684e-05 eta: 3:24:29 time: 0.3898 data_time: 0.0172 memory: 5384 loss: 0.2074 decode.loss_ce: 0.2074 decode.acc_seg: 89.9670 +2024/10/28 06:43:05 - mmengine - INFO - Iter(train) [129300/160000] base_lr: 3.8573e-05 lr: 3.8573e-05 eta: 3:24:09 time: 0.3886 data_time: 0.0164 memory: 5386 loss: 0.2349 decode.loss_ce: 0.2349 decode.acc_seg: 89.8561 +2024/10/28 06:43:25 - mmengine - INFO - Iter(train) [129350/160000] base_lr: 3.8463e-05 lr: 3.8463e-05 eta: 3:23:49 time: 0.3818 data_time: 0.0174 memory: 5384 loss: 0.2390 decode.loss_ce: 0.2390 decode.acc_seg: 90.1093 +2024/10/28 06:43:44 - mmengine - INFO - Iter(train) [129400/160000] base_lr: 3.8354e-05 lr: 3.8354e-05 eta: 3:23:29 time: 0.3776 data_time: 0.0175 memory: 5384 loss: 0.2269 decode.loss_ce: 0.2269 decode.acc_seg: 91.4653 +2024/10/28 06:44:03 - mmengine - INFO - Iter(train) [129450/160000] base_lr: 3.8244e-05 lr: 3.8244e-05 eta: 3:23:08 time: 0.3840 data_time: 0.0168 memory: 5384 loss: 0.2636 decode.loss_ce: 0.2636 decode.acc_seg: 87.6825 +2024/10/28 06:44:25 - mmengine - INFO - Iter(train) [129500/160000] base_lr: 3.8134e-05 lr: 3.8134e-05 eta: 3:22:49 time: 0.3813 data_time: 0.0178 memory: 5384 loss: 0.2292 decode.loss_ce: 0.2292 decode.acc_seg: 90.4246 +2024/10/28 06:44:44 - mmengine - INFO - Iter(train) [129550/160000] base_lr: 3.8024e-05 lr: 3.8024e-05 eta: 3:22:29 time: 0.3811 data_time: 0.0173 memory: 5383 loss: 0.1997 decode.loss_ce: 0.1997 decode.acc_seg: 94.6256 +2024/10/28 06:45:03 - mmengine - INFO - Iter(train) [129600/160000] base_lr: 3.7915e-05 lr: 3.7915e-05 eta: 3:22:09 time: 0.3842 data_time: 0.0177 memory: 5384 loss: 0.2166 decode.loss_ce: 0.2166 decode.acc_seg: 92.5071 +2024/10/28 06:45:25 - mmengine - INFO - Iter(train) [129650/160000] base_lr: 3.7805e-05 lr: 3.7805e-05 eta: 3:21:49 time: 0.3794 data_time: 0.0165 memory: 5385 loss: 0.2265 decode.loss_ce: 0.2265 decode.acc_seg: 88.2538 +2024/10/28 06:45:44 - mmengine - INFO - Iter(train) [129700/160000] base_lr: 3.7696e-05 lr: 3.7696e-05 eta: 3:21:29 time: 0.4003 data_time: 0.0163 memory: 5384 loss: 0.2421 decode.loss_ce: 0.2421 decode.acc_seg: 89.6476 +2024/10/28 06:46:04 - mmengine - INFO - Iter(train) [129750/160000] base_lr: 3.7586e-05 lr: 3.7586e-05 eta: 3:21:09 time: 0.3849 data_time: 0.0168 memory: 5383 loss: 0.2184 decode.loss_ce: 0.2184 decode.acc_seg: 86.0640 +2024/10/28 06:46:24 - mmengine - INFO - Iter(train) [129800/160000] base_lr: 3.7477e-05 lr: 3.7477e-05 eta: 3:20:49 time: 0.3825 data_time: 0.0174 memory: 5384 loss: 0.2372 decode.loss_ce: 0.2372 decode.acc_seg: 87.3754 +2024/10/28 06:46:44 - mmengine - INFO - Iter(train) [129850/160000] base_lr: 3.7368e-05 lr: 3.7368e-05 eta: 3:20:29 time: 0.3804 data_time: 0.0167 memory: 5385 loss: 0.2056 decode.loss_ce: 0.2056 decode.acc_seg: 92.8889 +2024/10/28 06:47:03 - mmengine - INFO - Iter(train) [129900/160000] base_lr: 3.7259e-05 lr: 3.7259e-05 eta: 3:20:09 time: 0.3807 data_time: 0.0159 memory: 5385 loss: 0.2426 decode.loss_ce: 0.2426 decode.acc_seg: 83.2516 +2024/10/28 06:47:25 - mmengine - INFO - Iter(train) [129950/160000] base_lr: 3.7150e-05 lr: 3.7150e-05 eta: 3:19:49 time: 0.3758 data_time: 0.0160 memory: 5384 loss: 0.2182 decode.loss_ce: 0.2182 decode.acc_seg: 90.8429 +2024/10/28 06:47:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:47:44 - mmengine - INFO - Iter(train) [130000/160000] base_lr: 3.7041e-05 lr: 3.7041e-05 eta: 3:19:29 time: 0.3820 data_time: 0.0166 memory: 5383 loss: 0.2179 decode.loss_ce: 0.2179 decode.acc_seg: 85.3249 +2024/10/28 06:48:03 - mmengine - INFO - Iter(train) [130050/160000] base_lr: 3.6932e-05 lr: 3.6932e-05 eta: 3:19:09 time: 0.3834 data_time: 0.0162 memory: 5384 loss: 0.2154 decode.loss_ce: 0.2154 decode.acc_seg: 92.7064 +2024/10/28 06:48:25 - mmengine - INFO - Iter(train) [130100/160000] base_lr: 3.6824e-05 lr: 3.6824e-05 eta: 3:18:50 time: 0.3935 data_time: 0.0159 memory: 5384 loss: 0.2647 decode.loss_ce: 0.2647 decode.acc_seg: 83.1261 +2024/10/28 06:48:45 - mmengine - INFO - Iter(train) [130150/160000] base_lr: 3.6715e-05 lr: 3.6715e-05 eta: 3:18:30 time: 0.4021 data_time: 0.0151 memory: 5384 loss: 0.1905 decode.loss_ce: 0.1905 decode.acc_seg: 93.9867 +2024/10/28 06:49:05 - mmengine - INFO - Iter(train) [130200/160000] base_lr: 3.6607e-05 lr: 3.6607e-05 eta: 3:18:10 time: 0.4027 data_time: 0.0161 memory: 5383 loss: 0.2369 decode.loss_ce: 0.2369 decode.acc_seg: 90.0953 +2024/10/28 06:49:26 - mmengine - INFO - Iter(train) [130250/160000] base_lr: 3.6498e-05 lr: 3.6498e-05 eta: 3:17:50 time: 0.3797 data_time: 0.0178 memory: 5384 loss: 0.2354 decode.loss_ce: 0.2354 decode.acc_seg: 92.5438 +2024/10/28 06:49:45 - mmengine - INFO - Iter(train) [130300/160000] base_lr: 3.6390e-05 lr: 3.6390e-05 eta: 3:17:30 time: 0.3763 data_time: 0.0174 memory: 5384 loss: 0.2229 decode.loss_ce: 0.2229 decode.acc_seg: 90.2755 +2024/10/28 06:50:04 - mmengine - INFO - Iter(train) [130350/160000] base_lr: 3.6281e-05 lr: 3.6281e-05 eta: 3:17:10 time: 0.3817 data_time: 0.0180 memory: 5383 loss: 0.2177 decode.loss_ce: 0.2177 decode.acc_seg: 90.8548 +2024/10/28 06:50:24 - mmengine - INFO - Iter(train) [130400/160000] base_lr: 3.6173e-05 lr: 3.6173e-05 eta: 3:16:50 time: 0.3804 data_time: 0.0176 memory: 5384 loss: 0.2106 decode.loss_ce: 0.2106 decode.acc_seg: 89.7237 +2024/10/28 06:50:44 - mmengine - INFO - Iter(train) [130450/160000] base_lr: 3.6065e-05 lr: 3.6065e-05 eta: 3:16:30 time: 0.3784 data_time: 0.0165 memory: 5384 loss: 0.2530 decode.loss_ce: 0.2530 decode.acc_seg: 84.9437 +2024/10/28 06:51:03 - mmengine - INFO - Iter(train) [130500/160000] base_lr: 3.5957e-05 lr: 3.5957e-05 eta: 3:16:10 time: 0.3831 data_time: 0.0157 memory: 5385 loss: 0.2266 decode.loss_ce: 0.2266 decode.acc_seg: 92.6994 +2024/10/28 06:51:25 - mmengine - INFO - Iter(train) [130550/160000] base_lr: 3.5849e-05 lr: 3.5849e-05 eta: 3:15:50 time: 0.3989 data_time: 0.0155 memory: 5384 loss: 0.2057 decode.loss_ce: 0.2057 decode.acc_seg: 93.8115 +2024/10/28 06:51:45 - mmengine - INFO - Iter(train) [130600/160000] base_lr: 3.5742e-05 lr: 3.5742e-05 eta: 3:15:30 time: 0.3824 data_time: 0.0186 memory: 5384 loss: 0.2090 decode.loss_ce: 0.2090 decode.acc_seg: 89.1441 +2024/10/28 06:52:04 - mmengine - INFO - Iter(train) [130650/160000] base_lr: 3.5634e-05 lr: 3.5634e-05 eta: 3:15:10 time: 0.3816 data_time: 0.0170 memory: 5384 loss: 0.2079 decode.loss_ce: 0.2079 decode.acc_seg: 93.6635 +2024/10/28 06:52:24 - mmengine - INFO - Iter(train) [130700/160000] base_lr: 3.5526e-05 lr: 3.5526e-05 eta: 3:14:50 time: 0.3788 data_time: 0.0173 memory: 5383 loss: 0.2343 decode.loss_ce: 0.2343 decode.acc_seg: 87.3339 +2024/10/28 06:52:43 - mmengine - INFO - Iter(train) [130750/160000] base_lr: 3.5419e-05 lr: 3.5419e-05 eta: 3:14:30 time: 0.3800 data_time: 0.0172 memory: 5383 loss: 0.1835 decode.loss_ce: 0.1835 decode.acc_seg: 93.6550 +2024/10/28 06:53:03 - mmengine - INFO - Iter(train) [130800/160000] base_lr: 3.5311e-05 lr: 3.5311e-05 eta: 3:14:10 time: 0.3826 data_time: 0.0177 memory: 5384 loss: 0.1994 decode.loss_ce: 0.1994 decode.acc_seg: 94.7475 +2024/10/28 06:53:25 - mmengine - INFO - Iter(train) [130850/160000] base_lr: 3.5204e-05 lr: 3.5204e-05 eta: 3:13:51 time: 0.3788 data_time: 0.0178 memory: 5384 loss: 0.2129 decode.loss_ce: 0.2129 decode.acc_seg: 92.4853 +2024/10/28 06:53:45 - mmengine - INFO - Iter(train) [130900/160000] base_lr: 3.5097e-05 lr: 3.5097e-05 eta: 3:13:31 time: 0.4046 data_time: 0.0159 memory: 5384 loss: 0.2246 decode.loss_ce: 0.2246 decode.acc_seg: 91.1540 +2024/10/28 06:54:05 - mmengine - INFO - Iter(train) [130950/160000] base_lr: 3.4990e-05 lr: 3.4990e-05 eta: 3:13:11 time: 0.3850 data_time: 0.0197 memory: 5384 loss: 0.2462 decode.loss_ce: 0.2462 decode.acc_seg: 88.9010 +2024/10/28 06:54:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 06:54:25 - mmengine - INFO - Iter(train) [131000/160000] base_lr: 3.4883e-05 lr: 3.4883e-05 eta: 3:12:51 time: 0.3809 data_time: 0.0155 memory: 5386 loss: 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[131250/160000] base_lr: 3.4349e-05 lr: 3.4349e-05 eta: 3:11:11 time: 0.3795 data_time: 0.0173 memory: 5383 loss: 0.2100 decode.loss_ce: 0.2100 decode.acc_seg: 91.4817 +2024/10/28 06:56:25 - mmengine - INFO - Iter(train) [131300/160000] base_lr: 3.4242e-05 lr: 3.4242e-05 eta: 3:10:51 time: 0.3816 data_time: 0.0155 memory: 5386 loss: 0.2460 decode.loss_ce: 0.2460 decode.acc_seg: 91.0143 +2024/10/28 06:56:44 - mmengine - INFO - Iter(train) [131350/160000] base_lr: 3.4136e-05 lr: 3.4136e-05 eta: 3:10:31 time: 0.3796 data_time: 0.0166 memory: 5384 loss: 0.2174 decode.loss_ce: 0.2174 decode.acc_seg: 90.1189 +2024/10/28 06:57:03 - mmengine - INFO - Iter(train) [131400/160000] base_lr: 3.4030e-05 lr: 3.4030e-05 eta: 3:10:11 time: 0.3816 data_time: 0.0173 memory: 5384 loss: 0.2281 decode.loss_ce: 0.2281 decode.acc_seg: 93.7491 +2024/10/28 06:57:24 - mmengine - INFO - Iter(train) [131450/160000] base_lr: 3.3924e-05 lr: 3.3924e-05 eta: 3:09:51 time: 0.3794 data_time: 0.0166 memory: 5384 loss: 0.2331 decode.loss_ce: 0.2331 decode.acc_seg: 91.2980 +2024/10/28 06:57:44 - mmengine - INFO - Iter(train) [131500/160000] base_lr: 3.3818e-05 lr: 3.3818e-05 eta: 3:09:31 time: 0.3802 data_time: 0.0164 memory: 5384 loss: 0.2148 decode.loss_ce: 0.2148 decode.acc_seg: 91.1640 +2024/10/28 06:58:03 - mmengine - INFO - Iter(train) [131550/160000] base_lr: 3.3712e-05 lr: 3.3712e-05 eta: 3:09:11 time: 0.3820 data_time: 0.0164 memory: 5384 loss: 0.2011 decode.loss_ce: 0.2011 decode.acc_seg: 94.6784 +2024/10/28 06:58:25 - mmengine - INFO - Iter(train) [131600/160000] base_lr: 3.3606e-05 lr: 3.3606e-05 eta: 3:08:51 time: 0.3779 data_time: 0.0175 memory: 5383 loss: 0.2209 decode.loss_ce: 0.2209 decode.acc_seg: 91.7165 +2024/10/28 06:58:44 - mmengine - INFO - Iter(train) [131650/160000] base_lr: 3.3500e-05 lr: 3.3500e-05 eta: 3:08:31 time: 0.3816 data_time: 0.0179 memory: 5384 loss: 0.2101 decode.loss_ce: 0.2101 decode.acc_seg: 93.1307 +2024/10/28 06:59:03 - mmengine - INFO - Iter(train) [131700/160000] base_lr: 3.3394e-05 lr: 3.3394e-05 eta: 3:08:11 time: 0.3815 data_time: 0.0174 memory: 5384 loss: 0.2349 decode.loss_ce: 0.2349 decode.acc_seg: 94.1341 +2024/10/28 06:59:24 - mmengine - INFO - Iter(train) [131750/160000] base_lr: 3.3289e-05 lr: 3.3289e-05 eta: 3:07:51 time: 0.3769 data_time: 0.0174 memory: 5384 loss: 0.1894 decode.loss_ce: 0.1894 decode.acc_seg: 87.5064 +2024/10/28 06:59:43 - mmengine - INFO - Iter(train) [131800/160000] base_lr: 3.3183e-05 lr: 3.3183e-05 eta: 3:07:31 time: 0.3734 data_time: 0.0169 memory: 5383 loss: 0.2170 decode.loss_ce: 0.2170 decode.acc_seg: 91.0605 +2024/10/28 07:00:02 - mmengine - INFO - Iter(train) [131850/160000] base_lr: 3.3078e-05 lr: 3.3078e-05 eta: 3:07:11 time: 0.3927 data_time: 0.0171 memory: 5384 loss: 0.2240 decode.loss_ce: 0.2240 decode.acc_seg: 90.8218 +2024/10/28 07:00:26 - mmengine - INFO - Iter(train) [131900/160000] base_lr: 3.2973e-05 lr: 3.2973e-05 eta: 3:06:52 time: 0.3813 data_time: 0.0166 memory: 5385 loss: 0.2607 decode.loss_ce: 0.2607 decode.acc_seg: 88.5747 +2024/10/28 07:00:45 - mmengine - INFO - Iter(train) [131950/160000] base_lr: 3.2868e-05 lr: 3.2868e-05 eta: 3:06:32 time: 0.3804 data_time: 0.0174 memory: 5384 loss: 0.2222 decode.loss_ce: 0.2222 decode.acc_seg: 93.6596 +2024/10/28 07:01:04 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:01:04 - mmengine - INFO - Iter(train) [132000/160000] base_lr: 3.2763e-05 lr: 3.2763e-05 eta: 3:06:12 time: 0.3795 data_time: 0.0168 memory: 5386 loss: 0.1946 decode.loss_ce: 0.1946 decode.acc_seg: 95.3420 +2024/10/28 07:01:25 - mmengine - INFO - Iter(train) [132050/160000] base_lr: 3.2658e-05 lr: 3.2658e-05 eta: 3:05:52 time: 0.3803 data_time: 0.0177 memory: 5384 loss: 0.1992 decode.loss_ce: 0.1992 decode.acc_seg: 93.5051 +2024/10/28 07:01:44 - mmengine - INFO - Iter(train) [132100/160000] base_lr: 3.2553e-05 lr: 3.2553e-05 eta: 3:05:32 time: 0.3794 data_time: 0.0188 memory: 5384 loss: 0.2346 decode.loss_ce: 0.2346 decode.acc_seg: 91.4561 +2024/10/28 07:02:03 - mmengine - INFO - Iter(train) [132150/160000] base_lr: 3.2448e-05 lr: 3.2448e-05 eta: 3:05:12 time: 0.3823 data_time: 0.0177 memory: 5385 loss: 0.2535 decode.loss_ce: 0.2535 decode.acc_seg: 89.8271 +2024/10/28 07:02:25 - mmengine - INFO - Iter(train) [132200/160000] base_lr: 3.2344e-05 lr: 3.2344e-05 eta: 3:04:52 time: 0.3804 data_time: 0.0178 memory: 5384 loss: 0.2242 decode.loss_ce: 0.2242 decode.acc_seg: 92.3620 +2024/10/28 07:02:44 - mmengine - INFO - Iter(train) [132250/160000] base_lr: 3.2239e-05 lr: 3.2239e-05 eta: 3:04:32 time: 0.3784 data_time: 0.0174 memory: 5384 loss: 0.2395 decode.loss_ce: 0.2395 decode.acc_seg: 93.2321 +2024/10/28 07:03:04 - mmengine - INFO - Iter(train) [132300/160000] base_lr: 3.2135e-05 lr: 3.2135e-05 eta: 3:04:12 time: 0.3843 data_time: 0.0174 memory: 5384 loss: 0.2672 decode.loss_ce: 0.2672 decode.acc_seg: 89.4548 +2024/10/28 07:03:25 - mmengine - INFO - Iter(train) [132350/160000] base_lr: 3.2030e-05 lr: 3.2030e-05 eta: 3:03:52 time: 0.3770 data_time: 0.0161 memory: 5384 loss: 0.1994 decode.loss_ce: 0.1994 decode.acc_seg: 88.7940 +2024/10/28 07:03:44 - mmengine - INFO - Iter(train) [132400/160000] base_lr: 3.1926e-05 lr: 3.1926e-05 eta: 3:03:32 time: 0.3823 data_time: 0.0181 memory: 5384 loss: 0.2537 decode.loss_ce: 0.2537 decode.acc_seg: 90.3512 +2024/10/28 07:04:04 - mmengine - INFO - Iter(train) [132450/160000] base_lr: 3.1822e-05 lr: 3.1822e-05 eta: 3:03:12 time: 0.3876 data_time: 0.0163 memory: 5384 loss: 0.2413 decode.loss_ce: 0.2413 decode.acc_seg: 89.2841 +2024/10/28 07:04:26 - mmengine - INFO - Iter(train) [132500/160000] base_lr: 3.1718e-05 lr: 3.1718e-05 eta: 3:02:53 time: 0.3811 data_time: 0.0175 memory: 5384 loss: 0.2332 decode.loss_ce: 0.2332 decode.acc_seg: 87.9480 +2024/10/28 07:04:45 - mmengine - INFO - Iter(train) [132550/160000] base_lr: 3.1614e-05 lr: 3.1614e-05 eta: 3:02:32 time: 0.3784 data_time: 0.0175 memory: 5384 loss: 0.2275 decode.loss_ce: 0.2275 decode.acc_seg: 91.9420 +2024/10/28 07:05:04 - mmengine - INFO - Iter(train) [132600/160000] base_lr: 3.1511e-05 lr: 3.1511e-05 eta: 3:02:12 time: 0.3810 data_time: 0.0171 memory: 5384 loss: 0.2439 decode.loss_ce: 0.2439 decode.acc_seg: 89.1664 +2024/10/28 07:05:26 - mmengine - INFO - Iter(train) [132650/160000] base_lr: 3.1407e-05 lr: 3.1407e-05 eta: 3:01:53 time: 0.3821 data_time: 0.0168 memory: 5384 loss: 0.2451 decode.loss_ce: 0.2451 decode.acc_seg: 86.6415 +2024/10/28 07:05:45 - mmengine - INFO - Iter(train) [132700/160000] base_lr: 3.1304e-05 lr: 3.1304e-05 eta: 3:01:33 time: 0.3793 data_time: 0.0177 memory: 5384 loss: 0.2095 decode.loss_ce: 0.2095 decode.acc_seg: 91.9267 +2024/10/28 07:06:04 - mmengine - INFO - Iter(train) [132750/160000] base_lr: 3.1200e-05 lr: 3.1200e-05 eta: 3:01:12 time: 0.3841 data_time: 0.0177 memory: 5384 loss: 0.1998 decode.loss_ce: 0.1998 decode.acc_seg: 90.5195 +2024/10/28 07:06:25 - mmengine - INFO - Iter(train) [132800/160000] base_lr: 3.1097e-05 lr: 3.1097e-05 eta: 3:00:53 time: 0.3818 data_time: 0.0172 memory: 5384 loss: 0.1920 decode.loss_ce: 0.1920 decode.acc_seg: 88.4957 +2024/10/28 07:06:44 - mmengine - INFO - Iter(train) [132850/160000] base_lr: 3.0994e-05 lr: 3.0994e-05 eta: 3:00:32 time: 0.3889 data_time: 0.0170 memory: 5384 loss: 0.2490 decode.loss_ce: 0.2490 decode.acc_seg: 92.0921 +2024/10/28 07:07:03 - mmengine - INFO - Iter(train) [132900/160000] base_lr: 3.0891e-05 lr: 3.0891e-05 eta: 3:00:12 time: 0.3796 data_time: 0.0164 memory: 5383 loss: 0.2091 decode.loss_ce: 0.2091 decode.acc_seg: 94.3911 +2024/10/28 07:07:24 - mmengine - INFO - Iter(train) [132950/160000] base_lr: 3.0788e-05 lr: 3.0788e-05 eta: 2:59:53 time: 0.3820 data_time: 0.0173 memory: 5384 loss: 0.2128 decode.loss_ce: 0.2128 decode.acc_seg: 94.0613 +2024/10/28 07:07:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:07:44 - mmengine - INFO - Iter(train) [133000/160000] base_lr: 3.0685e-05 lr: 3.0685e-05 eta: 2:59:33 time: 0.3821 data_time: 0.0166 memory: 5384 loss: 0.2053 decode.loss_ce: 0.2053 decode.acc_seg: 91.5795 +2024/10/28 07:08:03 - mmengine - INFO - Iter(train) [133050/160000] base_lr: 3.0582e-05 lr: 3.0582e-05 eta: 2:59:12 time: 0.3906 data_time: 0.0169 memory: 5384 loss: 0.2182 decode.loss_ce: 0.2182 decode.acc_seg: 89.0275 +2024/10/28 07:08:25 - mmengine - INFO - Iter(train) [133100/160000] base_lr: 3.0479e-05 lr: 3.0479e-05 eta: 2:58:53 time: 0.3809 data_time: 0.0169 memory: 5384 loss: 0.2097 decode.loss_ce: 0.2097 decode.acc_seg: 89.3197 +2024/10/28 07:08:44 - mmengine - INFO - Iter(train) [133150/160000] base_lr: 3.0377e-05 lr: 3.0377e-05 eta: 2:58:33 time: 0.3819 data_time: 0.0166 memory: 5384 loss: 0.1948 decode.loss_ce: 0.1948 decode.acc_seg: 95.5195 +2024/10/28 07:09:03 - mmengine - INFO - Iter(train) [133200/160000] base_lr: 3.0275e-05 lr: 3.0275e-05 eta: 2:58:13 time: 0.3828 data_time: 0.0165 memory: 5385 loss: 0.1998 decode.loss_ce: 0.1998 decode.acc_seg: 90.7257 +2024/10/28 07:09:24 - mmengine - INFO - Iter(train) [133250/160000] base_lr: 3.0172e-05 lr: 3.0172e-05 eta: 2:57:53 time: 0.3766 data_time: 0.0168 memory: 5384 loss: 0.2286 decode.loss_ce: 0.2286 decode.acc_seg: 86.4075 +2024/10/28 07:09:43 - mmengine - INFO - Iter(train) [133300/160000] base_lr: 3.0070e-05 lr: 3.0070e-05 eta: 2:57:33 time: 0.3773 data_time: 0.0163 memory: 5386 loss: 0.2281 decode.loss_ce: 0.2281 decode.acc_seg: 84.5110 +2024/10/28 07:10:02 - mmengine - INFO - Iter(train) [133350/160000] base_lr: 2.9968e-05 lr: 2.9968e-05 eta: 2:57:13 time: 0.3791 data_time: 0.0185 memory: 5384 loss: 0.2027 decode.loss_ce: 0.2027 decode.acc_seg: 90.8686 +2024/10/28 07:10:25 - mmengine - INFO - Iter(train) [133400/160000] base_lr: 2.9866e-05 lr: 2.9866e-05 eta: 2:56:53 time: 0.3800 data_time: 0.0160 memory: 5383 loss: 0.2049 decode.loss_ce: 0.2049 decode.acc_seg: 90.1925 +2024/10/28 07:10:45 - mmengine - INFO - Iter(train) [133450/160000] base_lr: 2.9764e-05 lr: 2.9764e-05 eta: 2:56:33 time: 0.3809 data_time: 0.0168 memory: 5382 loss: 0.2523 decode.loss_ce: 0.2523 decode.acc_seg: 87.8262 +2024/10/28 07:11:04 - mmengine - INFO - Iter(train) [133500/160000] base_lr: 2.9663e-05 lr: 2.9663e-05 eta: 2:56:13 time: 0.3817 data_time: 0.0171 memory: 5384 loss: 0.2467 decode.loss_ce: 0.2467 decode.acc_seg: 90.8795 +2024/10/28 07:11:25 - mmengine - INFO - Iter(train) [133550/160000] base_lr: 2.9561e-05 lr: 2.9561e-05 eta: 2:55:53 time: 0.3818 