2023/06/07 08:21:47 - mmengine - INFO - ------------------------------------------------------------ System environment: sys.platform: linux Python: 3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1998644929 GPU 0,1: NVIDIA GeForce RTX 3090 CUDA_HOME: /opt/conda NVCC: Cuda compilation tools, release 11.6, V11.6.124 GCC: gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0 PyTorch: 2.0.1+cu117 PyTorch compiling details: PyTorch built with: - GCC 9.3 - C++ Version: 201703 - Intel(R) oneAPI Math Kernel Library Version 2023.1-Product Build 20230303 for Intel(R) 64 architecture applications - Intel(R) MKL-DNN v2.7.3 (Git Hash 6dbeffbae1f23cbbeae17adb7b5b13f1f37c080e) - OpenMP 201511 (a.k.a. OpenMP 4.5) - LAPACK is enabled (usually provided by MKL) - NNPACK is enabled - CPU capability usage: AVX2 - CUDA Runtime 11.7 - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 - CuDNN 8.5 - Magma 2.6.1 - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.7, CUDNN_VERSION=8.5.0, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -D_GLIBCXX_USE_CXX11_ABI=0 -fabi-version=11 -Wno-deprecated -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 -Wunused-local-typedefs -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_DISABLE_GPU_ASSERTS=ON, TORCH_VERSION=2.0.1, 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: 0.15.2 OpenCV: 4.7.0 MMEngine: 0.7.4 Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: 1998644929 Distributed launcher: pytorch Distributed training: True GPU number: 2 ------------------------------------------------------------ 2023/06/07 08:21:47 - mmengine - INFO - Config: norm_cfg = dict(type='SyncBN', requires_grad=True) data_preprocessor = dict( type='SegDataPreProcessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_val=0, seg_pad_val=255, size=(416, 416)) model = dict( type='EncoderDecoder', data_preprocessor=dict( type='SegDataPreProcessor', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], bgr_to_rgb=True, pad_val=0, seg_pad_val=255, size=(416, 416)), pretrained='mmcls://mobilenet_v2', backbone=dict( type='MobileNetV2', widen_factor=1.0, strides=(1, 2, 2, 1, 1, 1, 1), dilations=(1, 1, 1, 2, 2, 4, 4), out_indices=(1, 2, 4, 6), norm_cfg=dict(type='SyncBN', requires_grad=True)), decode_head=dict( type='DepthwiseSeparableASPPHead', in_channels=320, in_index=3, channels=128, dilations=(1, 12, 24, 36), c1_in_channels=24, c1_channels=12, dropout_ratio=0.1, num_classes=3, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), auxiliary_head=dict( type='FCNHead', in_channels=96, in_index=2, channels=64, num_convs=1, concat_input=False, dropout_ratio=0.1, num_classes=3, norm_cfg=dict(type='SyncBN', requires_grad=True), align_corners=False, loss_decode=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), train_cfg=dict(), test_cfg=dict(mode='whole')) dataset_type = 'DroneDataset' data_root = 'data/drone_custom_dataset' crop_size = (416, 416) train_pipeline = [ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='RandomResize', scale=(2048, 416), ratio_range=(0.5, 2.0), keep_ratio=True), dict(type='RandomCrop', crop_size=(416, 416), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict(type='PackSegInputs') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict(type='Resize', scale=(2048, 416), keep_ratio=True), dict(type='LoadAnnotations'), dict(type='PackSegInputs') ] img_ratios = [0.5, 0.75, 1.0, 1.25, 1.5, 1.75] tta_pipeline = [ dict(type='LoadImageFromFile', backend_args=None), dict( type='TestTimeAug', transforms=[[{ 'type': 'Resize', 'scale_factor': 0.5, 'keep_ratio': True }, { 'type': 'Resize', 'scale_factor': 0.75, 'keep_ratio': True }, { 'type': 'Resize', 'scale_factor': 1.0, 'keep_ratio': True }, { 'type': 'Resize', 'scale_factor': 1.25, 'keep_ratio': True }, { 'type': 'Resize', 'scale_factor': 1.5, 'keep_ratio': True }, { 'type': 'Resize', 'scale_factor': 1.75, 'keep_ratio': True }], [{ 'type': 'RandomFlip', 'prob': 0.0, 'direction': 'horizontal' }, { 'type': 'RandomFlip', 'prob': 1.0, 'direction': 'horizontal' }], [{ 'type': 'LoadAnnotations' }], [{ 'type': 'PackSegInputs' }]]) ] train_dataloader = dict( batch_size=24, num_workers=1, persistent_workers=True, sampler=dict(type='InfiniteSampler', shuffle=True), dataset=dict( type='DroneDataset', data_root='data/drone_custom_dataset', data_prefix=dict(img_path='images', seg_map_path='anns'), ann_file='train.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='LoadAnnotations'), dict( type='RandomResize', scale=(2048, 416), ratio_range=(0.5, 2.0), keep_ratio=True), dict(type='RandomCrop', crop_size=(416, 416), cat_max_ratio=0.75), dict(type='RandomFlip', prob=0.5), dict(type='PhotoMetricDistortion'), dict(type='PackSegInputs') ])) val_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='DroneDataset', data_root='data/drone_custom_dataset', data_prefix=dict(img_path='images', seg_map_path='anns'), ann_file='val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', scale=(2048, 416), keep_ratio=True), dict(type='LoadAnnotations'), dict(type='PackSegInputs') ])) test_dataloader = dict( batch_size=1, num_workers=4, persistent_workers=True, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='DroneDataset', data_root='data/drone_custom_dataset', data_prefix=dict(img_path='images', seg_map_path='anns'), ann_file='val.txt', pipeline=[ dict(type='LoadImageFromFile'), dict(type='Resize', scale=(2048, 416), keep_ratio=True), dict(type='LoadAnnotations'), dict(type='PackSegInputs') ])) val_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) test_evaluator = dict(type='IoUMetric', iou_metrics=['mIoU']) default_scope = 'mmseg' env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='SegLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict(by_epoch=False) log_level = 'INFO' load_from = None resume = False tta_model = dict(type='SegTTAModel') optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) optim_wrapper = dict( type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005), clip_grad=None) param_scheduler = [ dict( type='PolyLR', eta_min=0.0001, power=0.9, begin=0, end=240000, by_epoch=False) ] train_cfg = dict( type='IterBasedTrainLoop', max_iters=240000, val_interval=24000) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), param_scheduler=dict(type='ParamSchedulerHook'), checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=24000), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='SegVisualizationHook')) launcher = 'pytorch' work_dir = './work_dirs/mobilenet_deeplab_drone' 2023/06/07 08:21:48 - 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_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_train: (VERY_LOW ) CheckpointHook -------------------- 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_run: (BELOW_NORMAL) LoggerHook -------------------- 2023/06/07 08:21:49 - mmengine - WARNING - The prefix is not set in metric class IoUMetric. 2023/06/07 08:21:49 - mmengine - INFO - load model from: mmcls://mobilenet_v2 2023/06/07 08:21:49 - mmengine - INFO - Loads checkpoint by mmcls backend from path: mmcls://mobilenet_v2 2023/06/07 08:21:49 - mmengine - WARNING - The model and loaded state dict do not match exactly unexpected key in source state_dict: conv2.conv.weight, conv2.bn.weight, conv2.bn.bias, conv2.bn.running_mean, conv2.bn.running_var, conv2.bn.num_batches_tracked Name of parameter - Initialization information backbone.conv1.conv.weight - torch.Size([32, 3, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.conv1.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.conv1.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.0.conv.weight - torch.Size([32, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.0.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.0.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.1.conv.weight - torch.Size([16, 32, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.1.bn.weight - torch.Size([16]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer1.0.conv.1.bn.bias - torch.Size([16]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.0.conv.weight - torch.Size([96, 16, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.0.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.0.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.1.conv.weight - torch.Size([96, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.1.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.1.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.2.conv.weight - torch.Size([24, 96, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.2.bn.weight - torch.Size([24]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.0.conv.2.bn.bias - torch.Size([24]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.0.conv.weight - torch.Size([144, 24, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.0.bn.weight - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.0.bn.bias - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.1.conv.weight - torch.Size([144, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.1.bn.weight - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.1.bn.bias - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.2.conv.weight - torch.Size([24, 144, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.2.bn.weight - torch.Size([24]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer2.1.conv.2.bn.bias - torch.Size([24]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.0.conv.weight - torch.Size([144, 24, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.0.bn.weight - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.0.bn.bias - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.1.conv.weight - torch.Size([144, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.1.bn.weight - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.1.bn.bias - torch.Size([144]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.2.conv.weight - torch.Size([32, 144, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.2.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.0.conv.2.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.0.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.0.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.1.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.1.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.2.conv.weight - torch.Size([32, 192, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.2.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.1.conv.2.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.0.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.0.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.1.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.1.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.2.conv.weight - torch.Size([32, 192, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.2.bn.weight - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer3.2.conv.2.bn.bias - torch.Size([32]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.0.conv.weight - torch.Size([192, 32, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.0.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.0.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.1.conv.weight - torch.Size([192, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.1.bn.weight - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.1.bn.bias - torch.Size([192]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.2.conv.weight - torch.Size([64, 192, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.0.conv.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.0.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.0.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.1.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.1.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.1.conv.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.0.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.0.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.1.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.1.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.2.conv.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.0.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.0.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.1.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.1.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.2.conv.weight - torch.Size([64, 384, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.2.bn.weight - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer4.3.conv.2.bn.bias - torch.Size([64]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.0.conv.weight - torch.Size([384, 64, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.0.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.0.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.1.conv.weight - torch.Size([384, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.1.bn.weight - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.1.bn.bias - torch.Size([384]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.2.conv.weight - torch.Size([96, 384, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.2.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.0.conv.2.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.0.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.0.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.1.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.1.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.2.conv.weight - torch.Size([96, 576, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.2.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.1.conv.2.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.0.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.0.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.1.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.1.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.2.conv.weight - torch.Size([96, 576, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.2.bn.weight - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer5.2.conv.2.bn.bias - torch.Size([96]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.0.conv.weight - torch.Size([576, 96, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.0.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.0.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.1.conv.weight - torch.Size([576, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.1.bn.weight - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.1.bn.bias - torch.Size([576]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.2.conv.weight - torch.Size([160, 576, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.2.bn.weight - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.0.conv.2.bn.bias - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.0.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.0.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.1.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.1.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.2.conv.weight - torch.Size([160, 960, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.2.bn.weight - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.1.conv.2.bn.bias - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.0.