data_time: 0.0166 memory: 5384 loss: 0.2007 decode.loss_ce: 0.2007 decode.acc_seg: 87.8587 +2024/10/28 07:11:44 - mmengine - INFO - Iter(train) [133600/160000] base_lr: 2.9460e-05 lr: 2.9460e-05 eta: 2:55:33 time: 0.3771 data_time: 0.0163 memory: 5385 loss: 0.2248 decode.loss_ce: 0.2248 decode.acc_seg: 88.9643 +2024/10/28 07:12:03 - mmengine - INFO - Iter(train) [133650/160000] base_lr: 2.9358e-05 lr: 2.9358e-05 eta: 2:55:13 time: 0.3817 data_time: 0.0165 memory: 5384 loss: 0.2456 decode.loss_ce: 0.2456 decode.acc_seg: 93.5749 +2024/10/28 07:12:25 - mmengine - INFO - Iter(train) [133700/160000] base_lr: 2.9257e-05 lr: 2.9257e-05 eta: 2:54:54 time: 0.3766 data_time: 0.0167 memory: 5384 loss: 0.2295 decode.loss_ce: 0.2295 decode.acc_seg: 86.0600 +2024/10/28 07:12:44 - mmengine - INFO - Iter(train) [133750/160000] base_lr: 2.9156e-05 lr: 2.9156e-05 eta: 2:54:33 time: 0.3758 data_time: 0.0170 memory: 5383 loss: 0.2469 decode.loss_ce: 0.2469 decode.acc_seg: 92.9856 +2024/10/28 07:13:03 - mmengine - INFO - Iter(train) [133800/160000] base_lr: 2.9055e-05 lr: 2.9055e-05 eta: 2:54:13 time: 0.3787 data_time: 0.0168 memory: 5384 loss: 0.2404 decode.loss_ce: 0.2404 decode.acc_seg: 90.8177 +2024/10/28 07:13:26 - mmengine - INFO - Iter(train) [133850/160000] base_lr: 2.8954e-05 lr: 2.8954e-05 eta: 2:53:54 time: 0.3745 data_time: 0.0163 memory: 5385 loss: 0.2410 decode.loss_ce: 0.2410 decode.acc_seg: 88.3180 +2024/10/28 07:13:45 - mmengine - INFO - Iter(train) [133900/160000] base_lr: 2.8853e-05 lr: 2.8853e-05 eta: 2:53:34 time: 0.3839 data_time: 0.0174 memory: 5384 loss: 0.2319 decode.loss_ce: 0.2319 decode.acc_seg: 89.2209 +2024/10/28 07:14:04 - mmengine - INFO - Iter(train) [133950/160000] base_lr: 2.8753e-05 lr: 2.8753e-05 eta: 2:53:14 time: 0.4028 data_time: 0.0149 memory: 5383 loss: 0.2600 decode.loss_ce: 0.2600 decode.acc_seg: 87.9964 +2024/10/28 07:14:26 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:14:26 - mmengine - INFO - Iter(train) [134000/160000] base_lr: 2.8652e-05 lr: 2.8652e-05 eta: 2:52:54 time: 0.3990 data_time: 0.0143 memory: 5384 loss: 0.2053 decode.loss_ce: 0.2053 decode.acc_seg: 93.4169 +2024/10/28 07:14:46 - mmengine - INFO - Iter(train) [134050/160000] base_lr: 2.8552e-05 lr: 2.8552e-05 eta: 2:52:34 time: 0.3987 data_time: 0.0140 memory: 5385 loss: 0.2198 decode.loss_ce: 0.2198 decode.acc_seg: 90.2712 +2024/10/28 07:15:06 - mmengine - INFO - Iter(train) [134100/160000] base_lr: 2.8451e-05 lr: 2.8451e-05 eta: 2:52:14 time: 0.3829 data_time: 0.0173 memory: 5384 loss: 0.2105 decode.loss_ce: 0.2105 decode.acc_seg: 91.7294 +2024/10/28 07:15:25 - mmengine - INFO - Iter(train) [134150/160000] base_lr: 2.8351e-05 lr: 2.8351e-05 eta: 2:51:54 time: 0.3806 data_time: 0.0169 memory: 5384 loss: 0.2206 decode.loss_ce: 0.2206 decode.acc_seg: 92.4826 +2024/10/28 07:15:44 - mmengine - INFO - Iter(train) [134200/160000] base_lr: 2.8251e-05 lr: 2.8251e-05 eta: 2:51:34 time: 0.3771 data_time: 0.0165 memory: 5383 loss: 0.2273 decode.loss_ce: 0.2273 decode.acc_seg: 94.2138 +2024/10/28 07:16:03 - mmengine - INFO - Iter(train) [134250/160000] base_lr: 2.8151e-05 lr: 2.8151e-05 eta: 2:51:14 time: 0.3831 data_time: 0.0170 memory: 5384 loss: 0.2376 decode.loss_ce: 0.2376 decode.acc_seg: 86.5777 +2024/10/28 07:16:24 - mmengine - INFO - Iter(train) [134300/160000] base_lr: 2.8052e-05 lr: 2.8052e-05 eta: 2:50:54 time: 0.3802 data_time: 0.0162 memory: 5384 loss: 0.2278 decode.loss_ce: 0.2278 decode.acc_seg: 93.9483 +2024/10/28 07:16:43 - mmengine - INFO - Iter(train) [134350/160000] base_lr: 2.7952e-05 lr: 2.7952e-05 eta: 2:50:34 time: 0.3808 data_time: 0.0161 memory: 5384 loss: 0.2183 decode.loss_ce: 0.2183 decode.acc_seg: 90.0195 +2024/10/28 07:17:03 - mmengine - INFO - Iter(train) [134400/160000] base_lr: 2.7852e-05 lr: 2.7852e-05 eta: 2:50:14 time: 0.3793 data_time: 0.0183 memory: 5384 loss: 0.1916 decode.loss_ce: 0.1916 decode.acc_seg: 92.0862 +2024/10/28 07:17:25 - mmengine - INFO - Iter(train) [134450/160000] base_lr: 2.7753e-05 lr: 2.7753e-05 eta: 2:49:54 time: 0.3801 data_time: 0.0190 memory: 5385 loss: 0.2023 decode.loss_ce: 0.2023 decode.acc_seg: 89.3904 +2024/10/28 07:17:45 - mmengine - INFO - Iter(train) [134500/160000] base_lr: 2.7654e-05 lr: 2.7654e-05 eta: 2:49:34 time: 0.3982 data_time: 0.0169 memory: 5385 loss: 0.2361 decode.loss_ce: 0.2361 decode.acc_seg: 86.7122 +2024/10/28 07:18:05 - mmengine - INFO - Iter(train) [134550/160000] base_lr: 2.7555e-05 lr: 2.7555e-05 eta: 2:49:15 time: 0.3891 data_time: 0.0187 memory: 5383 loss: 0.2607 decode.loss_ce: 0.2607 decode.acc_seg: 89.9989 +2024/10/28 07:18:24 - mmengine - INFO - Iter(train) [134600/160000] base_lr: 2.7455e-05 lr: 2.7455e-05 eta: 2:48:54 time: 0.3787 data_time: 0.0192 memory: 5384 loss: 0.2286 decode.loss_ce: 0.2286 decode.acc_seg: 92.1533 +2024/10/28 07:18:44 - mmengine - INFO - Iter(train) [134650/160000] base_lr: 2.7357e-05 lr: 2.7357e-05 eta: 2:48:34 time: 0.4064 data_time: 0.0178 memory: 5384 loss: 0.2259 decode.loss_ce: 0.2259 decode.acc_seg: 87.4273 +2024/10/28 07:19:04 - mmengine - INFO - Iter(train) [134700/160000] base_lr: 2.7258e-05 lr: 2.7258e-05 eta: 2:48:15 time: 0.4098 data_time: 0.0172 memory: 5384 loss: 0.2015 decode.loss_ce: 0.2015 decode.acc_seg: 84.5128 +2024/10/28 07:19:24 - mmengine - INFO - Iter(train) [134750/160000] base_lr: 2.7159e-05 lr: 2.7159e-05 eta: 2:47:55 time: 0.3813 data_time: 0.0165 memory: 5384 loss: 0.2591 decode.loss_ce: 0.2591 decode.acc_seg: 90.6248 +2024/10/28 07:19:43 - mmengine - INFO - Iter(train) [134800/160000] base_lr: 2.7061e-05 lr: 2.7061e-05 eta: 2:47:34 time: 0.3776 data_time: 0.0160 memory: 5384 loss: 0.2151 decode.loss_ce: 0.2151 decode.acc_seg: 90.8061 +2024/10/28 07:20:03 - mmengine - INFO - Iter(train) [134850/160000] base_lr: 2.6962e-05 lr: 2.6962e-05 eta: 2:47:14 time: 0.3826 data_time: 0.0160 memory: 5385 loss: 0.1960 decode.loss_ce: 0.1960 decode.acc_seg: 94.2439 +2024/10/28 07:20:24 - mmengine - INFO - Iter(train) [134900/160000] base_lr: 2.6864e-05 lr: 2.6864e-05 eta: 2:46:55 time: 0.3817 data_time: 0.0177 memory: 5384 loss: 0.2038 decode.loss_ce: 0.2038 decode.acc_seg: 84.3553 +2024/10/28 07:20:44 - mmengine - INFO - Iter(train) [134950/160000] base_lr: 2.6766e-05 lr: 2.6766e-05 eta: 2:46:35 time: 0.3777 data_time: 0.0169 memory: 5385 loss: 0.2050 decode.loss_ce: 0.2050 decode.acc_seg: 95.4997 +2024/10/28 07:21:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:21:03 - mmengine - INFO - Iter(train) [135000/160000] base_lr: 2.6668e-05 lr: 2.6668e-05 eta: 2:46:15 time: 0.3833 data_time: 0.0176 memory: 5383 loss: 0.1998 decode.loss_ce: 0.1998 decode.acc_seg: 89.3887 +2024/10/28 07:21:24 - mmengine - INFO - Iter(train) [135050/160000] base_lr: 2.6570e-05 lr: 2.6570e-05 eta: 2:45:55 time: 0.3807 data_time: 0.0173 memory: 5384 loss: 0.1921 decode.loss_ce: 0.1921 decode.acc_seg: 90.4422 +2024/10/28 07:21:44 - mmengine - INFO - Iter(train) [135100/160000] base_lr: 2.6472e-05 lr: 2.6472e-05 eta: 2:45:35 time: 0.3805 data_time: 0.0173 memory: 5384 loss: 0.2382 decode.loss_ce: 0.2382 decode.acc_seg: 92.6212 +2024/10/28 07:22:03 - mmengine - INFO - Iter(train) [135150/160000] base_lr: 2.6374e-05 lr: 2.6374e-05 eta: 2:45:15 time: 0.3837 data_time: 0.0170 memory: 5383 loss: 0.2036 decode.loss_ce: 0.2036 decode.acc_seg: 88.9702 +2024/10/28 07:22:26 - mmengine - INFO - Iter(train) 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0.2387 decode.loss_ce: 0.2387 decode.acc_seg: 91.3297 +2024/10/28 07:24:03 - mmengine - INFO - Iter(train) [135450/160000] base_lr: 2.5791e-05 lr: 2.5791e-05 eta: 2:43:15 time: 0.3843 data_time: 0.0168 memory: 5384 loss: 0.2084 decode.loss_ce: 0.2084 decode.acc_seg: 90.7651 +2024/10/28 07:24:24 - mmengine - INFO - Iter(train) [135500/160000] base_lr: 2.5695e-05 lr: 2.5695e-05 eta: 2:42:55 time: 0.3827 data_time: 0.0167 memory: 5384 loss: 0.2172 decode.loss_ce: 0.2172 decode.acc_seg: 91.7841 +2024/10/28 07:24:43 - mmengine - INFO - Iter(train) [135550/160000] base_lr: 2.5598e-05 lr: 2.5598e-05 eta: 2:42:35 time: 0.3813 data_time: 0.0171 memory: 5384 loss: 0.2214 decode.loss_ce: 0.2214 decode.acc_seg: 91.6491 +2024/10/28 07:25:03 - mmengine - INFO - Iter(train) [135600/160000] base_lr: 2.5502e-05 lr: 2.5502e-05 eta: 2:42:15 time: 0.3843 data_time: 0.0175 memory: 5384 loss: 0.2357 decode.loss_ce: 0.2357 decode.acc_seg: 91.1523 +2024/10/28 07:25:24 - mmengine - INFO - Iter(train) 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0.2116 decode.loss_ce: 0.2116 decode.acc_seg: 92.8900 +2024/10/28 07:27:03 - mmengine - INFO - Iter(train) [135900/160000] base_lr: 2.4926e-05 lr: 2.4926e-05 eta: 2:40:16 time: 0.3878 data_time: 0.0171 memory: 5384 loss: 0.2024 decode.loss_ce: 0.2024 decode.acc_seg: 93.0784 +2024/10/28 07:27:26 - mmengine - INFO - Iter(train) [135950/160000] base_lr: 2.4830e-05 lr: 2.4830e-05 eta: 2:39:56 time: 0.3813 data_time: 0.0161 memory: 5384 loss: 0.2036 decode.loss_ce: 0.2036 decode.acc_seg: 94.2654 +2024/10/28 07:27:45 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:27:45 - mmengine - INFO - Iter(train) [136000/160000] base_lr: 2.4735e-05 lr: 2.4735e-05 eta: 2:39:36 time: 0.3813 data_time: 0.0172 memory: 5385 loss: 0.2414 decode.loss_ce: 0.2414 decode.acc_seg: 90.4552 +2024/10/28 07:28:04 - mmengine - INFO - Iter(train) [136050/160000] base_lr: 2.4640e-05 lr: 2.4640e-05 eta: 2:39:16 time: 0.3829 data_time: 0.0180 memory: 5384 loss: 0.2152 decode.loss_ce: 0.2152 decode.acc_seg: 88.4079 +2024/10/28 07:28:25 - mmengine - INFO - Iter(train) [136100/160000] base_lr: 2.4544e-05 lr: 2.4544e-05 eta: 2:38:56 time: 0.3824 data_time: 0.0165 memory: 5384 loss: 0.1916 decode.loss_ce: 0.1916 decode.acc_seg: 92.3018 +2024/10/28 07:28:44 - mmengine - INFO - Iter(train) [136150/160000] base_lr: 2.4449e-05 lr: 2.4449e-05 eta: 2:38:36 time: 0.3831 data_time: 0.0161 memory: 5385 loss: 0.2073 decode.loss_ce: 0.2073 decode.acc_seg: 91.6816 +2024/10/28 07:29:04 - mmengine - INFO - Iter(train) [136200/160000] base_lr: 2.4355e-05 lr: 2.4355e-05 eta: 2:38:16 time: 0.3863 data_time: 0.0172 memory: 5383 loss: 0.1754 decode.loss_ce: 0.1754 decode.acc_seg: 92.3974 +2024/10/28 07:29:25 - mmengine - INFO - Iter(train) [136250/160000] base_lr: 2.4260e-05 lr: 2.4260e-05 eta: 2:37:56 time: 0.3767 data_time: 0.0175 memory: 5384 loss: 0.1954 decode.loss_ce: 0.1954 decode.acc_seg: 92.3020 +2024/10/28 07:29:44 - mmengine - INFO - Iter(train) [136300/160000] base_lr: 2.4165e-05 lr: 2.4165e-05 eta: 2:37:36 time: 0.3794 data_time: 0.0181 memory: 5383 loss: 0.2000 decode.loss_ce: 0.2000 decode.acc_seg: 92.2814 +2024/10/28 07:30:03 - mmengine - INFO - Iter(train) [136350/160000] base_lr: 2.4071e-05 lr: 2.4071e-05 eta: 2:37:16 time: 0.3845 data_time: 0.0187 memory: 5383 loss: 0.2031 decode.loss_ce: 0.2031 decode.acc_seg: 92.5449 +2024/10/28 07:30:25 - mmengine - INFO - Iter(train) [136400/160000] base_lr: 2.3977e-05 lr: 2.3977e-05 eta: 2:36:56 time: 0.3819 data_time: 0.0185 memory: 5384 loss: 0.2042 decode.loss_ce: 0.2042 decode.acc_seg: 91.7685 +2024/10/28 07:30:44 - mmengine - INFO - Iter(train) [136450/160000] base_lr: 2.3883e-05 lr: 2.3883e-05 eta: 2:36:36 time: 0.3803 data_time: 0.0180 memory: 5383 loss: 0.1963 decode.loss_ce: 0.1963 decode.acc_seg: 90.0632 +2024/10/28 07:31:03 - mmengine - INFO - Iter(train) [136500/160000] base_lr: 2.3789e-05 lr: 2.3789e-05 eta: 2:36:16 time: 0.3812 data_time: 0.0162 memory: 5384 loss: 0.2366 decode.loss_ce: 0.2366 decode.acc_seg: 90.5086 +2024/10/28 07:31:24 - mmengine - INFO - Iter(train) [136550/160000] base_lr: 2.3695e-05 lr: 2.3695e-05 eta: 2:35:57 time: 0.3822 data_time: 0.0167 memory: 5383 loss: 0.2204 decode.loss_ce: 0.2204 decode.acc_seg: 91.4061 +2024/10/28 07:31:44 - mmengine - INFO - Iter(train) [136600/160000] base_lr: 2.3601e-05 lr: 2.3601e-05 eta: 2:35:36 time: 0.3776 data_time: 0.0168 memory: 5384 loss: 0.2361 decode.loss_ce: 0.2361 decode.acc_seg: 93.5843 +2024/10/28 07:32:03 - mmengine - INFO - Iter(train) [136650/160000] base_lr: 2.3507e-05 lr: 2.3507e-05 eta: 2:35:16 time: 0.3832 data_time: 0.0177 memory: 5386 loss: 0.1807 decode.loss_ce: 0.1807 decode.acc_seg: 92.6592 +2024/10/28 07:32:25 - mmengine - INFO - Iter(train) [136700/160000] base_lr: 2.3414e-05 lr: 2.3414e-05 eta: 2:34:57 time: 0.3973 data_time: 0.0163 memory: 5385 loss: 0.2735 decode.loss_ce: 0.2735 decode.acc_seg: 91.7401 +2024/10/28 07:32:45 - mmengine - INFO - Iter(train) [136750/160000] base_lr: 2.3321e-05 lr: 2.3321e-05 eta: 2:34:37 time: 0.3769 data_time: 0.0165 memory: 5384 loss: 0.2179 decode.loss_ce: 0.2179 decode.acc_seg: 88.7861 +2024/10/28 07:33:04 - mmengine - INFO - Iter(train) [136800/160000] base_lr: 2.3227e-05 lr: 2.3227e-05 eta: 2:34:17 time: 0.3791 data_time: 0.0171 memory: 5385 loss: 0.2039 decode.loss_ce: 0.2039 decode.acc_seg: 92.8241 +2024/10/28 07:33:25 - mmengine - INFO - Iter(train) [136850/160000] base_lr: 2.3134e-05 lr: 2.3134e-05 eta: 2:33:57 time: 0.3778 data_time: 0.0175 memory: 5384 loss: 0.2050 decode.loss_ce: 0.2050 decode.acc_seg: 91.9915 +2024/10/28 07:33:44 - mmengine - INFO - Iter(train) [136900/160000] base_lr: 2.3042e-05 lr: 2.3042e-05 eta: 2:33:37 time: 0.3766 data_time: 0.0167 memory: 5384 loss: 0.2024 decode.loss_ce: 0.2024 decode.acc_seg: 88.2096 +2024/10/28 07:34:03 - mmengine - INFO - Iter(train) [136950/160000] base_lr: 2.2949e-05 lr: 2.2949e-05 eta: 2:33:17 time: 0.3835 data_time: 0.0161 memory: 5384 loss: 0.2212 decode.loss_ce: 0.2212 decode.acc_seg: 89.7258 +2024/10/28 07:34:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:34:25 - mmengine - INFO - Iter(train) [137000/160000] base_lr: 2.2856e-05 lr: 2.2856e-05 eta: 2:32:57 time: 0.3990 data_time: 0.0139 memory: 5384 loss: 0.2087 decode.loss_ce: 0.2087 decode.acc_seg: 88.2565 +2024/10/28 07:34:44 - mmengine - INFO - Iter(train) [137050/160000] base_lr: 2.2764e-05 lr: 2.2764e-05 eta: 2:32:37 time: 0.3783 data_time: 0.0178 memory: 5384 loss: 0.2151 decode.loss_ce: 0.2151 decode.acc_seg: 87.8518 +2024/10/28 07:35:03 - mmengine - INFO - Iter(train) [137100/160000] base_lr: 2.2671e-05 lr: 2.2671e-05 eta: 2:32:17 time: 0.3768 data_time: 0.0167 memory: 5384 loss: 0.2099 decode.loss_ce: 0.2099 decode.acc_seg: 91.6171 +2024/10/28 07:35:25 - mmengine - INFO - Iter(train) [137150/160000] base_lr: 2.2579e-05 lr: 2.2579e-05 eta: 2:31:57 time: 0.3771 data_time: 0.0159 memory: 5384 loss: 0.2490 decode.loss_ce: 0.2490 decode.acc_seg: 79.1915 +2024/10/28 07:35:44 - mmengine - INFO - Iter(train) [137200/160000] base_lr: 2.2487e-05 lr: 2.2487e-05 eta: 2:31:37 time: 0.3770 data_time: 0.0164 memory: 5384 loss: 0.1968 decode.loss_ce: 0.1968 decode.acc_seg: 93.7564 +2024/10/28 07:36:03 - mmengine - INFO - Iter(train) [137250/160000] base_lr: 2.2395e-05 lr: 2.2395e-05 eta: 2:31:17 time: 0.3854 data_time: 0.0171 memory: 5384 loss: 0.2086 decode.loss_ce: 0.2086 decode.acc_seg: 90.2778 +2024/10/28 07:36:25 - mmengine - INFO - Iter(train) [137300/160000] base_lr: 2.2304e-05 lr: 2.2304e-05 eta: 2:30:58 time: 0.3790 data_time: 0.0187 memory: 5384 loss: 0.2058 decode.loss_ce: 0.2058 decode.acc_seg: 91.1101 +2024/10/28 07:36:45 - mmengine - INFO - Iter(train) [137350/160000] base_lr: 2.2212e-05 lr: 2.2212e-05 eta: 2:30:37 time: 0.3760 data_time: 0.0181 memory: 5385 loss: 0.2268 decode.loss_ce: 0.2268 decode.acc_seg: 93.2518 +2024/10/28 07:37:04 - mmengine - INFO - Iter(train) [137400/160000] base_lr: 2.2121e-05 lr: 2.2121e-05 eta: 2:30:17 time: 0.3864 data_time: 0.0175 memory: 5384 loss: 0.2010 decode.loss_ce: 0.2010 decode.acc_seg: 94.6276 +2024/10/28 07:37:24 - mmengine - INFO - Iter(train) [137450/160000] base_lr: 2.2029e-05 lr: 2.2029e-05 eta: 2:29:57 time: 0.3802 data_time: 0.0171 memory: 5384 loss: 0.2425 decode.loss_ce: 0.2425 decode.acc_seg: 93.7664 +2024/10/28 07:37:43 - mmengine - INFO - Iter(train) [137500/160000] base_lr: 2.1938e-05 lr: 2.1938e-05 eta: 2:29:37 time: 0.3803 data_time: 0.0157 memory: 5383 loss: 0.2280 decode.loss_ce: 0.2280 decode.acc_seg: 91.9283 +2024/10/28 07:38:03 - mmengine - INFO - Iter(train) [137550/160000] base_lr: 2.1847e-05 lr: 2.1847e-05 eta: 2:29:17 time: 0.4031 data_time: 0.0149 memory: 5384 loss: 0.1949 decode.loss_ce: 0.1949 decode.acc_seg: 93.4413 +2024/10/28 07:38:25 - mmengine - INFO - Iter(train) [137600/160000] base_lr: 2.1756e-05 lr: 2.1756e-05 eta: 2:28:58 time: 0.3763 data_time: 0.0156 memory: 5382 loss: 0.2555 decode.loss_ce: 0.2555 decode.acc_seg: 90.7825 +2024/10/28 07:38:44 - mmengine - INFO - Iter(train) [137650/160000] base_lr: 2.1666e-05 lr: 2.1666e-05 eta: 2:28:38 time: 0.3813 data_time: 0.0162 memory: 5384 loss: 0.2208 decode.loss_ce: 0.2208 decode.acc_seg: 92.5567 +2024/10/28 07:39:03 - mmengine - INFO - Iter(train) [137700/160000] base_lr: 2.1575e-05 lr: 2.1575e-05 eta: 2:28:18 time: 0.3880 data_time: 0.0158 memory: 5386 loss: 0.2059 decode.loss_ce: 0.2059 decode.acc_seg: 92.7048 +2024/10/28 07:39:25 - mmengine - INFO - Iter(train) [137750/160000] base_lr: 2.1485e-05 lr: 2.1485e-05 eta: 2:27:58 time: 0.3804 data_time: 0.0166 memory: 5384 loss: 0.1833 decode.loss_ce: 0.1833 decode.acc_seg: 92.2324 +2024/10/28 07:39:45 - mmengine - INFO - Iter(train) [137800/160000] base_lr: 2.1394e-05 lr: 2.1394e-05 eta: 2:27:38 time: 0.3799 data_time: 0.0167 memory: 5384 loss: 0.1858 decode.loss_ce: 0.1858 decode.acc_seg: 91.5028 +2024/10/28 07:40:04 - mmengine - INFO - Iter(train) [137850/160000] base_lr: 2.1304e-05 lr: 2.1304e-05 eta: 2:27:18 time: 0.3823 data_time: 0.0170 memory: 5384 loss: 0.1565 decode.loss_ce: 0.1565 decode.acc_seg: 94.3998 +2024/10/28 07:40:25 - mmengine - INFO - Iter(train) [137900/160000] base_lr: 2.1214e-05 lr: 2.1214e-05 eta: 2:26:58 time: 0.3772 data_time: 0.0176 memory: 5384 loss: 0.2093 decode.loss_ce: 0.2093 decode.acc_seg: 88.5645 +2024/10/28 07:40:44 - mmengine - INFO - Iter(train) [137950/160000] base_lr: 2.1125e-05 lr: 2.1125e-05 eta: 2:26:38 time: 0.3862 data_time: 0.0167 memory: 5384 loss: 0.1813 decode.loss_ce: 0.1813 decode.acc_seg: 90.4846 +2024/10/28 07:41:04 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:41:04 - mmengine - INFO - Iter(train) [138000/160000] base_lr: 2.1035e-05 lr: 2.1035e-05 eta: 2:26:18 time: 0.3853 data_time: 0.0187 memory: 5383 loss: 0.1664 decode.loss_ce: 0.1664 decode.acc_seg: 93.3086 +2024/10/28 07:41:25 - mmengine - INFO - Iter(train) [138050/160000] base_lr: 2.0945e-05 lr: 2.0945e-05 eta: 2:25:58 time: 0.3800 data_time: 0.0164 memory: 5383 loss: 0.1861 decode.loss_ce: 0.1861 decode.acc_seg: 92.4624 +2024/10/28 07:41:44 - mmengine - INFO - Iter(train) [138100/160000] base_lr: 2.0856e-05 lr: 2.0856e-05 eta: 2:25:38 time: 0.3753 data_time: 0.0180 memory: 5384 loss: 0.1996 decode.loss_ce: 0.1996 decode.acc_seg: 90.2918 +2024/10/28 07:42:04 - mmengine - INFO - Iter(train) [138150/160000] base_lr: 2.0767e-05 lr: 2.0767e-05 eta: 2:25:18 time: 0.3867 data_time: 0.0180 memory: 5384 loss: 0.1906 decode.loss_ce: 0.1906 decode.acc_seg: 93.8789 +2024/10/28 07:42:25 - mmengine - INFO - Iter(train) [138200/160000] base_lr: 2.0678e-05 lr: 2.0678e-05 eta: 2:24:58 time: 0.3793 data_time: 0.0173 memory: 5384 loss: 0.1948 decode.loss_ce: 0.1948 decode.acc_seg: 86.2474 +2024/10/28 07:42:44 - mmengine - INFO - Iter(train) [138250/160000] base_lr: 2.0589e-05 lr: 2.0589e-05 eta: 2:24:38 time: 0.3838 data_time: 0.0171 memory: 5384 loss: 0.2607 decode.loss_ce: 0.2607 decode.acc_seg: 89.3547 +2024/10/28 07:43:03 - mmengine - INFO - Iter(train) [138300/160000] base_lr: 2.0500e-05 lr: 2.0500e-05 eta: 2:24:18 time: 0.3786 data_time: 0.0161 memory: 5384 loss: 0.2126 decode.loss_ce: 0.2126 decode.acc_seg: 94.8193 +2024/10/28 07:43:24 - mmengine - INFO - Iter(train) [138350/160000] base_lr: 2.0412e-05 lr: 2.0412e-05 eta: 2:23:58 time: 0.3797 data_time: 0.0153 memory: 5383 loss: 0.2084 decode.loss_ce: 0.2084 decode.acc_seg: 88.3699 +2024/10/28 07:43:43 - mmengine - INFO - Iter(train) [138400/160000] base_lr: 2.0323e-05 lr: 2.0323e-05 eta: 2:23:38 time: 0.3813 data_time: 0.0176 memory: 5384 loss: 0.1821 decode.loss_ce: 0.1821 decode.acc_seg: 91.4454 +2024/10/28 07:44:02 - mmengine - INFO - Iter(train) [138450/160000] base_lr: 2.0235e-05 lr: 2.0235e-05 eta: 2:23:18 time: 0.3829 data_time: 0.0173 memory: 5385 loss: 0.1765 decode.loss_ce: 0.1765 decode.acc_seg: 89.6835 +2024/10/28 07:44:24 - mmengine - INFO - Iter(train) [138500/160000] base_lr: 2.0147e-05 lr: 2.0147e-05 eta: 2:22:59 time: 0.3836 data_time: 0.0183 memory: 5384 loss: 0.2519 decode.loss_ce: 0.2519 decode.acc_seg: 94.1080 +2024/10/28 07:44:43 - mmengine - INFO - Iter(train) [138550/160000] base_lr: 2.0059e-05 lr: 2.0059e-05 eta: 2:22:38 time: 0.3818 data_time: 0.0184 memory: 5384 loss: 0.1771 decode.loss_ce: 0.1771 decode.acc_seg: 96.6527 +2024/10/28 07:45:03 - mmengine - INFO - Iter(train) [138600/160000] base_lr: 1.9971e-05 lr: 1.9971e-05 eta: 2:22:18 time: 0.4061 data_time: 0.0148 memory: 5384 loss: 0.2069 decode.loss_ce: 0.2069 decode.acc_seg: 93.7456 +2024/10/28 07:45:25 - mmengine - INFO - Iter(train) [138650/160000] base_lr: 1.9883e-05 lr: 1.9883e-05 eta: 2:21:59 time: 0.4014 data_time: 0.0163 memory: 5384 loss: 0.2269 decode.loss_ce: 0.2269 decode.acc_seg: 90.0641 +2024/10/28 07:45:45 - mmengine - INFO - Iter(train) [138700/160000] base_lr: 1.9796e-05 lr: 1.9796e-05 eta: 2:21:39 time: 0.4052 data_time: 0.0167 memory: 5384 loss: 0.2172 decode.loss_ce: 0.2172 decode.acc_seg: 91.7261 +2024/10/28 07:46:05 - mmengine - INFO - Iter(train) [138750/160000] base_lr: 1.9708e-05 lr: 1.9708e-05 eta: 2:21:19 time: 0.3871 data_time: 0.0174 memory: 5386 loss: 0.2051 decode.loss_ce: 0.2051 decode.acc_seg: 93.6471 +2024/10/28 07:46:25 - mmengine - INFO - Iter(train) [138800/160000] base_lr: 1.9621e-05 lr: 1.9621e-05 eta: 2:20:59 time: 0.3825 data_time: 0.0162 memory: 5386 loss: 0.2003 decode.loss_ce: 0.2003 decode.acc_seg: 93.8792 +2024/10/28 07:46:44 - mmengine - INFO - Iter(train) [138850/160000] base_lr: 1.9534e-05 lr: 1.9534e-05 eta: 2:20:39 time: 0.3791 data_time: 0.0171 memory: 5384 loss: 0.2286 decode.loss_ce: 0.2286 decode.acc_seg: 92.3412 +2024/10/28 07:47:03 - mmengine - INFO - Iter(train) [138900/160000] base_lr: 1.9447e-05 lr: 1.9447e-05 eta: 2:20:19 time: 0.3855 data_time: 0.0169 memory: 5384 loss: 0.1897 decode.loss_ce: 0.1897 decode.acc_seg: 95.5939 +2024/10/28 07:47:26 - mmengine - INFO - Iter(train) [138950/160000] base_lr: 1.9360e-05 lr: 1.9360e-05 eta: 2:19:59 time: 0.3840 data_time: 0.0178 memory: 5384 loss: 0.2297 decode.loss_ce: 0.2297 decode.acc_seg: 90.4030 +2024/10/28 07:47:45 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:47:45 - mmengine - INFO - Iter(train) [139000/160000] base_lr: 1.9274e-05 lr: 1.9274e-05 eta: 2:19:39 time: 0.3788 data_time: 0.0184 memory: 5384 loss: 0.1849 decode.loss_ce: 0.1849 decode.acc_seg: 93.0847 +2024/10/28 07:48:04 - mmengine - INFO - Iter(train) [139050/160000] base_lr: 1.9187e-05 lr: 1.9187e-05 eta: 2:19:19 time: 0.3890 data_time: 0.0183 memory: 5384 loss: 0.1995 decode.loss_ce: 0.1995 decode.acc_seg: 96.3890 +2024/10/28 07:48:26 - mmengine - INFO - Iter(train) [139100/160000] base_lr: 1.9101e-05 lr: 1.9101e-05 eta: 2:19:00 time: 0.3818 data_time: 0.0172 memory: 5385 loss: 0.2127 decode.loss_ce: 0.2127 decode.acc_seg: 88.0596 +2024/10/28 07:48:45 - mmengine - INFO - Iter(train) [139150/160000] base_lr: 1.9015e-05 lr: 1.9015e-05 eta: 2:18:39 time: 0.3844 data_time: 0.0188 memory: 5385 loss: 0.2115 decode.loss_ce: 0.2115 decode.acc_seg: 91.0223 +2024/10/28 07:49:06 - mmengine - INFO - Iter(train) [139200/160000] base_lr: 1.8929e-05 lr: 1.8929e-05 eta: 2:18:20 time: 0.4278 data_time: 0.0166 memory: 5384 loss: 0.2214 decode.loss_ce: 0.2214 decode.acc_seg: 90.4730 +2024/10/28 07:49:26 - mmengine - INFO - Iter(train) [139250/160000] base_lr: 1.8843e-05 lr: 1.8843e-05 eta: 2:18:00 time: 0.3890 data_time: 0.0188 memory: 5382 loss: 0.2112 decode.loss_ce: 0.2112 decode.acc_seg: 86.1201 +2024/10/28 07:49:45 - mmengine - INFO - Iter(train) [139300/160000] base_lr: 1.8757e-05 lr: 1.8757e-05 eta: 2:17:40 time: 0.3792 data_time: 0.0177 memory: 5384 loss: 0.2242 decode.loss_ce: 0.2242 decode.acc_seg: 89.5676 +2024/10/28 07:50:04 - mmengine - INFO - Iter(train) [139350/160000] base_lr: 1.8672e-05 lr: 1.8672e-05 eta: 2:17:19 time: 0.3954 data_time: 0.0159 memory: 5384 loss: 0.2112 decode.loss_ce: 0.2112 decode.acc_seg: 93.0769 +2024/10/28 07:50:25 - mmengine - INFO - Iter(train) [139400/160000] base_lr: 1.8587e-05 lr: 1.8587e-05 eta: 2:17:00 time: 0.3781 data_time: 0.0186 memory: 5384 loss: 0.1981 decode.loss_ce: 0.1981 decode.acc_seg: 90.8774 +2024/10/28 07:50:45 - mmengine - INFO - Iter(train) [139450/160000] base_lr: 1.8501e-05 lr: 1.8501e-05 eta: 2:16:40 time: 0.3821 data_time: 0.0167 memory: 5385 loss: 0.1885 decode.loss_ce: 0.1885 decode.acc_seg: 94.0058 +2024/10/28 07:51:04 - mmengine - INFO - Iter(train) [139500/160000] base_lr: 1.8416e-05 lr: 1.8416e-05 eta: 2:16:20 time: 0.3906 data_time: 0.0162 memory: 5385 loss: 0.2135 decode.loss_ce: 0.2135 decode.acc_seg: 91.9017 +2024/10/28 07:51:24 - mmengine - INFO - Iter(train) [139550/160000] base_lr: 1.8332e-05 lr: 1.8332e-05 eta: 2:16:00 time: 0.3801 data_time: 0.0175 memory: 5384 loss: 0.2148 decode.loss_ce: 0.2148 decode.acc_seg: 91.9287 +2024/10/28 07:51:44 - mmengine - INFO - Iter(train) [139600/160000] base_lr: 1.8247e-05 lr: 1.8247e-05 eta: 2:15:40 time: 0.3784 data_time: 0.0189 memory: 5384 loss: 0.1811 decode.loss_ce: 0.1811 decode.acc_seg: 94.1401 +2024/10/28 07:52:04 - mmengine - INFO - Iter(train) [139650/160000] base_lr: 1.8162e-05 lr: 1.8162e-05 eta: 2:15:20 time: 0.4105 data_time: 0.0151 memory: 5386 loss: 0.2245 decode.loss_ce: 0.2245 decode.acc_seg: 89.2534 +2024/10/28 07:52:24 - mmengine - INFO - Iter(train) [139700/160000] base_lr: 1.8078e-05 lr: 1.8078e-05 eta: 2:15:00 time: 0.3792 data_time: 0.0188 memory: 5384 loss: 0.2079 decode.loss_ce: 0.2079 decode.acc_seg: 89.6279 +2024/10/28 07:52:43 - mmengine - INFO - Iter(train) [139750/160000] base_lr: 1.7994e-05 lr: 1.7994e-05 eta: 2:14:40 time: 0.3816 data_time: 0.0172 memory: 5384 loss: 0.2286 decode.loss_ce: 0.2286 decode.acc_seg: 89.9851 +2024/10/28 07:53:02 - mmengine - INFO - Iter(train) [139800/160000] base_lr: 1.7910e-05 lr: 1.7910e-05 eta: 2:14:20 time: 0.3735 data_time: 0.0154 memory: 5384 loss: 0.2037 decode.loss_ce: 0.2037 decode.acc_seg: 87.6530 +2024/10/28 07:53:25 - mmengine - INFO - Iter(train) [139850/160000] base_lr: 1.7826e-05 lr: 1.7826e-05 eta: 2:14:00 time: 0.3815 data_time: 0.0170 memory: 5384 loss: 0.2002 decode.loss_ce: 0.2002 decode.acc_seg: 91.2049 +2024/10/28 07:53:44 - mmengine - INFO - Iter(train) [139900/160000] base_lr: 1.7742e-05 lr: 1.7742e-05 eta: 2:13:40 time: 0.3801 data_time: 0.0170 memory: 5384 loss: 0.2239 decode.loss_ce: 0.2239 decode.acc_seg: 90.6081 +2024/10/28 07:54:03 - mmengine - INFO - Iter(train) [139950/160000] base_lr: 1.7659e-05 lr: 1.7659e-05 eta: 2:13:20 time: 0.3804 data_time: 0.0162 memory: 5384 loss: 0.2409 decode.loss_ce: 0.2409 decode.acc_seg: 91.4162 +2024/10/28 07:54:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 07:54:24 - mmengine - INFO - Iter(train) [140000/160000] base_lr: 1.7575e-05 lr: 1.7575e-05 eta: 2:13:00 time: 0.3785 data_time: 0.0165 memory: 5383 loss: 0.2276 decode.loss_ce: 0.2276 decode.acc_seg: 85.4972 +2024/10/28 07:54:43 - mmengine - INFO - Iter(train) [140050/160000] base_lr: 1.7492e-05 lr: 1.7492e-05 eta: 2:12:40 time: 0.3744 data_time: 0.0154 memory: 5384 loss: 0.1877 decode.loss_ce: 0.1877 decode.acc_seg: 93.9881 +2024/10/28 07:55:02 - mmengine - INFO - Iter(train) [140100/160000] base_lr: 1.7409e-05 lr: 1.7409e-05 eta: 2:12:20 time: 0.3746 data_time: 0.0154 memory: 5384 loss: 0.1722 decode.loss_ce: 0.1722 decode.acc_seg: 94.3945 +2024/10/28 07:55:25 - mmengine - INFO - Iter(train) [140150/160000] base_lr: 1.7326e-05 lr: 1.7326e-05 eta: 2:12:00 time: 0.3742 data_time: 0.0152 memory: 5384 loss: 0.2566 decode.loss_ce: 0.2566 decode.acc_seg: 91.4365 +2024/10/28 07:55:45 - mmengine - INFO - Iter(train) [140200/160000] base_lr: 1.7243e-05 lr: 1.7243e-05 eta: 2:11:40 time: 0.3897 data_time: 0.0154 memory: 5386 loss: 0.2063 decode.loss_ce: 0.2063 decode.acc_seg: 92.6859 +2024/10/28 07:56:04 - mmengine - INFO - Iter(train) [140250/160000] base_lr: 1.7161e-05 lr: 1.7161e-05 eta: 2:11:20 time: 0.3803 data_time: 0.0163 memory: 5386 loss: 0.2736 decode.loss_ce: 0.2736 decode.acc_seg: 90.4809 +2024/10/28 07:56:26 - mmengine - INFO - Iter(train) [140300/160000] base_lr: 1.7078e-05 lr: 1.7078e-05 eta: 2:11:01 time: 0.3823 data_time: 0.0165 memory: 5383 loss: 0.2127 decode.loss_ce: 0.2127 decode.acc_seg: 92.3996 +2024/10/28 07:56:44 - mmengine - INFO - Iter(train) [140350/160000] base_lr: 1.6996e-05 lr: 1.6996e-05 eta: 2:10:41 time: 0.3767 data_time: 0.0167 memory: 5384 loss: 0.2368 decode.loss_ce: 0.2368 decode.acc_seg: 91.2396 +2024/10/28 07:57:03 - mmengine - INFO - Iter(train) [140400/160000] base_lr: 1.6914e-05 lr: 1.6914e-05 eta: 2:10:20 time: 0.3817 data_time: 0.0159 memory: 5384 loss: 0.1980 decode.loss_ce: 0.1980 decode.acc_seg: 94.9071 +2024/10/28 07:57:25 - mmengine - INFO - Iter(train) [140450/160000] base_lr: 1.6832e-05 lr: 1.6832e-05 eta: 2:10:01 time: 0.3778 data_time: 0.0165 memory: 5383 loss: 0.2278 decode.loss_ce: 0.2278 decode.acc_seg: 92.4429 +2024/10/28 07:57:44 - mmengine - INFO - Iter(train) [140500/160000] base_lr: 1.6750e-05 lr: 1.6750e-05 eta: 2:09:41 time: 0.3822 data_time: 0.0165 memory: 5384 loss: 0.2354 decode.loss_ce: 0.2354 decode.acc_seg: 88.3048 +2024/10/28 07:58:04 - mmengine - INFO - Iter(train) [140550/160000] base_lr: 1.6669e-05 lr: 1.6669e-05 eta: 2:09:21 time: 0.3843 data_time: 0.0166 memory: 5386 loss: 0.2054 decode.loss_ce: 0.2054 decode.acc_seg: 90.7411 +2024/10/28 07:58:26 - mmengine - INFO - Iter(train) [140600/160000] base_lr: 1.6587e-05 lr: 1.6587e-05 eta: 2:09:01 time: 0.3768 data_time: 0.0177 memory: 5384 loss: 0.2257 decode.loss_ce: 0.2257 decode.acc_seg: 92.4929 +2024/10/28 07:58:45 - mmengine - INFO - Iter(train) [140650/160000] base_lr: 1.6506e-05 lr: 1.6506e-05 eta: 2:08:41 time: 0.3776 data_time: 0.0173 memory: 5384 loss: 0.2020 decode.loss_ce: 0.2020 decode.acc_seg: 93.6497 +2024/10/28 07:59:04 - mmengine - INFO - Iter(train) [140700/160000] base_lr: 1.6425e-05 lr: 1.6425e-05 eta: 2:08:21 time: 0.3774 data_time: 0.0165 memory: 5384 loss: 0.2081 decode.loss_ce: 0.2081 decode.acc_seg: 91.8137 +2024/10/28 07:59:26 - mmengine - INFO - Iter(train) [140750/160000] base_lr: 1.6344e-05 lr: 1.6344e-05 eta: 2:08:01 time: 0.3786 data_time: 0.0182 memory: 5384 loss: 0.1987 decode.loss_ce: 0.1987 decode.acc_seg: 94.7196 +2024/10/28 07:59:45 - mmengine - INFO - Iter(train) [140800/160000] base_lr: 1.6263e-05 lr: 1.6263e-05 eta: 2:07:41 time: 0.3780 data_time: 0.0176 memory: 5384 loss: 0.1689 decode.loss_ce: 0.1689 decode.acc_seg: 92.2073 +2024/10/28 08:00:04 - mmengine - INFO - Iter(train) [140850/160000] base_lr: 1.6183e-05 lr: 1.6183e-05 eta: 2:07:21 time: 0.4031 data_time: 0.0163 memory: 5384 loss: 0.2281 decode.loss_ce: 0.2281 decode.acc_seg: 87.9903 +2024/10/28 08:00:25 - mmengine - INFO - Iter(train) [140900/160000] base_lr: 1.6103e-05 lr: 1.6103e-05 eta: 2:07:01 time: 0.3813 data_time: 0.0168 memory: 5384 loss: 0.2143 decode.loss_ce: 0.2143 decode.acc_seg: 90.5366 +2024/10/28 08:00:44 - mmengine - INFO - Iter(train) [140950/160000] base_lr: 1.6022e-05 lr: 1.6022e-05 eta: 2:06:41 time: 0.3834 data_time: 0.0168 memory: 5386 loss: 0.1789 decode.loss_ce: 0.1789 decode.acc_seg: 93.1182 +2024/10/28 08:01:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:01:03 - mmengine - INFO - Iter(train) [141000/160000] base_lr: 1.5942e-05 lr: 1.5942e-05 eta: 2:06:21 time: 0.3839 data_time: 0.0165 memory: 5384 loss: 0.2088 decode.loss_ce: 0.2088 decode.acc_seg: 93.1020 +2024/10/28 08:01:25 - mmengine - INFO - Iter(train) [141050/160000] base_lr: 1.5862e-05 lr: 1.5862e-05 eta: 2:06:01 time: 0.3792 data_time: 0.0166 memory: 5384 loss: 0.2475 decode.loss_ce: 0.2475 decode.acc_seg: 92.5837 +2024/10/28 08:01:44 - mmengine - INFO - Iter(train) [141100/160000] base_lr: 1.5783e-05 lr: 1.5783e-05 eta: 2:05:41 time: 0.3806 data_time: 0.0164 memory: 5386 loss: 0.1986 decode.loss_ce: 0.1986 decode.acc_seg: 86.2187 +2024/10/28 08:02:03 - mmengine - INFO - Iter(train) [141150/160000] base_lr: 1.5703e-05 lr: 1.5703e-05 eta: 2:05:21 time: 0.3831 data_time: 0.0158 memory: 5384 loss: 0.1945 decode.loss_ce: 0.1945 decode.acc_seg: 94.3946 +2024/10/28 08:02:25 - mmengine - INFO - Iter(train) [141200/160000] base_lr: 1.5624e-05 lr: 1.5624e-05 eta: 2:05:01 time: 0.3793 data_time: 0.0167 memory: 5384 loss: 0.1920 decode.loss_ce: 0.1920 decode.acc_seg: 92.5479 +2024/10/28 08:02:44 - mmengine - INFO - Iter(train) [141250/160000] base_lr: 1.5545e-05 lr: 1.5545e-05 eta: 2:04:41 time: 0.3781 data_time: 0.0170 memory: 5384 loss: 0.2468 decode.loss_ce: 0.2468 decode.acc_seg: 91.4356 +2024/10/28 08:03:03 - mmengine - INFO - Iter(train) [141300/160000] base_lr: 1.5465e-05 lr: 1.5465e-05 eta: 2:04:21 time: 0.3844 data_time: 0.0174 memory: 5384 loss: 0.2109 decode.loss_ce: 0.2109 decode.acc_seg: 89.4184 +2024/10/28 08:03:24 - mmengine - INFO - Iter(train) [141350/160000] base_lr: 1.5387e-05 lr: 1.5387e-05 eta: 2:04:01 time: 0.3796 data_time: 0.0181 memory: 5384 loss: 0.2262 decode.loss_ce: 0.2262 decode.acc_seg: 86.6154 +2024/10/28 08:03:43 - mmengine - INFO - Iter(train) [141400/160000] base_lr: 1.5308e-05 lr: 1.5308e-05 eta: 2:03:41 time: 0.3815 data_time: 0.0184 memory: 5384 loss: 0.2599 decode.loss_ce: 0.2599 decode.acc_seg: 91.4163 +2024/10/28 08:04:02 - mmengine - INFO - Iter(train) [141450/160000] base_lr: 1.5229e-05 lr: 1.5229e-05 eta: 2:03:21 time: 0.3790 data_time: 0.0184 memory: 5384 loss: 0.1937 decode.loss_ce: 0.1937 decode.acc_seg: 87.6569 +2024/10/28 08:04:25 - mmengine - INFO - Iter(train) [141500/160000] base_lr: 1.5151e-05 lr: 1.5151e-05 eta: 2:03:02 time: 0.3766 data_time: 0.0182 memory: 5384 loss: 0.1929 decode.loss_ce: 0.1929 decode.acc_seg: 92.1474 +2024/10/28 08:04:44 - mmengine - INFO - Iter(train) [141550/160000] base_lr: 1.5073e-05 lr: 1.5073e-05 eta: 2:02:42 time: 0.3774 data_time: 0.0175 memory: 5386 loss: 0.1920 decode.loss_ce: 0.1920 decode.acc_seg: 92.2571 +2024/10/28 08:05:03 - mmengine - INFO - Iter(train) [141600/160000] base_lr: 1.4995e-05 lr: 1.4995e-05 eta: 2:02:22 time: 0.3795 data_time: 0.0168 memory: 5384 loss: 0.2020 decode.loss_ce: 0.2020 decode.acc_seg: 91.9848 +2024/10/28 08:05:24 - mmengine - INFO - Iter(train) [141650/160000] base_lr: 1.4917e-05 lr: 1.4917e-05 eta: 2:02:02 time: 0.3778 data_time: 0.0168 memory: 5385 loss: 0.1897 decode.loss_ce: 0.1897 decode.acc_seg: 92.9774 +2024/10/28 08:05:43 - mmengine - INFO - Iter(train) [141700/160000] base_lr: 1.4839e-05 lr: 1.4839e-05 eta: 2:01:42 time: 0.3787 data_time: 0.0171 memory: 5384 loss: 0.1804 decode.loss_ce: 0.1804 decode.acc_seg: 94.1280 +2024/10/28 08:06:02 - mmengine - INFO - Iter(train) [141750/160000] base_lr: 1.4762e-05 lr: 1.4762e-05 eta: 2:01:22 time: 0.3804 data_time: 0.0167 memory: 5383 loss: 0.2207 decode.loss_ce: 0.2207 decode.acc_seg: 91.5282 +2024/10/28 08:06:24 - mmengine - INFO - Iter(train) [141800/160000] base_lr: 1.4685e-05 lr: 1.4685e-05 eta: 2:01:02 time: 0.3738 data_time: 0.0169 memory: 5383 loss: 0.1999 decode.loss_ce: 0.1999 decode.acc_seg: 83.6946 +2024/10/28 08:06:43 - mmengine - INFO - Iter(train) [141850/160000] base_lr: 1.4608e-05 lr: 1.4608e-05 eta: 2:00:42 time: 0.3793 data_time: 0.0153 memory: 5385 loss: 0.2064 decode.loss_ce: 0.2064 decode.acc_seg: 87.8846 +2024/10/28 08:07:02 - mmengine - INFO - Iter(train) [141900/160000] base_lr: 1.4531e-05 lr: 1.4531e-05 eta: 2:00:22 time: 0.3781 data_time: 0.0157 memory: 5384 loss: 0.1944 decode.loss_ce: 0.1944 decode.acc_seg: 94.5688 +2024/10/28 08:07:25 - mmengine - INFO - Iter(train) [141950/160000] base_lr: 1.4454e-05 lr: 1.4454e-05 eta: 2:00:02 time: 0.3807 data_time: 0.0160 memory: 5384 loss: 0.2148 decode.loss_ce: 0.2148 decode.acc_seg: 90.6532 +2024/10/28 08:07:44 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:07:44 - mmengine - INFO - Iter(train) [142000/160000] base_lr: 1.4377e-05 lr: 1.4377e-05 eta: 1:59:42 time: 0.3762 data_time: 0.0160 memory: 5384 loss: 0.2088 decode.loss_ce: 0.2088 decode.acc_seg: 92.0864 +2024/10/28 08:08:03 - mmengine - INFO - Iter(train) [142050/160000] base_lr: 1.4301e-05 lr: 1.4301e-05 eta: 1:59:22 time: 0.3785 data_time: 0.0163 memory: 5384 loss: 0.2025 decode.loss_ce: 0.2025 decode.acc_seg: 92.0765 +2024/10/28 08:08:26 - mmengine - INFO - Iter(train) [142100/160000] base_lr: 1.4224e-05 lr: 1.4224e-05 eta: 1:59:02 time: 0.3845 data_time: 0.0164 memory: 5384 loss: 0.1978 decode.loss_ce: 0.1978 decode.acc_seg: 94.5116 +2024/10/28 08:08:45 - mmengine - INFO - Iter(train) [142150/160000] base_lr: 1.4148e-05 lr: 1.4148e-05 eta: 1:58:42 time: 0.3774 data_time: 0.0174 