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.0.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.1.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.1.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.2.conv.weight - torch.Size([160, 960, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.2.bn.weight - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer6.2.conv.2.bn.bias - torch.Size([160]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.0.conv.weight - torch.Size([960, 160, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.0.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.0.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.1.conv.weight - torch.Size([960, 1, 3, 3]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.1.bn.weight - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.1.bn.bias - torch.Size([960]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.2.conv.weight - torch.Size([320, 960, 1, 1]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.2.bn.weight - torch.Size([320]): PretrainedInit: load from mmcls://mobilenet_v2 backbone.layer7.0.conv.2.bn.bias - torch.Size([320]): PretrainedInit: load from mmcls://mobilenet_v2 decode_head.conv_seg.weight - torch.Size([3, 128, 1, 1]): NormalInit: mean=0, std=0.01, bias=0 decode_head.conv_seg.bias - torch.Size([3]): NormalInit: mean=0, std=0.01, bias=0 decode_head.image_pool.1.conv.weight - torch.Size([128, 320, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.image_pool.1.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.image_pool.1.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.0.conv.weight - torch.Size([128, 320, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.0.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.0.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.1.depthwise_conv.conv.weight - torch.Size([320, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.1.depthwise_conv.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.1.depthwise_conv.bn.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.1.pointwise_conv.conv.weight - torch.Size([128, 320, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.1.pointwise_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.1.pointwise_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.2.depthwise_conv.conv.weight - torch.Size([320, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.2.depthwise_conv.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.2.depthwise_conv.bn.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.2.pointwise_conv.conv.weight - torch.Size([128, 320, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.2.pointwise_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.2.pointwise_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.3.depthwise_conv.conv.weight - torch.Size([320, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.3.depthwise_conv.bn.weight - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.3.depthwise_conv.bn.bias - torch.Size([320]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.3.pointwise_conv.conv.weight - torch.Size([128, 320, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.3.pointwise_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.aspp_modules.3.pointwise_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.bottleneck.conv.weight - torch.Size([128, 640, 3, 3]): Initialized by user-defined `init_weights` in ConvModule decode_head.bottleneck.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.bottleneck.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.c1_bottleneck.conv.weight - torch.Size([12, 24, 1, 1]): Initialized by user-defined `init_weights` in ConvModule decode_head.c1_bottleneck.bn.weight - torch.Size([12]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.c1_bottleneck.bn.bias - torch.Size([12]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.0.depthwise_conv.conv.weight - torch.Size([140, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.0.depthwise_conv.bn.weight - torch.Size([140]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.0.depthwise_conv.bn.bias - torch.Size([140]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.0.pointwise_conv.conv.weight - torch.Size([128, 140, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.0.pointwise_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.0.pointwise_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.1.depthwise_conv.conv.weight - torch.Size([128, 1, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.1.depthwise_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.1.depthwise_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.1.pointwise_conv.conv.weight - torch.Size([128, 128, 1, 1]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.1.pointwise_conv.bn.weight - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder decode_head.sep_bottleneck.1.pointwise_conv.bn.bias - torch.Size([128]): The value is the same before and after calling `init_weights` of EncoderDecoder auxiliary_head.conv_seg.weight - torch.Size([3, 64, 1, 1]): NormalInit: mean=0, std=0.01, bias=0 auxiliary_head.conv_seg.bias - torch.Size([3]): NormalInit: mean=0, std=0.01, bias=0 auxiliary_head.convs.0.conv.weight - torch.Size([64, 96, 3, 3]): The value is the same before and after calling `init_weights` of EncoderDecoder auxiliary_head.convs.0.bn.weight - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder auxiliary_head.convs.0.bn.bias - torch.Size([64]): The value is the same before and after calling `init_weights` of EncoderDecoder 2023/06/07 08:21:49 - mmengine - WARNING - "FileClient" will be deprecated in future. Please use io functions in https://mmengine.readthedocs.io/en/latest/api/fileio.html#file-io 2023/06/07 08:21:49 - mmengine - WARNING - "HardDiskBackend" is the alias of "LocalBackend" and the former will be deprecated in future. 2023/06/07 08:21:49 - mmengine - INFO - Checkpoints will be saved to /workspace/mmsegmentation/work_dirs/mobilenet_deeplab_drone. 2023/06/07 08:22:29 - mmengine - INFO - Iter(train) [ 50/240000] lr: 9.9982e-03 eta: 2 days, 5:17:14 time: 0.7278 data_time: 0.2624 memory: 19289 loss: 0.8325 decode.loss_ce: 0.5750 decode.acc_seg: 70.1639 aux.loss_ce: 0.2575 aux.acc_seg: 73.7644 2023/06/07 08:23:05 - mmengine - INFO - Iter(train) [ 100/240000] lr: 9.9963e-03 eta: 2 days, 2:38:06 time: 0.7261 data_time: 0.3195 memory: 17393 loss: 0.6319 decode.loss_ce: 0.4384 decode.acc_seg: 82.3955 aux.loss_ce: 0.1935 aux.acc_seg: 80.9960 2023/06/07 08:23:41 - mmengine - INFO - Iter(train) [ 150/240000] lr: 9.9945e-03 eta: 2 days, 1:57:47 time: 0.7293 data_time: 0.2307 memory: 17393 loss: 0.5814 decode.loss_ce: 0.4075 decode.acc_seg: 81.8291 aux.loss_ce: 0.1739 aux.acc_seg: 81.2621 2023/06/07 08:24:17 - mmengine - INFO - Iter(train) [ 200/240000] lr: 9.9926e-03 eta: 2 days, 1:28:52 time: 0.7142 data_time: 0.3783 memory: 17393 loss: 0.5667 decode.loss_ce: 0.3933 decode.acc_seg: 78.7280 aux.loss_ce: 0.1735 aux.acc_seg: 77.5627 2023/06/07 08:24:53 - mmengine - INFO - Iter(train) [ 250/240000] lr: 9.9908e-03 eta: 2 days, 1:06:28 time: 0.7180 data_time: 0.3918 memory: 17391 loss: 0.5533 decode.loss_ce: 0.3885 decode.acc_seg: 76.1210 aux.loss_ce: 0.1648 aux.acc_seg: 76.8160 2023/06/07 08:25:29 - mmengine - INFO - Iter(train) [ 300/240000] lr: 9.9889e-03 eta: 2 days, 0:54:35 time: 0.7293 data_time: 0.3850 memory: 17393 loss: 0.5435 decode.loss_ce: 0.3792 decode.acc_seg: 84.0982 aux.loss_ce: 0.1643 aux.acc_seg: 79.7279 2023/06/07 08:26:06 - mmengine - INFO - Iter(train) [ 350/240000] lr: 9.9870e-03 eta: 2 days, 0:51:57 time: 0.7411 data_time: 0.3891 memory: 17394 loss: 0.5166 decode.loss_ce: 0.3627 decode.acc_seg: 84.3475 aux.loss_ce: 0.1539 aux.acc_seg: 83.6045 2023/06/07 08:26:42 - mmengine - INFO - Iter(train) [ 400/240000] lr: 9.9852e-03 eta: 2 days, 0:46:21 time: 0.7339 data_time: 0.3943 memory: 17392 loss: 0.4910 decode.loss_ce: 0.3411 decode.acc_seg: 85.4769 aux.loss_ce: 0.1499 aux.acc_seg: 83.4603 2023/06/07 08:27:18 - mmengine - INFO - Iter(train) [ 450/240000] lr: 9.9833e-03 eta: 2 days, 0:41:27 time: 0.7252 data_time: 0.3876 memory: 17394 loss: 0.4589 decode.loss_ce: 0.3184 decode.acc_seg: 85.4586 aux.loss_ce: 0.1406 aux.acc_seg: 83.1537 2023/06/07 08:27:54 - mmengine - INFO - Iter(train) [ 500/240000] lr: 9.9815e-03 eta: 2 days, 0:38:06 time: 0.7209 data_time: 0.3775 memory: 17393 loss: 0.4945 decode.loss_ce: 0.3437 decode.acc_seg: 86.0555 aux.loss_ce: 0.1508 aux.acc_seg: 81.9062 2023/06/07 08:28:30 - mmengine - INFO - Iter(train) [ 550/240000] lr: 9.9796e-03 eta: 2 days, 0:33:06 time: 0.7109 data_time: 0.3840 memory: 17394 loss: 0.4388 decode.loss_ce: 0.3019 decode.acc_seg: 86.7880 aux.loss_ce: 0.1369 aux.acc_seg: 84.2811 2023/06/07 08:29:07 - mmengine - INFO - Iter(train) [ 600/240000] lr: 9.9778e-03 eta: 2 days, 0:32:31 time: 0.7274 data_time: 0.3836 memory: 17391 loss: 0.4491 decode.loss_ce: 0.3109 decode.acc_seg: 84.2190 aux.loss_ce: 0.1382 aux.acc_seg: 81.1930 2023/06/07 08:29:43 - mmengine - INFO - Iter(train) [ 650/240000] lr: 9.9759e-03 eta: 2 days, 0:31:45 time: 0.7318 data_time: 0.3790 memory: 17394 loss: 0.4315 decode.loss_ce: 0.2982 decode.acc_seg: 86.8267 aux.loss_ce: 0.1333 aux.acc_seg: 85.1578 2023/06/07 08:30:19 - mmengine - INFO - Iter(train) [ 700/240000] lr: 9.9740e-03 eta: 2 days, 0:29:08 time: 0.7398 data_time: 0.3981 memory: 17392 loss: 0.4410 decode.loss_ce: 0.3049 decode.acc_seg: 87.3562 aux.loss_ce: 0.1361 aux.acc_seg: 86.2129 2023/06/07 08:30:56 - mmengine - INFO - Iter(train) [ 750/240000] lr: 9.9722e-03 eta: 2 days, 0:28:38 time: 0.7392 data_time: 0.3922 memory: 17392 loss: 0.4375 decode.loss_ce: 0.3016 decode.acc_seg: 88.0438 aux.loss_ce: 0.1359 aux.acc_seg: 86.8285 2023/06/07 08:31:32 - mmengine - INFO - Iter(train) [ 800/240000] lr: 9.9703e-03 eta: 2 days, 0:28:11 time: 0.7267 data_time: 0.3810 memory: 17392 loss: 0.4065 decode.loss_ce: 0.2810 decode.acc_seg: 88.3210 aux.loss_ce: 0.1255 aux.acc_seg: 86.7530 2023/06/07 08:32:09 - mmengine - INFO - Iter(train) [ 850/240000] lr: 9.9685e-03 eta: 2 days, 0:27:31 time: 0.7218 data_time: 0.3804 memory: 17394 loss: 0.4327 decode.loss_ce: 0.2964 decode.acc_seg: 87.4225 aux.loss_ce: 0.1363 aux.acc_seg: 83.7222 2023/06/07 08:32:45 - mmengine - INFO - Iter(train) [ 900/240000] lr: 9.9666e-03 eta: 2 days, 0:26:00 time: 0.7343 data_time: 0.3996 memory: 17391 loss: 0.4232 decode.loss_ce: 0.2897 decode.acc_seg: 85.3209 aux.loss_ce: 0.1335 aux.acc_seg: 84.5729 2023/06/07 08:33:22 - mmengine - INFO - Iter(train) [ 950/240000] lr: 9.9648e-03 eta: 2 days, 0:25:26 time: 0.7298 data_time: 0.4010 memory: 17394 loss: 0.4038 decode.loss_ce: 0.2743 decode.acc_seg: 90.1587 aux.loss_ce: 0.1295 aux.acc_seg: 87.5932 2023/06/07 08:33:38 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 08:33:58 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 08:33:58 - mmengine - INFO - Iter(train) [ 1000/240000] lr: 9.9629e-03 eta: 2 days, 0:24:52 time: 0.7322 data_time: 0.3922 memory: 17393 loss: 0.4130 decode.loss_ce: 0.2845 decode.acc_seg: 88.9051 aux.loss_ce: 0.1285 aux.acc_seg: 87.4262 2023/06/07 08:34:34 - mmengine - INFO - Iter(train) [ 1050/240000] lr: 9.9610e-03 eta: 2 days, 0:22:49 time: 0.7196 data_time: 0.3799 memory: 17392 loss: 0.3951 decode.loss_ce: 0.2712 decode.acc_seg: 89.8859 aux.loss_ce: 0.1238 aux.acc_seg: 88.8799 2023/06/07 08:35:10 - mmengine - INFO - Iter(train) [ 1100/240000] lr: 9.9592e-03 eta: 2 days, 0:21:13 time: 0.7172 data_time: 0.3860 memory: 17390 loss: 0.3864 decode.loss_ce: 0.2629 decode.acc_seg: 89.8206 aux.loss_ce: 0.1236 aux.acc_seg: 88.2360 2023/06/07 08:35:47 - mmengine - INFO - Iter(train) [ 1150/240000] lr: 9.9573e-03 eta: 2 days, 0:20:12 time: 0.7291 data_time: 0.3904 memory: 17391 loss: 0.4056 decode.loss_ce: 0.2751 decode.acc_seg: 87.4646 aux.loss_ce: 0.1305 aux.acc_seg: 84.9306 2023/06/07 08:36:23 - mmengine - INFO - Iter(train) [ 1200/240000] lr: 9.9555e-03 eta: 2 days, 0:18:58 time: 0.7230 data_time: 0.3844 memory: 17392 loss: 0.3863 decode.loss_ce: 0.2646 decode.acc_seg: 85.6615 aux.loss_ce: 0.1217 aux.acc_seg: 83.5950 2023/06/07 08:36:59 - mmengine - INFO - Iter(train) [ 1250/240000] lr: 9.9536e-03 eta: 2 days, 0:17:58 time: 0.7268 data_time: 0.3797 memory: 17395 loss: 0.3915 decode.loss_ce: 0.2671 decode.acc_seg: 87.2680 aux.loss_ce: 0.1244 aux.acc_seg: 83.2477 2023/06/07 08:37:36 - mmengine - INFO - Iter(train) [ 1300/240000] lr: 9.9518e-03 eta: 2 days, 0:17:46 time: 0.7263 data_time: 0.0120 memory: 17394 loss: 0.3717 decode.loss_ce: 0.2559 decode.acc_seg: 89.4016 aux.loss_ce: 0.1158 aux.acc_seg: 87.0072 2023/06/07 08:38:12 - mmengine - INFO - Iter(train) [ 1350/240000] lr: 9.9499e-03 eta: 2 days, 0:17:53 time: 0.7430 data_time: 0.0122 memory: 17395 loss: 0.3898 decode.loss_ce: 0.2661 decode.acc_seg: 87.9344 aux.loss_ce: 0.1237 aux.acc_seg: 84.7718 2023/06/07 08:38:49 - mmengine - INFO - Iter(train) [ 1400/240000] lr: 9.9480e-03 eta: 2 days, 0:16:21 time: 0.7107 data_time: 0.0119 memory: 17394 loss: 0.3620 decode.loss_ce: 0.2472 decode.acc_seg: 88.2480 aux.loss_ce: 0.1148 aux.acc_seg: 86.4280 2023/06/07 08:39:24 - mmengine - INFO - Iter(train) [ 1450/240000] lr: 9.9462e-03 eta: 2 days, 0:14:11 time: 0.7196 data_time: 0.0120 memory: 17391 loss: 0.3619 decode.loss_ce: 0.2461 decode.acc_seg: 92.2529 aux.loss_ce: 0.1158 aux.acc_seg: 90.8195 2023/06/07 08:40:00 - mmengine - INFO - Iter(train) [ 1500/240000] lr: 9.9443e-03 eta: 2 days, 0:11:34 time: 0.7130 data_time: 0.0119 memory: 17391 loss: 0.3628 decode.loss_ce: 0.2471 decode.acc_seg: 88.9759 aux.loss_ce: 0.1158 aux.acc_seg: 87.3308 2023/06/07 08:40:36 - mmengine - INFO - Iter(train) [ 1550/240000] lr: 9.9425e-03 eta: 2 days, 0:09:13 time: 0.7030 data_time: 0.0120 memory: 17391 loss: 0.3663 decode.loss_ce: 0.2490 decode.acc_seg: 88.7688 aux.loss_ce: 0.1173 aux.acc_seg: 86.0841 2023/06/07 08:41:11 - mmengine - INFO - Iter(train) [ 1600/240000] lr: 9.9406e-03 eta: 2 days, 0:06:41 time: 0.7100 data_time: 0.0122 memory: 17393 loss: 0.3702 decode.loss_ce: 0.2539 decode.acc_seg: 88.9615 aux.loss_ce: 0.1163 aux.acc_seg: 85.9056 2023/06/07 08:41:48 - mmengine - INFO - Iter(train) [ 1650/240000] lr: 9.9388e-03 eta: 2 days, 0:06:19 time: 0.7248 data_time: 0.0115 memory: 17393 loss: 0.3621 decode.loss_ce: 0.2460 decode.acc_seg: 89.9897 aux.loss_ce: 0.1161 aux.acc_seg: 87.7559 2023/06/07 08:42:24 - mmengine - INFO - Iter(train) [ 1700/240000] lr: 9.9369e-03 eta: 2 days, 0:06:44 time: 0.7419 data_time: 0.0125 memory: 17393 loss: 0.3472 decode.loss_ce: 0.2368 decode.acc_seg: 92.3443 aux.loss_ce: 0.1104 aux.acc_seg: 90.3317 2023/06/07 08:43:01 - mmengine - INFO - Iter(train) [ 1750/240000] lr: 9.9350e-03 eta: 2 days, 0:06:25 time: 0.7371 data_time: 0.0124 memory: 17392 loss: 0.3861 decode.loss_ce: 0.2636 decode.acc_seg: 87.4701 aux.loss_ce: 0.1225 aux.acc_seg: 85.4371 2023/06/07 08:43:37 - mmengine - INFO - Iter(train) [ 1800/240000] lr: 9.9332e-03 eta: 2 days, 0:05:20 time: 0.7282 data_time: 0.0137 memory: 17393 loss: 0.3594 decode.loss_ce: 0.2414 decode.acc_seg: 88.9618 