memory: 5384 loss: 0.2203 decode.loss_ce: 0.2203 decode.acc_seg: 87.7575 +2024/10/28 08:09:04 - mmengine - INFO - Iter(train) [142200/160000] base_lr: 1.4073e-05 lr: 1.4073e-05 eta: 1:58:22 time: 0.3816 data_time: 0.0170 memory: 5383 loss: 0.2300 decode.loss_ce: 0.2300 decode.acc_seg: 87.3787 +2024/10/28 08:09:25 - mmengine - INFO - Iter(train) [142250/160000] base_lr: 1.3997e-05 lr: 1.3997e-05 eta: 1:58:02 time: 0.3731 data_time: 0.0156 memory: 5385 loss: 0.1720 decode.loss_ce: 0.1720 decode.acc_seg: 92.2329 +2024/10/28 08:09:44 - mmengine - INFO - Iter(train) [142300/160000] base_lr: 1.3921e-05 lr: 1.3921e-05 eta: 1:57:42 time: 0.3815 data_time: 0.0155 memory: 5384 loss: 0.2113 decode.loss_ce: 0.2113 decode.acc_seg: 90.8353 +2024/10/28 08:10:03 - mmengine - INFO - Iter(train) [142350/160000] base_lr: 1.3846e-05 lr: 1.3846e-05 eta: 1:57:22 time: 0.3795 data_time: 0.0166 memory: 5384 loss: 0.1935 decode.loss_ce: 0.1935 decode.acc_seg: 91.2026 +2024/10/28 08:10:25 - mmengine - INFO - Iter(train) [142400/160000] base_lr: 1.3771e-05 lr: 1.3771e-05 eta: 1:57:03 time: 0.3832 data_time: 0.0160 memory: 5383 loss: 0.2086 decode.loss_ce: 0.2086 decode.acc_seg: 90.7155 +2024/10/28 08:10:44 - mmengine - INFO - Iter(train) [142450/160000] base_lr: 1.3696e-05 lr: 1.3696e-05 eta: 1:56:43 time: 0.3727 data_time: 0.0153 memory: 5384 loss: 0.2505 decode.loss_ce: 0.2505 decode.acc_seg: 92.6507 +2024/10/28 08:11:02 - mmengine - INFO - Iter(train) [142500/160000] base_lr: 1.3621e-05 lr: 1.3621e-05 eta: 1:56:22 time: 0.3781 data_time: 0.0156 memory: 5383 loss: 0.1755 decode.loss_ce: 0.1755 decode.acc_seg: 91.0383 +2024/10/28 08:11:24 - mmengine - INFO - Iter(train) [142550/160000] base_lr: 1.3546e-05 lr: 1.3546e-05 eta: 1:56:03 time: 0.3739 data_time: 0.0168 memory: 5383 loss: 0.1913 decode.loss_ce: 0.1913 decode.acc_seg: 89.1193 +2024/10/28 08:11:43 - mmengine - INFO - Iter(train) [142600/160000] base_lr: 1.3472e-05 lr: 1.3472e-05 eta: 1:55:43 time: 0.3785 data_time: 0.0163 memory: 5383 loss: 0.2057 decode.loss_ce: 0.2057 decode.acc_seg: 90.0883 +2024/10/28 08:12:02 - mmengine - INFO - Iter(train) [142650/160000] base_lr: 1.3397e-05 lr: 1.3397e-05 eta: 1:55:22 time: 0.3797 data_time: 0.0172 memory: 5384 loss: 0.1686 decode.loss_ce: 0.1686 decode.acc_seg: 92.3350 +2024/10/28 08:12:24 - mmengine - INFO - Iter(train) [142700/160000] base_lr: 1.3323e-05 lr: 1.3323e-05 eta: 1:55:03 time: 0.3777 data_time: 0.0169 memory: 5385 loss: 0.2020 decode.loss_ce: 0.2020 decode.acc_seg: 94.1716 +2024/10/28 08:12:43 - mmengine - INFO - Iter(train) [142750/160000] base_lr: 1.3249e-05 lr: 1.3249e-05 eta: 1:54:43 time: 0.3946 data_time: 0.0170 memory: 5383 loss: 0.2171 decode.loss_ce: 0.2171 decode.acc_seg: 92.3433 +2024/10/28 08:13:02 - mmengine - INFO - Iter(train) [142800/160000] base_lr: 1.3176e-05 lr: 1.3176e-05 eta: 1:54:23 time: 0.3768 data_time: 0.0167 memory: 5384 loss: 0.2031 decode.loss_ce: 0.2031 decode.acc_seg: 91.5157 +2024/10/28 08:13:25 - mmengine - INFO - Iter(train) [142850/160000] base_lr: 1.3102e-05 lr: 1.3102e-05 eta: 1:54:03 time: 0.3759 data_time: 0.0172 memory: 5384 loss: 0.2124 decode.loss_ce: 0.2124 decode.acc_seg: 92.3786 +2024/10/28 08:13:44 - mmengine - INFO - Iter(train) [142900/160000] base_lr: 1.3029e-05 lr: 1.3029e-05 eta: 1:53:43 time: 0.3749 data_time: 0.0165 memory: 5384 loss: 0.1907 decode.loss_ce: 0.1907 decode.acc_seg: 90.3600 +2024/10/28 08:14:03 - mmengine - INFO - Iter(train) [142950/160000] base_lr: 1.2955e-05 lr: 1.2955e-05 eta: 1:53:23 time: 0.3824 data_time: 0.0171 memory: 5386 loss: 0.1928 decode.loss_ce: 0.1928 decode.acc_seg: 90.6797 +2024/10/28 08:14:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:14:26 - mmengine - INFO - Iter(train) [143000/160000] base_lr: 1.2882e-05 lr: 1.2882e-05 eta: 1:53:03 time: 0.3999 data_time: 0.0173 memory: 5383 loss: 0.1830 decode.loss_ce: 0.1830 decode.acc_seg: 95.0333 +2024/10/28 08:14:45 - mmengine - INFO - Iter(train) [143050/160000] base_lr: 1.2810e-05 lr: 1.2810e-05 eta: 1:52:43 time: 0.3794 data_time: 0.0168 memory: 5384 loss: 0.1906 decode.loss_ce: 0.1906 decode.acc_seg: 91.0039 +2024/10/28 08:15:04 - mmengine - INFO - Iter(train) [143100/160000] base_lr: 1.2737e-05 lr: 1.2737e-05 eta: 1:52:23 time: 0.3778 data_time: 0.0168 memory: 5384 loss: 0.1745 decode.loss_ce: 0.1745 decode.acc_seg: 94.9933 +2024/10/28 08:15:25 - mmengine - INFO - Iter(train) [143150/160000] base_lr: 1.2664e-05 lr: 1.2664e-05 eta: 1:52:03 time: 0.3767 data_time: 0.0174 memory: 5384 loss: 0.2125 decode.loss_ce: 0.2125 decode.acc_seg: 90.2202 +2024/10/28 08:15:44 - mmengine - INFO - Iter(train) [143200/160000] base_lr: 1.2592e-05 lr: 1.2592e-05 eta: 1:51:43 time: 0.3790 data_time: 0.0169 memory: 5384 loss: 0.1873 decode.loss_ce: 0.1873 decode.acc_seg: 91.6775 +2024/10/28 08:16:04 - mmengine - INFO - Iter(train) [143250/160000] base_lr: 1.2520e-05 lr: 1.2520e-05 eta: 1:51:23 time: 0.3829 data_time: 0.0171 memory: 5384 loss: 0.2515 decode.loss_ce: 0.2515 decode.acc_seg: 91.2409 +2024/10/28 08:16:25 - mmengine - INFO - Iter(train) [143300/160000] base_lr: 1.2448e-05 lr: 1.2448e-05 eta: 1:51:04 time: 0.3792 data_time: 0.0172 memory: 5384 loss: 0.2183 decode.loss_ce: 0.2183 decode.acc_seg: 91.1751 +2024/10/28 08:16:44 - mmengine - INFO - Iter(train) [143350/160000] base_lr: 1.2376e-05 lr: 1.2376e-05 eta: 1:50:44 time: 0.3784 data_time: 0.0170 memory: 5383 loss: 0.2082 decode.loss_ce: 0.2082 decode.acc_seg: 88.7323 +2024/10/28 08:17:03 - mmengine - INFO - Iter(train) [143400/160000] base_lr: 1.2305e-05 lr: 1.2305e-05 eta: 1:50:23 time: 0.3815 data_time: 0.0166 memory: 5384 loss: 0.2003 decode.loss_ce: 0.2003 decode.acc_seg: 92.8129 +2024/10/28 08:17:25 - mmengine - INFO - Iter(train) [143450/160000] base_lr: 1.2233e-05 lr: 1.2233e-05 eta: 1:50:04 time: 0.4044 data_time: 0.0157 memory: 5384 loss: 0.1715 decode.loss_ce: 0.1715 decode.acc_seg: 92.1006 +2024/10/28 08:17:45 - mmengine - INFO - Iter(train) [143500/160000] base_lr: 1.2162e-05 lr: 1.2162e-05 eta: 1:49:44 time: 0.4063 data_time: 0.0161 memory: 5384 loss: 0.2172 decode.loss_ce: 0.2172 decode.acc_seg: 91.0804 +2024/10/28 08:18:05 - mmengine - INFO - Iter(train) [143550/160000] base_lr: 1.2091e-05 lr: 1.2091e-05 eta: 1:49:24 time: 0.3850 data_time: 0.0172 memory: 5384 loss: 0.2356 decode.loss_ce: 0.2356 decode.acc_seg: 88.4597 +2024/10/28 08:18:25 - mmengine - INFO - Iter(train) [143600/160000] base_lr: 1.2020e-05 lr: 1.2020e-05 eta: 1:49:04 time: 0.3807 data_time: 0.0174 memory: 5384 loss: 0.2250 decode.loss_ce: 0.2250 decode.acc_seg: 91.8324 +2024/10/28 08:18:44 - mmengine - INFO - Iter(train) [143650/160000] base_lr: 1.1950e-05 lr: 1.1950e-05 eta: 1:48:44 time: 0.3759 data_time: 0.0172 memory: 5384 loss: 0.1930 decode.loss_ce: 0.1930 decode.acc_seg: 94.0523 +2024/10/28 08:19:03 - mmengine - INFO - Iter(train) [143700/160000] base_lr: 1.1879e-05 lr: 1.1879e-05 eta: 1:48:24 time: 0.3866 data_time: 0.0171 memory: 5384 loss: 0.2223 decode.loss_ce: 0.2223 decode.acc_seg: 92.1652 +2024/10/28 08:19:25 - mmengine - INFO - Iter(train) [143750/160000] base_lr: 1.1809e-05 lr: 1.1809e-05 eta: 1:48:04 time: 0.3814 data_time: 0.0167 memory: 5383 loss: 0.2231 decode.loss_ce: 0.2231 decode.acc_seg: 90.4819 +2024/10/28 08:19:44 - mmengine - INFO - Iter(train) [143800/160000] base_lr: 1.1739e-05 lr: 1.1739e-05 eta: 1:47:44 time: 0.3780 data_time: 0.0184 memory: 5384 loss: 0.2391 decode.loss_ce: 0.2391 decode.acc_seg: 89.6873 +2024/10/28 08:20:03 - mmengine - INFO - Iter(train) [143850/160000] base_lr: 1.1669e-05 lr: 1.1669e-05 eta: 1:47:24 time: 0.3779 data_time: 0.0169 memory: 5384 loss: 0.2049 decode.loss_ce: 0.2049 decode.acc_seg: 89.6500 +2024/10/28 08:20:25 - mmengine - INFO - Iter(train) [143900/160000] base_lr: 1.1599e-05 lr: 1.1599e-05 eta: 1:47:04 time: 0.3775 data_time: 0.0166 memory: 5383 loss: 0.1881 decode.loss_ce: 0.1881 decode.acc_seg: 94.0349 +2024/10/28 08:20:43 - mmengine - INFO - Iter(train) [143950/160000] base_lr: 1.1530e-05 lr: 1.1530e-05 eta: 1:46:44 time: 0.3783 data_time: 0.0161 memory: 5384 loss: 0.1969 decode.loss_ce: 0.1969 decode.acc_seg: 91.4440 +2024/10/28 08:21:02 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:21:02 - mmengine - INFO - Iter(train) [144000/160000] base_lr: 1.1460e-05 lr: 1.1460e-05 eta: 1:46:24 time: 0.3807 data_time: 0.0170 memory: 5385 loss: 0.1877 decode.loss_ce: 0.1877 decode.acc_seg: 93.8767 +2024/10/28 08:21:05 - mmengine - INFO - Saving checkpoint at 144000 iterations +2024/10/28 08:21:10 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:00:15 time: 0.0330 data_time: 0.0014 memory: 980 +2024/10/28 08:21:12 - mmengine - INFO - Iter(val) [100/500] eta: 0:00:13 time: 0.0329 data_time: 0.0013 memory: 1050 +2024/10/28 08:21:13 - mmengine - INFO - Iter(val) [150/500] eta: 0:00:11 time: 0.0333 data_time: 0.0015 memory: 767 +2024/10/28 08:21:15 - mmengine - INFO - Iter(val) [200/500] eta: 0:00:09 time: 0.0328 data_time: 0.0014 memory: 800 +2024/10/28 08:21:17 - mmengine - INFO - Iter(val) [250/500] eta: 0:00:08 time: 0.0333 data_time: 0.0015 memory: 839 +2024/10/28 08:21:18 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:06 time: 0.0336 data_time: 0.0017 memory: 1961 +2024/10/28 08:21:20 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:04 time: 0.0324 data_time: 0.0013 memory: 765 +2024/10/28 08:21:21 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:03 time: 0.0337 data_time: 0.0014 memory: 837 +2024/10/28 08:21:23 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:01 time: 0.0326 data_time: 0.0013 memory: 772 +2024/10/28 08:21:25 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.0334 data_time: 0.0014 memory: 822 +2024/10/28 08:21:26 - mmengine - INFO - per class results: +2024/10/28 08:21:26 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 72.09 | 86.75 | +| building | 78.43 | 89.87 | +| sky | 92.89 | 96.62 | +| floor | 76.43 | 88.61 | +| tree | 70.74 | 86.44 | +| ceiling | 80.36 | 91.17 | +| road | 83.02 | 90.19 | +| bed | 86.33 | 93.44 | +| windowpane | 56.26 | 71.9 | +| grass | 63.0 | 78.93 | +| cabinet | 54.18 | 65.24 | +| sidewalk | 61.71 | 78.01 | +| person | 72.87 | 87.4 | +| earth | 35.9 | 51.08 | +| door | 41.49 | 56.39 | +| table | 51.23 | 69.82 | +| mountain | 49.7 | 63.08 | +| plant | 48.19 | 59.67 | +| curtain | 67.63 | 80.26 | +| chair | 49.49 | 63.22 | +| car | 79.04 | 87.93 | +| water | 51.55 | 64.5 | +| painting | 64.96 | 79.43 | +| sofa | 60.59 | 76.12 | +| shelf | 34.11 | 51.45 | +| house | 31.99 | 40.3 | +| sea | 56.05 | 84.91 | +| mirror | 59.75 | 71.22 | +| rug | 54.45 | 68.06 | +| field | 28.54 | 53.35 | +| armchair | 39.92 | 56.36 | +| seat | 58.02 | 79.56 | +| fence | 31.88 | 48.75 | +| desk | 44.57 | 59.53 | +| rock | 39.15 | 59.89 | +| wardrobe | 42.06 | 64.9 | +| lamp | 50.93 | 63.31 | +| bathtub | 70.33 | 77.61 | +| railing | 32.74 | 44.43 | +| cushion | 50.61 | 65.8 | +| base | 13.37 | 17.72 | +| box | 17.04 | 25.96 | +| column | 35.83 | 44.18 | +| signboard | 27.76 | 37.6 | +| chest of drawers | 33.47 | 49.73 | +| counter | 23.38 | 29.06 | +| sand | 42.1 | 66.32 | +| sink | 61.32 | 73.69 | +| skyscraper | 51.61 | 67.13 | +| fireplace | 69.88 | 82.81 | +| refrigerator | 65.67 | 86.52 | +| grandstand | 37.04 | 68.77 | +| path | 22.66 | 31.05 | +| stairs | 24.96 | 30.94 | +| runway | 71.65 | 94.33 | +| case | 43.88 | 65.43 | +| pool table | 88.11 | 91.73 | +| pillow | 48.79 | 57.26 | +| screen door | 51.39 | 62.62 | +| stairway | 25.28 | 33.13 | +| river | 6.87 | 11.87 | +| bridge | 65.32 | 75.42 | +| bookcase | 31.19 | 47.82 | +| blind | 34.93 | 38.63 | +| coffee table | 54.02 | 80.21 | +| toilet | 79.93 | 84.73 | +| flower | 31.01 | 41.45 | +| book | 36.96 | 55.84 | +| hill | 3.4 | 4.2 | +| bench | 34.77 | 44.3 | +| countertop | 50.39 | 68.04 | +| stove | 68.04 | 75.78 | +| palm | 44.29 | 60.34 | +| kitchen island | 32.36 | 56.31 | +| computer | 50.29 | 60.09 | +| swivel chair | 31.45 | 43.16 | +| boat | 57.17 | 73.98 | +| bar | 42.44 | 50.77 | +| arcade machine | 51.16 | 56.86 | +| hovel | 15.4 | 17.59 | +| bus | 80.79 | 91.65 | +| towel | 53.6 | 64.76 | +| light | 35.99 | 41.43 | +| truck | 30.17 | 43.91 | +| tower | 24.93 | 44.2 | +| chandelier | 55.9 | 70.39 | +| awning | 15.76 | 17.89 | +| streetlight | 12.33 | 15.86 | +| booth | 60.47 | 74.25 | +| television receiver | 64.29 | 74.93 | +| airplane | 52.38 | 58.96 | +| dirt track | 18.61 | 29.52 | +| apparel | 21.17 | 24.33 | +| pole | 16.04 | 23.3 | +| land | 5.49 | 8.47 | +| bannister | 4.99 | 7.3 | +| escalator | 6.72 | 7.0 | +| ottoman | 42.41 | 55.77 | +| bottle | 26.43 | 43.12 | +| buffet | 40.59 | 46.68 | +| poster | 25.76 | 34.04 | +| stage | 12.68 | 17.47 | +| van | 40.99 | 51.32 | +| ship | 14.16 | 15.9 | +| fountain | 13.96 | 14.84 | +| conveyer belt | 50.77 | 66.03 | +| canopy | 14.43 | 15.19 | +| washer | 52.42 | 61.04 | +| plaything | 15.79 | 28.05 | +| swimming pool | 43.28 | 56.07 | +| stool | 33.46 | 50.97 | +| barrel | 35.58 | 64.97 | +| basket | 22.46 | 29.33 | +| waterfall | 46.31 | 62.7 | +| tent | 90.41 | 96.22 | +| bag | 7.26 | 10.17 | +| minibike | 54.49 | 76.79 | +| cradle | 71.57 | 93.97 | +| oven | 34.12 | 41.1 | +| ball | 35.02 | 41.22 | +| food | 25.96 | 28.16 | +| step | 11.41 | 13.5 | +| tank | 45.07 | 46.94 | +| trade name | 17.6 | 19.93 | +| microwave | 36.84 | 40.58 | +| pot | 36.67 | 44.11 | +| animal | 47.53 | 52.21 | +| bicycle | 42.32 | 59.61 | +| lake | 52.89 | 62.14 | +| dishwasher | 54.02 | 62.22 | +| screen | 71.49 | 83.24 | +| blanket | 5.44 | 6.27 | +| sculpture | 42.33 | 56.66 | +| hood | 55.4 | 58.23 | +| sconce | 32.65 | 38.06 | +| vase | 27.1 | 36.73 | +| traffic light | 21.42 | 32.56 | +| tray | 6.36 | 9.09 | +| ashcan | 33.07 | 42.24 | +| fan | 43.12 | 56.31 | +| pier | 14.48 | 16.54 | +| crt screen | 7.8 | 15.07 | +| plate | 37.09 | 49.73 | +| monitor | 37.73 | 42.28 | +| bulletin board | 31.3 | 36.67 | +| shower | 1.91 | 8.94 | +| radiator | 45.66 | 52.39 | +| glass | 3.52 | 3.69 | +| clock | 10.9 | 30.43 | +| flag | 34.38 | 37.9 | ++---------------------+-------+-------+ +2024/10/28 08:21:26 - mmengine - INFO - Iter(val) [500/500] aAcc: 79.6800 mIoU: 41.7800 mAcc: 52.8200 data_time: 0.0014 time: 0.0331 +2024/10/28 08:21:46 - mmengine - INFO - Iter(train) [144050/160000] base_lr: 1.1391e-05 lr: 1.1391e-05 eta: 1:46:04 time: 0.4020 data_time: 0.0141 memory: 5384 loss: 0.1840 decode.loss_ce: 0.1840 decode.acc_seg: 94.9868 +2024/10/28 08:22:07 - mmengine - INFO - Iter(train) [144100/160000] base_lr: 1.1322e-05 lr: 1.1322e-05 eta: 1:45:44 time: 0.4021 data_time: 0.0143 memory: 5384 loss: 0.1896 decode.loss_ce: 0.1896 decode.acc_seg: 92.4930 +2024/10/28 08:22:27 - mmengine - INFO - Iter(train) [144150/160000] base_lr: 1.1253e-05 lr: 1.1253e-05 eta: 1:45:24 time: 0.3944 data_time: 0.0141 memory: 5384 loss: 0.2355 decode.loss_ce: 0.2355 decode.acc_seg: 93.0641 +2024/10/28 08:22:46 - mmengine - INFO - Iter(train) [144200/160000] base_lr: 1.1185e-05 lr: 1.1185e-05 eta: 1:45:04 time: 0.3877 data_time: 0.0178 memory: 5384 loss: 0.2094 decode.loss_ce: 0.2094 decode.acc_seg: 92.6206 +2024/10/28 08:23:05 - mmengine - INFO - Iter(train) [144250/160000] base_lr: 1.1116e-05 lr: 1.1116e-05 eta: 1:44:44 time: 0.3806 data_time: 0.0162 memory: 5384 loss: 0.1952 decode.loss_ce: 0.1952 decode.acc_seg: 91.5840 +2024/10/28 08:23:24 - mmengine - INFO - Iter(train) [144300/160000] base_lr: 1.1048e-05 lr: 1.1048e-05 eta: 1:44:24 time: 0.3775 data_time: 0.0167 memory: 5384 loss: 0.2091 decode.loss_ce: 0.2091 decode.acc_seg: 92.4314 +2024/10/28 08:23:43 - mmengine - INFO - Iter(train) [144350/160000] base_lr: 1.0980e-05 lr: 1.0980e-05 eta: 1:44:04 time: 0.3754 data_time: 0.0155 memory: 5385 loss: 0.1778 decode.loss_ce: 0.1778 decode.acc_seg: 91.0638 +2024/10/28 08:24:03 - mmengine - INFO - Iter(train) [144400/160000] base_lr: 1.0912e-05 lr: 1.0912e-05 eta: 1:43:44 time: 0.3820 data_time: 0.0167 memory: 5384 loss: 0.1819 decode.loss_ce: 0.1819 decode.acc_seg: 88.6759 +2024/10/28 08:24:24 - mmengine - INFO - Iter(train) [144450/160000] base_lr: 1.0845e-05 lr: 1.0845e-05 eta: 1:43:24 time: 0.3865 data_time: 0.0162 memory: 5384 loss: 0.1781 decode.loss_ce: 0.1781 decode.acc_seg: 92.5866 +2024/10/28 08:24:44 - mmengine - INFO - Iter(train) [144500/160000] base_lr: 1.0777e-05 lr: 1.0777e-05 eta: 1:43:04 time: 0.3824 data_time: 0.0179 memory: 5385 loss: 0.2626 decode.loss_ce: 0.2626 decode.acc_seg: 91.1501 +2024/10/28 08:25:03 - mmengine - INFO - Iter(train) [144550/160000] base_lr: 1.0710e-05 lr: 1.0710e-05 eta: 1:42:44 time: 0.3795 data_time: 0.0180 memory: 5384 loss: 0.2096 decode.loss_ce: 0.2096 decode.acc_seg: 92.4159 +2024/10/28 08:25:25 - mmengine - INFO - Iter(train) [144600/160000] base_lr: 1.0643e-05 lr: 1.0643e-05 eta: 1:42:25 time: 0.3782 data_time: 0.0172 memory: 5384 loss: 0.1761 decode.loss_ce: 0.1761 decode.acc_seg: 93.8542 +2024/10/28 08:25:45 - mmengine - INFO - Iter(train) [144650/160000] base_lr: 1.0576e-05 lr: 1.0576e-05 eta: 1:42:05 time: 0.3799 data_time: 0.0173 memory: 5383 loss: 0.1980 decode.loss_ce: 0.1980 decode.acc_seg: 92.9697 +2024/10/28 08:26:04 - mmengine - INFO - Iter(train) [144700/160000] base_lr: 1.0509e-05 lr: 1.0509e-05 eta: 1:41:45 time: 0.3858 data_time: 0.0153 memory: 5384 loss: 0.2023 decode.loss_ce: 0.2023 decode.acc_seg: 93.1948 +2024/10/28 08:26:24 - mmengine - INFO - Iter(train) [144750/160000] base_lr: 1.0443e-05 lr: 1.0443e-05 eta: 1:41:25 time: 0.3807 data_time: 0.0169 memory: 5386 loss: 0.1692 decode.loss_ce: 0.1692 decode.acc_seg: 93.6528 +2024/10/28 08:26:44 - mmengine - INFO - Iter(train) [144800/160000] base_lr: 1.0376e-05 lr: 1.0376e-05 eta: 1:41:05 time: 0.3997 data_time: 0.0162 memory: 5384 loss: 0.1905 decode.loss_ce: 0.1905 decode.acc_seg: 92.8746 +2024/10/28 08:27:04 - mmengine - INFO - Iter(train) [144850/160000] base_lr: 1.0310e-05 lr: 1.0310e-05 eta: 1:40:45 time: 0.3927 data_time: 0.0165 memory: 5386 loss: 0.1895 decode.loss_ce: 0.1895 decode.acc_seg: 92.0087 +2024/10/28 08:27:24 - mmengine - INFO - Iter(train) [144900/160000] base_lr: 1.0244e-05 lr: 1.0244e-05 eta: 1:40:25 time: 0.3761 data_time: 0.0182 memory: 5384 loss: 0.2180 decode.loss_ce: 0.2180 decode.acc_seg: 93.8852 +2024/10/28 08:27:43 - mmengine - INFO - Iter(train) [144950/160000] base_lr: 1.0179e-05 lr: 1.0179e-05 eta: 1:40:05 time: 0.3803 data_time: 0.0179 memory: 5384 loss: 0.1770 decode.loss_ce: 0.1770 decode.acc_seg: 88.9764 +2024/10/28 08:28:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:28:03 - mmengine - INFO - Iter(train) [145000/160000] base_lr: 1.0113e-05 lr: 1.0113e-05 eta: 1:39:45 time: 0.4103 data_time: 0.0161 memory: 5385 loss: 0.1964 decode.loss_ce: 0.1964 decode.acc_seg: 92.8014 +2024/10/28 08:28:24 - mmengine - INFO - Iter(train) [145050/160000] base_lr: 1.0048e-05 lr: 1.0048e-05 eta: 1:39:25 time: 0.3804 data_time: 0.0174 memory: 5385 loss: 0.1629 decode.loss_ce: 0.1629 decode.acc_seg: 95.2478 +2024/10/28 08:28:44 - mmengine - INFO - Iter(train) [145100/160000] base_lr: 9.9826e-06 lr: 9.9826e-06 eta: 1:39:05 time: 0.3819 data_time: 0.0173 memory: 5383 loss: 0.1859 decode.loss_ce: 0.1859 decode.acc_seg: 89.5515 +2024/10/28 08:29:03 - mmengine - INFO - Iter(train) [145150/160000] base_lr: 9.9176e-06 lr: 9.9176e-06 eta: 1:38:45 time: 0.3824 data_time: 0.0160 memory: 5384 loss: 0.2194 decode.loss_ce: 0.2194 decode.acc_seg: 90.1677 +2024/10/28 08:29:25 - mmengine - INFO - Iter(train) [145200/160000] base_lr: 9.8529e-06 lr: 9.8529e-06 eta: 1:38:25 time: 0.4027 data_time: 0.0183 memory: 5383 loss: 0.1740 decode.loss_ce: 0.1740 decode.acc_seg: 96.4472 +2024/10/28 08:29:44 - mmengine - INFO - Iter(train) [145250/160000] base_lr: 9.7883e-06 lr: 9.7883e-06 eta: 1:38:05 time: 0.3796 data_time: 0.0180 memory: 5384 loss: 0.2138 decode.loss_ce: 0.2138 decode.acc_seg: 91.7813 +2024/10/28 08:30:03 - mmengine - INFO - Iter(train) [145300/160000] base_lr: 9.7239e-06 lr: 9.7239e-06 eta: 1:37:45 time: 0.3829 data_time: 0.0173 memory: 5384 loss: 0.1860 decode.loss_ce: 0.1860 decode.acc_seg: 94.0499 +2024/10/28 08:30:24 - mmengine - INFO - Iter(train) [145350/160000] base_lr: 9.6597e-06 lr: 9.6597e-06 eta: 1:37:25 time: 0.3796 data_time: 0.0172 memory: 5384 loss: 0.2167 decode.loss_ce: 0.2167 decode.acc_seg: 93.2538 +2024/10/28 08:30:44 - mmengine - INFO - Iter(train) [145400/160000] base_lr: 9.5957e-06 lr: 9.5957e-06 eta: 1:37:05 time: 0.3816 data_time: 0.0177 memory: 5386 loss: 0.1981 decode.loss_ce: 0.1981 decode.acc_seg: 93.0714 +2024/10/28 08:31:03 - mmengine - INFO - Iter(train) [145450/160000] base_lr: 9.5319e-06 lr: 9.5319e-06 eta: 1:36:45 time: 0.3798 data_time: 0.0172 memory: 5383 loss: 0.1982 decode.loss_ce: 0.1982 decode.acc_seg: 90.5425 +2024/10/28 08:31:24 - mmengine - INFO - Iter(train) [145500/160000] base_lr: 9.4682e-06 lr: 9.4682e-06 eta: 1:36:25 time: 0.3792 data_time: 0.0169 memory: 5383 loss: 0.2014 decode.loss_ce: 0.2014 decode.acc_seg: 96.1767 +2024/10/28 08:31:43 - mmengine - INFO - Iter(train) [145550/160000] base_lr: 9.4048e-06 lr: 9.4048e-06 eta: 1:36:05 time: 0.3789 data_time: 0.0170 memory: 5384 loss: 0.2154 decode.loss_ce: 0.2154 decode.acc_seg: 88.8943 +2024/10/28 08:32:02 - mmengine - INFO - Iter(train) [145600/160000] base_lr: 9.3416e-06 lr: 9.3416e-06 eta: 1:35:45 time: 0.3794 data_time: 0.0171 memory: 5386 loss: 0.2220 decode.loss_ce: 0.2220 decode.acc_seg: 92.7184 +2024/10/28 08:32:24 - mmengine - INFO - Iter(train) [145650/160000] base_lr: 9.2786e-06 lr: 9.2786e-06 eta: 1:35:25 time: 0.3751 data_time: 0.0168 memory: 5384 loss: 0.1833 decode.loss_ce: 0.1833 decode.acc_seg: 92.3865 +2024/10/28 08:32:42 - mmengine - INFO - Iter(train) [145700/160000] base_lr: 9.2157e-06 lr: 9.2157e-06 eta: 1:35:05 time: 0.3800 data_time: 0.0169 memory: 5386 loss: 0.2120 decode.loss_ce: 0.2120 decode.acc_seg: 88.8070 +2024/10/28 08:33:01 - mmengine - INFO - Iter(train) [145750/160000] base_lr: 9.1531e-06 lr: 9.1531e-06 eta: 1:34:45 time: 0.3820 data_time: 0.0167 memory: 5384 loss: 0.1644 decode.loss_ce: 0.1644 decode.acc_seg: 93.6761 +2024/10/28 08:33:21 - mmengine - INFO - Iter(train) [145800/160000] base_lr: 9.0906e-06 lr: 9.0906e-06 eta: 1:34:25 time: 0.3783 data_time: 0.0176 memory: 5384 loss: 0.2206 decode.loss_ce: 0.2206 decode.acc_seg: 89.7591 +2024/10/28 08:33:39 - mmengine - INFO - Iter(train) [145850/160000] base_lr: 9.0284e-06 lr: 9.0284e-06 eta: 1:34:05 time: 0.3769 data_time: 0.0162 memory: 5384 loss: 0.1921 decode.loss_ce: 0.1921 decode.acc_seg: 95.6155 +2024/10/28 08:33:59 - mmengine - INFO - Iter(train) [145900/160000] base_lr: 8.9663e-06 lr: 8.9663e-06 eta: 1:33:45 time: 0.3810 data_time: 0.0168 memory: 5383 loss: 0.1742 decode.loss_ce: 0.1742 decode.acc_seg: 93.7770 +2024/10/28 08:34:18 - mmengine - INFO - Iter(train) [145950/160000] base_lr: 8.9045e-06 lr: 8.9045e-06 eta: 1:33:25 time: 0.3800 data_time: 0.0167 memory: 5384 loss: 0.1775 decode.loss_ce: 0.1775 decode.acc_seg: 91.1163 +2024/10/28 08:34:38 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:34:38 - mmengine - INFO - Iter(train) [146000/160000] base_lr: 8.8428e-06 lr: 8.8428e-06 eta: 1:33:05 time: 0.3798 data_time: 0.0173 memory: 5386 loss: 0.1898 decode.loss_ce: 0.1898 decode.acc_seg: 92.9079 +2024/10/28 08:34:56 - mmengine - INFO - Iter(train) [146050/160000] base_lr: 8.7814e-06 lr: 8.7814e-06 eta: 1:32:45 time: 0.3775 data_time: 0.0171 memory: 5382 loss: 0.2105 decode.loss_ce: 0.2105 decode.acc_seg: 89.4302 +2024/10/28 08:35:15 - mmengine - INFO - Iter(train) [146100/160000] base_lr: 8.7201e-06 lr: 8.7201e-06 eta: 1:32:25 time: 0.3783 data_time: 0.0161 memory: 5384 loss: 0.1985 decode.loss_ce: 0.1985 decode.acc_seg: 88.8880 +2024/10/28 08:35:34 - mmengine - INFO - Iter(train) [146150/160000] base_lr: 8.6590e-06 lr: 8.6590e-06 eta: 1:32:05 time: 0.3781 data_time: 0.0165 memory: 5385 loss: 0.1945 decode.loss_ce: 0.1945 decode.acc_seg: 89.0541 +2024/10/28 08:35:54 - mmengine - INFO - Iter(train) [146200/160000] base_lr: 8.5982e-06 lr: 8.5982e-06 eta: 1:31:45 time: 0.3807 data_time: 0.0170 memory: 5384 loss: 0.1741 decode.loss_ce: 0.1741 decode.acc_seg: 92.5114 +2024/10/28 08:36:13 - mmengine - INFO - Iter(train) [146250/160000] base_lr: 8.5375e-06 lr: 8.5375e-06 eta: 1:31:25 time: 0.3754 data_time: 0.0169 memory: 5384 loss: 0.1743 decode.loss_ce: 0.1743 decode.acc_seg: 90.6855 +2024/10/28 08:36:32 - mmengine - INFO - Iter(train) [146300/160000] base_lr: 8.4770e-06 lr: 8.4770e-06 eta: 1:31:05 time: 0.3810 data_time: 0.0173 memory: 5384 loss: 0.1811 decode.loss_ce: 0.1811 decode.acc_seg: 94.1999 +2024/10/28 08:36:51 - mmengine - INFO - Iter(train) [146350/160000] base_lr: 8.4168e-06 lr: 8.4168e-06 eta: 1:30:45 time: 0.3785 data_time: 0.0167 memory: 5384 loss: 0.1756 decode.loss_ce: 0.1756 decode.acc_seg: 94.3021 +2024/10/28 08:37:10 - mmengine - INFO - Iter(train) [146400/160000] base_lr: 8.3567e-06 lr: 8.3567e-06 eta: 1:30:25 time: 0.3825 data_time: 0.0162 memory: 5385 loss: 0.1941 decode.loss_ce: 0.1941 decode.acc_seg: 93.7882 +2024/10/28 08:37:29 - mmengine - INFO - Iter(train) [146450/160000] base_lr: 8.2968e-06 lr: 8.2968e-06 eta: 1:30:05 time: 0.3792 data_time: 0.0173 memory: 5386 loss: 0.1935 decode.loss_ce: 0.1935 decode.acc_seg: 94.8591 +2024/10/28 08:37:49 - mmengine - INFO - Iter(train) [146500/160000] base_lr: 8.2371e-06 lr: 8.2371e-06 eta: 1:29:45 time: 0.3784 data_time: 0.0173 memory: 5385 loss: 0.2366 decode.loss_ce: 0.2366 decode.acc_seg: 88.4756 +2024/10/28 08:38:08 - mmengine - INFO - Iter(train) [146550/160000] base_lr: 8.1777e-06 lr: 8.1777e-06 eta: 1:29:25 time: 0.3816 data_time: 0.0167 memory: 5384 loss: 0.2070 decode.loss_ce: 0.2070 decode.acc_seg: 91.4183 +2024/10/28 08:38:27 - mmengine - INFO - Iter(train) [146600/160000] base_lr: 8.1184e-06 lr: 8.1184e-06 eta: 1:29:05 time: 0.3780 data_time: 0.0169 memory: 5384 loss: 0.1706 decode.loss_ce: 0.1706 decode.acc_seg: 95.3264 +2024/10/28 08:38:46 - mmengine - INFO - Iter(train) [146650/160000] base_lr: 8.0593e-06 lr: 8.0593e-06 eta: 1:28:45 time: 0.3825 data_time: 0.0171 memory: 5384 loss: 0.1820 decode.loss_ce: 0.1820 decode.acc_seg: 88.6948 +2024/10/28 08:39:05 - mmengine - INFO - Iter(train) [146700/160000] base_lr: 8.0004e-06 lr: 8.0004e-06 eta: 1:28:25 time: 0.4017 data_time: 0.0163 memory: 5384 loss: 0.1765 decode.loss_ce: 0.1765 decode.acc_seg: 94.2407 +2024/10/28 08:39:25 - mmengine - INFO - Iter(train) [146750/160000] base_lr: 7.9418e-06 lr: 7.9418e-06 eta: 1:28:05 time: 0.3797 data_time: 0.0176 memory: 5383 loss: 0.1959 decode.loss_ce: 0.1959 decode.acc_seg: 92.6452 +2024/10/28 08:39:44 - mmengine - INFO - Iter(train) [146800/160000] base_lr: 7.8833e-06 lr: 7.8833e-06 eta: 1:27:45 time: 0.3793 data_time: 0.0174 memory: 5384 loss: 0.2113 decode.loss_ce: 0.2113 decode.acc_seg: 90.4686 +2024/10/28 08:40:03 - mmengine - INFO - Iter(train) [146850/160000] base_lr: 7.8250e-06 lr: 7.8250e-06 eta: 1:27:25 time: 0.3814 data_time: 0.0171 memory: 5384 loss: 0.1817 decode.loss_ce: 0.1817 decode.acc_seg: 92.9824 +2024/10/28 08:40:24 - mmengine - INFO - Iter(train) [146900/160000] base_lr: 7.7669e-06 lr: 7.7669e-06 eta: 1:27:05 time: 0.3917 data_time: 0.0159 memory: 5383 loss: 0.1782 decode.loss_ce: 0.1782 decode.acc_seg: 93.9286 +2024/10/28 08:40:44 - mmengine - INFO - Iter(train) [146950/160000] base_lr: 7.7091e-06 lr: 7.7091e-06 eta: 1:26:45 time: 0.3774 data_time: 0.0172 memory: 5383 loss: 0.1837 decode.loss_ce: 0.1837 decode.acc_seg: 92.9638 +2024/10/28 08:41:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:41:03 - mmengine - INFO - Iter(train) [147000/160000] base_lr: 7.6514e-06 lr: 7.6514e-06 eta: 1:26:25 time: 0.4030 data_time: 0.0160 memory: 5384 loss: 0.1787 decode.loss_ce: 0.1787 decode.acc_seg: 90.0873 +2024/10/28 08:41:26 - mmengine - INFO - Iter(train) [147050/160000] base_lr: 7.5939e-06 lr: 7.5939e-06 eta: 1:26:05 time: 0.4052 data_time: 0.0156 memory: 5384 loss: 0.1676 decode.loss_ce: 0.1676 decode.acc_seg: 90.3001 +2024/10/28 08:41:46 - mmengine - INFO - Iter(train) [147100/160000] base_lr: 7.5367e-06 lr: 7.5367e-06 eta: 1:25:45 time: 0.3808 data_time: 0.0171 memory: 5384 loss: 0.2117 decode.loss_ce: 0.2117 decode.acc_seg: 89.7637 +2024/10/28 08:42:05 - mmengine - INFO - Iter(train) [147150/160000] base_lr: 7.4796e-06 lr: 7.4796e-06 eta: 1:25:25 time: 0.3870 data_time: 0.0184 memory: 5384 loss: 0.1780 decode.loss_ce: 0.1780 decode.acc_seg: 92.2458 +2024/10/28 08:42:25 - mmengine - INFO - Iter(train) [147200/160000] base_lr: 7.4227e-06 lr: 7.4227e-06 eta: 1:25:05 time: 0.3822 data_time: 0.0180 memory: 5385 loss: 0.2199 decode.loss_ce: 0.2199 decode.acc_seg: 94.5628 +2024/10/28 08:42:44 - mmengine - INFO - Iter(train) [147250/160000] base_lr: 7.3661e-06 lr: 7.3661e-06 eta: 1:24:45 time: 0.3797 data_time: 0.0180 memory: 5384 loss: 0.1880 decode.loss_ce: 0.1880 decode.acc_seg: 95.1542 +2024/10/28 08:43:03 - mmengine - INFO - Iter(train) [147300/160000] base_lr: 7.3096e-06 lr: 7.3096e-06 eta: 1:24:25 time: 0.3823 data_time: 0.0169 memory: 5384 loss: 0.1609 decode.loss_ce: 0.1609 decode.acc_seg: 88.6528 +2024/10/28 08:43:24 - mmengine - INFO - Iter(train) [147350/160000] base_lr: 7.2534e-06 lr: 7.2534e-06 eta: 1:24:05 time: 0.3876 data_time: 0.0167 memory: 5385 loss: 0.1890 decode.loss_ce: 0.1890 decode.acc_seg: 90.3218 +2024/10/28 08:43:43 - mmengine - INFO - Iter(train) [147400/160000] base_lr: 7.1973e-06 lr: 7.1973e-06 eta: 1:23:45 time: 0.3823 data_time: 0.0177 memory: 5384 loss: 0.1627 decode.loss_ce: 0.1627 decode.acc_seg: 93.0335 +2024/10/28 08:44:02 - mmengine - INFO - Iter(train) [147450/160000] base_lr: 7.1415e-06 lr: 7.1415e-06 eta: 1:23:25 time: 0.3841 data_time: 0.0166 memory: 5382 loss: 0.2367 decode.loss_ce: 0.2367 decode.acc_seg: 89.5343 +2024/10/28 08:44:24 - mmengine - INFO - Iter(train) [147500/160000] base_lr: 7.0858e-06 lr: 7.0858e-06 eta: 1:23:05 time: 0.3812 data_time: 0.0162 memory: 5383 loss: 0.2013 decode.loss_ce: 0.2013 decode.acc_seg: 94.9632 +2024/10/28 08:44:43 - mmengine - INFO - Iter(train) [147550/160000] base_lr: 7.0304e-06 lr: 7.0304e-06 eta: 1:22:45 time: 0.3929 data_time: 0.0157 memory: 5384 loss: 0.2071 decode.loss_ce: 0.2071 decode.acc_seg: 91.3328 +2024/10/28 08:45:04 - mmengine - INFO - Iter(train) [147600/160000] base_lr: 6.9752e-06 lr: 6.9752e-06 eta: 1:22:26 time: 0.4046 data_time: 0.0165 memory: 5384 loss: 0.1736 decode.loss_ce: 0.1736 decode.acc_seg: 90.5734 +2024/10/28 08:45:25 - mmengine - INFO - Iter(train) [147650/160000] base_lr: 6.9201e-06 lr: 6.9201e-06 eta: 1:22:06 time: 0.4004 data_time: 0.0155 memory: 5384 loss: 0.2059 decode.loss_ce: 0.2059 decode.acc_seg: 90.0285 +2024/10/28 08:45:44 - mmengine - INFO - Iter(train) [147700/160000] base_lr: 6.8653e-06 lr: 6.8653e-06 eta: 1:21:46 time: 0.3817 data_time: 0.0153 memory: 5384 loss: 0.1598 decode.loss_ce: 0.1598 decode.acc_seg: 90.0498 +2024/10/28 08:46:03 - mmengine - INFO - Iter(train) [147750/160000] base_lr: 6.8107e-06 lr: 6.8107e-06 eta: 1:21:26 time: 0.3806 data_time: 0.0162 memory: 5385 loss: 0.2022 decode.loss_ce: 0.2022 decode.acc_seg: 91.3589 +2024/10/28 08:46:24 - mmengine - INFO - Iter(train) [147800/160000] base_lr: 6.7563e-06 lr: 6.7563e-06 eta: 1:21:06 time: 0.3774 data_time: 0.0164 memory: 5384 loss: 0.1945 decode.loss_ce: 0.1945 decode.acc_seg: 92.6411 +2024/10/28 08:46:43 - mmengine - INFO - Iter(train) [147850/160000] base_lr: 6.7021e-06 lr: 6.7021e-06 eta: 1:20:46 time: 0.3801 data_time: 0.0164 memory: 5384 loss: 0.2758 decode.loss_ce: 0.2758 decode.acc_seg: 88.5221 +2024/10/28 08:47:02 - mmengine - INFO - Iter(train) [147900/160000] base_lr: 6.6481e-06 lr: 6.6481e-06 eta: 1:20:26 time: 0.3821 data_time: 0.0165 memory: 5384 loss: 0.2037 decode.loss_ce: 0.2037 decode.acc_seg: 91.4564 +2024/10/28 08:47:24 - mmengine - INFO - Iter(train) [147950/160000] base_lr: 6.5943e-06 lr: 6.5943e-06 eta: 1:20:06 time: 0.3805 data_time: 0.0158 memory: 5384 loss: 0.1877 decode.loss_ce: 0.1877 decode.acc_seg: 93.5324 +2024/10/28 08:47:43 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:47:43 - mmengine - INFO - Iter(train) [148000/160000] base_lr: 6.5407e-06 lr: 6.5407e-06 eta: 1:19:46 time: 0.3802 data_time: 0.0155 memory: 5384 loss: 0.1986 decode.loss_ce: 0.1986 decode.acc_seg: 93.4289 +2024/10/28 08:48:03 - mmengine - INFO - Iter(train) [148050/160000] base_lr: 6.4873e-06 lr: 6.4873e-06 eta: 1:19:26 time: 0.3829 data_time: 0.0164 memory: 5385 loss: 0.1891 decode.loss_ce: 0.1891 decode.acc_seg: 89.3907 +2024/10/28 08:48:24 - mmengine - INFO - Iter(train) [148100/160000] base_lr: 6.4341e-06 lr: 6.4341e-06 eta: 1:19:06 time: 0.3762 data_time: 0.0163 memory: 5383 loss: 0.1829 decode.loss_ce: 0.1829 decode.acc_seg: 91.5058 +2024/10/28 08:48:43 - mmengine - INFO - Iter(train) [148150/160000] base_lr: 6.3811e-06 lr: 6.3811e-06 eta: 1:18:46 time: 0.3803 data_time: 0.0160 memory: 5384 loss: 0.1746 decode.loss_ce: 0.1746 decode.acc_seg: 90.0877 +2024/10/28 08:49:02 - mmengine - INFO - Iter(train) [148200/160000] base_lr: 6.3284e-06 lr: 6.3284e-06 eta: 1:18:26 time: 0.3829 data_time: 0.0170 memory: 5384 loss: 0.2086 decode.loss_ce: 0.2086 decode.acc_seg: 92.5505 +2024/10/28 08:49:23 - mmengine - INFO - Iter(train) [148250/160000] base_lr: 6.2758e-06 lr: 6.2758e-06 eta: 1:18:06 time: 0.3791 data_time: 0.0160 memory: 5384 loss: 0.2116 decode.loss_ce: 0.2116 decode.acc_seg: 89.5696 +2024/10/28 08:49:42 - mmengine - INFO - Iter(train) [148300/160000] base_lr: 6.2235e-06 lr: 6.2235e-06 eta: 1:17:46 time: 0.3759 data_time: 0.0168 memory: 5383 loss: 0.1874 decode.loss_ce: 0.1874 decode.acc_seg: 90.6861 +2024/10/28 08:50:01 - mmengine - INFO - Iter(train) [148350/160000] base_lr: 6.1713e-06 lr: 6.1713e-06 eta: 1:17:26 time: 0.3776 data_time: 0.0155 memory: 5384 loss: 0.2119 decode.loss_ce: 0.2119 decode.acc_seg: 89.2241 +2024/10/28 08:50:20 - mmengine - INFO - Iter(train) [148400/160000] base_lr: 6.1194e-06 lr: 6.1194e-06 eta: 1:17:06 time: 0.3792 data_time: 0.0157 memory: 5384 loss: 0.2314 decode.loss_ce: 0.2314 decode.acc_seg: 85.7741 +2024/10/28 08:50:39 - mmengine - INFO - Iter(train) [148450/160000] base_lr: 6.0677e-06 lr: 6.0677e-06 eta: 1:16:46 time: 0.3799 data_time: 0.0160 memory: 5385 loss: 0.2046 decode.loss_ce: 0.2046 decode.acc_seg: 91.0071 +2024/10/28 08:50:58 - mmengine - INFO - Iter(train) [148500/160000] base_lr: 6.0161e-06 lr: 6.0161e-06 eta: 1:16:26 time: 0.3739 data_time: 0.0152 memory: 5384 loss: 0.2055 decode.loss_ce: 0.2055 decode.acc_seg: 92.2371 +2024/10/28 08:51:17 - mmengine - INFO - Iter(train) [148550/160000] base_lr: 5.9648e-06 lr: 5.9648e-06 eta: 1:16:06 time: 0.3739 data_time: 0.0155 memory: 5385 loss: 0.1770 decode.loss_ce: 0.1770 decode.acc_seg: 93.5018 +2024/10/28 08:51:36 - mmengine - INFO - Iter(train) [148600/160000] base_lr: 5.9137e-06 lr: 5.9137e-06 eta: 1:15:46 time: 0.3755 data_time: 0.0159 memory: 5383 loss: 0.1680 decode.loss_ce: 0.1680 decode.acc_seg: 95.2368 +2024/10/28 08:51:55 - mmengine - INFO - Iter(train) [148650/160000] base_lr: 5.8628e-06 lr: 5.8628e-06 eta: 1:15:26 time: 0.3741 data_time: 0.0161 memory: 5385 loss: 0.2085 decode.loss_ce: 0.2085 decode.acc_seg: 95.4269 +2024/10/28 08:52:14 - mmengine - INFO - Iter(train) [148700/160000] base_lr: 5.8121e-06 lr: 5.8121e-06 eta: 1:15:06 time: 0.3739 data_time: 0.0156 memory: 5384 loss: 0.2025 decode.loss_ce: 0.2025 decode.acc_seg: 90.3647 +2024/10/28 08:52:33 - mmengine - INFO - Iter(train) [148750/160000] base_lr: 5.7616e-06 lr: 5.7616e-06 eta: 1:14:46 time: 0.3954 data_time: 0.0172 memory: 5384 loss: 0.1883 decode.loss_ce: 0.1883 decode.acc_seg: 92.2959 +2024/10/28 08:52:52 - mmengine - INFO - Iter(train) [148800/160000] base_lr: 5.7114e-06 lr: 5.7114e-06 eta: 1:14:26 time: 0.3802 data_time: 0.0176 memory: 5384 loss: 0.1998 decode.loss_ce: 0.1998 decode.acc_seg: 92.5381 +2024/10/28 08:53:11 - mmengine - INFO - Iter(train) [148850/160000] base_lr: 5.6613e-06 lr: 5.6613e-06 eta: 1:14:06 time: 0.3772 data_time: 0.0176 memory: 5383 loss: 0.2198 decode.loss_ce: 0.2198 decode.acc_seg: 92.1375 +2024/10/28 08:53:30 - mmengine - INFO - Iter(train) [148900/160000] base_lr: 5.6115e-06 lr: 5.6115e-06 eta: 1:13:46 time: 0.3782 data_time: 0.0179 memory: 5383 loss: 0.2034 decode.loss_ce: 0.2034 decode.acc_seg: 92.5591 +2024/10/28 08:53:49 - mmengine - INFO - Iter(train) [148950/160000] base_lr: 5.5618e-06 lr: 5.5618e-06 eta: 1:13:26 time: 0.3783 data_time: 0.0174 memory: 5384 loss: 0.1933 decode.loss_ce: 0.1933 decode.acc_seg: 93.5451 +2024/10/28 08:54:08 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 08:54:08 - mmengine - INFO - Iter(train) [149000/160000] base_lr: 5.5124e-06 lr: 5.5124e-06 eta: 1:13:06 time: 0.3793 data_time: 0.0181 memory: 5384 loss: 0.1797 decode.loss_ce: 0.1797 decode.acc_seg: 89.1641 +2024/10/28 08:54:28 - mmengine - INFO - Iter(train) [149050/160000] base_lr: 5.4632e-06 lr: 5.4632e-06 eta: 1:12:46 