aux.loss_ce: 0.1180 aux.acc_seg: 87.0503 2023/06/07 08:44:13 - mmengine - INFO - Iter(train) [ 1850/240000] lr: 9.9313e-03 eta: 2 days, 0:03:54 time: 0.7146 data_time: 0.0121 memory: 17394 loss: 0.3589 decode.loss_ce: 0.2422 decode.acc_seg: 89.2836 aux.loss_ce: 0.1167 aux.acc_seg: 86.1874 2023/06/07 08:44:49 - mmengine - INFO - Iter(train) [ 1900/240000] lr: 9.9295e-03 eta: 2 days, 0:02:55 time: 0.7216 data_time: 0.0124 memory: 17393 loss: 0.3605 decode.loss_ce: 0.2428 decode.acc_seg: 90.6996 aux.loss_ce: 0.1177 aux.acc_seg: 87.8883 2023/06/07 08:45:26 - mmengine - INFO - Iter(train) [ 1950/240000] lr: 9.9276e-03 eta: 2 days, 0:02:37 time: 0.7254 data_time: 0.0119 memory: 17393 loss: 0.3606 decode.loss_ce: 0.2424 decode.acc_seg: 89.8805 aux.loss_ce: 0.1182 aux.acc_seg: 87.8911 2023/06/07 08:46:02 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 08:46:02 - mmengine - INFO - Iter(train) [ 2000/240000] lr: 9.9258e-03 eta: 2 days, 0:01:22 time: 0.7319 data_time: 0.0118 memory: 17391 loss: 0.3604 decode.loss_ce: 0.2457 decode.acc_seg: 88.8957 aux.loss_ce: 0.1147 aux.acc_seg: 87.5121 2023/06/07 08:46:38 - mmengine - INFO - Iter(train) [ 2050/240000] lr: 9.9239e-03 eta: 2 days, 0:00:35 time: 0.7177 data_time: 0.0122 memory: 17391 loss: 0.3471 decode.loss_ce: 0.2349 decode.acc_seg: 91.0538 aux.loss_ce: 0.1122 aux.acc_seg: 89.3779 2023/06/07 08:47:14 - mmengine - INFO - Iter(train) [ 2100/240000] lr: 9.9220e-03 eta: 2 days, 0:00:10 time: 0.7205 data_time: 0.0121 memory: 17392 loss: 0.3471 decode.loss_ce: 0.2362 decode.acc_seg: 89.9077 aux.loss_ce: 0.1109 aux.acc_seg: 88.3505 2023/06/07 08:47:50 - mmengine - INFO - Iter(train) [ 2150/240000] lr: 9.9202e-03 eta: 1 day, 23:59:20 time: 0.7255 data_time: 0.0121 memory: 17392 loss: 0.3722 decode.loss_ce: 0.2555 decode.acc_seg: 88.0637 aux.loss_ce: 0.1167 aux.acc_seg: 87.1525 2023/06/07 08:48:27 - mmengine - INFO - Iter(train) [ 2200/240000] lr: 9.9183e-03 eta: 1 day, 23:59:55 time: 0.7491 data_time: 0.0124 memory: 17394 loss: 0.3398 decode.loss_ce: 0.2265 decode.acc_seg: 86.6737 aux.loss_ce: 0.1133 aux.acc_seg: 84.2594 2023/06/07 08:49:04 - mmengine - INFO - Iter(train) [ 2250/240000] lr: 9.9165e-03 eta: 1 day, 23:59:18 time: 0.7247 data_time: 0.0125 memory: 17392 loss: 0.3360 decode.loss_ce: 0.2276 decode.acc_seg: 90.0693 aux.loss_ce: 0.1083 aux.acc_seg: 87.6791 2023/06/07 08:49:40 - mmengine - INFO - Iter(train) [ 2300/240000] lr: 9.9146e-03 eta: 1 day, 23:57:39 time: 0.7146 data_time: 0.0123 memory: 17392 loss: 0.3354 decode.loss_ce: 0.2270 decode.acc_seg: 89.3767 aux.loss_ce: 0.1084 aux.acc_seg: 86.5148 2023/06/07 08:50:15 - mmengine - INFO - Iter(train) [ 2350/240000] lr: 9.9128e-03 eta: 1 day, 23:55:22 time: 0.7074 data_time: 0.2976 memory: 17391 loss: 0.3402 decode.loss_ce: 0.2313 decode.acc_seg: 88.9656 aux.loss_ce: 0.1089 aux.acc_seg: 86.9368 2023/06/07 08:50:51 - mmengine - INFO - Iter(train) [ 2400/240000] lr: 9.9109e-03 eta: 1 day, 23:54:10 time: 0.7127 data_time: 0.0120 memory: 17393 loss: 0.3640 decode.loss_ce: 0.2470 decode.acc_seg: 90.5126 aux.loss_ce: 0.1170 aux.acc_seg: 88.7816 2023/06/07 08:51:26 - mmengine - INFO - Iter(train) [ 2450/240000] lr: 9.9090e-03 eta: 1 day, 23:51:43 time: 0.6993 data_time: 0.0150 memory: 17392 loss: 0.3553 decode.loss_ce: 0.2430 decode.acc_seg: 83.2516 aux.loss_ce: 0.1122 aux.acc_seg: 82.0690 2023/06/07 08:52:02 - mmengine - INFO - Iter(train) [ 2500/240000] lr: 9.9072e-03 eta: 1 day, 23:50:06 time: 0.7117 data_time: 0.0120 memory: 17395 loss: 0.3458 decode.loss_ce: 0.2348 decode.acc_seg: 89.1497 aux.loss_ce: 0.1110 aux.acc_seg: 87.3798 2023/06/07 08:52:37 - mmengine - INFO - Iter(train) [ 2550/240000] lr: 9.9053e-03 eta: 1 day, 23:47:55 time: 0.7095 data_time: 0.0347 memory: 17392 loss: 0.3420 decode.loss_ce: 0.2340 decode.acc_seg: 88.8743 aux.loss_ce: 0.1080 aux.acc_seg: 86.7731 2023/06/07 08:53:12 - mmengine - INFO - Iter(train) [ 2600/240000] lr: 9.9035e-03 eta: 1 day, 23:46:04 time: 0.7090 data_time: 0.0120 memory: 17391 loss: 0.3311 decode.loss_ce: 0.2252 decode.acc_seg: 88.8144 aux.loss_ce: 0.1059 aux.acc_seg: 86.3185 2023/06/07 08:53:48 - mmengine - INFO - Iter(train) [ 2650/240000] lr: 9.9016e-03 eta: 1 day, 23:44:31 time: 0.7048 data_time: 0.0120 memory: 17393 loss: 0.3144 decode.loss_ce: 0.2109 decode.acc_seg: 91.2077 aux.loss_ce: 0.1035 aux.acc_seg: 86.6307 2023/06/07 08:54:23 - mmengine - INFO - Iter(train) [ 2700/240000] lr: 9.8997e-03 eta: 1 day, 23:42:52 time: 0.7154 data_time: 0.0119 memory: 17392 loss: 0.3129 decode.loss_ce: 0.2086 decode.acc_seg: 90.1970 aux.loss_ce: 0.1043 aux.acc_seg: 88.6720 2023/06/07 08:54:59 - mmengine - INFO - Iter(train) [ 2750/240000] lr: 9.8979e-03 eta: 1 day, 23:41:18 time: 0.7112 data_time: 0.0119 memory: 17392 loss: 0.3330 decode.loss_ce: 0.2229 decode.acc_seg: 90.1929 aux.loss_ce: 0.1101 aux.acc_seg: 87.3512 2023/06/07 08:55:34 - mmengine - INFO - Iter(train) [ 2800/240000] lr: 9.8960e-03 eta: 1 day, 23:39:45 time: 0.7044 data_time: 0.0120 memory: 17394 loss: 0.3258 decode.loss_ce: 0.2204 decode.acc_seg: 90.5963 aux.loss_ce: 0.1054 aux.acc_seg: 88.3478 2023/06/07 08:56:10 - mmengine - INFO - Iter(train) [ 2850/240000] lr: 9.8942e-03 eta: 1 day, 23:38:33 time: 0.7170 data_time: 0.0118 memory: 17392 loss: 0.3316 decode.loss_ce: 0.2272 decode.acc_seg: 89.3413 aux.loss_ce: 0.1045 aux.acc_seg: 87.1939 2023/06/07 08:56:46 - mmengine - INFO - Iter(train) [ 2900/240000] lr: 9.8923e-03 eta: 1 day, 23:37:16 time: 0.7084 data_time: 0.0120 memory: 17391 loss: 0.3289 decode.loss_ce: 0.2231 decode.acc_seg: 90.9027 aux.loss_ce: 0.1058 aux.acc_seg: 88.9762 2023/06/07 08:57:21 - mmengine - INFO - Iter(train) [ 2950/240000] lr: 9.8905e-03 eta: 1 day, 23:35:47 time: 0.7236 data_time: 0.0120 memory: 17390 loss: 0.3260 decode.loss_ce: 0.2212 decode.acc_seg: 91.1301 aux.loss_ce: 0.1049 aux.acc_seg: 88.1778 2023/06/07 08:57:56 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 08:57:56 - mmengine - INFO - Iter(train) [ 3000/240000] lr: 9.8886e-03 eta: 1 day, 23:34:00 time: 0.6993 data_time: 0.0156 memory: 17394 loss: 0.3138 decode.loss_ce: 0.2131 decode.acc_seg: 89.0682 aux.loss_ce: 0.1007 aux.acc_seg: 87.1804 2023/06/07 08:58:32 - mmengine - INFO - Iter(train) [ 3050/240000] lr: 9.8867e-03 eta: 1 day, 23:32:40 time: 0.7040 data_time: 0.0119 memory: 17392 loss: 0.3151 decode.loss_ce: 0.2142 decode.acc_seg: 90.8330 aux.loss_ce: 0.1009 aux.acc_seg: 89.5455 2023/06/07 08:59:07 - mmengine - INFO - Iter(train) [ 3100/240000] lr: 9.8849e-03 eta: 1 day, 23:31:10 time: 0.7000 data_time: 0.1946 memory: 17391 loss: 0.3381 decode.loss_ce: 0.2264 decode.acc_seg: 91.6955 aux.loss_ce: 0.1116 aux.acc_seg: 89.9924 2023/06/07 08:59:43 - mmengine - INFO - Iter(train) [ 3150/240000] lr: 9.8830e-03 eta: 1 day, 23:30:09 time: 0.7124 data_time: 0.1530 memory: 17391 loss: 0.3254 decode.loss_ce: 0.2229 decode.acc_seg: 90.1092 aux.loss_ce: 0.1025 aux.acc_seg: 88.3012 2023/06/07 09:00:19 - mmengine - INFO - Iter(train) [ 3200/240000] lr: 9.8812e-03 eta: 1 day, 23:28:48 time: 0.7018 data_time: 0.3214 memory: 17392 loss: 0.2951 decode.loss_ce: 0.1983 decode.acc_seg: 89.2626 aux.loss_ce: 0.0968 aux.acc_seg: 85.1935 2023/06/07 09:00:54 - mmengine - INFO - Iter(train) [ 3250/240000] lr: 9.8793e-03 eta: 1 day, 23:27:12 time: 0.7107 data_time: 0.3222 memory: 17392 loss: 0.3167 decode.loss_ce: 0.2150 decode.acc_seg: 89.1420 aux.loss_ce: 0.1018 aux.acc_seg: 86.2191 2023/06/07 09:01:29 - mmengine - INFO - Iter(train) [ 3300/240000] lr: 9.8774e-03 eta: 1 day, 23:25:34 time: 0.6957 data_time: 0.2715 memory: 17394 loss: 0.3049 decode.loss_ce: 0.2053 decode.acc_seg: 92.6060 aux.loss_ce: 0.0995 aux.acc_seg: 89.9015 2023/06/07 09:02:05 - mmengine - INFO - Iter(train) [ 3350/240000] lr: 9.8756e-03 eta: 1 day, 23:24:31 time: 0.7221 data_time: 0.0731 memory: 17391 loss: 0.3106 decode.loss_ce: 0.2093 decode.acc_seg: 92.3409 aux.loss_ce: 0.1013 aux.acc_seg: 91.6726 2023/06/07 09:02:40 - mmengine - INFO - Iter(train) [ 3400/240000] lr: 9.8737e-03 eta: 1 day, 23:23:06 time: 0.7138 data_time: 0.3889 memory: 17393 loss: 0.3123 decode.loss_ce: 0.2095 decode.acc_seg: 92.2068 aux.loss_ce: 0.1027 aux.acc_seg: 90.7471 2023/06/07 09:03:16 - mmengine - INFO - Iter(train) [ 3450/240000] lr: 9.8719e-03 eta: 1 day, 23:21:40 time: 0.7056 data_time: 0.3148 memory: 17395 loss: 0.3025 decode.loss_ce: 0.2031 decode.acc_seg: 90.8506 aux.loss_ce: 0.0994 aux.acc_seg: 87.0286 2023/06/07 09:03:51 - mmengine - INFO - Iter(train) [ 3500/240000] lr: 9.8700e-03 eta: 1 day, 23:20:32 time: 0.7248 data_time: 0.2580 memory: 17392 loss: 0.3182 decode.loss_ce: 0.2170 decode.acc_seg: 92.4509 aux.loss_ce: 0.1013 aux.acc_seg: 91.3298 2023/06/07 09:04:27 - mmengine - INFO - Iter(train) [ 3550/240000] lr: 9.8681e-03 eta: 1 day, 23:19:26 time: 0.7168 data_time: 0.0788 memory: 17393 loss: 0.3097 decode.loss_ce: 0.2089 decode.acc_seg: 88.5057 aux.loss_ce: 0.1007 aux.acc_seg: 86.3356 2023/06/07 09:05:02 - mmengine - INFO - Iter(train) [ 3600/240000] lr: 9.8663e-03 eta: 1 day, 23:18:12 time: 0.7126 data_time: 0.0117 memory: 17392 loss: 0.3042 decode.loss_ce: 0.2031 decode.acc_seg: 89.5452 aux.loss_ce: 0.1011 aux.acc_seg: 86.3796 2023/06/07 09:05:38 - mmengine - INFO - Iter(train) [ 3650/240000] lr: 9.8644e-03 eta: 1 day, 23:16:59 time: 0.7071 data_time: 0.0600 memory: 17393 loss: 0.3077 decode.loss_ce: 0.2079 decode.acc_seg: 91.7805 aux.loss_ce: 0.0998 aux.acc_seg: 88.7055 2023/06/07 09:06:13 - mmengine - INFO - Iter(train) [ 3700/240000] lr: 9.8626e-03 eta: 1 day, 23:15:44 time: 0.7066 data_time: 0.1462 memory: 17392 loss: 0.3081 decode.loss_ce: 0.2069 decode.acc_seg: 88.2817 aux.loss_ce: 0.1012 aux.acc_seg: 86.3034 2023/06/07 09:06:48 - mmengine - INFO - Iter(train) [ 3750/240000] lr: 9.8607e-03 eta: 1 day, 23:14:33 time: 0.7098 data_time: 0.2509 memory: 17393 loss: 0.3140 decode.loss_ce: 0.2107 decode.acc_seg: 92.4028 aux.loss_ce: 0.1034 aux.acc_seg: 91.2961 2023/06/07 09:07:25 - mmengine - INFO - Iter(train) [ 3800/240000] lr: 9.8588e-03 eta: 1 day, 23:14:16 time: 0.7398 data_time: 0.0121 memory: 17393 loss: 0.3302 decode.loss_ce: 0.2222 decode.acc_seg: 91.5318 aux.loss_ce: 0.1080 aux.acc_seg: 90.2118 2023/06/07 09:08:01 - mmengine - INFO - Iter(train) [ 3850/240000] lr: 9.8570e-03 eta: 1 day, 23:14:13 time: 0.7281 data_time: 0.0118 memory: 17393 loss: 0.2943 decode.loss_ce: 0.1989 decode.acc_seg: 91.4078 aux.loss_ce: 0.0954 aux.acc_seg: 89.8647 2023/06/07 09:08:38 - mmengine - INFO - Iter(train) [ 3900/240000] lr: 9.8551e-03 eta: 1 day, 23:14:49 time: 0.7341 data_time: 0.0118 memory: 17395 loss: 0.3140 decode.loss_ce: 0.2117 decode.acc_seg: 91.1357 aux.loss_ce: 0.1023 aux.acc_seg: 88.5519 2023/06/07 09:09:15 - mmengine - INFO - Iter(train) [ 3950/240000] lr: 9.8533e-03 eta: 1 day, 23:14:29 time: 0.7187 data_time: 0.0120 memory: 17392 loss: 0.2940 decode.loss_ce: 0.1984 decode.acc_seg: 90.2773 aux.loss_ce: 0.0956 aux.acc_seg: 89.0762 2023/06/07 09:09:52 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 09:09:52 - mmengine - INFO - Iter(train) [ 4000/240000] lr: 9.8514e-03 eta: 1 day, 23:14:52 time: 0.7307 data_time: 0.0119 memory: 17391 loss: 0.3028 decode.loss_ce: 0.2024 decode.acc_seg: 88.7642 aux.loss_ce: 0.1004 aux.acc_seg: 85.8144 2023/06/07 09:10:29 - mmengine - INFO - Iter(train) [ 4050/240000] lr: 9.8496e-03 eta: 1 day, 23:15:11 time: 0.7441 data_time: 0.0129 memory: 17395 loss: 0.3095 decode.loss_ce: 0.2095 decode.acc_seg: 90.0875 aux.loss_ce: 0.0999 aux.acc_seg: 87.6843 2023/06/07 09:11:05 - mmengine - INFO - Iter(train) [ 4100/240000] lr: 9.8477e-03 eta: 1 day, 23:14:52 time: 0.7124 data_time: 0.0123 memory: 17391 loss: 0.3167 decode.loss_ce: 0.2148 decode.acc_seg: 81.9400 aux.loss_ce: 0.1020 aux.acc_seg: 80.9353 2023/06/07 09:11:42 - mmengine - INFO - Iter(train) [ 4150/240000] lr: 9.8458e-03 eta: 1 day, 23:15:08 time: 0.7398 data_time: 0.2459 memory: 17391 loss: 0.3036 decode.loss_ce: 0.2039 decode.acc_seg: 90.8928 aux.loss_ce: 0.0997 aux.acc_seg: 89.9686 2023/06/07 09:12:19 - mmengine - INFO - Iter(train) [ 4200/240000] lr: 9.8440e-03 eta: 1 day, 23:15:29 time: 0.7431 data_time: 0.0134 memory: 17393 loss: 0.3110 decode.loss_ce: 0.2132 decode.acc_seg: 92.8284 aux.loss_ce: 0.0978 aux.acc_seg: 90.7083 2023/06/07 09:12:56 - mmengine - INFO - Iter(train) [ 4250/240000] lr: 9.8421e-03 eta: 1 day, 23:15:37 time: 0.7497 data_time: 0.0127 memory: 17393 loss: 0.3054 decode.loss_ce: 0.2054 decode.acc_seg: 92.3860 aux.loss_ce: 0.1000 aux.acc_seg: 88.4425 2023/06/07 09:13:34 - mmengine - INFO - Iter(train) [ 4300/240000] lr: 9.8403e-03 eta: 1 day, 23:16:16 time: 0.7495 data_time: 0.0129 memory: 17393 loss: 0.2968 decode.loss_ce: 0.2005 decode.acc_seg: 90.7223 aux.loss_ce: 0.0963 aux.acc_seg: 88.4221 2023/06/07 09:14:10 - mmengine - INFO - Iter(train) [ 4350/240000] lr: 9.8384e-03 eta: 1 day, 23:16:19 time: 0.7304 data_time: 0.0113 memory: 17391 loss: 0.2861 decode.loss_ce: 0.1939 decode.acc_seg: 90.8431 aux.loss_ce: 0.0922 aux.acc_seg: 89.0580 2023/06/07 09:14:47 - mmengine - INFO - Iter(train) [ 4400/240000] lr: 9.8365e-03 eta: 1 day, 23:16:30 time: 0.7487 data_time: 0.0123 memory: 17392 loss: 0.3094 decode.loss_ce: 0.2085 decode.acc_seg: 90.3941 aux.loss_ce: 0.1009 aux.acc_seg: 86.1273 2023/06/07 09:15:24 - mmengine - INFO - Iter(train) [ 4450/240000] lr: 9.8347e-03 eta: 1 day, 23:16:43 time: 0.7316 data_time: 0.0121 memory: 17392 loss: 0.3257 decode.loss_ce: 0.2200 decode.acc_seg: 92.7882 aux.loss_ce: 0.1057 aux.acc_seg: 90.1986 2023/06/07 09:16:00 - mmengine - INFO - Iter(train) [ 4500/240000] lr: 9.8328e-03 eta: 1 day, 23:16:03 time: 0.7270 data_time: 0.0357 memory: 17393 loss: 0.2804 decode.loss_ce: 0.1882 decode.acc_seg: 93.1827 aux.loss_ce: 0.0922 aux.acc_seg: 92.0178 2023/06/07 09:16:36 - mmengine - INFO - Iter(train) [ 4550/240000] lr: 9.8310e-03 eta: 1 day, 23:15:16 time: 0.7149 data_time: 0.0817 memory: 17391 loss: 0.3310 decode.loss_ce: 0.2255 decode.acc_seg: 89.9744 aux.loss_ce: 0.1055 aux.acc_seg: 91.3251 2023/06/07 09:17:13 - mmengine - INFO - Iter(train) [ 4600/240000] lr: 9.8291e-03 eta: 1 day, 23:14:54 time: 0.7099 data_time: 0.0205 memory: 17391 loss: 0.3319 decode.loss_ce: 0.2223 decode.acc_seg: 89.5469 aux.loss_ce: 0.1096 aux.acc_seg: 84.4781 2023/06/07 09:17:49 - mmengine - INFO - Iter(train) [ 4650/240000] lr: 9.8272e-03 eta: 1 day, 23:14:35 time: 0.7283 data_time: 0.0157 memory: 17392 loss: 0.2983 decode.loss_ce: 0.1997 decode.acc_seg: 89.8924 aux.loss_ce: 0.0986 aux.acc_seg: 88.7986 2023/06/07 09:18:25 - mmengine - INFO - Iter(train) [ 4700/240000] lr: 9.8254e-03 eta: 1 day, 23:14:04 time: 0.7137 data_time: 0.0111 memory: 17393 loss: 0.3073 decode.loss_ce: 0.2065 decode.acc_seg: 85.2027 aux.loss_ce: 0.1008 aux.acc_seg: 82.8983 2023/06/07 09:19:01 - mmengine - INFO - Iter(train) [ 4750/240000] lr: 9.8235e-03 eta: 1 day, 23:13:19 time: 0.7205 data_time: 0.0133 memory: 17392 loss: 0.3031 decode.loss_ce: 0.2043 decode.acc_seg: 90.7955 aux.loss_ce: 0.0987 aux.acc_seg: 89.0645 2023/06/07 09:19:37 - mmengine - INFO - Iter(train) [ 4800/240000] lr: 9.8217e-03 eta: 1 day, 23:11:59 time: 0.7056 data_time: 0.1486 memory: 17394 loss: 0.2901 decode.loss_ce: 0.1954 decode.acc_seg: 91.6853 aux.loss_ce: 0.0947 aux.acc_seg: 89.8530 2023/06/07 