time: 0.3775 data_time: 0.0178 memory: 5384 loss: 0.1779 decode.loss_ce: 0.1779 decode.acc_seg: 90.0581 +2024/10/28 08:54:46 - mmengine - INFO - Iter(train) [149100/160000] base_lr: 5.4142e-06 lr: 5.4142e-06 eta: 1:12:26 time: 0.3781 data_time: 0.0178 memory: 5383 loss: 0.1921 decode.loss_ce: 0.1921 decode.acc_seg: 91.3034 +2024/10/28 08:55:06 - mmengine - INFO - Iter(train) [149150/160000] base_lr: 5.3654e-06 lr: 5.3654e-06 eta: 1:12:06 time: 0.3826 data_time: 0.0170 memory: 5383 loss: 0.1763 decode.loss_ce: 0.1763 decode.acc_seg: 90.0671 +2024/10/28 08:55:26 - mmengine - INFO - Iter(train) [149200/160000] base_lr: 5.3168e-06 lr: 5.3168e-06 eta: 1:11:46 time: 0.3805 data_time: 0.0174 memory: 5384 loss: 0.2089 decode.loss_ce: 0.2089 decode.acc_seg: 90.0360 +2024/10/28 08:55:45 - mmengine - INFO - Iter(train) [149250/160000] base_lr: 5.2684e-06 lr: 5.2684e-06 eta: 1:11:26 time: 0.3759 data_time: 0.0176 memory: 5385 loss: 0.2323 decode.loss_ce: 0.2323 decode.acc_seg: 91.2796 +2024/10/28 08:56:05 - mmengine - INFO - Iter(train) [149300/160000] base_lr: 5.2202e-06 lr: 5.2202e-06 eta: 1:11:06 time: 0.4026 data_time: 0.0148 memory: 5383 loss: 0.1978 decode.loss_ce: 0.1978 decode.acc_seg: 88.7730 +2024/10/28 08:56:26 - mmengine - INFO - Iter(train) [149350/160000] base_lr: 5.1723e-06 lr: 5.1723e-06 eta: 1:10:46 time: 0.4045 data_time: 0.0161 memory: 5384 loss: 0.2181 decode.loss_ce: 0.2181 decode.acc_seg: 92.3183 +2024/10/28 08:56:45 - mmengine - INFO - Iter(train) [149400/160000] base_lr: 5.1245e-06 lr: 5.1245e-06 eta: 1:10:26 time: 0.3803 data_time: 0.0164 memory: 5384 loss: 0.1765 decode.loss_ce: 0.1765 decode.acc_seg: 93.2343 +2024/10/28 08:57:05 - mmengine - INFO - Iter(train) [149450/160000] base_lr: 5.0770e-06 lr: 5.0770e-06 eta: 1:10:06 time: 0.3910 data_time: 0.0155 memory: 5384 loss: 0.2041 decode.loss_ce: 0.2041 decode.acc_seg: 92.2698 +2024/10/28 08:57:24 - mmengine - INFO - Iter(train) [149500/160000] base_lr: 5.0297e-06 lr: 5.0297e-06 eta: 1:09:46 time: 0.3810 data_time: 0.0167 memory: 5384 loss: 0.2002 decode.loss_ce: 0.2002 decode.acc_seg: 88.7934 +2024/10/28 08:57:44 - mmengine - INFO - Iter(train) [149550/160000] base_lr: 4.9826e-06 lr: 4.9826e-06 eta: 1:09:26 time: 0.3803 data_time: 0.0161 memory: 5384 loss: 0.2138 decode.loss_ce: 0.2138 decode.acc_seg: 92.1561 +2024/10/28 08:58:03 - mmengine - INFO - Iter(train) [149600/160000] base_lr: 4.9357e-06 lr: 4.9357e-06 eta: 1:09:06 time: 0.3819 data_time: 0.0168 memory: 5384 loss: 0.2453 decode.loss_ce: 0.2453 decode.acc_seg: 92.6865 +2024/10/28 08:58:23 - mmengine - INFO - Iter(train) [149650/160000] base_lr: 4.8890e-06 lr: 4.8890e-06 eta: 1:08:46 time: 0.3743 data_time: 0.0148 memory: 5384 loss: 0.1834 decode.loss_ce: 0.1834 decode.acc_seg: 87.7814 +2024/10/28 08:58:42 - mmengine - INFO - Iter(train) [149700/160000] base_lr: 4.8425e-06 lr: 4.8425e-06 eta: 1:08:26 time: 0.3756 data_time: 0.0162 memory: 5384 loss: 0.2065 decode.loss_ce: 0.2065 decode.acc_seg: 93.3742 +2024/10/28 08:59:01 - mmengine - INFO - Iter(train) [149750/160000] base_lr: 4.7962e-06 lr: 4.7962e-06 eta: 1:08:06 time: 0.3768 data_time: 0.0164 memory: 5385 loss: 0.2000 decode.loss_ce: 0.2000 decode.acc_seg: 94.9496 +2024/10/28 08:59:20 - mmengine - INFO - Iter(train) [149800/160000] base_lr: 4.7502e-06 lr: 4.7502e-06 eta: 1:07:46 time: 0.3785 data_time: 0.0157 memory: 5384 loss: 0.2085 decode.loss_ce: 0.2085 decode.acc_seg: 91.7606 +2024/10/28 08:59:39 - mmengine - INFO - Iter(train) [149850/160000] base_lr: 4.7044e-06 lr: 4.7044e-06 eta: 1:07:26 time: 0.3750 data_time: 0.0161 memory: 5384 loss: 0.1703 decode.loss_ce: 0.1703 decode.acc_seg: 93.8954 +2024/10/28 08:59:58 - mmengine - INFO - Iter(train) [149900/160000] base_lr: 4.6587e-06 lr: 4.6587e-06 eta: 1:07:06 time: 0.3752 data_time: 0.0154 memory: 5384 loss: 0.1908 decode.loss_ce: 0.1908 decode.acc_seg: 91.5265 +2024/10/28 09:00:18 - mmengine - INFO - Iter(train) [149950/160000] base_lr: 4.6133e-06 lr: 4.6133e-06 eta: 1:06:46 time: 0.3778 data_time: 0.0167 memory: 5385 loss: 0.1899 decode.loss_ce: 0.1899 decode.acc_seg: 93.5275 +2024/10/28 09:00:37 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:00:37 - mmengine - INFO - Iter(train) [150000/160000] base_lr: 4.5681e-06 lr: 4.5681e-06 eta: 1:06:26 time: 0.3807 data_time: 0.0159 memory: 5384 loss: 0.2122 decode.loss_ce: 0.2122 decode.acc_seg: 91.8808 +2024/10/28 09:00:56 - mmengine - INFO - Iter(train) [150050/160000] base_lr: 4.5231e-06 lr: 4.5231e-06 eta: 1:06:06 time: 0.3811 data_time: 0.0162 memory: 5384 loss: 0.2014 decode.loss_ce: 0.2014 decode.acc_seg: 94.5385 +2024/10/28 09:01:16 - mmengine - INFO - Iter(train) [150100/160000] base_lr: 4.4784e-06 lr: 4.4784e-06 eta: 1:05:46 time: 0.3823 data_time: 0.0159 memory: 5384 loss: 0.1661 decode.loss_ce: 0.1661 decode.acc_seg: 91.1133 +2024/10/28 09:01:35 - mmengine - INFO - Iter(train) [150150/160000] base_lr: 4.4338e-06 lr: 4.4338e-06 eta: 1:05:26 time: 0.3809 data_time: 0.0159 memory: 5384 loss: 0.1933 decode.loss_ce: 0.1933 decode.acc_seg: 92.6748 +2024/10/28 09:01:54 - mmengine - INFO - Iter(train) [150200/160000] base_lr: 4.3895e-06 lr: 4.3895e-06 eta: 1:05:06 time: 0.3784 data_time: 0.0156 memory: 5383 loss: 0.1855 decode.loss_ce: 0.1855 decode.acc_seg: 91.2560 +2024/10/28 09:02:14 - mmengine - INFO - Iter(train) [150250/160000] base_lr: 4.3454e-06 lr: 4.3454e-06 eta: 1:04:46 time: 0.3801 data_time: 0.0169 memory: 5384 loss: 0.1833 decode.loss_ce: 0.1833 decode.acc_seg: 90.6578 +2024/10/28 09:02:33 - mmengine - INFO - Iter(train) [150300/160000] base_lr: 4.3015e-06 lr: 4.3015e-06 eta: 1:04:26 time: 0.3807 data_time: 0.0164 memory: 5384 loss: 0.1827 decode.loss_ce: 0.1827 decode.acc_seg: 89.7026 +2024/10/28 09:02:52 - mmengine - INFO - Iter(train) [150350/160000] base_lr: 4.2578e-06 lr: 4.2578e-06 eta: 1:04:06 time: 0.3840 data_time: 0.0159 memory: 5384 loss: 0.1913 decode.loss_ce: 0.1913 decode.acc_seg: 92.4070 +2024/10/28 09:03:11 - mmengine - INFO - Iter(train) [150400/160000] base_lr: 4.2143e-06 lr: 4.2143e-06 eta: 1:03:46 time: 0.3771 data_time: 0.0158 memory: 5384 loss: 0.2077 decode.loss_ce: 0.2077 decode.acc_seg: 91.0586 +2024/10/28 09:03:30 - mmengine - INFO - Iter(train) [150450/160000] base_lr: 4.1710e-06 lr: 4.1710e-06 eta: 1:03:27 time: 0.3843 data_time: 0.0155 memory: 5385 loss: 0.1683 decode.loss_ce: 0.1683 decode.acc_seg: 92.9915 +2024/10/28 09:03:50 - mmengine - INFO - Iter(train) [150500/160000] base_lr: 4.1280e-06 lr: 4.1280e-06 eta: 1:03:07 time: 0.3806 data_time: 0.0156 memory: 5384 loss: 0.1633 decode.loss_ce: 0.1633 decode.acc_seg: 95.7214 +2024/10/28 09:04:09 - mmengine - INFO - Iter(train) [150550/160000] base_lr: 4.0851e-06 lr: 4.0851e-06 eta: 1:02:47 time: 0.3829 data_time: 0.0171 memory: 5385 loss: 0.1866 decode.loss_ce: 0.1866 decode.acc_seg: 90.5055 +2024/10/28 09:04:28 - mmengine - INFO - Iter(train) [150600/160000] base_lr: 4.0425e-06 lr: 4.0425e-06 eta: 1:02:27 time: 0.3753 data_time: 0.0169 memory: 5386 loss: 0.1855 decode.loss_ce: 0.1855 decode.acc_seg: 93.8443 +2024/10/28 09:04:48 - mmengine - INFO - Iter(train) [150650/160000] base_lr: 4.0001e-06 lr: 4.0001e-06 eta: 1:02:07 time: 0.4009 data_time: 0.0143 memory: 5384 loss: 0.2473 decode.loss_ce: 0.2473 decode.acc_seg: 89.6840 +2024/10/28 09:05:07 - mmengine - INFO - Iter(train) [150700/160000] base_lr: 3.9579e-06 lr: 3.9579e-06 eta: 1:01:47 time: 0.3838 data_time: 0.0180 memory: 5384 loss: 0.2096 decode.loss_ce: 0.2096 decode.acc_seg: 93.8686 +2024/10/28 09:05:26 - mmengine - INFO - Iter(train) [150750/160000] base_lr: 3.9159e-06 lr: 3.9159e-06 eta: 1:01:27 time: 0.3770 data_time: 0.0177 memory: 5384 loss: 0.1751 decode.loss_ce: 0.1751 decode.acc_seg: 91.1933 +2024/10/28 09:05:45 - mmengine - INFO - Iter(train) [150800/160000] base_lr: 3.8742e-06 lr: 3.8742e-06 eta: 1:01:07 time: 0.3756 data_time: 0.0174 memory: 5384 loss: 0.2304 decode.loss_ce: 0.2304 decode.acc_seg: 89.0935 +2024/10/28 09:06:05 - mmengine - INFO - Iter(train) [150850/160000] base_lr: 3.8327e-06 lr: 3.8327e-06 eta: 1:00:47 time: 0.3828 data_time: 0.0165 memory: 5384 loss: 0.2074 decode.loss_ce: 0.2074 decode.acc_seg: 94.4705 +2024/10/28 09:06:24 - mmengine - INFO - Iter(train) [150900/160000] base_lr: 3.7913e-06 lr: 3.7913e-06 eta: 1:00:27 time: 0.3777 data_time: 0.0171 memory: 5384 loss: 0.2067 decode.loss_ce: 0.2067 decode.acc_seg: 90.9096 +2024/10/28 09:06:43 - mmengine - INFO - Iter(train) [150950/160000] base_lr: 3.7502e-06 lr: 3.7502e-06 eta: 1:00:07 time: 0.3750 data_time: 0.0172 memory: 5384 loss: 0.1929 decode.loss_ce: 0.1929 decode.acc_seg: 90.4340 +2024/10/28 09:07:02 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:07:02 - mmengine - INFO - Iter(train) [151000/160000] base_lr: 3.7093e-06 lr: 3.7093e-06 eta: 0:59:47 time: 0.3759 data_time: 0.0175 memory: 5384 loss: 0.1856 decode.loss_ce: 0.1856 decode.acc_seg: 91.8975 +2024/10/28 09:07:23 - mmengine - INFO - Iter(train) [151050/160000] base_lr: 3.6687e-06 lr: 3.6687e-06 eta: 0:59:27 time: 0.3776 data_time: 0.0177 memory: 5384 loss: 0.1775 decode.loss_ce: 0.1775 decode.acc_seg: 90.1120 +2024/10/28 09:07:43 - mmengine - INFO - Iter(train) [151100/160000] base_lr: 3.6282e-06 lr: 3.6282e-06 eta: 0:59:07 time: 0.3774 data_time: 0.0176 memory: 5384 loss: 0.1750 decode.loss_ce: 0.1750 decode.acc_seg: 94.0882 +2024/10/28 09:08:02 - mmengine - INFO - Iter(train) [151150/160000] base_lr: 3.5880e-06 lr: 3.5880e-06 eta: 0:58:47 time: 0.3865 data_time: 0.0173 memory: 5384 loss: 0.2127 decode.loss_ce: 0.2127 decode.acc_seg: 91.0823 +2024/10/28 09:08:24 - mmengine - INFO - Iter(train) [151200/160000] base_lr: 3.5480e-06 lr: 3.5480e-06 eta: 0:58:27 time: 0.3802 data_time: 0.0177 memory: 5384 loss: 0.2206 decode.loss_ce: 0.2206 decode.acc_seg: 92.7200 +2024/10/28 09:08:43 - mmengine - INFO - Iter(train) [151250/160000] base_lr: 3.5081e-06 lr: 3.5081e-06 eta: 0:58:07 time: 0.3772 data_time: 0.0167 memory: 5384 loss: 0.1938 decode.loss_ce: 0.1938 decode.acc_seg: 88.0339 +2024/10/28 09:09:03 - mmengine - INFO - Iter(train) [151300/160000] base_lr: 3.4686e-06 lr: 3.4686e-06 eta: 0:57:47 time: 0.3823 data_time: 0.0154 memory: 5384 loss: 0.1891 decode.loss_ce: 0.1891 decode.acc_seg: 90.5440 +2024/10/28 09:09:24 - mmengine - INFO - Iter(train) [151350/160000] base_lr: 3.4292e-06 lr: 3.4292e-06 eta: 0:57:27 time: 0.3768 data_time: 0.0182 memory: 5384 loss: 0.1900 decode.loss_ce: 0.1900 decode.acc_seg: 91.5920 +2024/10/28 09:09:43 - mmengine - INFO - Iter(train) [151400/160000] base_lr: 3.3901e-06 lr: 3.3901e-06 eta: 0:57:07 time: 0.3866 data_time: 0.0185 memory: 5385 loss: 0.1668 decode.loss_ce: 0.1668 decode.acc_seg: 93.5213 +2024/10/28 09:10:02 - mmengine - INFO - Iter(train) [151450/160000] base_lr: 3.3511e-06 lr: 3.3511e-06 eta: 0:56:47 time: 0.3842 data_time: 0.0184 memory: 5384 loss: 0.1990 decode.loss_ce: 0.1990 decode.acc_seg: 89.7103 +2024/10/28 09:10:24 - mmengine - INFO - Iter(train) [151500/160000] base_lr: 3.3124e-06 lr: 3.3124e-06 eta: 0:56:28 time: 0.3808 data_time: 0.0189 memory: 5384 loss: 0.2003 decode.loss_ce: 0.2003 decode.acc_seg: 92.1091 +2024/10/28 09:10:43 - mmengine - INFO - Iter(train) [151550/160000] base_lr: 3.2739e-06 lr: 3.2739e-06 eta: 0:56:08 time: 0.3776 data_time: 0.0182 memory: 5384 loss: 0.1878 decode.loss_ce: 0.1878 decode.acc_seg: 93.7252 +2024/10/28 09:11:03 - mmengine - INFO - Iter(train) [151600/160000] base_lr: 3.2356e-06 lr: 3.2356e-06 eta: 0:55:48 time: 0.3832 data_time: 0.0177 memory: 5384 loss: 0.1899 decode.loss_ce: 0.1899 decode.acc_seg: 87.3900 +2024/10/28 09:11:24 - mmengine - INFO - Iter(train) [151650/160000] base_lr: 3.1976e-06 lr: 3.1976e-06 eta: 0:55:28 time: 0.3802 data_time: 0.0171 memory: 5384 loss: 0.1786 decode.loss_ce: 0.1786 decode.acc_seg: 94.1989 +2024/10/28 09:11:43 - mmengine - INFO - Iter(train) [151700/160000] base_lr: 3.1597e-06 lr: 3.1597e-06 eta: 0:55:08 time: 0.3832 data_time: 0.0173 memory: 5384 loss: 0.1971 decode.loss_ce: 0.1971 decode.acc_seg: 94.8942 +2024/10/28 09:12:02 - mmengine - INFO - Iter(train) [151750/160000] base_lr: 3.1221e-06 lr: 3.1221e-06 eta: 0:54:48 time: 0.3829 data_time: 0.0170 memory: 5384 loss: 0.1956 decode.loss_ce: 0.1956 decode.acc_seg: 89.4046 +2024/10/28 09:12:24 - mmengine - INFO - Iter(train) [151800/160000] base_lr: 3.0847e-06 lr: 3.0847e-06 eta: 0:54:28 time: 0.3803 data_time: 0.0177 memory: 5384 loss: 0.2102 decode.loss_ce: 0.2102 decode.acc_seg: 92.7581 +2024/10/28 09:12:43 - mmengine - INFO - Iter(train) [151850/160000] base_lr: 3.0476e-06 lr: 3.0476e-06 eta: 0:54:08 time: 0.3779 data_time: 0.0178 memory: 5384 loss: 0.1848 decode.loss_ce: 0.1848 decode.acc_seg: 90.8104 +2024/10/28 09:13:02 - mmengine - INFO - Iter(train) [151900/160000] base_lr: 3.0106e-06 lr: 3.0106e-06 eta: 0:53:48 time: 0.3971 data_time: 0.0145 memory: 5384 loss: 0.1634 decode.loss_ce: 0.1634 decode.acc_seg: 93.1110 +2024/10/28 09:13:24 - mmengine - INFO - Iter(train) [151950/160000] base_lr: 2.9739e-06 lr: 2.9739e-06 eta: 0:53:28 time: 0.3888 data_time: 0.0160 memory: 5384 loss: 0.1730 decode.loss_ce: 0.1730 decode.acc_seg: 93.1298 +2024/10/28 09:13:43 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:13:43 - mmengine - INFO - Iter(train) [152000/160000] base_lr: 2.9373e-06 lr: 2.9373e-06 eta: 0:53:08 time: 0.3818 data_time: 0.0176 memory: 5384 loss: 0.1756 decode.loss_ce: 0.1756 decode.acc_seg: 92.6363 +2024/10/28 09:14:02 - mmengine - INFO - Iter(train) [152050/160000] base_lr: 2.9010e-06 lr: 2.9010e-06 eta: 0:52:48 time: 0.3848 data_time: 0.0185 memory: 5385 loss: 0.1891 decode.loss_ce: 0.1891 decode.acc_seg: 92.3678 +2024/10/28 09:14:24 - mmengine - INFO - Iter(train) [152100/160000] base_lr: 2.8650e-06 lr: 2.8650e-06 eta: 0:52:28 time: 0.3778 data_time: 0.0189 memory: 5384 loss: 0.1667 decode.loss_ce: 0.1667 decode.acc_seg: 91.5094 +2024/10/28 09:14:43 - mmengine - INFO - Iter(train) [152150/160000] base_lr: 2.8291e-06 lr: 2.8291e-06 eta: 0:52:08 time: 0.3814 data_time: 0.0173 memory: 5384 loss: 0.1876 decode.loss_ce: 0.1876 decode.acc_seg: 90.5188 +2024/10/28 09:15:03 - mmengine - INFO - Iter(train) [152200/160000] base_lr: 2.7935e-06 lr: 2.7935e-06 eta: 0:51:49 time: 0.3856 data_time: 0.0169 memory: 5384 loss: 0.1776 decode.loss_ce: 0.1776 decode.acc_seg: 93.1279 +2024/10/28 09:15:24 - mmengine - INFO - Iter(train) [152250/160000] base_lr: 2.7580e-06 lr: 2.7580e-06 eta: 0:51:29 time: 0.3801 data_time: 0.0159 memory: 5384 loss: 0.2074 decode.loss_ce: 0.2074 decode.acc_seg: 88.9694 +2024/10/28 09:15:44 - mmengine - INFO - Iter(train) [152300/160000] base_lr: 2.7228e-06 lr: 2.7228e-06 eta: 0:51:09 time: 0.4036 data_time: 0.0154 memory: 5384 loss: 0.1892 decode.loss_ce: 0.1892 decode.acc_seg: 95.4160 +2024/10/28 09:16:03 - mmengine - INFO - Iter(train) [152350/160000] base_lr: 2.6879e-06 lr: 2.6879e-06 eta: 0:50:49 time: 0.3851 data_time: 0.0161 memory: 5385 loss: 0.1886 decode.loss_ce: 0.1886 decode.acc_seg: 92.2432 +2024/10/28 09:16:24 - mmengine - INFO - Iter(train) [152400/160000] base_lr: 2.6531e-06 lr: 2.6531e-06 eta: 0:50:29 time: 0.3858 data_time: 0.0179 memory: 5383 loss: 0.1683 decode.loss_ce: 0.1683 decode.acc_seg: 94.2395 +2024/10/28 09:16:43 - mmengine - INFO - Iter(train) [152450/160000] base_lr: 2.6186e-06 lr: 2.6186e-06 eta: 0:50:09 time: 0.3853 data_time: 0.0185 memory: 5385 loss: 0.2202 decode.loss_ce: 0.2202 decode.acc_seg: 91.7172 +2024/10/28 09:17:02 - mmengine - INFO - Iter(train) [152500/160000] base_lr: 2.5843e-06 lr: 2.5843e-06 eta: 0:49:49 time: 0.3833 data_time: 0.0189 memory: 5384 loss: 0.1693 decode.loss_ce: 0.1693 decode.acc_seg: 94.8922 +2024/10/28 09:17:24 - mmengine - INFO - Iter(train) [152550/160000] base_lr: 2.5502e-06 lr: 2.5502e-06 eta: 0:49:29 time: 0.3813 data_time: 0.0176 memory: 5385 loss: 0.2145 decode.loss_ce: 0.2145 decode.acc_seg: 89.1556 +2024/10/28 09:17:43 - mmengine - INFO - Iter(train) [152600/160000] base_lr: 2.5163e-06 lr: 2.5163e-06 eta: 0:49:09 time: 0.3829 data_time: 0.0163 memory: 5384 loss: 0.1984 decode.loss_ce: 0.1984 decode.acc_seg: 95.0635 +2024/10/28 09:18:02 - mmengine - INFO - Iter(train) [152650/160000] base_lr: 2.4827e-06 lr: 2.4827e-06 eta: 0:48:49 time: 0.3782 data_time: 0.0167 memory: 5384 loss: 0.1912 decode.loss_ce: 0.1912 decode.acc_seg: 90.0010 +2024/10/28 09:18:24 - mmengine - INFO - Iter(train) [152700/160000] base_lr: 2.4492e-06 lr: 2.4492e-06 eta: 0:48:29 time: 0.3828 data_time: 0.0153 memory: 5384 loss: 0.1687 decode.loss_ce: 0.1687 decode.acc_seg: 92.4560 +2024/10/28 09:18:43 - mmengine - INFO - Iter(train) [152750/160000] base_lr: 2.4160e-06 lr: 2.4160e-06 eta: 0:48:09 time: 0.3863 data_time: 0.0175 memory: 5384 loss: 0.1909 decode.loss_ce: 0.1909 decode.acc_seg: 85.1628 +2024/10/28 09:19:02 - mmengine - INFO - Iter(train) [152800/160000] base_lr: 2.3830e-06 lr: 2.3830e-06 eta: 0:47:49 time: 0.3842 data_time: 0.0162 memory: 5385 loss: 0.2011 decode.loss_ce: 0.2011 decode.acc_seg: 90.7553 +2024/10/28 09:19:24 - mmengine - INFO - Iter(train) [152850/160000] base_lr: 2.3503e-06 lr: 2.3503e-06 eta: 0:47:30 time: 0.3818 data_time: 0.0167 memory: 5385 loss: 0.2139 decode.loss_ce: 0.2139 decode.acc_seg: 91.9232 +2024/10/28 09:19:43 - mmengine - INFO - Iter(train) [152900/160000] base_lr: 2.3177e-06 lr: 2.3177e-06 eta: 0:47:10 time: 0.3834 data_time: 0.0170 memory: 5384 loss: 0.1691 decode.loss_ce: 0.1691 decode.acc_seg: 92.6095 +2024/10/28 09:20:03 - mmengine - INFO - Iter(train) [152950/160000] base_lr: 2.2854e-06 lr: 2.2854e-06 eta: 0:46:50 time: 0.3839 data_time: 0.0173 memory: 5383 loss: 0.2074 decode.loss_ce: 0.2074 decode.acc_seg: 95.1005 +2024/10/28 09:20:24 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:20:24 - mmengine - INFO - Iter(train) [153000/160000] base_lr: 2.2533e-06 lr: 2.2533e-06 eta: 0:46:30 time: 0.3831 data_time: 0.0171 memory: 5384 loss: 0.1801 decode.loss_ce: 0.1801 decode.acc_seg: 90.7357 +2024/10/28 09:20:43 - mmengine - INFO - Iter(train) [153050/160000] base_lr: 2.2215e-06 lr: 2.2215e-06 eta: 0:46:10 time: 0.3799 data_time: 0.0167 memory: 5385 loss: 0.1838 decode.loss_ce: 0.1838 decode.acc_seg: 90.2184 +2024/10/28 09:21:03 - mmengine - INFO - Iter(train) [153100/160000] base_lr: 2.1898e-06 lr: 2.1898e-06 eta: 0:45:50 time: 0.3819 data_time: 0.0174 memory: 5384 loss: 0.1752 decode.loss_ce: 0.1752 decode.acc_seg: 92.2934 +2024/10/28 09:21:25 - mmengine - INFO - Iter(train) [153150/160000] base_lr: 2.1584e-06 lr: 2.1584e-06 eta: 0:45:30 time: 0.3793 data_time: 0.0181 memory: 5384 loss: 0.1982 decode.loss_ce: 0.1982 decode.acc_seg: 93.4438 +2024/10/28 09:21:44 - mmengine - INFO - Iter(train) [153200/160000] base_lr: 2.1272e-06 lr: 2.1272e-06 eta: 0:45:10 time: 0.3835 data_time: 0.0176 memory: 5384 loss: 0.2064 decode.loss_ce: 0.2064 decode.acc_seg: 90.9375 +2024/10/28 09:22:03 - mmengine - INFO - Iter(train) [153250/160000] base_lr: 2.0962e-06 lr: 2.0962e-06 eta: 0:44:50 time: 0.3862 data_time: 0.0165 memory: 5384 loss: 0.1766 decode.loss_ce: 0.1766 decode.acc_seg: 92.1506 +2024/10/28 09:22:25 - mmengine - INFO - Iter(train) [153300/160000] base_lr: 2.0654e-06 lr: 2.0654e-06 eta: 0:44:30 time: 0.3820 data_time: 0.0159 memory: 5384 loss: 0.1806 decode.loss_ce: 0.1806 decode.acc_seg: 93.3175 +2024/10/28 09:22:45 - mmengine - INFO - Iter(train) [153350/160000] base_lr: 2.0349e-06 lr: 2.0349e-06 eta: 0:44:10 time: 0.3893 data_time: 0.0174 memory: 5383 loss: 0.1731 decode.loss_ce: 0.1731 decode.acc_seg: 93.5810 +2024/10/28 09:23:04 - mmengine - INFO - Iter(train) [153400/160000] base_lr: 2.0046e-06 lr: 2.0046e-06 eta: 0:43:50 time: 0.3818 data_time: 0.0169 memory: 5384 loss: 0.1847 decode.loss_ce: 0.1847 decode.acc_seg: 91.4449 +2024/10/28 09:23:23 - mmengine - INFO - Iter(train) [153450/160000] base_lr: 1.9745e-06 lr: 1.9745e-06 eta: 0:43:30 time: 0.3788 data_time: 0.0169 memory: 5384 loss: 0.1776 decode.loss_ce: 0.1776 decode.acc_seg: 93.7520 +2024/10/28 09:23:42 - mmengine - INFO - Iter(train) [153500/160000] base_lr: 1.9446e-06 lr: 1.9446e-06 eta: 0:43:10 time: 0.3785 data_time: 0.0169 memory: 5385 loss: 0.1861 decode.loss_ce: 0.1861 decode.acc_seg: 91.5242 +2024/10/28 09:24:02 - mmengine - INFO - Iter(train) [153550/160000] base_lr: 1.9150e-06 lr: 1.9150e-06 eta: 0:42:50 time: 0.3813 data_time: 0.0172 memory: 5384 loss: 0.1929 decode.loss_ce: 0.1929 decode.acc_seg: 91.8237 +2024/10/28 09:24:23 - mmengine - INFO - Iter(train) [153600/160000] base_lr: 1.8856e-06 lr: 1.8856e-06 eta: 0:42:31 time: 0.3797 data_time: 0.0171 memory: 5384 loss: 0.1944 decode.loss_ce: 0.1944 decode.acc_seg: 90.9472 +2024/10/28 09:24:43 - mmengine - INFO - Iter(train) [153650/160000] base_lr: 1.8564e-06 lr: 1.8564e-06 eta: 0:42:11 time: 0.3944 data_time: 0.0152 memory: 5384 loss: 0.1904 decode.loss_ce: 0.1904 decode.acc_seg: 94.4941 +2024/10/28 09:25:02 - mmengine - INFO - Iter(train) [153700/160000] base_lr: 1.8274e-06 lr: 1.8274e-06 eta: 0:41:51 time: 0.3983 data_time: 0.0157 memory: 5384 loss: 0.2044 decode.loss_ce: 0.2044 decode.acc_seg: 92.7977 +2024/10/28 09:25:24 - mmengine - INFO - Iter(train) [153750/160000] base_lr: 1.7987e-06 lr: 1.7987e-06 eta: 0:41:31 time: 0.3772 data_time: 0.0170 memory: 5384 loss: 0.1857 decode.loss_ce: 0.1857 decode.acc_seg: 88.7998 +2024/10/28 09:25:43 - mmengine - INFO - Iter(train) [153800/160000] base_lr: 1.7702e-06 lr: 1.7702e-06 eta: 0:41:11 time: 0.3848 data_time: 0.0175 memory: 5384 loss: 0.1942 decode.loss_ce: 0.1942 decode.acc_seg: 92.9127 +2024/10/28 09:26:03 - mmengine - INFO - Iter(train) [153850/160000] base_lr: 1.7419e-06 lr: 1.7419e-06 eta: 0:40:51 time: 0.3830 data_time: 0.0169 memory: 5384 loss: 0.1848 decode.loss_ce: 0.1848 decode.acc_seg: 93.2382 +2024/10/28 09:26:25 - mmengine - INFO - Iter(train) [153900/160000] base_lr: 1.7138e-06 lr: 1.7138e-06 eta: 0:40:31 time: 0.4053 data_time: 0.0160 memory: 5384 loss: 0.1702 decode.loss_ce: 0.1702 decode.acc_seg: 92.3542 +2024/10/28 09:26:45 - mmengine - INFO - Iter(train) [153950/160000] base_lr: 1.6860e-06 lr: 1.6860e-06 eta: 0:40:11 time: 0.4017 data_time: 0.0167 memory: 5384 loss: 0.1826 decode.loss_ce: 0.1826 decode.acc_seg: 90.9301 +2024/10/28 09:27:06 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:27:06 - mmengine - INFO - Iter(train) [154000/160000] base_lr: 1.6584e-06 lr: 1.6584e-06 eta: 0:39:51 time: 0.4105 data_time: 0.0161 memory: 5383 loss: 0.1810 decode.loss_ce: 0.1810 decode.acc_seg: 93.7936 +2024/10/28 09:27:25 - mmengine - INFO - Iter(train) [154050/160000] base_lr: 1.6310e-06 lr: 1.6310e-06 eta: 0:39:31 time: 0.3794 data_time: 0.0184 memory: 5384 loss: 0.1747 decode.loss_ce: 0.1747 decode.acc_seg: 94.0277 +2024/10/28 09:27:45 - mmengine - INFO - Iter(train) [154100/160000] base_lr: 1.6038e-06 lr: 1.6038e-06 eta: 0:39:11 time: 0.3784 data_time: 0.0186 memory: 5384 loss: 0.1855 decode.loss_ce: 0.1855 decode.acc_seg: 90.6601 +2024/10/28 09:28:03 - mmengine - INFO - Iter(train) [154150/160000] base_lr: 1.5768e-06 lr: 1.5768e-06 eta: 0:38:51 time: 0.3828 data_time: 0.0176 memory: 5386 loss: 0.1797 decode.loss_ce: 0.1797 decode.acc_seg: 92.8425 +2024/10/28 09:28:24 - mmengine - INFO - Iter(train) [154200/160000] base_lr: 1.5501e-06 lr: 1.5501e-06 eta: 0:38:31 time: 0.3778 data_time: 0.0169 memory: 5384 loss: 0.1568 decode.loss_ce: 0.1568 decode.acc_seg: 93.6906 +2024/10/28 09:28:43 - mmengine - INFO - Iter(train) [154250/160000] base_lr: 1.5236e-06 lr: 1.5236e-06 eta: 0:38:11 time: 0.3770 data_time: 0.0167 memory: 5384 loss: 0.1838 decode.loss_ce: 0.1838 decode.acc_seg: 90.2856 +2024/10/28 09:29:02 - mmengine - INFO - Iter(train) [154300/160000] base_lr: 1.4974e-06 lr: 1.4974e-06 eta: 0:37:52 time: 0.3779 data_time: 0.0184 memory: 5384 loss: 0.2010 decode.loss_ce: 0.2010 decode.acc_seg: 95.1880 +2024/10/28 09:29:24 - mmengine - INFO - Iter(train) [154350/160000] base_lr: 1.4713e-06 lr: 1.4713e-06 eta: 0:37:32 time: 0.3816 data_time: 0.0167 memory: 5384 loss: 0.1931 decode.loss_ce: 0.1931 decode.acc_seg: 95.0054 +2024/10/28 09:29:43 - mmengine - INFO - Iter(train) [154400/160000] base_lr: 1.4455e-06 lr: 1.4455e-06 eta: 0:37:12 time: 0.3773 data_time: 0.0180 memory: 5383 loss: 0.2039 decode.loss_ce: 0.2039 decode.acc_seg: 92.4800 +2024/10/28 09:30:02 - mmengine - INFO - Iter(train) [154450/160000] base_lr: 1.4199e-06 lr: 1.4199e-06 eta: 0:36:52 time: 0.3822 data_time: 0.0169 memory: 5384 loss: 0.2239 decode.loss_ce: 0.2239 decode.acc_seg: 92.6227 +2024/10/28 09:30:24 - mmengine - INFO - Iter(train) [154500/160000] base_lr: 1.3946e-06 lr: 1.3946e-06 eta: 0:36:32 time: 0.3747 data_time: 0.0173 memory: 5385 loss: 0.1961 decode.loss_ce: 0.1961 decode.acc_seg: 92.6362 +2024/10/28 09:30:43 - mmengine - INFO - Iter(train) [154550/160000] base_lr: 1.3694e-06 lr: 1.3694e-06 eta: 0:36:12 time: 0.3838 data_time: 0.0188 memory: 5384 loss: 0.1942 decode.loss_ce: 0.1942 decode.acc_seg: 89.8602 +2024/10/28 09:31:02 - mmengine - INFO - Iter(train) [154600/160000] base_lr: 1.3445e-06 lr: 1.3445e-06 eta: 0:35:52 time: 0.3846 data_time: 0.0175 memory: 5384 loss: 0.1898 decode.loss_ce: 0.1898 decode.acc_seg: 93.2529 +2024/10/28 09:31:23 - mmengine - INFO - Iter(train) [154650/160000] base_lr: 1.3198e-06 lr: 1.3198e-06 eta: 0:35:32 time: 0.3804 data_time: 0.0167 memory: 5384 loss: 0.1686 decode.loss_ce: 0.1686 decode.acc_seg: 91.3691 +2024/10/28 09:31:42 - mmengine - INFO - Iter(train) [154700/160000] base_lr: 1.2954e-06 lr: 1.2954e-06 eta: 0:35:12 time: 0.3767 data_time: 0.0163 memory: 5384 loss: 0.2036 decode.loss_ce: 0.2036 decode.acc_seg: 90.0673 +2024/10/28 09:32:02 - mmengine - INFO - Iter(train) [154750/160000] base_lr: 1.2711e-06 lr: 1.2711e-06 eta: 0:34:52 time: 0.3793 data_time: 0.0159 memory: 5384 loss: 0.2187 decode.loss_ce: 0.2187 decode.acc_seg: 88.9994 +2024/10/28 09:32:23 - mmengine - INFO - Iter(train) [154800/160000] base_lr: 1.2471e-06 lr: 1.2471e-06 eta: 0:34:32 time: 0.3812 data_time: 0.0166 memory: 5384 loss: 0.1573 decode.loss_ce: 0.1573 decode.acc_seg: 92.9547 +2024/10/28 09:32:42 - mmengine - INFO - Iter(train) [154850/160000] base_lr: 1.2233e-06 lr: 1.2233e-06 eta: 0:34:12 time: 0.3824 data_time: 0.0173 memory: 5384 loss: 0.2214 decode.loss_ce: 0.2214 decode.acc_seg: 89.9447 +2024/10/28 09:33:01 - mmengine - INFO - Iter(train) [154900/160000] base_lr: 1.1998e-06 lr: 1.1998e-06 eta: 0:33:52 time: 0.3843 data_time: 0.0182 memory: 5384 loss: 0.2042 decode.loss_ce: 0.2042 decode.acc_seg: 94.6126 +2024/10/28 09:33:21 - mmengine - INFO - Iter(train) [154950/160000] base_lr: 1.1764e-06 lr: 1.1764e-06 eta: 0:33:32 time: 0.3796 data_time: 0.0159 memory: 5384 loss: 0.1813 decode.loss_ce: 0.1813 decode.acc_seg: 91.2717 +2024/10/28 09:33:40 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:33:40 - mmengine - INFO - Iter(train) [155000/160000] base_lr: 1.1533e-06 lr: 1.1533e-06 eta: 0:33:12 time: 0.3818 data_time: 0.0171 memory: 5384 loss: 0.1984 decode.loss_ce: 0.1984 decode.acc_seg: 92.2749 +2024/10/28 09:34:00 - mmengine - INFO - Iter(train) [155050/160000] base_lr: 1.1305e-06 lr: 1.1305e-06 eta: 0:32:53 time: 0.4031 data_time: 0.0148 memory: 5384 loss: 0.2000 decode.loss_ce: 0.2000 decode.acc_seg: 91.9494 +2024/10/28 09:34:20 - mmengine - INFO - Iter(train) [155100/160000] base_lr: 1.1078e-06 lr: 1.1078e-06 eta: 0:32:33 time: 0.4062 data_time: 0.0145 memory: 5384 loss: 0.1716 decode.loss_ce: 0.1716 decode.acc_seg: 93.1508 +2024/10/28 09:34:40 - mmengine - INFO - Iter(train) [155150/160000] base_lr: 1.0854e-06 lr: 1.0854e-06 eta: 0:32:13 time: 0.4061 data_time: 0.0149 memory: 5385 loss: 0.1858 decode.loss_ce: 0.1858 decode.acc_seg: 93.2775 +2024/10/28 09:35:01 - mmengine - INFO - Iter(train) [155200/160000] base_lr: 1.0632e-06 lr: 1.0632e-06 eta: 0:31:53 time: 0.4072 data_time: 0.0151 memory: 5384 loss: 0.1675 decode.loss_ce: 0.1675 decode.acc_seg: 91.8768 +2024/10/28 09:35:20 - mmengine - INFO - Iter(train) [155250/160000] base_lr: 1.0412e-06 lr: 1.0412e-06 eta: 0:31:33 time: 0.3803 data_time: 0.0165 memory: 5384 loss: 0.1899 decode.loss_ce: 0.1899 decode.acc_seg: 90.3033 +2024/10/28 09:35:39 - mmengine - INFO - Iter(train) [155300/160000] base_lr: 1.0195e-06 lr: 1.0195e-06 eta: 0:31:13 time: 0.3806 data_time: 0.0159 memory: 5385 loss: 0.2096 decode.loss_ce: 0.2096 decode.acc_seg: 87.1760 +2024/10/28 09:35:58 - mmengine - INFO - Iter(train) [155350/160000] base_lr: 9.9799e-07 lr: 9.9799e-07 eta: 0:30:53 time: 0.3795 data_time: 0.0167 memory: 5384 loss: 0.1875 decode.loss_ce: 0.1875 decode.acc_seg: 91.0602 +2024/10/28 09:36:18 - mmengine - INFO - Iter(train) [155400/160000] base_lr: 9.7671e-07 lr: 9.7671e-07 eta: 0:30:33 time: 0.3834 data_time: 0.0160 memory: 5384 loss: 0.1867 decode.loss_ce: 0.1867 decode.acc_seg: 93.1616 +2024/10/28 09:36:36 - mmengine - INFO - Iter(train) [155450/160000] base_lr: 9.5565e-07 lr: 9.5565e-07 eta: 0:30:13 time: 0.3751 data_time: 0.0170 memory: 5385 loss: 0.2269 decode.loss_ce: 0.2269 decode.acc_seg: 85.6218 +2024/10/28 09:36:56 - mmengine - INFO - Iter(train) [155500/160000] base_lr: 9.3482e-07 lr: 9.3482e-07 eta: 0:29:53 time: 0.3810 data_time: 0.0169 memory: 5384 loss: 0.1813 decode.loss_ce: 0.1813 decode.acc_seg: 94.0931 +2024/10/28 09:37:15 - mmengine - INFO - Iter(train) [155550/160000] base_lr: 9.1422e-07 lr: 9.1422e-07 eta: 0:29:33 time: 0.3786 data_time: 0.0166 memory: 5384 loss: 0.1862 decode.loss_ce: 0.1862 decode.acc_seg: 93.0671 +2024/10/28 09:37:34 - mmengine - INFO - Iter(train) [155600/160000] base_lr: 8.9385e-07 lr: 8.9385e-07 eta: 0:29:13 time: 0.3962 data_time: 0.0152 memory: 5384 loss: 0.1791 decode.loss_ce: 0.1791 decode.acc_seg: 92.5919 +2024/10/28 09:37:53 - mmengine - INFO - Iter(train) [155650/160000] base_lr: 8.7370e-07 lr: 8.7370e-07 eta: 0:28:53 time: 0.3986 data_time: 0.0164 memory: 5385 loss: 0.2144 decode.loss_ce: 0.2144 decode.acc_seg: 90.4055 +2024/10/28 09:38:13 - mmengine - INFO - Iter(train) [155700/160000] base_lr: 8.5378e-07 lr: 8.5378e-07 eta: 0:28:33 time: 0.3825 data_time: 0.0170 memory: 5384 loss: 0.2069 decode.loss_ce: 0.2069 decode.acc_seg: 94.8478 +2024/10/28 09:38:32 - mmengine - INFO - Iter(train) [155750/160000] base_lr: 8.3409e-07 lr: 8.3409e-07 eta: 0:28:13 time: 0.3823 data_time: 0.0169 memory: 5384 loss: 0.2037 decode.loss_ce: 0.2037 decode.acc_seg: 90.9688 +2024/10/28 09:38:51 - mmengine - INFO - Iter(train) [155800/160000] base_lr: 8.1463e-07 lr: 8.1463e-07 eta: 0:27:53 time: 0.3812 data_time: 0.0184 memory: 5384 loss: 0.1765 decode.loss_ce: 0.1765 decode.acc_seg: 96.2891 +2024/10/28 09:39:10 - mmengine - INFO - Iter(train) [155850/160000] base_lr: 7.9540e-07 lr: 7.9540e-07 eta: 0:27:33 time: 0.3798 data_time: 0.0182 memory: 5384 loss: 0.1711 decode.loss_ce: 0.1711 decode.acc_seg: 91.5775 +2024/10/28 09:39:30 - mmengine - INFO - Iter(train) [155900/160000] base_lr: 7.7639e-07 lr: 7.7639e-07 eta: 0:27:13 time: 0.3791 data_time: 0.0178 memory: 5384 loss: 0.2034 decode.loss_ce: 0.2034 decode.acc_seg: 91.3101 +2024/10/28 09:39:49 - mmengine - INFO - Iter(train) [155950/160000] base_lr: 7.5762e-07 lr: 7.5762e-07 eta: 0:26:54 time: 0.3947 data_time: 0.0180 memory: 5386 loss: 0.1711 decode.loss_ce: 0.1711 decode.acc_seg: 94.2604 +2024/10/28 09:40:09 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:40:09 - mmengine - INFO - Iter(train) [156000/160000] base_lr: 7.3907e-07 lr: 7.3907e-07 eta: 0:26:34 time: 0.3802 data_time: 0.0174 memory: 5385 loss: 0.1882 decode.loss_ce: 0.1882 decode.acc_seg: 90.5663 +2024/10/28 09:40:28 - mmengine - INFO - Iter(train) [156050/160000] base_lr: 7.2075e-07 lr: 7.2075e-07 eta: 0:26:14 time: 0.4061 data_time: 0.0190 memory: 5385 loss: 0.1769 decode.loss_ce: 0.1769 decode.acc_seg: 93.0071 +2024/10/28 09:40:49 - mmengine - INFO - Iter(train) [156100/160000] base_lr: 7.0266e-07 lr: 7.0266e-07 eta: 0:25:54 time: 0.4068 data_time: 0.0162 memory: 5383 loss: 0.2013 decode.loss_ce: 0.2013 decode.acc_seg: 94.6193 +2024/10/28 09:41:09 - mmengine - INFO - Iter(train) [156150/160000] base_lr: 6.8479e-07 lr: 6.8479e-07 eta: 0:25:34 time: 0.3820 data_time: 0.0173 memory: 5385 loss: 0.1901 decode.loss_ce: 0.1901 decode.acc_seg: 85.8077 +2024/10/28 09:41:28 - mmengine - INFO - Iter(train) [156200/160000] base_lr: 6.6716e-07 lr: 6.6716e-07 eta: 0:25:14 time: 0.3778 data_time: 0.0185 memory: 5384 loss: 0.2173 decode.loss_ce: 0.2173 decode.acc_seg: 90.7465 +2024/10/28 09:41:47 - mmengine - INFO - Iter(train) [156250/160000] base_lr: 6.4976e-07 lr: 6.4976e-07 eta: 0:24:54 time: 0.3823 data_time: 0.0182 memory: 5384 loss: 0.1701 decode.loss_ce: 0.1701 decode.acc_seg: 91.0267 +2024/10/28 09:42:06 - mmengine - INFO - Iter(train) [156300/160000] base_lr: 6.3258e-07 lr: 6.3258e-07 eta: 0:24:34 time: 0.4095 data_time: 0.0169 memory: 5384 loss: 0.2061 decode.loss_ce: 0.2061 decode.acc_seg: 93.9011 +2024/10/28 09:42:27 - mmengine - INFO - Iter(train) [156350/160000] base_lr: 6.1563e-07 lr: 6.1563e-07 eta: 0:24:14 time: 0.4081 data_time: 0.0162 memory: 5384 loss: 0.1594 decode.loss_ce: 0.1594 decode.acc_seg: 95.7508 +2024/10/28 09:42:48 - mmengine - INFO - Iter(train) [156400/160000] base_lr: 5.9891e-07 lr: 5.9891e-07 eta: 0:23:54 time: 0.4043 data_time: 0.0167 memory: 5384 loss: 0.2028 decode.loss_ce: 0.2028 decode.acc_seg: 92.3180 +2024/10/28 09:43:08 - mmengine - INFO - Iter(train) [156450/160000] base_lr: 5.8242e-07 lr: 5.8242e-07 eta: 0:23:34 time: 0.3884 data_time: 0.0157 memory: 5384 loss: 0.1960 decode.loss_ce: 0.1960 decode.acc_seg: 90.9074 +2024/10/28 09:43:27 - mmengine - INFO - Iter(train) [156500/160000] base_lr: 5.6616e-07 lr: 5.6616e-07 eta: 0:23:14 time: 0.3766 data_time: 0.0166 memory: 5385 loss: 0.2055 decode.loss_ce: 0.2055 decode.acc_seg: 89.0374 +2024/10/28 09:43:46 - mmengine - INFO - Iter(train) [156550/160000] base_lr: 5.5013e-07 lr: 5.5013e-07 eta: 0:22:54 time: 0.3783 data_time: 0.0171 memory: 5384 loss: 0.1942 decode.loss_ce: 0.1942 decode.acc_seg: 88.6496 +2024/10/28 09:44:05 - mmengine - INFO - Iter(train) [156600/160000] base_lr: 5.3433e-07 lr: 5.3433e-07 eta: 0:22:34 time: 0.3803 data_time: 0.0168 memory: 5384 loss: 0.1929 decode.loss_ce: 0.1929 decode.acc_seg: 92.8203 +2024/10/28 09:44:25 - mmengine - INFO - Iter(train) [156650/160000] base_lr: 5.1876e-07 lr: 5.1876e-07 eta: 0:22:14 time: 0.3958 data_time: 0.0173 memory: 5384 loss: 0.1600 decode.loss_ce: 0.1600 decode.acc_seg: 95.4188 +2024/10/28 09:44:44 - mmengine - INFO - Iter(train) [156700/160000] base_lr: 5.0341e-07 lr: 5.0341e-07 eta: 0:21:54 time: 0.3788 data_time: 0.0179 memory: 5384 loss: 0.2062 decode.loss_ce: 0.2062 decode.acc_seg: 88.8108 +2024/10/28 09:45:03 - mmengine - INFO - Iter(train) [156750/160000] base_lr: 4.8830e-07 lr: 4.8830e-07 eta: 0:21:35 time: 0.3789 data_time: 0.0173 memory: 5384 loss: 0.1751 decode.loss_ce: 0.1751 decode.acc_seg: 94.6881 +2024/10/28 09:45:25 - mmengine - INFO - Iter(train) [156800/160000] base_lr: 4.7341e-07 lr: 4.7341e-07 eta: 0:21:15 time: 0.3786 data_time: 0.0173 memory: 5384 loss: 0.1677 decode.loss_ce: 0.1677 decode.acc_seg: 93.8308 +2024/10/28 09:45:44 - mmengine - INFO - Iter(train) [156850/160000] base_lr: 4.5876e-07 lr: 4.5876e-07 eta: 0:20:55 time: 0.3793 data_time: 0.0159 memory: 5384 loss: 0.2314 decode.loss_ce: 0.2314 decode.acc_seg: 88.2146 +2024/10/28 09:46:03 - mmengine - INFO - Iter(train) [156900/160000] base_lr: 4.4433e-07 lr: 4.4433e-07 eta: 0:20:35 time: 0.3764 data_time: 0.0159 memory: 5383 loss: 0.2025 decode.loss_ce: 0.2025 decode.acc_seg: 91.7460 +2024/10/28 09:46:24 - mmengine - INFO - Iter(train) [156950/160000] base_lr: 4.3014e-07 lr: 4.3014e-07 eta: 0:20:15 time: 0.3743 data_time: 0.0163 memory: 5384 loss: 0.2070 decode.loss_ce: 0.2070 decode.acc_seg: 87.7563 +2024/10/28 09:46:43 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:46:43 - mmengine - INFO - Iter(train) [157000/160000] base_lr: 4.1617e-07 lr: 4.1617e-07 eta: 0:19:55 time: 0.3761 data_time: 0.0158 memory: 5384 loss: 0.1883 decode.loss_ce: 0.1883 decode.acc_seg: 91.6150 +2024/10/28 09:47:02 - mmengine - INFO - Iter(train) [157050/160000] base_lr: 4.0243e-07 lr: 4.0243e-07 eta: 0:19:35 time: 0.3767 data_time: 0.0159 memory: 5384 loss: 0.2155 decode.loss_ce: 0.2155 decode.acc_seg: 91.8658 +2024/10/28 09:47:24 - mmengine - INFO - Iter(train) [157100/160000] base_lr: 3.8893e-07 lr: 3.8893e-07 eta: 0:19:15 time: 0.3778 data_time: 0.0163 memory: 5384 loss: 0.2138 decode.loss_ce: 0.2138 decode.acc_seg: 88.7499 +2024/10/28 09:47:44 - mmengine - INFO - Iter(train) [157150/160000] base_lr: 3.7565e-07 lr: 3.7565e-07 eta: 0:18:55 time: 0.3946 data_time: 0.0160 memory: 5384 loss: 0.1756 decode.loss_ce: 0.1756 decode.acc_seg: 94.5566 +2024/10/28 09:48:03 - mmengine - INFO - Iter(train) [157200/160000] base_lr: 3.6260e-07 lr: 3.6260e-07 eta: 0:18:35 time: 0.3806 data_time: 0.0159 memory: 5385 loss: 0.1889 decode.loss_ce: 0.1889 decode.acc_seg: 94.6789 +2024/10/28 09:48:24 - mmengine - INFO - Iter(train) [157250/160000] base_lr: 