09:20:12 - mmengine - INFO - Iter(train) [ 4850/240000] lr: 9.8198e-03 eta: 1 day, 23:10:51 time: 0.6978 data_time: 0.3595 memory: 17392 loss: 0.3194 decode.loss_ce: 0.2132 decode.acc_seg: 88.5537 aux.loss_ce: 0.1062 aux.acc_seg: 88.8563 2023/06/07 09:20:48 - mmengine - INFO - Iter(train) [ 4900/240000] lr: 9.8179e-03 eta: 1 day, 23:09:51 time: 0.7209 data_time: 0.3963 memory: 17394 loss: 0.3223 decode.loss_ce: 0.2194 decode.acc_seg: 90.8292 aux.loss_ce: 0.1029 aux.acc_seg: 89.8603 2023/06/07 09:21:24 - mmengine - INFO - Iter(train) [ 4950/240000] lr: 9.8161e-03 eta: 1 day, 23:09:40 time: 0.7428 data_time: 0.3977 memory: 17393 loss: 0.2721 decode.loss_ce: 0.1830 decode.acc_seg: 89.4881 aux.loss_ce: 0.0891 aux.acc_seg: 86.9141 2023/06/07 09:22:00 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 09:22:00 - mmengine - INFO - Iter(train) [ 5000/240000] lr: 9.8142e-03 eta: 1 day, 23:08:56 time: 0.7256 data_time: 0.3927 memory: 17395 loss: 0.3079 decode.loss_ce: 0.2063 decode.acc_seg: 90.0592 aux.loss_ce: 0.1017 aux.acc_seg: 87.3060 2023/06/07 09:22:37 - mmengine - INFO - Iter(train) [ 5050/240000] lr: 9.8124e-03 eta: 1 day, 23:08:28 time: 0.7215 data_time: 0.3905 memory: 17391 loss: 0.2812 decode.loss_ce: 0.1910 decode.acc_seg: 91.6577 aux.loss_ce: 0.0902 aux.acc_seg: 89.8406 2023/06/07 09:23:13 - mmengine - INFO - Iter(train) [ 5100/240000] lr: 9.8105e-03 eta: 1 day, 23:07:58 time: 0.7215 data_time: 0.3877 memory: 17392 loss: 0.2717 decode.loss_ce: 0.1830 decode.acc_seg: 94.3244 aux.loss_ce: 0.0887 aux.acc_seg: 93.1862 2023/06/07 09:23:49 - mmengine - INFO - Iter(train) [ 5150/240000] lr: 9.8086e-03 eta: 1 day, 23:07:31 time: 0.7410 data_time: 0.4000 memory: 17392 loss: 0.3001 decode.loss_ce: 0.2028 decode.acc_seg: 89.3160 aux.loss_ce: 0.0973 aux.acc_seg: 87.0216 2023/06/07 09:24:26 - mmengine - INFO - Iter(train) [ 5200/240000] lr: 9.8068e-03 eta: 1 day, 23:07:16 time: 0.7263 data_time: 0.3866 memory: 17392 loss: 0.2847 decode.loss_ce: 0.1904 decode.acc_seg: 92.2133 aux.loss_ce: 0.0943 aux.acc_seg: 90.7899 2023/06/07 09:25:02 - mmengine - INFO - Iter(train) [ 5250/240000] lr: 9.8049e-03 eta: 1 day, 23:06:42 time: 0.7201 data_time: 0.3956 memory: 17392 loss: 0.2936 decode.loss_ce: 0.1964 decode.acc_seg: 90.0268 aux.loss_ce: 0.0972 aux.acc_seg: 87.7132 2023/06/07 09:25:38 - mmengine - INFO - Iter(train) [ 5300/240000] lr: 9.8031e-03 eta: 1 day, 23:06:03 time: 0.7282 data_time: 0.3914 memory: 17391 loss: 0.2930 decode.loss_ce: 0.1980 decode.acc_seg: 90.9685 aux.loss_ce: 0.0950 aux.acc_seg: 89.0443 2023/06/07 09:26:14 - mmengine - INFO - Iter(train) [ 5350/240000] lr: 9.8012e-03 eta: 1 day, 23:05:28 time: 0.7220 data_time: 0.3898 memory: 17392 loss: 0.2944 decode.loss_ce: 0.1992 decode.acc_seg: 90.1166 aux.loss_ce: 0.0953 aux.acc_seg: 88.4372 2023/06/07 09:26:50 - mmengine - INFO - Iter(train) [ 5400/240000] lr: 9.7993e-03 eta: 1 day, 23:04:51 time: 0.7212 data_time: 0.3923 memory: 17393 loss: 0.3013 decode.loss_ce: 0.2048 decode.acc_seg: 88.3465 aux.loss_ce: 0.0965 aux.acc_seg: 87.1033 2023/06/07 09:27:26 - mmengine - INFO - Iter(train) [ 5450/240000] lr: 9.7975e-03 eta: 1 day, 23:04:12 time: 0.7233 data_time: 0.3957 memory: 17391 loss: 0.2807 decode.loss_ce: 0.1903 decode.acc_seg: 90.6281 aux.loss_ce: 0.0903 aux.acc_seg: 87.5948 2023/06/07 09:28:02 - mmengine - INFO - Iter(train) [ 5500/240000] lr: 9.7956e-03 eta: 1 day, 23:03:32 time: 0.7148 data_time: 0.3746 memory: 17392 loss: 0.2971 decode.loss_ce: 0.1995 decode.acc_seg: 91.1075 aux.loss_ce: 0.0976 aux.acc_seg: 90.0791 2023/06/07 09:28:38 - mmengine - INFO - Iter(train) [ 5550/240000] lr: 9.7938e-03 eta: 1 day, 23:02:56 time: 0.7209 data_time: 0.3900 memory: 17393 loss: 0.3027 decode.loss_ce: 0.2043 decode.acc_seg: 91.2327 aux.loss_ce: 0.0984 aux.acc_seg: 90.1133 2023/06/07 09:29:15 - mmengine - INFO - Iter(train) [ 5600/240000] lr: 9.7919e-03 eta: 1 day, 23:02:33 time: 0.7343 data_time: 0.0992 memory: 17392 loss: 0.3003 decode.loss_ce: 0.2003 decode.acc_seg: 91.7955 aux.loss_ce: 0.1000 aux.acc_seg: 89.3675 2023/06/07 09:29:51 - mmengine - INFO - Iter(train) [ 5650/240000] lr: 9.7900e-03 eta: 1 day, 23:02:07 time: 0.7165 data_time: 0.2689 memory: 17391 loss: 0.2944 decode.loss_ce: 0.1990 decode.acc_seg: 89.9238 aux.loss_ce: 0.0954 aux.acc_seg: 86.7764 2023/06/07 09:30:28 - mmengine - INFO - Iter(train) [ 5700/240000] lr: 9.7882e-03 eta: 1 day, 23:01:35 time: 0.7176 data_time: 0.3817 memory: 17392 loss: 0.2977 decode.loss_ce: 0.2018 decode.acc_seg: 91.4578 aux.loss_ce: 0.0959 aux.acc_seg: 88.7671 2023/06/07 09:31:04 - mmengine - INFO - Iter(train) [ 5750/240000] lr: 9.7863e-03 eta: 1 day, 23:01:12 time: 0.7240 data_time: 0.1099 memory: 17392 loss: 0.2963 decode.loss_ce: 0.2018 decode.acc_seg: 88.3659 aux.loss_ce: 0.0944 aux.acc_seg: 88.0029 2023/06/07 09:31:40 - mmengine - INFO - Iter(train) [ 5800/240000] lr: 9.7844e-03 eta: 1 day, 23:00:40 time: 0.7282 data_time: 0.1795 memory: 17391 loss: 0.2921 decode.loss_ce: 0.1955 decode.acc_seg: 91.3060 aux.loss_ce: 0.0965 aux.acc_seg: 87.7446 2023/06/07 09:32:16 - mmengine - INFO - Iter(train) [ 5850/240000] lr: 9.7826e-03 eta: 1 day, 22:59:46 time: 0.7061 data_time: 0.3796 memory: 17392 loss: 0.2996 decode.loss_ce: 0.2008 decode.acc_seg: 92.3506 aux.loss_ce: 0.0988 aux.acc_seg: 90.9262 2023/06/07 09:32:52 - mmengine - INFO - Iter(train) [ 5900/240000] lr: 9.7807e-03 eta: 1 day, 22:59:10 time: 0.7236 data_time: 0.2488 memory: 17394 loss: 0.2921 decode.loss_ce: 0.1943 decode.acc_seg: 92.0152 aux.loss_ce: 0.0978 aux.acc_seg: 90.7200 2023/06/07 09:33:28 - mmengine - INFO - Iter(train) [ 5950/240000] lr: 9.7789e-03 eta: 1 day, 22:58:36 time: 0.7291 data_time: 0.0499 memory: 17392 loss: 0.2913 decode.loss_ce: 0.1965 decode.acc_seg: 92.1954 aux.loss_ce: 0.0948 aux.acc_seg: 89.4611 2023/06/07 09:34:04 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 09:34:04 - mmengine - INFO - Iter(train) [ 6000/240000] lr: 9.7770e-03 eta: 1 day, 22:57:55 time: 0.7262 data_time: 0.0828 memory: 17392 loss: 0.2817 decode.loss_ce: 0.1866 decode.acc_seg: 90.6490 aux.loss_ce: 0.0951 aux.acc_seg: 88.2022 2023/06/07 09:34:41 - mmengine - INFO - Iter(train) [ 6050/240000] lr: 9.7751e-03 eta: 1 day, 22:57:46 time: 0.7412 data_time: 0.0123 memory: 17393 loss: 0.2862 decode.loss_ce: 0.1929 decode.acc_seg: 92.1096 aux.loss_ce: 0.0933 aux.acc_seg: 90.3183 2023/06/07 09:35:18 - mmengine - INFO - Iter(train) [ 6100/240000] lr: 9.7733e-03 eta: 1 day, 22:57:56 time: 0.7392 data_time: 0.0122 memory: 17391 loss: 0.3029 decode.loss_ce: 0.2044 decode.acc_seg: 91.0712 aux.loss_ce: 0.0985 aux.acc_seg: 89.1642 2023/06/07 09:35:56 - mmengine - INFO - Iter(train) [ 6150/240000] lr: 9.7714e-03 eta: 1 day, 22:57:58 time: 0.7579 data_time: 0.0124 memory: 17391 loss: 0.2919 decode.loss_ce: 0.1965 decode.acc_seg: 90.2766 aux.loss_ce: 0.0954 aux.acc_seg: 88.4733 2023/06/07 09:36:32 - mmengine - INFO - Iter(train) [ 6200/240000] lr: 9.7696e-03 eta: 1 day, 22:57:18 time: 0.7194 data_time: 0.0123 memory: 17392 loss: 0.2799 decode.loss_ce: 0.1871 decode.acc_seg: 91.5350 aux.loss_ce: 0.0928 aux.acc_seg: 89.9602 2023/06/07 09:37:08 - mmengine - INFO - Iter(train) [ 6250/240000] lr: 9.7677e-03 eta: 1 day, 22:56:50 time: 0.7276 data_time: 0.0125 memory: 17393 loss: 0.2918 decode.loss_ce: 0.1950 decode.acc_seg: 92.4966 aux.loss_ce: 0.0968 aux.acc_seg: 91.0048 2023/06/07 09:37:44 - mmengine - INFO - Iter(train) [ 6300/240000] lr: 9.7658e-03 eta: 1 day, 22:56:21 time: 0.7280 data_time: 0.0123 memory: 17391 loss: 0.2894 decode.loss_ce: 0.1939 decode.acc_seg: 92.9523 aux.loss_ce: 0.0955 aux.acc_seg: 91.8513 2023/06/07 09:38:20 - mmengine - INFO - Iter(train) [ 6350/240000] lr: 9.7640e-03 eta: 1 day, 22:55:24 time: 0.7201 data_time: 0.0120 memory: 17392 loss: 0.2916 decode.loss_ce: 0.1953 decode.acc_seg: 91.1575 aux.loss_ce: 0.0963 aux.acc_seg: 89.2453 2023/06/07 09:38:56 - mmengine - INFO - Iter(train) [ 6400/240000] lr: 9.7621e-03 eta: 1 day, 22:54:32 time: 0.7350 data_time: 0.0127 memory: 17391 loss: 0.2930 decode.loss_ce: 0.1966 decode.acc_seg: 92.0453 aux.loss_ce: 0.0964 aux.acc_seg: 91.0255 2023/06/07 09:39:33 - mmengine - INFO - Iter(train) [ 6450/240000] lr: 9.7603e-03 eta: 1 day, 22:54:56 time: 0.7193 data_time: 0.0121 memory: 17392 loss: 0.2755 decode.loss_ce: 0.1847 decode.acc_seg: 91.8268 aux.loss_ce: 0.0908 aux.acc_seg: 89.7377 2023/06/07 09:40:09 - mmengine - INFO - Iter(train) [ 6500/240000] lr: 9.7584e-03 eta: 1 day, 22:54:10 time: 0.7093 data_time: 0.0118 memory: 17392 loss: 0.3020 decode.loss_ce: 0.2017 decode.acc_seg: 90.8922 aux.loss_ce: 0.1003 aux.acc_seg: 89.5513 2023/06/07 09:40:45 - mmengine - INFO - Iter(train) [ 6550/240000] lr: 9.7565e-03 eta: 1 day, 22:53:18 time: 0.7123 data_time: 0.0121 memory: 17392 loss: 0.2787 decode.loss_ce: 0.1875 decode.acc_seg: 90.4851 aux.loss_ce: 0.0912 aux.acc_seg: 88.7166 2023/06/07 09:41:21 - mmengine - INFO - Iter(train) [ 6600/240000] lr: 9.7547e-03 eta: 1 day, 22:52:41 time: 0.7246 data_time: 0.0119 memory: 17391 loss: 0.2650 decode.loss_ce: 0.1781 decode.acc_seg: 91.4795 aux.loss_ce: 0.0868 aux.acc_seg: 89.9706 2023/06/07 09:42:01 - mmengine - INFO - Iter(train) [ 6650/240000] lr: 9.7528e-03 eta: 1 day, 22:54:13 time: 0.9043 data_time: 0.0233 memory: 17390 loss: 0.2892 decode.loss_ce: 0.1951 decode.acc_seg: 91.5411 aux.loss_ce: 0.0941 aux.acc_seg: 88.5534 2023/06/07 09:42:41 - mmengine - INFO - Iter(train) [ 6700/240000] lr: 9.7509e-03 eta: 1 day, 22:56:03 time: 0.7754 data_time: 0.0132 memory: 17397 loss: 0.2933 decode.loss_ce: 0.1957 decode.acc_seg: 91.4918 aux.loss_ce: 0.0976 aux.acc_seg: 89.5159 2023/06/07 09:43:18 - mmengine - INFO - Iter(train) [ 6750/240000] lr: 9.7491e-03 eta: 1 day, 22:55:47 time: 0.7307 data_time: 0.0123 memory: 17393 loss: 0.3104 decode.loss_ce: 0.2078 decode.acc_seg: 92.2061 aux.loss_ce: 0.1026 aux.acc_seg: 90.1491 2023/06/07 09:43:54 - mmengine - INFO - Iter(train) [ 6800/240000] lr: 9.7472e-03 eta: 1 day, 22:55:17 time: 0.7355 data_time: 0.0119 memory: 17390 loss: 0.2823 decode.loss_ce: 0.1880 decode.acc_seg: 90.0018 aux.loss_ce: 0.0942 aux.acc_seg: 88.9522 2023/06/07 09:44:31 - mmengine - INFO - Iter(train) [ 6850/240000] lr: 9.7454e-03 eta: 1 day, 22:54:42 time: 0.7211 data_time: 0.0123 memory: 17391 loss: 0.2818 decode.loss_ce: 0.1905 decode.acc_seg: 92.3597 aux.loss_ce: 0.0913 aux.acc_seg: 90.5088 2023/06/07 09:45:07 - mmengine - INFO - Iter(train) [ 6900/240000] lr: 9.7435e-03 eta: 1 day, 22:54:14 time: 0.7339 data_time: 0.0116 memory: 17393 loss: 0.2826 decode.loss_ce: 0.1886 decode.acc_seg: 90.6221 aux.loss_ce: 0.0940 aux.acc_seg: 86.0949 2023/06/07 09:45:44 - mmengine - INFO - Iter(train) [ 6950/240000] lr: 9.7416e-03 eta: 1 day, 22:53:41 time: 0.7566 data_time: 0.0171 memory: 17392 loss: 0.2811 decode.loss_ce: 0.1893 decode.acc_seg: 91.8508 aux.loss_ce: 0.0918 aux.acc_seg: 90.3883 2023/06/07 09:46:20 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 09:46:20 - mmengine - INFO - Iter(train) [ 7000/240000] lr: 9.7398e-03 eta: 1 day, 22:53:11 time: 0.7318 data_time: 0.0380 memory: 17393 loss: 0.2829 decode.loss_ce: 0.1897 decode.acc_seg: 91.3906 aux.loss_ce: 0.0932 aux.acc_seg: 90.2378 2023/06/07 09:46:56 - mmengine - INFO - Iter(train) [ 7050/240000] lr: 9.7379e-03 eta: 1 day, 22:52:39 time: 0.7248 data_time: 0.0271 memory: 17391 loss: 0.2695 decode.loss_ce: 0.1789 decode.acc_seg: 91.6298 aux.loss_ce: 0.0906 aux.acc_seg: 89.8644 2023/06/07 09:47:33 - mmengine - INFO - Iter(train) [ 7100/240000] lr: 9.7361e-03 eta: 1 day, 22:52:11 time: 0.7230 data_time: 0.0118 memory: 17393 loss: 0.3025 decode.loss_ce: 0.2045 decode.acc_seg: 92.5023 aux.loss_ce: 0.0980 aux.acc_seg: 91.1836 2023/06/07 09:48:10 - mmengine - INFO - Iter(train) [ 7150/240000] lr: 9.7342e-03 eta: 1 day, 22:52:01 time: 0.7257 data_time: 0.0123 memory: 17391 loss: 0.2848 decode.loss_ce: 0.1919 decode.acc_seg: 91.2390 aux.loss_ce: 0.0929 aux.acc_seg: 90.0127 2023/06/07 09:48:46 - mmengine - INFO - Iter(train) [ 7200/240000] lr: 9.7323e-03 eta: 1 day, 22:51:35 time: 0.7211 data_time: 0.0121 memory: 17391 loss: 0.2923 decode.loss_ce: 0.1976 decode.acc_seg: 93.0736 aux.loss_ce: 0.0947 aux.acc_seg: 91.0359 2023/06/07 09:49:22 - mmengine - INFO - Iter(train) [ 7250/240000] lr: 9.7305e-03 eta: 1 day, 22:50:48 time: 0.7203 data_time: 0.3710 memory: 17391 loss: 0.2749 decode.loss_ce: 0.1800 decode.acc_seg: 91.8754 aux.loss_ce: 0.0949 aux.acc_seg: 88.9267 2023/06/07 09:49:59 - mmengine - INFO - Iter(train) [ 7300/240000] lr: 9.7286e-03 eta: 1 day, 22:50:15 time: 0.7206 data_time: 0.3876 memory: 17393 loss: 0.2836 decode.loss_ce: 0.1895 decode.acc_seg: 87.8303 aux.loss_ce: 0.0941 aux.acc_seg: 86.5153 2023/06/07 09:50:35 - mmengine - INFO - Iter(train) [ 7350/240000] lr: 9.7267e-03 eta: 1 day, 22:49:31 time: 0.7139 data_time: 0.1202 memory: 17395 loss: 0.2866 decode.loss_ce: 0.1906 decode.acc_seg: 90.8672 aux.loss_ce: 0.0960 aux.acc_seg: 86.5646 2023/06/07 09:51:10 - mmengine - INFO - Iter(train) [ 7400/240000] lr: 9.7249e-03 eta: 1 day, 22:48:46 time: 0.7193 data_time: 0.3696 memory: 17392 loss: 0.2877 decode.loss_ce: 0.1913 decode.acc_seg: 90.5392 aux.loss_ce: 0.0964 aux.acc_seg: 87.6497 2023/06/07 09:51:47 - mmengine - INFO - Iter(train) [ 7450/240000] lr: 9.7230e-03 eta: 1 day, 22:48:09 time: 0.7246 data_time: 0.3881 memory: 17392 loss: 0.2954 decode.loss_ce: 0.1953 decode.acc_seg: 91.6015 aux.loss_ce: 0.1001 aux.acc_seg: 87.6082 2023/06/07 09:52:23 - mmengine - INFO - Iter(train) [ 7500/240000] lr: 9.7212e-03 eta: 1 day, 22:47:36 time: 0.7342 data_time: 0.1942 memory: 17393 loss: 0.3034 decode.loss_ce: 0.2046 decode.acc_seg: 89.1338 aux.loss_ce: 0.0988 aux.acc_seg: 86.7781 2023/06/07 09:52:59 - mmengine - INFO - Iter(train) [ 7550/240000] lr: 9.7193e-03 eta: 1 day, 22:46:51 time: 0.7194 data_time: 0.1924 memory: 17391 loss: 0.2925 decode.loss_ce: 0.1953 decode.acc_seg: 93.1267 aux.loss_ce: 0.0972 aux.acc_seg: 92.2430 2023/06/07 09:53:35 - mmengine - INFO - Iter(train) [ 7600/240000] lr: 9.7174e-03 eta: 1 day, 22:46:06 time: 0.7089 data_time: 0.1983 memory: 17394 loss: 0.2994 decode.loss_ce: 0.1983 decode.acc_seg: 91.4219 aux.loss_ce: 0.1012 aux.acc_seg: 89.6260 2023/06/07 09:54:11 - mmengine - INFO - Iter(train) [ 7650/240000] lr: 9.7156e-03 eta: 1 day, 22:45:22 time: 0.7119 data_time: 0.0918 memory: 17390 loss: 0.2656 decode.loss_ce: 0.1781 decode.acc_seg: 90.0470 aux.loss_ce: 0.0875 aux.acc_seg: 87.3629 2023/06/07 09:54:49 - mmengine - INFO - Iter(train) [ 7700/240000] lr: 9.7137e-03 eta: 1 day, 22:45:46 time: 0.9023 data_time: 0.2614 memory: 17394 loss: 0.2714 decode.loss_ce: 0.1830 decode.acc_seg: 91.3121 aux.loss_ce: 0.0884 aux.acc_seg: 89.5106 2023/06/07 09:55:27 - mmengine - INFO - Iter(train) [ 7750/240000] lr: 9.7118e-03 eta: 1 day, 22:46:11 time: 0.7480 data_time: 0.1924 memory: 17392 loss: 0.2853 decode.loss_ce: 0.1931 decode.acc_seg: 90.6368 aux.loss_ce: 0.0923 aux.acc_seg: 87.0570 2023/06/07 09:56:03 - mmengine - INFO - Iter(train) [ 7800/240000] lr: 9.7100e-03 eta: 1 day, 22:45:24 time: 0.7102 data_time: 0.2074 memory: 17393 loss: 0.2816 decode.loss_ce: 0.1895 decode.acc_seg: 90.6119 aux.loss_ce: 0.0921 aux.acc_seg: 89.4038 2023/06/07 09:56:41 - mmengine - INFO - Iter(train) [ 7850/240000] lr: 9.7081e-03 eta: 1 day, 22:45:36 time: 0.7305 data_time: 0.4043 memory: 17393 loss: 0.2591 decode.loss_ce: 0.1730 decode.acc_seg: 92.6062 aux.loss_ce: 0.0861 aux.acc_seg: 91.5804 2023/06/07 09:57:17 - mmengine - INFO - Iter(train) [ 7900/240000] lr: 9.7063e-03 eta: 1 day, 22:44:43 time: 0.7204 data_time: 0.3388 memory: 17392 loss: 0.2957 decode.loss_ce: 0.2000 decode.acc_seg: 91.4947 aux.loss_ce: 0.0957 aux.acc_seg: 88.8660 2023/06/07 09:57:52 - mmengine - INFO - Iter(train) [ 7950/240000] lr: 9.7044e-03 eta: 1 day, 22:43:43 time: 0.7023 data_time: 0.3774 memory: 17394 loss: 0.2722 decode.loss_ce: 0.1820 decode.acc_seg: 91.8224 aux.loss_ce: 0.0903 aux.acc_seg: 90.2356 2023/06/07 09:58:29 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 09:58:29 - mmengine - INFO - Iter(train) [ 8000/240000] lr: 9.7025e-03 eta: 1 day, 22:43:29 time: 0.7350 data_time: 0.3955 memory: 17395 loss: 0.2806 decode.loss_ce: 0.1889 decode.acc_seg: 88.1850 aux.loss_ce: 0.0918 aux.acc_seg: 85.8667 2023/06/07 09:59:05 - mmengine - INFO - Iter(train) [ 8050/240000] lr: 9.7007e-03 eta: 1 day, 22:42:48 time: 0.7293 data_time: 0.4044 memory: 17392 loss: 0.2733 decode.loss_ce: 0.1823 decode.acc_seg: 92.1745 aux.loss_ce: 0.0910 aux.acc_seg: 91.1693 2023/06/07 09:59:41 - mmengine - INFO - Iter(train) [ 8100/240000] lr: 9.6988e-03 eta: 1 day, 22:41:54 time: 0.7041 data_time: 0.3800 memory: 17393 loss: 0.2756 decode.loss_ce: 0.1830 decode.acc_seg: 89.9808 aux.loss_ce: 0.0927 aux.acc_seg: 86.8030 2023/06/07 10:00:17 - mmengine - INFO - Iter(train) [ 8150/240000] lr: 9.6969e-03 eta: 1 day, 22:40:59 time: 0.7101 data_time: 0.3862 memory: 17393 loss: 0.2902 decode.loss_ce: 0.1957 decode.acc_seg: 91.6546 aux.loss_ce: 0.0946 aux.acc_seg: 88.6929 2023/06/07 10:00:52 - mmengine - INFO - Iter(train) [ 8200/240000] lr: 9.6951e-03 eta: 1 day, 22:40:06 time: 0.7173 data_time: 0.3927 memory: 17392 loss: 0.2955 decode.loss_ce: 0.2000 decode.acc_seg: 92.9596 aux.loss_ce: 0.0955 aux.acc_seg: 91.6757 2023/06/07 10:01:28 - mmengine - INFO - Iter(train) [ 8250/240000] lr: 9.6932e-03 eta: 1 day, 22:39:18 time: 0.7095 data_time: 0.3852 memory: 17397 loss: 0.2972 decode.loss_ce: 0.1990 decode.acc_seg: 91.0171 aux.loss_ce: 0.0983 aux.acc_seg: 88.2877 2023/06/07 10:02:04 - mmengine - INFO - Iter(train) [ 8300/240000] lr: 9.6914e-03 eta: 1 day, 22:38:29 time: 0.7132 data_time: 0.3892 memory: 17396 loss: 0.2775 decode.loss_ce: 0.1875 decode.acc_seg: 92.3358 aux.loss_ce: 0.0901 aux.acc_seg: 89.4230 2023/06/07 10:02:39 - mmengine - INFO - Iter(train) [ 8350/240000] lr: 9.6895e-03 eta: 1 day, 22:37:31 time: 0.7071 data_time: 0.3823 memory: 17395 loss: 0.2696 decode.loss_ce: 0.1816 decode.acc_seg: 92.5120 aux.loss_ce: 0.0881 aux.acc_seg: 90.3448 2023/06/07 10:03:15 - mmengine - INFO - Iter(train) [ 8400/240000] lr: 9.6876e-03 eta: 1 day, 22:36:33 time: 0.7094 data_time: 0.3854 memory: 17393 loss: 0.3177 decode.loss_ce: 0.2126 decode.acc_seg: 91.9743 aux.loss_ce: 0.1051 aux.acc_seg: 88.1320 2023/06/07 10:03:50 - mmengine - INFO - Iter(train) [ 8450/240000] lr: 9.6858e-03 eta: 1 day, 22:35:34 time: 0.7089 data_time: 0.3651 memory: 17390 loss: 0.2905 decode.loss_ce: 0.1944 decode.acc_seg: 91.1566 aux.loss_ce: 0.0961 aux.acc_seg: 85.9388 2023/06/07 10:04:26 - mmengine - INFO - Iter(train) [ 8500/240000] lr: 9.6839e-03 eta: 1 day, 22:34:39 time: 0.7186 data_time: 0.3906 memory: 17391 loss: 0.2641 decode.loss_ce: 0.1764 decode.acc_seg: 90.5549 aux.loss_ce: 0.0877 aux.acc_seg: 89.3050 2023/06/07 10:05:01 - mmengine - INFO - Iter(train) [ 8550/240000] lr: 9.6820e-03 eta: 1 day, 22:33:36 time: 0.7154 data_time: 0.3916 memory: 17390 loss: 0.2681 decode.loss_ce: 0.1804 decode.acc_seg: 92.9172 aux.loss_ce: 0.0877 aux.acc_seg: 90.2668 2023/06/07 10:05:37 - mmengine - INFO - Iter(train) [ 8600/240000] lr: 9.6802e-03 eta: 1 day, 22:32:44 time: 0.7047 data_time: 0.3811 memory: 17392 loss: 0.2733 decode.loss_ce: 0.1830 decode.acc_seg: 91.7869 aux.loss_ce: 0.0903 aux.acc_seg: 89.8504 2023/06/07 10:06:12 - mmengine - INFO - Iter(train) [ 8650/240000] lr: 9.6783e-03 eta: 1 day, 22:32:00 time: 0.7215 data_time: 0.3975 memory: 17393 loss: 0.2619 decode.loss_ce: 0.1758 decode.acc_seg: 91.9900 aux.loss_ce: 0.0861 aux.acc_seg: 90.7390 2023/06/07 10:06:48 - mmengine - INFO - Iter(train) [ 8700/240000] lr: 9.6765e-03 eta: 1 day, 22:31:09 time: 0.7316 data_time: 0.4074 memory: 17390 loss: 0.2636 decode.loss_ce: 0.1765 decode.acc_seg: 93.1497 aux.loss_ce: 0.0870 aux.acc_seg: 91.6939 2023/06/07 10:07:24 - mmengine - INFO - Iter(train) [ 8750/240000] lr: 9.6746e-03 eta: 1 day, 22:30:21 time: 0.7109 data_time: 0.3876 memory: 17397 loss: 0.2633 decode.loss_ce: 0.1764 decode.acc_seg: 91.9303 aux.loss_ce: 0.0869 aux.acc_seg: 90.9456 2023/06/07 10:07:59 - mmengine - INFO - Iter(train) [ 8800/240000] lr: 9.6727e-03 eta: 1 day, 22:29:15 time: 0.7079 data_time: 0.3842 memory: 17396 loss: 0.2843 decode.loss_ce: 0.1905 decode.acc_seg: 91.2676 aux.loss_ce: 0.0938 aux.acc_seg: 88.1181 2023/06/07 10:08:34 - mmengine - INFO - Iter(train) [ 8850/240000] lr: 9.6709e-03 eta: 1 day, 22:28:23 time: 0.7074 data_time: 0.3838 memory: 17393 loss: 0.2742 decode.loss_ce: 0.1819 decode.acc_seg: 89.7877 aux.loss_ce: 0.0922 aux.acc_seg: 87.2628 2023/06/07 10:09:10 - mmengine - INFO - Iter(train) [ 8900/240000] lr: 9.6690e-03 eta: 1 day, 22:27:33 time: 0.7247 data_time: 0.4007 memory: 17393 loss: 0.2886 decode.loss_ce: 0.1959 decode.acc_seg: 91.1422 aux.loss_ce: 0.0927 aux.acc_seg: 88.5662 2023/06/07 10:09:46 - mmengine - INFO - Iter(train) [ 8950/240000] lr: 9.6671e-03 eta: 1 day, 22:26:47 time: 0.7100 data_time: 0.3864 memory: 17392 loss: 0.2669 decode.loss_ce: 0.1818 decode.acc_seg: 92.6989 aux.loss_ce: 0.0852 aux.acc_seg: 91.6306 2023/06/07 10:10:21 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 10:10:21 - mmengine - INFO - Iter(train) [ 9000/240000] lr: 9.6653e-03 eta: 1 day, 22:25:46 time: 0.7088 data_time: 0.3842 memory: 17395 loss: 0.2939 decode.loss_ce: 0.1980 decode.acc_seg: 90.5891 aux.loss_ce: 0.0959 aux.acc_seg: 89.1311 2023/06/07 10:10:57 - mmengine - INFO - Iter(train) [ 9050/240000] lr: 9.6634e-03 eta: 1 day, 22:24:59 time: 0.7096 data_time: 0.3861 memory: 17393 loss: 0.2667 decode.loss_ce: 0.1780 decode.acc_seg: 92.7145 aux.loss_ce: 0.0887 aux.acc_seg: 91.6240 2023/06/07 10:11:32 - mmengine - INFO - Iter(train) [ 9100/240000] lr: 9.6615e-03 eta: 1 day, 22:24:07 time: 0.7041 data_time: 0.3802 memory: 17392 loss: 0.2803 decode.loss_ce: 0.1888 decode.acc_seg: 92.6007 aux.loss_ce: 0.0915 aux.acc_seg: 91.2226 2023/06/07 10:12:08 - mmengine - INFO - Iter(train) [ 9150/240000] lr: 9.6597e-03 eta: 1 day, 22:23:10 time: 0.7116 data_time: 0.3877 memory: 17390 loss: 0.2785 decode.loss_ce: 0.1876 decode.acc_seg: 91.9915 aux.loss_ce: 0.0909 aux.acc_seg: 91.3390 2023/06/07 10:12:43 - mmengine - INFO - Iter(train) [ 9200/240000] lr: 9.6578e-03 eta: 1 day, 22:22:21 time: 0.7006 data_time: 0.3769 memory: 17390 loss: 0.2612 decode.loss_ce: 0.1764 decode.acc_seg: 91.8866 aux.loss_ce: 0.0848 aux.acc_seg: 90.6628 2023/06/07 10:13:19 - mmengine - INFO - Iter(train) [ 9250/240000] lr: 9.6560e-03 eta: 1 day, 22:21:29 time: 0.7048 data_time: 0.3799 memory: 17395 loss: 0.3037 decode.loss_ce: 0.2044 decode.acc_seg: 92.7474 aux.loss_ce: 0.0993 aux.acc_seg: 90.8686 2023/06/07 10:13:55 - mmengine - INFO - Iter(train) [ 9300/240000] lr: 9.6541e-03 eta: 1 day, 22:20:39 time: 0.7078 data_time: 0.3848 memory: 17392 loss: 0.2806 decode.loss_ce: 0.1911 decode.acc_seg: 90.9254 aux.loss_ce: 0.0896 aux.acc_seg: 88.9171 2023/06/07 10:14:30 - mmengine - INFO - Iter(train) [ 9350/240000] lr: 9.6522e-03 eta: 1 day, 22:19:54 time: 0.7287 data_time: 0.4057 memory: 17394 loss: 0.2698 decode.loss_ce: 0.1806 decode.acc_seg: 93.0468 aux.loss_ce: 0.0892 aux.acc_seg: 91.0950 2023/06/07 10:15:06 - mmengine - INFO - Iter(train) [ 9400/240000] lr: 9.6504e-03 eta: 1 day, 22:19:08 time: 0.7224 data_time: 0.3992 memory: 17392 loss: 0.2545 decode.loss_ce: 0.1673 decode.acc_seg: 92.8774 aux.loss_ce: 0.0872 aux.acc_seg: 90.2643 2023/06/07 10:15:42 - mmengine - INFO - Iter(train) [ 9450/240000] lr: 9.6485e-03 eta: 1 day, 22:18:14 time: 0.7009 data_time: 0.3778 memory: 17391 loss: 0.2851 decode.loss_ce: 0.1892 decode.acc_seg: 92.6236 aux.loss_ce: 0.0959 aux.acc_seg: 91.6209 2023/06/07 10:16:17 - mmengine - INFO - Iter(train) [ 9500/240000] lr: 9.6466e-03 eta: 1 day, 22:17:16 time: 0.7076 data_time: 0.3838 memory: 17395 loss: 0.2778 decode.loss_ce: 0.1856 decode.acc_seg: 91.6182 aux.loss_ce: 0.0922 aux.acc_seg: 90.0850 2023/06/07 10:16:52 - mmengine - INFO - Iter(train) [ 9550/240000] lr: 9.6448e-03 eta: 1 day, 22:16:21 time: 0.7014 data_time: 0.3780 memory: 17393 loss: 0.2741 decode.loss_ce: 0.1835 decode.acc_seg: 90.3616 aux.loss_ce: 0.0907 aux.acc_seg: 88.3600 2023/06/07 10:17:28 - mmengine - INFO - Iter(train) [ 9600/240000] lr: 9.6429e-03 eta: 1 day, 22:15:27 time: 0.7101 data_time: 0.3100 memory: 17392 loss: 0.2894 decode.loss_ce: 0.1942 decode.acc_seg: 91.2488 aux.loss_ce: 0.0952 aux.acc_seg: 88.1249 2023/06/07 10:18:03 - mmengine - INFO - Iter(train) [ 9650/240000] lr: 9.6410e-03 eta: 1 day, 22:14:29 time: 0.7069 data_time: 0.3508 memory: 17391 loss: 0.2784 decode.loss_ce: 0.1890 decode.acc_seg: 90.2511 aux.loss_ce: 0.0894 aux.acc_seg: 89.3289 2023/06/07 10:18:38 - mmengine - INFO - Iter(train) [ 9700/240000] lr: 9.6392e-03 eta: 1 day, 22:13:34 time: 0.7086 data_time: 0.3049 memory: 17393 loss: 0.2555 decode.loss_ce: 0.1684 decode.acc_seg: 92.8046 aux.loss_ce: 0.0870 aux.acc_seg: 88.9960 2023/06/07 10:19:14 - mmengine - INFO - Iter(train) [ 9750/240000] lr: 9.6373e-03 eta: 1 day, 22:12:43 time: 0.7145 data_time: 0.1251 memory: 17392 loss: 0.2656 decode.loss_ce: 0.1775 decode.acc_seg: 90.9835 aux.loss_ce: 0.0881 aux.acc_seg: 89.6516 2023/06/07 10:19:49 - mmengine - INFO - Iter(train) [ 9800/240000] lr: 9.6355e-03 eta: 1 day, 22:11:56 time: 0.6981 data_time: 0.0118 memory: 17394 loss: 0.2581 decode.loss_ce: 0.1737 decode.acc_seg: 91.0230 aux.loss_ce: 0.0844 aux.acc_seg: 89.0424 2023/06/07 10:20:25 - mmengine - INFO - Iter(train) [ 9850/240000] lr: 9.6336e-03 eta: 1 day, 22:11:08 time: 0.7183 data_time: 0.0148 memory: 17392 loss: 0.2691 decode.loss_ce: 0.1783 decode.acc_seg: 93.3552 aux.loss_ce: 0.0908 aux.acc_seg: 92.0842 2023/06/07 10:21:00 - mmengine - INFO - Iter(train) [ 9900/240000] lr: 9.6317e-03 eta: 1 day, 22:10:13 time: 0.7011 data_time: 0.0120 memory: 17390 loss: 0.2539 decode.loss_ce: 0.1697 decode.acc_seg: 91.8369 aux.loss_ce: 0.0842 aux.acc_seg: 90.2036 2023/06/07 10:21:36 - mmengine - INFO - Iter(train) [ 9950/240000] lr: 9.6299e-03 eta: 1 day, 22:09:28 time: 0.7093 data_time: 0.0118 memory: 17396 loss: 0.2854 decode.loss_ce: 0.1914 decode.acc_seg: 92.4980 aux.loss_ce: 0.0941 aux.acc_seg: 88.8509 2023/06/07 10:22:12 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 10:22:12 - mmengine - INFO - Iter(train) [ 10000/240000] lr: 9.6280e-03 eta: 1 day, 22:08:41 time: 0.7246 data_time: 0.0122 memory: 17393 loss: 0.2643 decode.loss_ce: 0.1763 decode.acc_seg: 92.8060 aux.loss_ce: 0.0880 aux.acc_seg: 91.0273 2023/06/07 10:22:48 - mmengine - INFO - Iter(train) [ 10050/240000] lr: 9.6261e-03 eta: 1 day, 22:08:00 time: 0.7131 data_time: 0.0119 memory: 17392 loss: 0.2790 decode.loss_ce: 0.1872 decode.acc_seg: 91.8129 aux.loss_ce: 0.0917 aux.acc_seg: 90.5952 2023/06/07 10:23:23 - mmengine - INFO - Iter(train) [ 10100/240000] lr: 9.6243e-03 eta: 1 day, 22:07:04 time: 0.7053 data_time: 0.0260 memory: 17391 loss: 0.2833 decode.loss_ce: 0.1903 decode.acc_seg: 92.7289 aux.loss_ce: 0.0929 aux.acc_seg: 89.7548 2023/06/07 10:23:58 - mmengine - INFO - Iter(train) [ 10150/240000] lr: 9.6224e-03 eta: 1 day, 22:06:10 time: 0.6948 data_time: 0.1722 memory: 17392 loss: 0.2727 decode.loss_ce: 0.1803 decode.acc_seg: 92.0243 aux.loss_ce: 0.0924 aux.acc_seg: 90.3564 2023/06/07 10:24:34 - mmengine - INFO - Iter(train) [ 10200/240000] lr: 9.6205e-03 eta: 1 day, 22:05:20 time: 0.7039 data_time: 0.2706 memory: 17394 loss: 0.2801 decode.loss_ce: 0.1877 decode.acc_seg: 89.7011 aux.loss_ce: 0.0924 aux.acc_seg: 88.5746 2023/06/07 10:25:09 - mmengine - INFO - Iter(train) [ 10250/240000] lr: 9.6187e-03 eta: 1 day, 22:04:28 time: 0.7005 data_time: 0.0923 memory: 17392 loss: 0.2893 decode.loss_ce: 0.1954 decode.acc_seg: 89.3577 aux.loss_ce: 0.0939 aux.acc_seg: 87.0920 2023/06/07 10:25:44 - mmengine - INFO - Iter(train) [ 10300/240000] lr: 9.6168e-03 eta: 1 day, 22:03:31 time: 0.7137 data_time: 0.3902 memory: 17391 loss: 0.2691 decode.loss_ce: 0.1798 decode.acc_seg: 89.7945 aux.loss_ce: 0.0893 aux.acc_seg: 87.8655 2023/06/07 10:26:19 - mmengine - INFO - Iter(train) [ 10350/240000] lr: 9.6149e-03 eta: 1 day, 22:02:38 time: 0.7230 data_time: 0.3999 memory: 17392 loss: 0.2542 decode.loss_ce: 0.1706 decode.acc_seg: 89.6378 aux.loss_ce: 0.0836 aux.acc_seg: 87.5187 2023/06/07 10:26:55 - mmengine - INFO - Iter(train) [ 10400/240000] lr: 9.6131e-03 eta: 1 day, 22:01:41 time: 0.6947 data_time: 0.2323 memory: 17388 loss: 0.2775 decode.loss_ce: 0.1849 decode.acc_seg: 91.2362 aux.loss_ce: 0.0926 aux.acc_seg: 88.7549 2023/06/07 10:27:30 - mmengine - INFO - Iter(train) [ 10450/240000] lr: 9.6112e-03 eta: 1 day, 22:00:57 time: 0.6984 data_time: 0.0683 memory: 17392 loss: 0.2623 decode.loss_ce: 0.1758 decode.acc_seg: 89.4422 aux.loss_ce: 0.0865 aux.acc_seg: 86.8631 2023/06/07 10:28:06 - mmengine - INFO - Iter(train) [ 10500/240000] lr: 9.6094e-03 eta: 1 day, 22:00:08 time: 0.7126 data_time: 0.3212 memory: 17392 loss: 0.2641 decode.loss_ce: 0.1779 decode.acc_seg: 91.9099 aux.loss_ce: 0.0862 aux.acc_seg: 90.6709 2023/06/07 10:28:42 - mmengine - INFO - Iter(train) [ 10550/240000] lr: 9.6075e-03 eta: 1 day, 21:59:23 time: 0.7189 data_time: 0.3957 memory: 17394 loss: 0.2803 decode.loss_ce: 0.1899 decode.acc_seg: 89.1457 aux.loss_ce: 0.0904 aux.acc_seg: 85.9269 2023/06/07 10:29:17 - mmengine - INFO - Iter(train) [ 10600/240000] lr: 9.6056e-03 eta: 1 day, 21:58:35 time: 0.7185 data_time: 0.3956 memory: 17391 loss: 0.2779 decode.loss_ce: 0.1845 decode.acc_seg: 90.9087 aux.loss_ce: 0.0934 aux.acc_seg: 88.9550 2023/06/07 10:29:52 - mmengine - INFO - Iter(train) [ 10650/240000] lr: 9.6038e-03 eta: 1 day, 21:57:43 time: 0.7020 data_time: 0.3791 memory: 17391 loss: 0.2748 decode.loss_ce: 0.1838 decode.acc_seg: 92.5827 aux.loss_ce: 0.0910 aux.acc_seg: 92.0386 2023/06/07 10:30:28 - mmengine - INFO - Iter(train) [ 10700/240000] lr: 9.6019e-03 eta: 1 day, 21:56:49 time: 0.7079 data_time: 0.3846 memory: 17394 loss: 0.2969 decode.loss_ce: 0.2028 decode.acc_seg: 88.2169 aux.loss_ce: 0.0941 aux.acc_seg: 86.9193 2023/06/07 10:31:03 - mmengine - INFO - Iter(train) [ 10750/240000] lr: 9.6000e-03 eta: 1 day, 21:56:03 time: 0.7113 data_time: 0.3873 memory: 17393 loss: 0.2672 decode.loss_ce: 0.1796 decode.acc_seg: 92.2935 aux.loss_ce: 0.0876 aux.acc_seg: 90.7371 2023/06/07 10:31:39 - mmengine - INFO - Iter(train) [ 10800/240000] lr: 9.5982e-03 eta: 1 day, 21:55:17 time: 0.7065 data_time: 0.3831 memory: 17393 loss: 0.2560 decode.loss_ce: 0.1690 decode.acc_seg: 90.0955 aux.loss_ce: 0.0870 aux.acc_seg: 87.9578 2023/06/07 10:32:15 - mmengine - INFO - Iter(train) [ 10850/240000] lr: 