3.4978e-07 lr: 3.4978e-07 eta: 0:18:15 time: 0.3763 data_time: 0.0174 memory: 5384 loss: 0.1802 decode.loss_ce: 0.1802 decode.acc_seg: 91.2938 +2024/10/28 09:48:44 - mmengine - INFO - Iter(train) [157300/160000] base_lr: 3.3720e-07 lr: 3.3720e-07 eta: 0:17:55 time: 0.3768 data_time: 0.0173 memory: 5384 loss: 0.1965 decode.loss_ce: 0.1965 decode.acc_seg: 93.3042 +2024/10/28 09:49:03 - mmengine - INFO - Iter(train) [157350/160000] base_lr: 3.2484e-07 lr: 3.2484e-07 eta: 0:17:35 time: 0.3788 data_time: 0.0170 memory: 5386 loss: 0.2084 decode.loss_ce: 0.2084 decode.acc_seg: 90.1113 +2024/10/28 09:49:24 - mmengine - INFO - Iter(train) [157400/160000] base_lr: 3.1271e-07 lr: 3.1271e-07 eta: 0:17:16 time: 0.3793 data_time: 0.0186 memory: 5384 loss: 0.1935 decode.loss_ce: 0.1935 decode.acc_seg: 91.9750 +2024/10/28 09:49:43 - mmengine - INFO - Iter(train) [157450/160000] base_lr: 3.0081e-07 lr: 3.0081e-07 eta: 0:16:56 time: 0.3784 data_time: 0.0179 memory: 5384 loss: 0.1871 decode.loss_ce: 0.1871 decode.acc_seg: 94.0466 +2024/10/28 09:50:02 - mmengine - INFO - Iter(train) [157500/160000] base_lr: 2.8915e-07 lr: 2.8915e-07 eta: 0:16:36 time: 0.3868 data_time: 0.0182 memory: 5384 loss: 0.1766 decode.loss_ce: 0.1766 decode.acc_seg: 95.1941 +2024/10/28 09:50:25 - mmengine - INFO - Iter(train) [157550/160000] base_lr: 2.7771e-07 lr: 2.7771e-07 eta: 0:16:16 time: 0.3786 data_time: 0.0179 memory: 5384 loss: 0.1711 decode.loss_ce: 0.1711 decode.acc_seg: 92.2480 +2024/10/28 09:50:44 - mmengine - INFO - Iter(train) [157600/160000] base_lr: 2.6650e-07 lr: 2.6650e-07 eta: 0:15:56 time: 0.3787 data_time: 0.0181 memory: 5385 loss: 0.1992 decode.loss_ce: 0.1992 decode.acc_seg: 93.6367 +2024/10/28 09:51:03 - mmengine - INFO - Iter(train) [157650/160000] base_lr: 2.5553e-07 lr: 2.5553e-07 eta: 0:15:36 time: 0.3828 data_time: 0.0183 memory: 5384 loss: 0.2102 decode.loss_ce: 0.2102 decode.acc_seg: 88.9849 +2024/10/28 09:51:24 - mmengine - INFO - Iter(train) [157700/160000] base_lr: 2.4478e-07 lr: 2.4478e-07 eta: 0:15:16 time: 0.3929 data_time: 0.0177 memory: 5384 loss: 0.2014 decode.loss_ce: 0.2014 decode.acc_seg: 93.1498 +2024/10/28 09:51:43 - mmengine - INFO - Iter(train) [157750/160000] base_lr: 2.3427e-07 lr: 2.3427e-07 eta: 0:14:56 time: 0.3831 data_time: 0.0184 memory: 5384 loss: 0.1740 decode.loss_ce: 0.1740 decode.acc_seg: 93.8778 +2024/10/28 09:52:02 - mmengine - INFO - Iter(train) [157800/160000] base_lr: 2.2398e-07 lr: 2.2398e-07 eta: 0:14:36 time: 0.3825 data_time: 0.0182 memory: 5384 loss: 0.1869 decode.loss_ce: 0.1869 decode.acc_seg: 89.4764 +2024/10/28 09:52:25 - mmengine - INFO - Iter(train) [157850/160000] base_lr: 2.1393e-07 lr: 2.1393e-07 eta: 0:14:16 time: 0.3788 data_time: 0.0173 memory: 5384 loss: 0.1846 decode.loss_ce: 0.1846 decode.acc_seg: 91.2160 +2024/10/28 09:52:44 - mmengine - INFO - Iter(train) [157900/160000] base_lr: 2.0410e-07 lr: 2.0410e-07 eta: 0:13:56 time: 0.3715 data_time: 0.0155 memory: 5384 loss: 0.1947 decode.loss_ce: 0.1947 decode.acc_seg: 83.5556 +2024/10/28 09:53:03 - mmengine - INFO - Iter(train) [157950/160000] base_lr: 1.9451e-07 lr: 1.9451e-07 eta: 0:13:36 time: 0.3815 data_time: 0.0179 memory: 5384 loss: 0.2120 decode.loss_ce: 0.2120 decode.acc_seg: 93.7190 +2024/10/28 09:53:25 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 09:53:25 - mmengine - INFO - Iter(train) [158000/160000] base_lr: 1.8514e-07 lr: 1.8514e-07 eta: 0:13:16 time: 0.3793 data_time: 0.0180 memory: 5384 loss: 0.2118 decode.loss_ce: 0.2118 decode.acc_seg: 86.8916 +2024/10/28 09:53:44 - mmengine - INFO - Iter(train) [158050/160000] base_lr: 1.7601e-07 lr: 1.7601e-07 eta: 0:12:57 time: 0.3777 data_time: 0.0176 memory: 5384 loss: 0.1657 decode.loss_ce: 0.1657 decode.acc_seg: 93.1373 +2024/10/28 09:54:03 - mmengine - INFO - Iter(train) [158100/160000] base_lr: 1.6711e-07 lr: 1.6711e-07 eta: 0:12:37 time: 0.3798 data_time: 0.0170 memory: 5384 loss: 0.2107 decode.loss_ce: 0.2107 decode.acc_seg: 94.8790 +2024/10/28 09:54:26 - mmengine - INFO - Iter(train) [158150/160000] base_lr: 1.5844e-07 lr: 1.5844e-07 eta: 0:12:17 time: 0.3787 data_time: 0.0178 memory: 5384 loss: 0.1786 decode.loss_ce: 0.1786 decode.acc_seg: 89.5742 +2024/10/28 09:54:44 - mmengine - INFO - Iter(train) [158200/160000] base_lr: 1.5000e-07 lr: 1.5000e-07 eta: 0:11:57 time: 0.3777 data_time: 0.0173 memory: 5382 loss: 0.1725 decode.loss_ce: 0.1725 decode.acc_seg: 90.6445 +2024/10/28 09:55:03 - mmengine - INFO - Iter(train) [158250/160000] base_lr: 1.4179e-07 lr: 1.4179e-07 eta: 0:11:37 time: 0.3785 data_time: 0.0171 memory: 5386 loss: 0.1933 decode.loss_ce: 0.1933 decode.acc_seg: 92.3068 +2024/10/28 09:55:25 - mmengine - INFO - Iter(train) [158300/160000] base_lr: 1.3381e-07 lr: 1.3381e-07 eta: 0:11:17 time: 0.3756 data_time: 0.0169 memory: 5384 loss: 0.2050 decode.loss_ce: 0.2050 decode.acc_seg: 91.0008 +2024/10/28 09:55:44 - mmengine - INFO - Iter(train) [158350/160000] base_lr: 1.2606e-07 lr: 1.2606e-07 eta: 0:10:57 time: 0.3816 data_time: 0.0170 memory: 5382 loss: 0.1841 decode.loss_ce: 0.1841 decode.acc_seg: 88.1770 +2024/10/28 09:56:03 - mmengine - INFO - Iter(train) [158400/160000] base_lr: 1.1854e-07 lr: 1.1854e-07 eta: 0:10:37 time: 0.3841 data_time: 0.0162 memory: 5384 loss: 0.2077 decode.loss_ce: 0.2077 decode.acc_seg: 88.3026 +2024/10/28 09:56:25 - mmengine - INFO - Iter(train) [158450/160000] base_lr: 1.1126e-07 lr: 1.1126e-07 eta: 0:10:17 time: 0.3763 data_time: 0.0180 memory: 5384 loss: 0.1835 decode.loss_ce: 0.1835 decode.acc_seg: 94.9278 +2024/10/28 09:56:44 - mmengine - INFO - Iter(train) [158500/160000] base_lr: 1.0420e-07 lr: 1.0420e-07 eta: 0:09:57 time: 0.3803 data_time: 0.0186 memory: 5384 loss: 0.1987 decode.loss_ce: 0.1987 decode.acc_seg: 88.9342 +2024/10/28 09:57:03 - mmengine - INFO - Iter(train) [158550/160000] base_lr: 9.7377e-08 lr: 9.7377e-08 eta: 0:09:37 time: 0.3827 data_time: 0.0172 memory: 5384 loss: 0.1843 decode.loss_ce: 0.1843 decode.acc_seg: 92.2930 +2024/10/28 09:57:24 - mmengine - INFO - Iter(train) [158600/160000] base_lr: 9.0784e-08 lr: 9.0784e-08 eta: 0:09:17 time: 0.3847 data_time: 0.0176 memory: 5383 loss: 0.1602 decode.loss_ce: 0.1602 decode.acc_seg: 93.1270 +2024/10/28 09:57:43 - mmengine - INFO - Iter(train) [158650/160000] base_lr: 8.4421e-08 lr: 8.4421e-08 eta: 0:08:57 time: 0.3799 data_time: 0.0180 memory: 5383 loss: 0.2070 decode.loss_ce: 0.2070 decode.acc_seg: 94.6444 +2024/10/28 09:58:02 - mmengine - INFO - Iter(train) [158700/160000] base_lr: 7.8289e-08 lr: 7.8289e-08 eta: 0:08:38 time: 0.3808 data_time: 0.0173 memory: 5384 loss: 0.1918 decode.loss_ce: 0.1918 decode.acc_seg: 88.0194 +2024/10/28 09:58:25 - mmengine - INFO - Iter(train) [158750/160000] base_lr: 7.2388e-08 lr: 7.2388e-08 eta: 0:08:18 time: 0.3800 data_time: 0.0180 memory: 5384 loss: 0.1931 decode.loss_ce: 0.1931 decode.acc_seg: 93.9565 +2024/10/28 09:58:44 - mmengine - INFO - Iter(train) [158800/160000] base_lr: 6.6719e-08 lr: 6.6719e-08 eta: 0:07:58 time: 0.3780 data_time: 0.0185 memory: 5383 loss: 0.2071 decode.loss_ce: 0.2071 decode.acc_seg: 90.8017 +2024/10/28 09:59:03 - mmengine - INFO - Iter(train) [158850/160000] base_lr: 6.1280e-08 lr: 6.1280e-08 eta: 0:07:38 time: 0.3829 data_time: 0.0177 memory: 5386 loss: 0.1920 decode.loss_ce: 0.1920 decode.acc_seg: 89.8085 +2024/10/28 09:59:24 - mmengine - INFO - Iter(train) [158900/160000] base_lr: 5.6072e-08 lr: 5.6072e-08 eta: 0:07:18 time: 0.3803 data_time: 0.0169 memory: 5385 loss: 0.1707 decode.loss_ce: 0.1707 decode.acc_seg: 93.0203 +2024/10/28 09:59:44 - mmengine - INFO - Iter(train) [158950/160000] base_lr: 5.1096e-08 lr: 5.1096e-08 eta: 0:06:58 time: 0.3782 data_time: 0.0167 memory: 5385 loss: 0.1723 decode.loss_ce: 0.1723 decode.acc_seg: 93.3648 +2024/10/28 10:00:03 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 10:00:03 - mmengine - INFO - Iter(train) [159000/160000] base_lr: 4.6350e-08 lr: 4.6350e-08 eta: 0:06:38 time: 0.3837 data_time: 0.0160 memory: 5386 loss: 0.1745 decode.loss_ce: 0.1745 decode.acc_seg: 92.1610 +2024/10/28 10:00:25 - mmengine - INFO - Iter(train) [159050/160000] base_lr: 4.1836e-08 lr: 4.1836e-08 eta: 0:06:18 time: 0.3783 data_time: 0.0165 memory: 5385 loss: 0.2356 decode.loss_ce: 0.2356 decode.acc_seg: 89.6584 +2024/10/28 10:00:44 - mmengine - INFO - Iter(train) [159100/160000] base_lr: 3.7553e-08 lr: 3.7553e-08 eta: 0:05:58 time: 0.3800 data_time: 0.0170 memory: 5384 loss: 0.1815 decode.loss_ce: 0.1815 decode.acc_seg: 93.0202 +2024/10/28 10:01:03 - mmengine - INFO - Iter(train) [159150/160000] base_lr: 3.3501e-08 lr: 3.3501e-08 eta: 0:05:38 time: 0.3923 data_time: 0.0172 memory: 5383 loss: 0.1982 decode.loss_ce: 0.1982 decode.acc_seg: 93.5500 +2024/10/28 10:01:25 - mmengine - INFO - Iter(train) [159200/160000] base_lr: 2.9680e-08 lr: 2.9680e-08 eta: 0:05:18 time: 0.3766 data_time: 0.0178 memory: 5383 loss: 0.1944 decode.loss_ce: 0.1944 decode.acc_seg: 91.0478 +2024/10/28 10:01:44 - mmengine - INFO - Iter(train) [159250/160000] base_lr: 2.6091e-08 lr: 2.6091e-08 eta: 0:04:58 time: 0.3786 data_time: 0.0176 memory: 5386 loss: 0.1888 decode.loss_ce: 0.1888 decode.acc_seg: 93.2908 +2024/10/28 10:02:03 - mmengine - INFO - Iter(train) [159300/160000] base_lr: 2.2733e-08 lr: 2.2733e-08 eta: 0:04:38 time: 0.3834 data_time: 0.0173 memory: 5384 loss: 0.1993 decode.loss_ce: 0.1993 decode.acc_seg: 92.9516 +2024/10/28 10:02:25 - mmengine - INFO - Iter(train) [159350/160000] base_lr: 1.9606e-08 lr: 1.9606e-08 eta: 0:04:19 time: 0.3759 data_time: 0.0162 memory: 5384 loss: 0.1583 decode.loss_ce: 0.1583 decode.acc_seg: 92.2822 +2024/10/28 10:02:44 - mmengine - INFO - Iter(train) [159400/160000] base_lr: 1.6710e-08 lr: 1.6710e-08 eta: 0:03:59 time: 0.3791 data_time: 0.0172 memory: 5385 loss: 0.1804 decode.loss_ce: 0.1804 decode.acc_seg: 92.5879 +2024/10/28 10:03:04 - mmengine - INFO - Iter(train) [159450/160000] base_lr: 1.4045e-08 lr: 1.4045e-08 eta: 0:03:39 time: 0.3806 data_time: 0.0170 memory: 5384 loss: 0.1679 decode.loss_ce: 0.1679 decode.acc_seg: 91.2566 +2024/10/28 10:03:25 - mmengine - INFO - Iter(train) [159500/160000] base_lr: 1.1612e-08 lr: 1.1612e-08 eta: 0:03:19 time: 0.3791 data_time: 0.0161 memory: 5385 loss: 0.2036 decode.loss_ce: 0.2036 decode.acc_seg: 93.9336 +2024/10/28 10:03:44 - mmengine - INFO - Iter(train) [159550/160000] base_lr: 9.4099e-09 lr: 9.4099e-09 eta: 0:02:59 time: 0.3757 data_time: 0.0161 memory: 5383 loss: 0.1663 decode.loss_ce: 0.1663 decode.acc_seg: 92.8900 +2024/10/28 10:04:03 - mmengine - INFO - Iter(train) [159600/160000] base_lr: 7.4391e-09 lr: 7.4391e-09 eta: 0:02:39 time: 0.3814 data_time: 0.0164 memory: 5384 loss: 0.2173 decode.loss_ce: 0.2173 decode.acc_seg: 93.3559 +2024/10/28 10:04:25 - mmengine - INFO - Iter(train) [159650/160000] base_lr: 5.6997e-09 lr: 5.6997e-09 eta: 0:02:19 time: 0.3763 data_time: 0.0171 memory: 5384 loss: 0.1830 decode.loss_ce: 0.1830 decode.acc_seg: 92.5419 +2024/10/28 10:04:44 - mmengine - INFO - Iter(train) [159700/160000] base_lr: 4.1915e-09 lr: 4.1915e-09 eta: 0:01:59 time: 0.3787 data_time: 0.0171 memory: 5386 loss: 0.1943 decode.loss_ce: 0.1943 decode.acc_seg: 91.4890 +2024/10/28 10:05:03 - mmengine - INFO - Iter(train) [159750/160000] base_lr: 2.9146e-09 lr: 2.9146e-09 eta: 0:01:39 time: 0.3777 data_time: 0.0154 memory: 5384 loss: 0.1862 decode.loss_ce: 0.1862 decode.acc_seg: 94.7549 +2024/10/28 10:05:24 - mmengine - INFO - Iter(train) [159800/160000] base_lr: 1.8691e-09 lr: 1.8691e-09 eta: 0:01:19 time: 0.3769 data_time: 0.0164 memory: 5384 loss: 0.2240 decode.loss_ce: 0.2240 decode.acc_seg: 93.8348 +2024/10/28 10:05:44 - mmengine - INFO - Iter(train) [159850/160000] base_lr: 1.0549e-09 lr: 1.0549e-09 eta: 0:00:59 time: 0.3759 data_time: 0.0177 memory: 5384 loss: 0.1863 decode.loss_ce: 0.1863 decode.acc_seg: 92.9388 +2024/10/28 10:06:03 - mmengine - INFO - Iter(train) [159900/160000] base_lr: 4.7194e-10 lr: 4.7194e-10 eta: 0:00:39 time: 0.3813 data_time: 0.0179 memory: 5383 loss: 0.2055 decode.loss_ce: 0.2055 decode.acc_seg: 92.3662 +2024/10/28 10:06:24 - mmengine - INFO - Iter(train) [159950/160000] base_lr: 1.2033e-10 lr: 1.2033e-10 eta: 0:00:19 time: 0.3797 data_time: 0.0157 memory: 5384 loss: 0.1974 decode.loss_ce: 0.1974 decode.acc_seg: 92.9827 +2024/10/28 10:06:43 - mmengine - INFO - Exp name: fpn_mobilemamba_b4-160k_ade20k-512x512_20241027_180446 +2024/10/28 10:06:43 - mmengine - INFO - Iter(train) [160000/160000] base_lr: 4.6264e-14 lr: 4.6264e-14 eta: 0:00:00 time: 0.3775 data_time: 0.0161 memory: 5384 loss: 0.1937 decode.loss_ce: 0.1937 decode.acc_seg: 95.2668 +2024/10/28 10:06:43 - mmengine - INFO - Saving checkpoint at 160000 iterations +2024/10/28 10:06:47 - mmengine - INFO - Iter(val) [ 50/500] eta: 0:00:16 time: 0.0368 data_time: 0.0018 memory: 980 +2024/10/28 10:06:49 - mmengine - INFO - Iter(val) [100/500] eta: 0:00:14 time: 0.0352 data_time: 0.0016 memory: 1050 +2024/10/28 10:06:51 - mmengine - INFO - Iter(val) [150/500] eta: 0:00:12 time: 0.0357 data_time: 0.0017 memory: 767 +2024/10/28 10:06:53 - mmengine - INFO - Iter(val) [200/500] eta: 0:00:10 time: 0.0352 data_time: 0.0016 memory: 800 +2024/10/28 10:06:54 - mmengine - INFO - Iter(val) [250/500] eta: 0:00:08 time: 0.0361 data_time: 0.0017 memory: 839 +2024/10/28 10:06:56 - mmengine - INFO - Iter(val) [300/500] eta: 0:00:07 time: 0.0385 data_time: 0.0024 memory: 1961 +2024/10/28 10:06:58 - mmengine - INFO - Iter(val) [350/500] eta: 0:00:05 time: 0.0352 data_time: 0.0017 memory: 765 +2024/10/28 10:07:00 - mmengine - INFO - Iter(val) [400/500] eta: 0:00:03 time: 0.0355 data_time: 0.0017 memory: 837 +2024/10/28 10:07:01 - mmengine - INFO - Iter(val) [450/500] eta: 0:00:01 time: 0.0346 data_time: 0.0016 memory: 772 +2024/10/28 10:07:03 - mmengine - INFO - Iter(val) [500/500] eta: 0:00:00 time: 0.0339 data_time: 0.0014 memory: 822 +2024/10/28 10:07:07 - mmengine - INFO - per class results: +2024/10/28 10:07:07 - mmengine - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 72.69 | 86.74 | +| building | 78.32 | 90.42 | +| sky | 92.64 | 96.4 | +| floor | 77.23 | 89.16 | +| tree | 70.42 | 86.99 | +| ceiling | 80.32 | 90.72 | +| road | 82.88 | 90.8 | +| bed | 86.3 | 93.93 | +| windowpane | 57.31 | 71.95 | +| grass | 62.41 | 79.49 | +| cabinet | 54.8 | 66.32 | +| sidewalk | 61.94 | 76.18 | +| person | 72.43 | 88.1 | +| earth | 35.57 | 50.11 | +| door | 40.86 | 54.65 | +| table | 51.22 | 70.02 | +| mountain | 50.86 | 65.34 | +| plant | 47.31 | 57.69 | +| curtain | 68.1 | 82.71 | +| chair | 49.5 | 63.77 | +| car | 78.32 | 87.28 | +| water | 52.53 | 66.88 | +| painting | 65.23 | 79.99 | +| sofa | 60.87 | 75.38 | +| shelf | 35.74 | 53.08 | +| house | 28.79 | 36.57 | +| sea | 56.22 | 84.14 | +| mirror | 60.13 | 70.24 | +| rug | 59.06 | 69.75 | +| field | 25.17 | 43.69 | +| armchair | 40.69 | 58.65 | +| seat | 59.27 | 79.57 | +| fence | 33.48 | 47.83 | +| desk | 44.99 | 59.22 | +| rock | 37.48 | 59.11 | +| wardrobe | 41.58 | 65.43 | +| lamp | 51.34 | 63.19 | +| bathtub | 73.63 | 82.11 | +| railing | 31.31 | 44.87 | +| cushion | 52.05 | 63.97 | +| base | 13.17 | 16.55 | +| box | 16.94 | 24.92 | +| column | 37.1 | 46.62 | +| signboard | 28.81 | 39.64 | +| chest of drawers | 32.24 | 46.01 | +| counter | 27.33 | 34.02 | +| sand | 43.73 | 65.77 | +| sink | 61.42 | 73.99 | +| skyscraper | 50.76 | 64.09 | +| fireplace | 69.72 | 85.11 | +| refrigerator | 66.47 | 83.73 | +| grandstand | 37.56 | 69.39 | +| path | 20.67 | 28.97 | +| stairs | 26.31 | 31.77 | +| runway | 71.35 | 93.6 | +| case | 39.44 | 61.22 | +| pool table | 87.74 | 92.6 | +| pillow | 52.0 | 62.69 | +| screen door | 53.84 | 64.02 | +| stairway | 25.15 | 32.87 | +| river | 6.55 | 11.1 | +| bridge | 66.19 | 77.16 | +| bookcase | 31.65 | 50.14 | +| blind | 37.06 | 40.92 | +| coffee table | 54.19 | 77.1 | +| toilet | 79.28 | 83.87 | +| flower | 29.73 | 42.64 | +| book | 36.96 | 55.28 | +| hill | 3.49 | 5.02 | +| bench | 36.13 | 44.56 | +| countertop | 49.79 | 68.33 | +| stove | 67.61 | 74.58 | +| palm | 45.11 | 65.96 | +| kitchen island | 34.47 | 63.88 | +| computer | 49.94 | 60.62 | +| swivel chair | 30.99 | 43.24 | +| boat | 47.36 | 71.8 | +| bar | 37.26 | 42.97 | +| arcade machine | 51.07 | 56.77 | +| hovel | 24.72 | 28.34 | +| bus | 80.12 | 91.81 | +| towel | 51.09 | 61.78 | +| light | 36.75 | 41.93 | +| truck | 29.29 | 40.9 | +| tower | 23.71 | 46.34 | +| chandelier | 56.76 | 70.18 | +| awning | 17.16 | 20.02 | +| streetlight | 12.86 | 16.06 | +| booth | 61.04 | 75.93 | +| television receiver | 64.92 | 77.23 | +| airplane | 51.95 | 59.61 | +| dirt track | 24.43 | 44.12 | +| apparel | 22.77 | 27.23 | +| pole | 16.4 | 23.17 | +| land | 4.09 | 6.3 | +| bannister | 4.75 | 6.9 | +| escalator | 9.49 | 10.11 | +| ottoman | 42.88 | 55.15 | +| bottle | 25.96 | 44.22 | +| buffet | 41.95 | 48.3 | +| poster | 27.44 | 34.43 | +| stage | 13.85 | 21.3 | +| van | 41.41 | 52.11 | +| ship | 42.67 | 49.51 | +| fountain | 12.18 | 12.58 | +| conveyer belt | 55.87 | 65.0 | +| canopy | 15.42 | 16.62 | +| washer | 55.54 | 62.38 | +| plaything | 19.45 | 34.49 | +| swimming pool | 42.15 | 48.78 | +| stool | 33.21 | 50.85 | +| barrel | 36.19 | 65.1 | +| basket | 21.96 | 27.86 | +| waterfall | 39.41 | 53.78 | +| tent | 93.69 | 97.02 | +| bag | 7.71 | 9.97 | +| minibike | 56.24 | 77.72 | +| cradle | 72.76 | 92.06 | +| oven | 40.17 | 50.46 | +| ball | 38.95 | 47.01 | +| food | 23.31 | 25.04 | +| step | 9.01 | 9.85 | +| tank | 42.83 | 45.94 | +| trade name | 19.0 | 20.94 | +| microwave | 36.88 | 39.44 | +| pot | 37.17 | 44.44 | +| animal | 47.51 | 52.53 | +| bicycle | 41.78 | 58.7 | +| lake | 57.33 | 64.73 | +| dishwasher | 57.39 | 66.77 | +| screen | 72.23 | 82.97 | +| blanket | 6.51 | 7.54 | +| sculpture | 40.33 | 62.69 | +| hood | 58.02 | 62.71 | +| sconce | 31.64 | 37.99 | +| vase | 27.1 | 37.59 | +| traffic light | 23.16 | 35.62 | +| tray | 6.96 | 10.37 | +| ashcan | 34.67 | 42.24 | +| fan | 40.72 | 51.66 | +| pier | 14.78 | 16.65 | +| crt screen | 11.61 | 16.48 | +| plate | 36.76 | 50.18 | +| monitor | 59.06 | 66.81 | +| bulletin board | 32.43 | 37.28 | +| shower | 1.96 | 8.93 | +| radiator | 44.49 | 52.08 | +| glass | 4.62 | 5.0 | +| clock | 11.12 | 31.76 | +| flag | 38.08 | 42.68 | ++---------------------+-------+-------+ +2024/10/28 10:07:07 - mmengine - INFO - Iter(val) [500/500] aAcc: 79.8500 mIoU: 42.5000 mAcc: 53.6600 data_time: 0.0017 time: 0.0356