9.5963e-03 eta: 1 day, 21:54:40 time: 0.7220 data_time: 0.3987 memory: 17394 loss: 0.2657 decode.loss_ce: 0.1803 decode.acc_seg: 91.8955 aux.loss_ce: 0.0854 aux.acc_seg: 90.2936 2023/06/07 10:32:50 - mmengine - INFO - Iter(train) [ 10900/240000] lr: 9.5944e-03 eta: 1 day, 21:53:49 time: 0.7063 data_time: 0.3831 memory: 17392 loss: 0.2713 decode.loss_ce: 0.1811 decode.acc_seg: 91.8412 aux.loss_ce: 0.0903 aux.acc_seg: 89.5109 2023/06/07 10:33:26 - mmengine - INFO - Iter(train) [ 10950/240000] lr: 9.5926e-03 eta: 1 day, 21:53:05 time: 0.7222 data_time: 0.3988 memory: 17394 loss: 0.2819 decode.loss_ce: 0.1867 decode.acc_seg: 93.4919 aux.loss_ce: 0.0952 aux.acc_seg: 91.7995 2023/06/07 10:34:02 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 10:34:02 - mmengine - INFO - Iter(train) [ 11000/240000] lr: 9.5907e-03 eta: 1 day, 21:52:24 time: 0.7134 data_time: 0.3894 memory: 17392 loss: 0.2614 decode.loss_ce: 0.1733 decode.acc_seg: 92.2221 aux.loss_ce: 0.0881 aux.acc_seg: 88.8207 2023/06/07 10:34:37 - mmengine - INFO - Iter(train) [ 11050/240000] lr: 9.5888e-03 eta: 1 day, 21:51:28 time: 0.7060 data_time: 0.3828 memory: 17393 loss: 0.2876 decode.loss_ce: 0.1922 decode.acc_seg: 90.8951 aux.loss_ce: 0.0954 aux.acc_seg: 88.1949 2023/06/07 10:35:12 - mmengine - INFO - Iter(train) [ 11100/240000] lr: 9.5870e-03 eta: 1 day, 21:50:37 time: 0.7144 data_time: 0.3902 memory: 17393 loss: 0.2461 decode.loss_ce: 0.1637 decode.acc_seg: 94.3079 aux.loss_ce: 0.0825 aux.acc_seg: 91.3015 2023/06/07 10:35:48 - mmengine - INFO - Iter(train) [ 11150/240000] lr: 9.5851e-03 eta: 1 day, 21:49:56 time: 0.7258 data_time: 0.1995 memory: 17392 loss: 0.2708 decode.loss_ce: 0.1802 decode.acc_seg: 93.5567 aux.loss_ce: 0.0907 aux.acc_seg: 91.5029 2023/06/07 10:36:25 - mmengine - INFO - Iter(train) [ 11200/240000] lr: 9.5832e-03 eta: 1 day, 21:49:35 time: 0.7739 data_time: 0.1791 memory: 17393 loss: 0.2666 decode.loss_ce: 0.1754 decode.acc_seg: 93.7748 aux.loss_ce: 0.0912 aux.acc_seg: 91.1577 2023/06/07 10:37:03 - mmengine - INFO - Iter(train) [ 11250/240000] lr: 9.5814e-03 eta: 1 day, 21:49:49 time: 0.7816 data_time: 0.0543 memory: 17392 loss: 0.2657 decode.loss_ce: 0.1765 decode.acc_seg: 92.8285 aux.loss_ce: 0.0892 aux.acc_seg: 91.3697 2023/06/07 10:37:42 - mmengine - INFO - Iter(train) [ 11300/240000] lr: 9.5795e-03 eta: 1 day, 21:49:58 time: 0.7634 data_time: 0.1270 memory: 17393 loss: 0.2683 decode.loss_ce: 0.1784 decode.acc_seg: 92.7292 aux.loss_ce: 0.0899 aux.acc_seg: 90.8638 2023/06/07 10:38:19 - mmengine - INFO - Iter(train) [ 11350/240000] lr: 9.5777e-03 eta: 1 day, 21:49:54 time: 0.7455 data_time: 0.2575 memory: 17394 loss: 0.2740 decode.loss_ce: 0.1830 decode.acc_seg: 92.4844 aux.loss_ce: 0.0910 aux.acc_seg: 89.7382 2023/06/07 10:38:57 - mmengine - INFO - Iter(train) [ 11400/240000] lr: 9.5758e-03 eta: 1 day, 21:49:45 time: 0.7655 data_time: 0.3100 memory: 17393 loss: 0.2657 decode.loss_ce: 0.1785 decode.acc_seg: 90.5420 aux.loss_ce: 0.0873 aux.acc_seg: 88.5476 2023/06/07 10:39:36 - mmengine - INFO - Iter(train) [ 11450/240000] lr: 9.5739e-03 eta: 1 day, 21:50:06 time: 0.7598 data_time: 0.1273 memory: 17394 loss: 0.2601 decode.loss_ce: 0.1738 decode.acc_seg: 93.3331 aux.loss_ce: 0.0864 aux.acc_seg: 91.0690 2023/06/07 10:40:14 - mmengine - INFO - Iter(train) [ 11500/240000] lr: 9.5721e-03 eta: 1 day, 21:50:15 time: 0.7535 data_time: 0.4022 memory: 17390 loss: 0.2615 decode.loss_ce: 0.1750 decode.acc_seg: 91.3539 aux.loss_ce: 0.0865 aux.acc_seg: 87.8086 2023/06/07 10:40:51 - mmengine - INFO - Iter(train) [ 11550/240000] lr: 9.5702e-03 eta: 1 day, 21:50:06 time: 0.7495 data_time: 0.4127 memory: 17394 loss: 0.2885 decode.loss_ce: 0.1916 decode.acc_seg: 90.6510 aux.loss_ce: 0.0969 aux.acc_seg: 89.3975 2023/06/07 10:41:29 - mmengine - INFO - Iter(train) [ 11600/240000] lr: 9.5683e-03 eta: 1 day, 21:50:08 time: 0.7631 data_time: 0.3831 memory: 17393 loss: 0.2842 decode.loss_ce: 0.1908 decode.acc_seg: 92.5684 aux.loss_ce: 0.0934 aux.acc_seg: 89.4749 2023/06/07 10:42:07 - mmengine - INFO - Iter(train) [ 11650/240000] lr: 9.5665e-03 eta: 1 day, 21:49:59 time: 0.7633 data_time: 0.3837 memory: 17394 loss: 0.2712 decode.loss_ce: 0.1843 decode.acc_seg: 92.0357 aux.loss_ce: 0.0869 aux.acc_seg: 90.9158 2023/06/07 10:42:45 - mmengine - INFO - Iter(train) [ 11700/240000] lr: 9.5646e-03 eta: 1 day, 21:50:09 time: 0.7534 data_time: 0.4172 memory: 17392 loss: 0.2805 decode.loss_ce: 0.1889 decode.acc_seg: 87.7828 aux.loss_ce: 0.0916 aux.acc_seg: 87.0899 2023/06/07 10:43:23 - mmengine - INFO - Iter(train) [ 11750/240000] lr: 9.5627e-03 eta: 1 day, 21:50:09 time: 0.7464 data_time: 0.2204 memory: 17392 loss: 0.2548 decode.loss_ce: 0.1706 decode.acc_seg: 91.2235 aux.loss_ce: 0.0843 aux.acc_seg: 89.4615 2023/06/07 10:44:01 - mmengine - INFO - Iter(train) [ 11800/240000] lr: 9.5609e-03 eta: 1 day, 21:50:06 time: 0.7385 data_time: 0.1119 memory: 17390 loss: 0.2549 decode.loss_ce: 0.1707 decode.acc_seg: 93.7530 aux.loss_ce: 0.0843 aux.acc_seg: 91.5673 2023/06/07 10:44:40 - mmengine - INFO - Iter(train) [ 11850/240000] lr: 9.5590e-03 eta: 1 day, 21:50:24 time: 0.7741 data_time: 0.1766 memory: 17393 loss: 0.2562 decode.loss_ce: 0.1698 decode.acc_seg: 91.3718 aux.loss_ce: 0.0864 aux.acc_seg: 87.7037 2023/06/07 10:45:19 - mmengine - INFO - Iter(train) [ 11900/240000] lr: 9.5571e-03 eta: 1 day, 21:50:29 time: 0.7575 data_time: 0.4027 memory: 17393 loss: 0.2763 decode.loss_ce: 0.1852 decode.acc_seg: 90.9446 aux.loss_ce: 0.0911 aux.acc_seg: 89.8712 2023/06/07 10:45:56 - mmengine - INFO - Iter(train) [ 11950/240000] lr: 9.5553e-03 eta: 1 day, 21:50:21 time: 0.7510 data_time: 0.1451 memory: 17392 loss: 0.2619 decode.loss_ce: 0.1722 decode.acc_seg: 91.5723 aux.loss_ce: 0.0898 aux.acc_seg: 90.5350 2023/06/07 10:46:35 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 10:46:35 - mmengine - INFO - Iter(train) [ 12000/240000] lr: 9.5534e-03 eta: 1 day, 21:50:26 time: 0.7864 data_time: 0.0130 memory: 17395 loss: 0.2702 decode.loss_ce: 0.1819 decode.acc_seg: 91.9649 aux.loss_ce: 0.0883 aux.acc_seg: 88.8474 2023/06/07 10:47:13 - mmengine - INFO - Iter(train) [ 12050/240000] lr: 9.5515e-03 eta: 1 day, 21:50:23 time: 0.7761 data_time: 0.0141 memory: 17392 loss: 0.2598 decode.loss_ce: 0.1740 decode.acc_seg: 91.6719 aux.loss_ce: 0.0858 aux.acc_seg: 89.9929 2023/06/07 10:47:51 - mmengine - INFO - Iter(train) [ 12100/240000] lr: 9.5497e-03 eta: 1 day, 21:50:31 time: 0.7580 data_time: 0.0130 memory: 17390 loss: 0.2868 decode.loss_ce: 0.1925 decode.acc_seg: 90.9646 aux.loss_ce: 0.0943 aux.acc_seg: 89.1788 2023/06/07 10:48:29 - mmengine - INFO - Iter(train) [ 12150/240000] lr: 9.5478e-03 eta: 1 day, 21:50:30 time: 0.7798 data_time: 0.0218 memory: 17392 loss: 0.2499 decode.loss_ce: 0.1663 decode.acc_seg: 93.1110 aux.loss_ce: 0.0836 aux.acc_seg: 90.8763 2023/06/07 10:49:07 - mmengine - INFO - Iter(train) [ 12200/240000] lr: 9.5459e-03 eta: 1 day, 21:50:29 time: 0.7607 data_time: 0.3416 memory: 17393 loss: 0.2627 decode.loss_ce: 0.1760 decode.acc_seg: 92.7610 aux.loss_ce: 0.0867 aux.acc_seg: 90.3699 2023/06/07 10:49:45 - mmengine - INFO - Iter(train) [ 12250/240000] lr: 9.5441e-03 eta: 1 day, 21:50:21 time: 0.7628 data_time: 0.3892 memory: 17393 loss: 0.2648 decode.loss_ce: 0.1758 decode.acc_seg: 91.0243 aux.loss_ce: 0.0890 aux.acc_seg: 87.6849 2023/06/07 10:50:24 - mmengine - INFO - Iter(train) [ 12300/240000] lr: 9.5422e-03 eta: 1 day, 21:50:27 time: 0.7637 data_time: 0.4065 memory: 17394 loss: 0.2844 decode.loss_ce: 0.1935 decode.acc_seg: 91.5388 aux.loss_ce: 0.0909 aux.acc_seg: 89.1187 2023/06/07 10:51:04 - mmengine - INFO - Iter(train) [ 12350/240000] lr: 9.5403e-03 eta: 1 day, 21:51:03 time: 0.7918 data_time: 0.4510 memory: 17391 loss: 0.2780 decode.loss_ce: 0.1839 decode.acc_seg: 90.5587 aux.loss_ce: 0.0941 aux.acc_seg: 88.4859 2023/06/07 10:51:42 - mmengine - INFO - Iter(train) [ 12400/240000] lr: 9.5385e-03 eta: 1 day, 21:51:00 time: 0.7507 data_time: 0.4169 memory: 17392 loss: 0.2730 decode.loss_ce: 0.1822 decode.acc_seg: 92.1682 aux.loss_ce: 0.0907 aux.acc_seg: 90.3500 2023/06/07 10:52:20 - mmengine - INFO - Iter(train) [ 12450/240000] lr: 9.5366e-03 eta: 1 day, 21:50:55 time: 0.7587 data_time: 0.4267 memory: 17394 loss: 0.2431 decode.loss_ce: 0.1610 decode.acc_seg: 91.8070 aux.loss_ce: 0.0821 aux.acc_seg: 88.6935 2023/06/07 10:52:58 - mmengine - INFO - Iter(train) [ 12500/240000] lr: 9.5347e-03 eta: 1 day, 21:50:51 time: 0.7614 data_time: 0.4244 memory: 17393 loss: 0.2652 decode.loss_ce: 0.1785 decode.acc_seg: 90.8634 aux.loss_ce: 0.0867 aux.acc_seg: 89.2111 2023/06/07 10:53:37 - mmengine - INFO - Iter(train) [ 12550/240000] lr: 9.5329e-03 eta: 1 day, 21:51:15 time: 0.7721 data_time: 0.4284 memory: 17391 loss: 0.2756 decode.loss_ce: 0.1823 decode.acc_seg: 91.7332 aux.loss_ce: 0.0933 aux.acc_seg: 88.3887 2023/06/07 10:54:16 - mmengine - INFO - Iter(train) [ 12600/240000] lr: 9.5310e-03 eta: 1 day, 21:51:13 time: 0.7879 data_time: 0.3823 memory: 17392 loss: 0.2840 decode.loss_ce: 0.1928 decode.acc_seg: 87.6417 aux.loss_ce: 0.0912 aux.acc_seg: 85.2873 2023/06/07 10:54:54 - mmengine - INFO - Iter(train) [ 12650/240000] lr: 9.5291e-03 eta: 1 day, 21:51:15 time: 0.7698 data_time: 0.4018 memory: 17393 loss: 0.2698 decode.loss_ce: 0.1825 decode.acc_seg: 93.4163 aux.loss_ce: 0.0874 aux.acc_seg: 91.5460 2023/06/07 10:55:33 - mmengine - INFO - Iter(train) [ 12700/240000] lr: 9.5273e-03 eta: 1 day, 21:51:21 time: 0.7698 data_time: 0.4129 memory: 17392 loss: 0.2670 decode.loss_ce: 0.1793 decode.acc_seg: 91.6117 aux.loss_ce: 0.0877 aux.acc_seg: 89.4635 2023/06/07 10:56:12 - mmengine - INFO - Iter(train) [ 12750/240000] lr: 9.5254e-03 eta: 1 day, 21:51:37 time: 0.7964 data_time: 0.4420 memory: 17390 loss: 0.2542 decode.loss_ce: 0.1674 decode.acc_seg: 92.8811 aux.loss_ce: 0.0868 aux.acc_seg: 91.0312 2023/06/07 10:56:51 - mmengine - INFO - Iter(train) [ 12800/240000] lr: 9.5235e-03 eta: 1 day, 21:51:46 time: 0.7877 data_time: 0.4279 memory: 17393 loss: 0.2677 decode.loss_ce: 0.1785 decode.acc_seg: 90.4874 aux.loss_ce: 0.0892 aux.acc_seg: 87.2924 2023/06/07 10:57:30 - mmengine - INFO - Iter(train) [ 12850/240000] lr: 9.5217e-03 eta: 1 day, 21:51:51 time: 0.7819 data_time: 0.4277 memory: 17394 loss: 0.2515 decode.loss_ce: 0.1652 decode.acc_seg: 92.7937 aux.loss_ce: 0.0863 aux.acc_seg: 90.5770 2023/06/07 10:58:08 - mmengine - INFO - Iter(train) [ 12900/240000] lr: 9.5198e-03 eta: 1 day, 21:51:52 time: 0.7608 data_time: 0.4158 memory: 17393 loss: 0.2592 decode.loss_ce: 0.1709 decode.acc_seg: 91.4563 aux.loss_ce: 0.0882 aux.acc_seg: 85.3101 2023/06/07 10:58:47 - mmengine - INFO - Iter(train) [ 12950/240000] lr: 9.5179e-03 eta: 1 day, 21:51:54 time: 0.7743 data_time: 0.4405 memory: 17394 loss: 0.2640 decode.loss_ce: 0.1763 decode.acc_seg: 90.4144 aux.loss_ce: 0.0877 aux.acc_seg: 88.9466 2023/06/07 10:59:24 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 10:59:24 - mmengine - INFO - Iter(train) [ 13000/240000] lr: 9.5161e-03 eta: 1 day, 21:51:41 time: 0.7506 data_time: 0.4179 memory: 17390 loss: 0.2692 decode.loss_ce: 0.1789 decode.acc_seg: 92.8357 aux.loss_ce: 0.0903 aux.acc_seg: 89.5905 2023/06/07 11:00:03 - mmengine - INFO - Iter(train) [ 13050/240000] lr: 9.5142e-03 eta: 1 day, 21:51:42 time: 0.7678 data_time: 0.4226 memory: 17393 loss: 0.2701 decode.loss_ce: 0.1776 decode.acc_seg: 91.7558 aux.loss_ce: 0.0926 aux.acc_seg: 87.9526 2023/06/07 11:00:41 - mmengine - INFO - Iter(train) [ 13100/240000] lr: 9.5123e-03 eta: 1 day, 21:51:39 time: 0.7530 data_time: 0.4062 memory: 17393 loss: 0.2784 decode.loss_ce: 0.1853 decode.acc_seg: 90.9424 aux.loss_ce: 0.0931 aux.acc_seg: 87.4899 2023/06/07 11:01:19 - mmengine - INFO - Iter(train) [ 13150/240000] lr: 9.5105e-03 eta: 1 day, 21:51:35 time: 0.7506 data_time: 0.4228 memory: 17393 loss: 0.2602 decode.loss_ce: 0.1724 decode.acc_seg: 90.5831 aux.loss_ce: 0.0878 aux.acc_seg: 89.0038 2023/06/07 11:01:57 - mmengine - INFO - Iter(train) [ 13200/240000] lr: 9.5086e-03 eta: 1 day, 21:51:24 time: 0.7401 data_time: 0.4098 memory: 17390 loss: 0.2649 decode.loss_ce: 0.1774 decode.acc_seg: 92.5324 aux.loss_ce: 0.0874 aux.acc_seg: 90.7506 2023/06/07 11:02:35 - mmengine - INFO - Iter(train) [ 13250/240000] lr: 9.5067e-03 eta: 1 day, 21:51:18 time: 0.7402 data_time: 0.4069 memory: 17391 loss: 0.2872 decode.loss_ce: 0.1933 decode.acc_seg: 89.2980 aux.loss_ce: 0.0939 aux.acc_seg: 87.9829 2023/06/07 11:03:13 - mmengine - INFO - Iter(train) [ 13300/240000] lr: 9.5049e-03 eta: 1 day, 21:51:08 time: 0.7627 data_time: 0.4311 memory: 17390 loss: 0.2710 decode.loss_ce: 0.1814 decode.acc_seg: 92.7037 aux.loss_ce: 0.0895 aux.acc_seg: 89.8745 2023/06/07 11:03:51 - mmengine - INFO - Iter(train) [ 13350/240000] lr: 9.5030e-03 eta: 1 day, 21:50:49 time: 0.7517 data_time: 0.4241 memory: 17390 loss: 0.2654 decode.loss_ce: 0.1770 decode.acc_seg: 92.3570 aux.loss_ce: 0.0884 aux.acc_seg: 90.4638 2023/06/07 11:04:29 - mmengine - INFO - Iter(train) [ 13400/240000] lr: 9.5011e-03 eta: 1 day, 21:50:47 time: 0.7806 data_time: 0.4379 memory: 17390 loss: 0.2559 decode.loss_ce: 0.1705 decode.acc_seg: 92.1263 aux.loss_ce: 0.0853 aux.acc_seg: 90.4927 2023/06/07 11:05:08 - mmengine - INFO - Iter(train) [ 13450/240000] lr: 9.4993e-03 eta: 1 day, 21:50:48 time: 0.7661 data_time: 0.4251 memory: 17392 loss: 0.2773 decode.loss_ce: 0.1865 decode.acc_seg: 92.2337 aux.loss_ce: 0.0909 aux.acc_seg: 90.9524 2023/06/07 11:05:46 - mmengine - INFO - Iter(train) [ 13500/240000] lr: 9.4974e-03 eta: 1 day, 21:50:38 time: 0.7408 data_time: 0.4036 memory: 17390 loss: 0.2615 decode.loss_ce: 0.1745 decode.acc_seg: 92.2140 aux.loss_ce: 0.0870 aux.acc_seg: 89.8170 2023/06/07 11:06:24 - mmengine - INFO - Iter(train) [ 13550/240000] lr: 9.4955e-03 eta: 1 day, 21:50:35 time: 0.7798 data_time: 0.4461 memory: 17395 loss: 0.2682 decode.loss_ce: 0.1786 decode.acc_seg: 91.8947 aux.loss_ce: 0.0896 aux.acc_seg: 90.1450 2023/06/07 11:07:03 - mmengine - INFO - Iter(train) [ 13600/240000] lr: 9.4937e-03 eta: 1 day, 21:50:32 time: 0.7740 data_time: 0.4377 memory: 17395 loss: 0.2758 decode.loss_ce: 0.1866 decode.acc_seg: 90.9716 aux.loss_ce: 0.0892 aux.acc_seg: 89.0650 2023/06/07 11:07:41 - mmengine - INFO - Iter(train) [ 13650/240000] lr: 9.4918e-03 eta: 1 day, 21:50:24 time: 0.7645 data_time: 0.4291 memory: 17393 loss: 0.2750 decode.loss_ce: 0.1844 decode.acc_seg: 93.6733 aux.loss_ce: 0.0906 aux.acc_seg: 92.3346 2023/06/07 11:08:21 - mmengine - INFO - Iter(train) [ 13700/240000] lr: 9.4899e-03 eta: 1 day, 21:50:40 time: 0.7972 data_time: 0.4259 memory: 17390 loss: 0.2667 decode.loss_ce: 0.1781 decode.acc_seg: 92.7241 aux.loss_ce: 0.0886 aux.acc_seg: 91.2352 2023/06/07 11:08:59 - mmengine - INFO - Iter(train) [ 13750/240000] lr: 9.4881e-03 eta: 1 day, 21:50:31 time: 0.7542 data_time: 0.3756 memory: 17392 loss: 0.2662 decode.loss_ce: 0.1765 decode.acc_seg: 91.5944 aux.loss_ce: 0.0898 aux.acc_seg: 90.2924 2023/06/07 11:09:37 - mmengine - INFO - Iter(train) [ 13800/240000] lr: 9.4862e-03 eta: 1 day, 21:50:22 time: 0.7733 data_time: 0.2777 memory: 17393 loss: 0.2833 decode.loss_ce: 0.1918 decode.acc_seg: 92.0122 aux.loss_ce: 0.0915 aux.acc_seg: 92.0946 2023/06/07 11:10:16 - mmengine - INFO - Iter(train) [ 13850/240000] lr: 9.4843e-03 eta: 1 day, 21:50:28 time: 0.7842 data_time: 0.0933 memory: 17394 loss: 0.2466 decode.loss_ce: 0.1651 decode.acc_seg: 94.1855 aux.loss_ce: 0.0815 aux.acc_seg: 92.4854 2023/06/07 11:10:54 - mmengine - INFO - Iter(train) [ 13900/240000] lr: 9.4825e-03 eta: 1 day, 21:50:19 time: 0.7609 data_time: 0.0737 memory: 17392 loss: 0.2837 decode.loss_ce: 0.1912 decode.acc_seg: 92.6210 aux.loss_ce: 0.0925 aux.acc_seg: 91.2951 2023/06/07 11:11:32 - mmengine - INFO - Iter(train) [ 13950/240000] lr: 9.4806e-03 eta: 1 day, 21:50:03 time: 0.7836 data_time: 0.2192 memory: 17392 loss: 0.2561 decode.loss_ce: 0.1719 decode.acc_seg: 90.7872 aux.loss_ce: 0.0842 aux.acc_seg: 88.4783 2023/06/07 11:12:10 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 11:12:10 - mmengine - INFO - Iter(train) [ 14000/240000] lr: 9.4787e-03 eta: 1 day, 21:49:55 time: 0.7623 data_time: 0.0141 memory: 17390 loss: 0.2640 decode.loss_ce: 0.1754 decode.acc_seg: 87.7310 aux.loss_ce: 0.0886 aux.acc_seg: 82.8446 2023/06/07 11:12:49 - mmengine - INFO - Iter(train) [ 14050/240000] lr: 9.4769e-03 eta: 1 day, 21:49:55 time: 0.7767 data_time: 0.0133 memory: 17391 loss: 0.2647 decode.loss_ce: 0.1795 decode.acc_seg: 91.2873 aux.loss_ce: 0.0852 aux.acc_seg: 88.2750 2023/06/07 11:13:28 - mmengine - INFO - Iter(train) [ 14100/240000] lr: 9.4750e-03 eta: 1 day, 21:49:58 time: 0.7781 data_time: 0.0138 memory: 17389 loss: 0.2684 decode.loss_ce: 0.1799 decode.acc_seg: 92.5590 aux.loss_ce: 0.0886 aux.acc_seg: 89.8928 2023/06/07 11:14:06 - mmengine - INFO - Iter(train) [ 14150/240000] lr: 9.4731e-03 eta: 1 day, 21:49:53 time: 0.7842 data_time: 0.0132 memory: 17393 loss: 0.2594 decode.loss_ce: 0.1695 decode.acc_seg: 91.8135 aux.loss_ce: 0.0899 aux.acc_seg: 89.3908 2023/06/07 11:14:47 - mmengine - INFO - Iter(train) [ 14200/240000] lr: 9.4713e-03 eta: 1 day, 21:50:17 time: 0.8233 data_time: 0.0146 memory: 17393 loss: 0.2763 decode.loss_ce: 0.1835 decode.acc_seg: 91.7663 aux.loss_ce: 0.0928 aux.acc_seg: 89.7297 2023/06/07 11:15:25 - mmengine - INFO - Iter(train) [ 14250/240000] lr: 9.4694e-03 eta: 1 day, 21:50:15 time: 0.7614 data_time: 0.0132 memory: 17391 loss: 0.2721 decode.loss_ce: 0.1848 decode.acc_seg: 91.1730 aux.loss_ce: 0.0873 aux.acc_seg: 90.8505 2023/06/07 11:16:04 - mmengine - INFO - Iter(train) [ 14300/240000] lr: 9.4675e-03 eta: 1 day, 21:50:08 time: 0.7783 data_time: 0.0137 memory: 17394 loss: 0.2750 decode.loss_ce: 0.1819 decode.acc_seg: 91.7299 aux.loss_ce: 0.0931 aux.acc_seg: 88.2140 2023/06/07 11:16:43 - mmengine - INFO - Iter(train) [ 14350/240000] lr: 9.4657e-03 eta: 1 day, 21:50:08 time: 0.7843 data_time: 0.0133 memory: 17393 loss: 0.2463 decode.loss_ce: 0.1642 decode.acc_seg: 91.9486 aux.loss_ce: 0.0821 aux.acc_seg: 89.9887 2023/06/07 11:17:22 - mmengine - INFO - Iter(train) [ 14400/240000] lr: 9.4638e-03 eta: 1 day, 21:50:10 time: 0.7853 data_time: 0.0136 memory: 17391 loss: 0.2499 decode.loss_ce: 0.1684 decode.acc_seg: 92.9007 aux.loss_ce: 0.0815 aux.acc_seg: 91.0594 2023/06/07 11:18:01 - mmengine - INFO - Iter(train) [ 14450/240000] lr: 9.4619e-03 eta: 1 day, 21:50:20 time: 0.7904 data_time: 0.0129 memory: 17394 loss: 0.2712 decode.loss_ce: 0.1805 decode.acc_seg: 93.6752 aux.loss_ce: 0.0907 aux.acc_seg: 92.0798 2023/06/07 11:18:41 - mmengine - INFO - Iter(train) [ 14500/240000] lr: 9.4601e-03 eta: 1 day, 21:50:31 time: 0.7962 data_time: 0.0125 memory: 17393 loss: 0.2390 decode.loss_ce: 0.1594 decode.acc_seg: 93.6648 aux.loss_ce: 0.0795 aux.acc_seg: 92.6461 2023/06/07 11:19:20 - mmengine - INFO - Iter(train) [ 14550/240000] lr: 9.4582e-03 eta: 1 day, 21:50:40 time: 0.7661 data_time: 0.0150 memory: 17392 loss: 0.2621 decode.loss_ce: 0.1740 decode.acc_seg: 93.5930 aux.loss_ce: 0.0881 aux.acc_seg: 90.0749 2023/06/07 11:19:59 - mmengine - INFO - Iter(train) [ 14600/240000] lr: 9.4563e-03 eta: 1 day, 21:50:34 time: 0.7686 data_time: 0.0134 memory: 17392 loss: 0.2439 decode.loss_ce: 0.1625 decode.acc_seg: 91.6919 aux.loss_ce: 0.0815 aux.acc_seg: 88.5798 2023/06/07 11:20:37 - mmengine - INFO - Iter(train) [ 14650/240000] lr: 9.4545e-03 eta: 1 day, 21:50:22 time: 0.7658 data_time: 0.0148 memory: 17393 loss: 0.2477 decode.loss_ce: 0.1663 decode.acc_seg: 92.3558 aux.loss_ce: 0.0815 aux.acc_seg: 91.2787 2023/06/07 11:21:15 - mmengine - INFO - Iter(train) [ 14700/240000] lr: 9.4526e-03 eta: 1 day, 21:50:08 time: 0.7607 data_time: 0.1450 memory: 17393 loss: 0.2612 decode.loss_ce: 0.1757 decode.acc_seg: 92.1241 aux.loss_ce: 0.0855 aux.acc_seg: 90.9792 2023/06/07 11:21:52 - mmengine - INFO - Iter(train) [ 14750/240000] lr: 9.4507e-03 eta: 1 day, 21:49:40 time: 0.7453 data_time: 0.3202 memory: 17390 loss: 0.2692 decode.loss_ce: 0.1794 decode.acc_seg: 90.3127 aux.loss_ce: 0.0899 aux.acc_seg: 85.2188 2023/06/07 11:22:30 - mmengine - INFO - Iter(train) [ 14800/240000] lr: 9.4489e-03 eta: 1 day, 21:49:14 time: 0.7551 data_time: 0.4051 memory: 17392 loss: 0.2586 decode.loss_ce: 0.1700 decode.acc_seg: 93.5210 aux.loss_ce: 0.0885 aux.acc_seg: 90.0731 2023/06/07 11:23:07 - mmengine - INFO - Iter(train) [ 14850/240000] lr: 9.4470e-03 eta: 1 day, 21:48:48 time: 0.7387 data_time: 0.4052 memory: 17393 loss: 0.2723 decode.loss_ce: 0.1815 decode.acc_seg: 91.9407 aux.loss_ce: 0.0908 aux.acc_seg: 89.5505 2023/06/07 11:23:45 - mmengine - INFO - Iter(train) [ 14900/240000] lr: 9.4451e-03 eta: 1 day, 21:48:28 time: 0.7422 data_time: 0.4142 memory: 17392 loss: 0.2535 decode.loss_ce: 0.1689 decode.acc_seg: 90.3117 aux.loss_ce: 0.0846 aux.acc_seg: 88.3637 2023/06/07 11:24:22 - mmengine - INFO - Iter(train) [ 14950/240000] lr: 9.4432e-03 eta: 1 day, 21:48:02 time: 0.7513 data_time: 0.4235 memory: 17391 loss: 0.2338 decode.loss_ce: 0.1517 decode.acc_seg: 95.6294 aux.loss_ce: 0.0822 aux.acc_seg: 94.5238 2023/06/07 11:25:00 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 11:25:00 - mmengine - INFO - Iter(train) [ 15000/240000] lr: 9.4414e-03 eta: 1 day, 21:47:41 time: 0.7514 data_time: 0.4235 memory: 17395 loss: 0.2537 decode.loss_ce: 0.1678 decode.acc_seg: 91.9780 aux.loss_ce: 0.0859 aux.acc_seg: 90.5351 2023/06/07 11:25:37 - mmengine - INFO - Iter(train) [ 15050/240000] lr: 9.4395e-03 eta: 1 day, 21:47:16 time: 0.7516 data_time: 0.4236 memory: 17395 loss: 0.2669 decode.loss_ce: 0.1784 decode.acc_seg: 94.1834 aux.loss_ce: 0.0885 aux.acc_seg: 92.7025 2023/06/07 11:26:14 - mmengine - INFO - Iter(train) [ 15100/240000] lr: 9.4376e-03 eta: 1 day, 21:46:44 time: 0.7394 data_time: 0.3558 memory: 17392 loss: 0.2561 decode.loss_ce: 0.1710 decode.acc_seg: 91.2589 aux.loss_ce: 0.0851 aux.acc_seg: 88.3946 2023/06/07 11:26:52 - mmengine - INFO - Iter(train) [ 15150/240000] lr: 9.4358e-03 eta: 1 day, 21:46:24 time: 0.7579 data_time: 0.2284 memory: 17391 loss: 0.2621 decode.loss_ce: 0.1757 decode.acc_seg: 92.0431 aux.loss_ce: 0.0864 aux.acc_seg: 90.0155 2023/06/07 11:27:29 - mmengine - INFO - Iter(train) [ 15200/240000] lr: 9.4339e-03 eta: 1 day, 21:45:56 time: 0.7296 data_time: 0.3648 memory: 17389 loss: 0.2576 decode.loss_ce: 0.1694 decode.acc_seg: 91.7646 aux.loss_ce: 0.0883 aux.acc_seg: 89.8523 2023/06/07 11:28:07 - mmengine - INFO - Iter(train) [ 15250/240000] lr: 9.4320e-03 eta: 1 day, 21:45:42 time: 0.7675 data_time: 0.1713 memory: 17393 loss: 0.2558 decode.loss_ce: 0.1714 decode.acc_seg: 89.3077 aux.loss_ce: 0.0844 aux.acc_seg: 84.6917 2023/06/07 11:28:46 - mmengine - INFO - Iter(train) [ 15300/240000] lr: 9.4302e-03 eta: 1 day, 21:45:31 time: 0.7673 data_time: 0.2285 memory: 17392 loss: 0.2477 decode.loss_ce: 0.1658 decode.acc_seg: 90.1679 aux.loss_ce: 0.0819 aux.acc_seg: 88.6555 2023/06/07 11:29:24 - mmengine - INFO - Iter(train) [ 15350/240000] lr: 9.4283e-03 eta: 1 day, 21:45:24 time: 0.7814 data_time: 0.0149 memory: 17392 loss: 0.2706 decode.loss_ce: 0.1794 decode.acc_seg: 90.5422 aux.loss_ce: 0.0912 aux.acc_seg: 88.7276 2023/06/07 11:30:03 - mmengine - INFO - Iter(train) [ 15400/240000] lr: 9.4264e-03 eta: 1 day, 21:45:20 time: 0.7859 data_time: 0.0129 memory: 17391 loss: 0.2642 decode.loss_ce: 0.1781 decode.acc_seg: 93.4933 aux.loss_ce: 0.0861 aux.acc_seg: 92.2969 2023/06/07 11:30:42 - mmengine - INFO - Iter(train) [ 15450/240000] lr: 9.4246e-03 eta: 1 day, 21:45:11 time: 0.7621 data_time: 0.0130 memory: 17392 loss: 0.2534 decode.loss_ce: 0.1690 decode.acc_seg: 91.6027 aux.loss_ce: 0.0843 aux.acc_seg: 89.3835 2023/06/07 11:31:21 - mmengine - INFO - Iter(train) [ 15500/240000] lr: 9.4227e-03 eta: 1 day, 21:45:04 time: 0.7761 data_time: 0.0329 memory: 17392 loss: 0.2540 decode.loss_ce: 0.1689 decode.acc_seg: 90.8408 aux.loss_ce: 0.0850 aux.acc_seg: 88.5303 2023/06/07 11:32:00 - mmengine - INFO - Iter(train) [ 15550/240000] lr: 9.4208e-03 eta: 1 day, 21:45:02 time: 0.7819 data_time: 0.0134 memory: 17393 loss: 0.2516 decode.loss_ce: 0.1687 decode.acc_seg: 92.4578 aux.loss_ce: 0.0829 aux.acc_seg: 90.2080 2023/06/07 11:32:38 - mmengine - INFO - Iter(train) [ 15600/240000] lr: 9.4190e-03 eta: 1 day, 21:44:45 time: 0.7579 data_time: 0.0130 memory: 17391 loss: 0.2375 decode.loss_ce: 0.1580 decode.acc_seg: 93.4244 aux.loss_ce: 0.0795 aux.acc_seg: 91.2298 2023/06/07 11:33:16 - mmengine - INFO - Iter(train) [ 15650/240000] lr: 9.4171e-03 eta: 1 day, 21:44:33 time: 0.7708 data_time: 0.0129 memory: 17393 loss: 0.2453 decode.loss_ce: 0.1630 decode.acc_seg: 92.1400 aux.loss_ce: 0.0823 aux.acc_seg: 89.8792 2023/06/07 11:33:55 - mmengine - INFO - Iter(train) [ 15700/240000] lr: 9.4152e-03 eta: 1 day, 21:44:25 time: 0.7690 data_time: 0.0132 memory: 17392 loss: 0.2377 decode.loss_ce: 0.1578 decode.acc_seg: 92.0745 aux.loss_ce: 0.0798 aux.acc_seg: 90.7530 2023/06/07 11:34:33 - mmengine - INFO - Iter(train) [ 15750/240000] lr: 9.4133e-03 eta: 1 day, 21:44:08 time: 0.7560 data_time: 0.0656 memory: 17393 loss: 0.2558 decode.loss_ce: 0.1716 decode.acc_seg: 90.2811 aux.loss_ce: 0.0842 aux.acc_seg: 88.2807 2023/06/07 11:35:12 - mmengine - INFO - Iter(train) [ 15800/240000] lr: 9.4115e-03 eta: 1 day, 21:43:57 time: 0.7573 data_time: 0.0205 memory: 17392 loss: 0.2495 decode.loss_ce: 0.1665 decode.acc_seg: 92.9344 aux.loss_ce: 0.0830 aux.acc_seg: 90.4902 2023/06/07 11:35:50 - mmengine - INFO - Iter(train) [ 15850/240000] lr: 9.4096e-03 eta: 1 day, 21:43:43 time: 0.7653 data_time: 0.0182 memory: 17392 loss: 0.2522 decode.loss_ce: 0.1674 decode.acc_seg: 94.1744 aux.loss_ce: 0.0848 aux.acc_seg: 92.2537 2023/06/07 11:36:29 - mmengine - INFO - Iter(train) [ 15900/240000] lr: 9.4077e-03 eta: 1 day, 21:43:33 time: 0.7732 data_time: 0.0528 memory: 17393 loss: 0.2553 decode.loss_ce: 0.1701 decode.acc_seg: 90.9902 aux.loss_ce: 0.0852 aux.acc_seg: 89.1888 2023/06/07 11:37:07 - mmengine - INFO - Iter(train) [ 15950/240000] lr: 9.4059e-03 eta: 1 day, 21:43:17 time: 0.7651 data_time: 0.0176 memory: 17395 loss: 0.2538 decode.loss_ce: 0.1706 decode.acc_seg: 92.3975 aux.loss_ce: 0.0832 aux.acc_seg: 90.2458 2023/06/07 11:37:45 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 11:37:45 - mmengine - INFO - Iter(train) [ 16000/240000] lr: 9.4040e-03 eta: 1 day, 21:43:00 time: 0.7599 data_time: 0.2942 memory: 17392 loss: 0.2430 decode.loss_ce: 0.1617 decode.acc_seg: 94.6612 aux.loss_ce: 0.0813 aux.acc_seg: 92.1863 2023/06/07 11:38:23 - mmengine - INFO - Iter(train) [ 16050/240000] lr: 9.4021e-03 eta: 1 day, 21:42:43 time: 0.7589 data_time: 0.4017 memory: 17392 loss: 0.2402 decode.loss_ce: 0.1610 decode.acc_seg: 90.6290 aux.loss_ce: 0.0792 aux.acc_seg: 90.8471 2023/06/07 11:39:01 - mmengine - INFO - Iter(train) [ 16100/240000] lr: 9.4003e-03 eta: 1 day, 21:42:24 time: 0.7623 data_time: 0.4090 memory: 17393 loss: 0.2542 decode.loss_ce: 0.1684 decode.acc_seg: 91.8371 aux.loss_ce: 0.0858 aux.acc_seg: 88.1099 2023/06/07 11:39:39 - mmengine - INFO - Iter(train) [ 16150/240000] lr: 9.3984e-03 eta: 1 day, 21:42:09 time: 0.7797 data_time: 0.4217 memory: 17393 loss: 0.2671 decode.loss_ce: 0.1788 decode.acc_seg: 93.8237 aux.loss_ce: 0.0883 aux.acc_seg: 91.9971 2023/06/07 11:40:18 - mmengine - INFO - Iter(train) [ 16200/240000] lr: 9.3965e-03 eta: 1 day, 21:41:54 time: 0.7683 data_time: 0.4157 memory: 17391 loss: 0.2577 decode.loss_ce: 0.1720 decode.acc_seg: 92.7370 aux.loss_ce: 0.0857 aux.acc_seg: 90.7668 2023/06/07 11:40:56 - mmengine - INFO - Iter(train) [ 16250/240000] lr: 9.3947e-03 eta: 1 day, 21:41:38 time: 0.7670 data_time: 0.4094 memory: 17392 loss: 0.2752 decode.loss_ce: 0.1845 decode.acc_seg: 90.1472 aux.loss_ce: 0.0907 aux.acc_seg: 88.1634 2023/06/07 11:41:34 - mmengine - INFO - Iter(train) [ 16300/240000] lr: 9.3928e-03 eta: 1 day, 21:41:21 time: 0.7580 data_time: 0.4056 memory: 17393 loss: 0.2546 decode.loss_ce: 0.1702 decode.acc_seg: 91.7887 aux.loss_ce: 0.0843 aux.acc_seg: 90.2915 2023/06/07 11:42:12 - mmengine - INFO - Iter(train) [ 16350/240000] lr: 9.3909e-03 eta: 1 day, 21:40:58 time: 0.7589 data_time: 0.3965 memory: 17392 loss: 0.2502 decode.loss_ce: 0.1643 decode.acc_seg: 94.8539 aux.loss_ce: 0.0859 aux.acc_seg: 92.9650 2023/06/07 11:42:50 - mmengine - INFO - Iter(train) [ 16400/240000] lr: 9.3891e-03 eta: 1 day, 21:40:42 time: 0.7728 data_time: 0.4224 memory: 17394 loss: 0.2570 decode.loss_ce: 0.1716 decode.acc_seg: 92.1444 aux.loss_ce: 0.0854 aux.acc_seg: 89.9725 2023/06/07 11:43:29 - mmengine - INFO - Iter(train) [ 16450/240000] lr: 9.3872e-03 eta: 1 day, 21:40:27 time: 0.7638 data_time: 0.4062 memory: 17395 loss: 0.2353 decode.loss_ce: 0.1568 decode.acc_seg: 94.2049 aux.loss_ce: 0.0785 aux.acc_seg: 92.9149 2023/06/07 11:44:07 - mmengine - INFO - Iter(train) [ 16500/240000] lr: 9.3853e-03 eta: 1 day, 21:40:15 time: 0.7683 data_time: 0.4205 memory: 17395 loss: 0.2459 decode.loss_ce: 0.1632 decode.acc_seg: 93.0303 aux.loss_ce: 0.0827 aux.acc_seg: 90.7775 2023/06/07 11:44:46 - mmengine - INFO - Iter(train) [ 16550/240000] lr: 9.3834e-03 eta: 1 day, 21:40:02 time: 0.7667 data_time: 0.4089 memory: 17390 loss: 0.2580 decode.loss_ce: 0.1702 decode.acc_seg: 91.4408 aux.loss_ce: 0.0878 aux.acc_seg: 89.2976 2023/06/07 11:45:24 - mmengine - INFO - Iter(train) [ 16600/240000] lr: 9.3816e-03 eta: 1 day, 21:39:51 time: 0.7585 data_time: 0.4093 memory: 17392 loss: 0.2663 decode.loss_ce: 0.1769 decode.acc_seg: 85.7593 aux.loss_ce: 0.0894 aux.acc_seg: 83.5320 2023/06/07 11:46:03 - mmengine - INFO - Iter(train) [ 16650/240000] lr: 9.3797e-03 eta: 1 day, 21:39:39 time: 0.7745 data_time: 0.4129 memory: 17391 loss: 0.2516 decode.loss_ce: 0.1690 decode.acc_seg: 92.1248 aux.loss_ce: 0.0826 aux.acc_seg: 90.0367 2023/06/07 11:46:41 - mmengine - INFO - Iter(train) [ 16700/240000] lr: 9.3778e-03 eta: 1 day, 21:39:23 time: 0.7688 data_time: 0.4179 memory: 17393 loss: 0.2711 decode.loss_ce: 0.1805 decode.acc_seg: 92.2772 aux.loss_ce: 0.0906 aux.acc_seg: 89.7379 2023/06/07 11:47:20 - mmengine - INFO - Iter(train) [ 16750/240000] lr: 9.3760e-03 eta: 1 day, 21:39:08 time: 0.7845 data_time: 0.4261 memory: 17394 loss: 0.2534 decode.loss_ce: 0.1675 decode.acc_seg: 92.3926 aux.loss_ce: 0.0859 aux.acc_seg: 90.8506 2023/06/07 11:47:58 - mmengine - INFO - Iter(train) [ 16800/240000] lr: 9.3741e-03 eta: 1 day, 21:38:52 time: 0.7520 data_time: 0.4023 memory: 17395 loss: 0.2712 decode.loss_ce: 0.1810 decode.acc_seg: 89.8029 aux.loss_ce: 0.0902 aux.acc_seg: 88.3589 2023/06/07 11:48:36 - mmengine - INFO - Iter(train) [ 16850/240000] lr: 9.3722e-03 eta: 1 day, 21:38:32 time: 0.7491 data_time: 0.4226 memory: 17392 loss: 0.2583 decode.loss_ce: 0.1717 decode.acc_seg: 92.7495 aux.loss_ce: 0.0866 aux.acc_seg: 91.6245 2023/06/07 11:49:14 - mmengine - INFO - Iter(train) [ 16900/240000] lr: 9.3704e-03 eta: 1 day, 21:38:12 time: 0.7576 data_time: 0.4306 memory: 17392 loss: 0.2511 decode.loss_ce: 0.1670 decode.acc_seg: 92.7840 aux.loss_ce: 0.0841 aux.acc_seg: 90.8414 2023/06/07 11:49:52 - mmengine - INFO - Iter(train) [ 16950/240000] lr: 9.3685e-03 eta: 1 day, 21:37:42 time: 0.7422 data_time: 0.4159 memory: 17392 loss: 0.2495 decode.loss_ce: 0.1668 decode.acc_seg: 92.5923 aux.loss_ce: 0.0827 aux.acc_seg: 90.8106 2023/06/07 11:50:30 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 11:50:30 - mmengine - INFO - Iter(train) [ 17000/240000] lr: 9.3666e-03 eta: 1 day, 21:37:22 time: 0.7508 data_time: 0.4235 memory: 17395 loss: 0.2615 decode.loss_ce: 0.1742 decode.acc_seg: 92.4715 aux.loss_ce: 0.0873 aux.acc_seg: 89.7114 2023/06/07 11:51:07 - mmengine - INFO - Iter(train) [ 17050/240000] lr: 9.3647e-03 eta: 1 day, 21:36:54 time: 0.7388 data_time: 0.4123 memory: 17395 loss: 0.2497 decode.loss_ce: 0.1666 decode.acc_seg: 92.0670 aux.loss_ce: 0.0831 aux.acc_seg: 90.0088 2023/06/07 11:51:45 - mmengine - INFO - Iter(train) [ 17100/240000] lr: 9.3629e-03 eta: 1 day, 21:36:30 time: 0.7508 data_time: 0.4237 memory: 17395 loss: 0.2768 decode.loss_ce: 0.1798 decode.acc_seg: 90.1785 aux.loss_ce: 0.0970 aux.acc_seg: 85.5648 2023/06/07 11:52:23 - mmengine - INFO - Iter(train) [ 17150/240000] lr: 9.3610e-03 eta: 1 day, 21:36:05 time: 0.7592 data_time: 0.4323 memory: 17394 loss: 0.2622 decode.loss_ce: 0.1738 decode.acc_seg: 92.1765 aux.loss_ce: 0.0884 aux.acc_seg: 91.4430 2023/06/07 11:53:00 - mmengine - INFO - Iter(train) [ 17200/240000] lr: 9.3591e-03 eta: 1 day, 21:35:36 time: 0.7464 data_time: 0.4189 memory: 17392 loss: 0.2502 decode.loss_ce: 0.1659 decode.acc_seg: 92.7877 aux.loss_ce: 0.0844 aux.acc_seg: 91.0171 2023/06/07 11:53:38 - mmengine - INFO - Iter(train) [ 17250/240000] lr: 9.3573e-03 eta: 1 day, 21:35:10 time: 0.7502 data_time: 0.4234 memory: 17392 loss: 0.2479 decode.loss_ce: 0.1652 decode.acc_seg: 92.7532 aux.loss_ce: 0.0828 aux.acc_seg: 90.9661 2023/06/07 11:54:16 - mmengine - INFO - Iter(train) [ 17300/240000] lr: 9.3554e-03 eta: 1 day, 21:34:45 time: 0.7445 data_time: 0.4182 memory: 17394 loss: 0.2675 decode.loss_ce: 0.1772 decode.acc_seg: 92.1063 aux.loss_ce: 0.0903 aux.acc_seg: 90.3452 2023/06/07 11:54:53 - mmengine - INFO - Iter(train) [ 17350/240000] lr: 9.3535e-03 eta: 1 day, 21:34:16 time: 0.7566 data_time: 0.4300 memory: 17394 loss: 0.2533 decode.loss_ce: 0.1677 decode.acc_seg: 92.6207 aux.loss_ce: 0.0856 aux.acc_seg: 89.4040 2023/06/07 11:55:31 - mmengine - INFO - Iter(train) [ 17400/240000] lr: 9.3517e-03 eta: 1 day, 21:33:48 time: 0.7439 data_time: 0.4178 memory: 17393 loss: 0.2779 decode.loss_ce: 0.1825 decode.acc_seg: 92.8442 aux.loss_ce: 0.0954 aux.acc_seg: 90.8886 2023/06/07 11:56:09 - mmengine - INFO - Iter(train) [ 17450/240000] lr: 9.3498e-03 eta: 1 day, 21:33:23 time: 0.7582 data_time: 0.4317 memory: 17394 loss: 0.2664 decode.loss_ce: 0.1762 decode.acc_seg: 93.0853 aux.loss_ce: 0.0902 aux.acc_seg: 90.3913 2023/06/07 11:56:46 - mmengine - INFO - Iter(train) [ 17500/240000] lr: 9.3479e-03 eta: 1 day, 21:32:56 time: 0.7639 data_time: 0.4360 memory: 17393 loss: 0.3097 decode.loss_ce: 0.2061 decode.acc_seg: 92.2808 aux.loss_ce: 0.1037 aux.acc_seg: 89.6898 2023/06/07 11:57:24 - mmengine - INFO - Iter(train) [ 17550/240000] lr: 9.3460e-03 eta: 1 day, 21:32:25 time: 0.7543 data_time: 0.3822 memory: 17391 loss: 0.2637 decode.loss_ce: 0.1761 decode.acc_seg: 91.7416 aux.loss_ce: 0.0875 aux.acc_seg: 89.5717 2023/06/07 11:58:01 - mmengine - INFO - Iter(train) [ 17600/240000] lr: 9.3442e-03 eta: 1 day, 21:31:54 time: 0.7404 data_time: 0.3859 memory: 17394 loss: 0.2697 decode.loss_ce: 0.1803 decode.acc_seg: 89.5323 aux.loss_ce: 0.0894 aux.acc_seg: 86.8468 2023/06/07 11:58:38 - mmengine - INFO - Iter(train) [ 17650/240000] lr: 9.3423e-03 eta: 1 day, 21:31:24 time: 0.7453 data_time: 0.4173 memory: 17395 loss: 0.2628 decode.loss_ce: 0.1737 decode.acc_seg: 94.0079 aux.loss_ce: 0.0891 aux.acc_seg: 90.3763 2023/06/07 11:59:16 - mmengine - INFO - Iter(train) [ 17700/240000] lr: 9.3404e-03 eta: 1 day, 21:30:53 time: 0.7516 data_time: 0.4240 memory: 17393 loss: 0.2545 decode.loss_ce: 0.1698 decode.acc_seg: 91.9532 aux.loss_ce: 0.0846 aux.acc_seg: 89.0854 2023/06/07 11:59:53 - mmengine - INFO - Iter(train) [ 17750/240000] lr: 9.3386e-03 eta: 1 day, 21:30:21 time: 0.7517 data_time: 0.4245 memory: 17396 loss: 0.2614 decode.loss_ce: 0.1750 decode.acc_seg: 93.4001 aux.loss_ce: 0.0864 aux.acc_seg: 91.2097 2023/06/07 12:00:31 - mmengine - INFO - Iter(train) [ 17800/240000] lr: 9.3367e-03 eta: 1 day, 21:29:57 time: 0.7622 data_time: 0.4354 memory: 17391 loss: 0.2372 decode.loss_ce: 0.1548 decode.acc_seg: 93.4551 aux.loss_ce: 0.0824 aux.acc_seg: 92.8604 2023/06/07 12:01:08 - mmengine - INFO - Iter(train) [ 17850/240000] lr: 9.3348e-03 eta: 1 day, 21:29:29 time: 0.7565 data_time: 0.4292 memory: 17394 loss: 0.2749 decode.loss_ce: 0.1852 decode.acc_seg: 93.1722 aux.loss_ce: 0.0897 aux.acc_seg: 90.9262 2023/06/07 12:01:46 - mmengine - INFO - Iter(train) [ 17900/240000] lr: 9.3329e-03 eta: 1 day, 21:29:00 time: 0.7668 data_time: 0.4389 memory: 17394 loss: 0.2570 decode.loss_ce: 0.1709 decode.acc_seg: 92.3654 aux.loss_ce: 0.0861 aux.acc_seg: 90.8659 2023/06/07 12:02:23 - mmengine - INFO - Iter(train) [ 17950/240000] lr: 9.3311e-03 eta: 1 day, 21:28:34 time: 0.7648 data_time: 0.4373 memory: 17394 loss: 0.2510 decode.loss_ce: 0.1662 decode.acc_seg: 92.4577 aux.loss_ce: 0.0848 aux.acc_seg: 90.0574 2023/06/07 12:03:01 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 12:03:01 - mmengine - INFO - Iter(train) [ 18000/240000] lr: 9.3292e-03 eta: 1 day, 21:28:09 time: 0.7462 data_time: 0.4186 memory: 17393 loss: 0.2614 decode.loss_ce: 0.1748 decode.acc_seg: 91.5692 aux.loss_ce: 0.0866 aux.acc_seg: 89.4369 2023/06/07 12:03:39 - mmengine - INFO - Iter(train) [ 18050/240000] lr: 9.3273e-03 eta: 1 day, 21:27:36 time: 0.7471 data_time: 0.4213 memory: 17393 loss: 0.2272 decode.loss_ce: 0.1495 decode.acc_seg: 93.2570 aux.loss_ce: 0.0777 aux.acc_seg: 89.9116 2023/06/07 12:04:16 - mmengine - INFO - Iter(train) [ 18100/240000] lr: 9.3255e-03 eta: 1 day, 21:27:11 time: 0.7718 data_time: 0.4457 memory: 17391 loss: 0.2418 decode.loss_ce: 0.1585 decode.acc_seg: 92.5760 aux.loss_ce: 0.0834 aux.acc_seg: 90.8137 2023/06/07 12:04:54 - mmengine - INFO - Iter(train) [ 18150/240000] lr: 9.3236e-03 eta: 1 day, 21:26:48 time: 0.7516 data_time: 0.4249 memory: 17395 loss: 0.2435 decode.loss_ce: 0.1586 decode.acc_seg: 92.3554 aux.loss_ce: 0.0849 aux.acc_seg: 90.8483 2023/06/07 12:05:32 - mmengine - INFO - Iter(train) [ 18200/240000] lr: 9.3217e-03 eta: 1 day, 21:26:20 time: 0.7577 data_time: 0.4321 memory: 17392 loss: 0.2439 decode.loss_ce: 0.1629 decode.acc_seg: 93.4815 aux.loss_ce: 0.0810 aux.acc_seg: 90.6114 2023/06/07 12:06:10 - mmengine - INFO - Iter(train) [ 18250/240000] lr: 9.3199e-03 eta: 1 day, 21:25:52 time: 0.7572 data_time: 0.4242 memory: 17395 loss: 0.2481 decode.loss_ce: 0.1630 decode.acc_seg: 92.5017 aux.loss_ce: 0.0852 aux.acc_seg: 90.0690 2023/06/07 12:06:47 - mmengine - INFO - Iter(train) [ 18300/240000] lr: 9.3180e-03 eta: 1 day, 21:25:25 time: 0.7600 data_time: 0.4332 memory: 17393 loss: 0.2572 decode.loss_ce: 0.1711 decode.acc_seg: 92.7646 aux.loss_ce: 0.0861 aux.acc_seg: 90.8464 2023/06/07 12:07:25 - mmengine - INFO - Iter(train) [ 18350/240000] lr: 9.3161e-03 eta: 1 day, 21:24:56 time: 0.7495 data_time: 0.4236 memory: 17393 loss: 0.2376 decode.loss_ce: 0.1574 decode.acc_seg: 93.5283 aux.loss_ce: 0.0802 aux.acc_seg: 92.0238 2023/06/07 12:08:02 - mmengine - INFO - Iter(train) [ 18400/240000] lr: 9.3142e-03 eta: 1 day, 21:24:29 time: 0.7576 data_time: 0.4306 memory: 17393 loss: 0.2504 decode.loss_ce: 0.1637 decode.acc_seg: 93.2094 aux.loss_ce: 0.0867 aux.acc_seg: 90.6705 2023/06/07 12:08:40 - mmengine - INFO - Iter(train) [ 18450/240000] lr: 9.3124e-03 eta: 1 day, 21:24:02 time: 0.7450 data_time: 0.4187 memory: 17392 loss: 0.2404 decode.loss_ce: 0.1621 decode.acc_seg: 93.5300 aux.loss_ce: 0.0783 aux.acc_seg: 91.9573 2023/06/07 12:09:18 - mmengine - INFO - Iter(train) [ 18500/240000] lr: 9.3105e-03 eta: 1 day, 21:23:35 time: 0.7554 data_time: 0.4286 memory: 17393 loss: 0.2513 decode.loss_ce: 0.1668 decode.acc_seg: 93.2836 aux.loss_ce: 0.0845 aux.acc_seg: 91.5275 2023/06/07 12:09:55 - mmengine - INFO - Iter(train) [ 18550/240000] lr: 9.3086e-03 eta: 1 day, 21:23:04 time: 0.7512 data_time: 0.4245 memory: 17393 loss: 0.2356 decode.loss_ce: 0.1560 decode.acc_seg: 94.6388 aux.loss_ce: 0.0795 aux.acc_seg: 93.1504 2023/06/07 12:10:33 - mmengine - INFO - Iter(train) [ 18600/240000] lr: 9.3068e-03 eta: 1 day, 21:22:38 time: 0.7456 data_time: 0.4199 memory: 17392 loss: 0.2540 decode.loss_ce: 0.1692 decode.acc_seg: 92.4169 aux.loss_ce: 0.0848 aux.acc_seg: 90.8017 2023/06/07 12:11:10 - mmengine - INFO - Iter(train) [ 18650/240000] lr: 9.3049e-03 eta: 1 day, 21:22:02 time: 0.7434 data_time: 0.4138 memory: 17396 loss: 0.2424 decode.loss_ce: 0.1610 decode.acc_seg: 92.6046 aux.loss_ce: 0.0814 aux.acc_seg: 89.1400 2023/06/07 12:11:48 - mmengine - INFO - Iter(train) [ 18700/240000] lr: 9.3030e-03 eta: 1 day, 21:21:41 time: 0.7628 data_time: 0.1622 memory: 17391 loss: 0.2515 decode.loss_ce: 0.1665 decode.acc_seg: 91.5628 aux.loss_ce: 0.0850 aux.acc_seg: 89.4598 2023/06/07 12:12:26 - mmengine - INFO - Iter(train) [ 18750/240000] lr: 9.3011e-03 eta: 1 day, 21:21:18 time: 0.7666 data_time: 0.4037 memory: 17392 loss: 0.2549 decode.loss_ce: 0.1710 decode.acc_seg: 91.2915 aux.loss_ce: 0.0839 aux.acc_seg: 89.2276 2023/06/07 12:13:05 - mmengine - INFO - Iter(train) [ 18800/240000] lr: 9.2993e-03 eta: 1 day, 21:20:58 time: 0.7695 data_time: 0.4110 memory: 17390 loss: 0.2563 decode.loss_ce: 0.1712 decode.acc_seg: 91.2899 aux.loss_ce: 0.0851 aux.acc_seg: 89.8436 2023/06/07 12:13:43 - mmengine - INFO - Iter(train) [ 18850/240000] lr: 9.2974e-03 eta: 1 day, 21:20:41 time: 0.7648 data_time: 0.4167 memory: 17390 loss: 0.2432 decode.loss_ce: 0.1603 decode.acc_seg: 93.4014 aux.loss_ce: 0.0829 aux.acc_seg: 92.2136 2023/06/07 12:14:22 - mmengine - INFO - Iter(train) [ 18900/240000] lr: 9.2955e-03 eta: 1 day, 21:20:21 time: 0.7565 data_time: 0.3983 memory: 17392 loss: 0.2584 decode.loss_ce: 0.1735 decode.acc_seg: 92.6133 aux.loss_ce: 0.0848 aux.acc_seg: 90.8409 2023/06/07 12:15:00 - mmengine - INFO - Iter(train) [ 18950/240000] lr: 9.2937e-03 eta: 1 day, 21:20:01 time: 0.7574 data_time: 0.4099 memory: 17394 loss: 0.2429 decode.loss_ce: 0.1612 decode.acc_seg: 91.5887 aux.loss_ce: 0.0818 aux.acc_seg: 88.6549 2023/06/07 12:15:38 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 12:15:38 - mmengine - INFO - Iter(train) [ 19000/240000] lr: 9.2918e-03 eta: 1 day, 21:19:41 time: 0.7815 data_time: 0.4185 memory: 17393 loss: 0.2473 decode.loss_ce: 0.1662 decode.acc_seg: 91.8771 aux.loss_ce: 0.0812 aux.acc_seg: 90.2079 2023/06/07 12:16:16 - mmengine - INFO - Iter(train) [ 19050/240000] lr: 9.2899e-03 eta: 1 day, 21:19:17 time: 0.7543 data_time: 0.4041 memory: 17390 loss: 0.2665 decode.loss_ce: 0.1770 decode.acc_seg: 91.9213 aux.loss_ce: 0.0895 aux.acc_seg: 90.6657 2023/06/07 12:16:55 - mmengine - INFO - Iter(train) [ 19100/240000] lr: 9.2880e-03 eta: 1 day, 21:18:55 time: 0.7593 data_time: 0.3389 memory: 17394 loss: 0.2822 decode.loss_ce: 0.1862 decode.acc_seg: 90.5943 aux.loss_ce: 0.0960 aux.acc_seg: 87.8015 2023/06/07 12:17:33 - mmengine - INFO - Iter(train) [ 19150/240000] lr: 9.2862e-03 eta: 1 day, 21:18:33 time: 0.7688 data_time: 0.4183 memory: 17395 loss: 0.2585 decode.loss_ce: 0.1695 decode.acc_seg: 93.5891 aux.loss_ce: 0.0890 aux.acc_seg: 90.9752 2023/06/07 12:18:12 - mmengine - INFO - Iter(train) [ 19200/240000] lr: 9.2843e-03 eta: 1 day, 21:18:18 time: 0.7936 data_time: 0.4375 memory: 17395 loss: 0.2305 decode.loss_ce: 0.1540 decode.acc_seg: 92.7730 aux.loss_ce: 0.0764 aux.acc_seg: 91.8088 2023/06/07 12:18:50 - mmengine - INFO - Iter(train) [ 19250/240000] lr: 9.2824e-03 eta: 1 day, 21:17:57 time: 0.7640 data_time: 0.4129 memory: 17393 loss: 0.2420 decode.loss_ce: 0.1616 decode.acc_seg: 92.9770 aux.loss_ce: 0.0803 aux.acc_seg: 91.3818 2023/06/07 12:19:29 - mmengine - INFO - Iter(train) [ 19300/240000] lr: 9.2806e-03 eta: 1 day, 21:17:38 time: 0.7575 data_time: 0.3993 memory: 17393 loss: 0.2692 decode.loss_ce: 0.1795 decode.acc_seg: 91.8622 aux.loss_ce: 0.0897 aux.acc_seg: 89.1087 2023/06/07 12:20:07 - mmengine - INFO - Iter(train) [ 19350/240000] lr: 9.2787e-03 eta: 1 day, 21:17:22 time: 0.7796 data_time: 0.4291 memory: 17392 loss: 0.2562 decode.loss_ce: 0.1697 decode.acc_seg: 94.2386 aux.loss_ce: 0.0865 aux.acc_seg: 92.0608 2023/06/07 12:20:46 - mmengine - INFO - Iter(train) [ 19400/240000] lr: 9.2768e-03 eta: 1 day, 21:17:07 time: 0.7770 data_time: 0.4219 memory: 17391 loss: 0.2642 decode.loss_ce: 0.1730 decode.acc_seg: 92.3525 aux.loss_ce: 0.0912 aux.acc_seg: 88.8179 2023/06/07 12:21:24 - mmengine - INFO - Iter(train) [ 19450/240000] lr: 9.2749e-03 eta: 1 day, 21:16:44 time: 0.7711 data_time: 0.4192 memory: 17393 loss: 0.2775 decode.loss_ce: 0.1881 decode.acc_seg: 91.9255 aux.loss_ce: 0.0894 aux.acc_seg: 90.5840 2023/06/07 12:22:03 - mmengine - INFO - Iter(train) [ 19500/240000] lr: 9.2731e-03 eta: 1 day, 21:16:24 time: 0.7554 data_time: 0.3995 memory: 17392 loss: 0.2556 decode.loss_ce: 0.1721 decode.acc_seg: 93.1465 aux.loss_ce: 0.0834 aux.acc_seg: 91.5634 2023/06/07 12:22:41 - mmengine - INFO - Iter(train) [ 19550/240000] lr: 9.2712e-03 eta: 1 day, 21:15:58 time: 0.7738 data_time: 0.1480 memory: 17392 loss: 0.2492 decode.loss_ce: 0.1640 decode.acc_seg: 93.0323 aux.loss_ce: 0.0851 aux.acc_seg: 90.0115 2023/06/07 12:23:19 - mmengine - INFO - Iter(train) [ 19600/240000] lr: 9.2693e-03 eta: 1 day, 21:15:38 time: 0.7773 data_time: 0.0130 memory: 17393 loss: 0.2468 decode.loss_ce: 0.1597 decode.acc_seg: 93.6687 aux.loss_ce: 0.0872 aux.acc_seg: 91.3914 2023/06/07 12:23:58 - mmengine - INFO - Iter(train) [ 19650/240000] lr: 9.2674e-03 eta: 1 day, 21:15:18 time: 0.7625 data_time: 0.0144 memory: 17391 loss: 0.2627 decode.loss_ce: 0.1776 decode.acc_seg: 90.5760 aux.loss_ce: 0.0851 aux.acc_seg: 87.5606 2023/06/07 12:24:36 - mmengine - INFO - Iter(train) [ 19700/240000] lr: 9.2656e-03 eta: 1 day, 21:14:58 time: 0.7634 data_time: 0.0155 memory: 17389 loss: 0.2469 decode.loss_ce: 0.1656 decode.acc_seg: 91.9215 aux.loss_ce: 0.0812 aux.acc_seg: 89.6701 2023/06/07 12:25:15 - mmengine - INFO - Iter(train) [ 19750/240000] lr: 9.2637e-03 eta: 1 day, 21:14:38 time: 0.7711 data_time: 0.0134 memory: 17392 loss: 0.2394 decode.loss_ce: 0.1585 decode.acc_seg: 94.6774 aux.loss_ce: 0.0808 aux.acc_seg: 93.0212 2023/06/07 12:25:53 - mmengine - INFO - Iter(train) [ 19800/240000] lr: 9.2618e-03 eta: 1 day, 21:14:18 time: 0.7592 data_time: 0.0131 memory: 17395 loss: 0.2551 decode.loss_ce: 0.1693 decode.acc_seg: 92.6243 aux.loss_ce: 0.0859 aux.acc_seg: 89.4607 2023/06/07 12:26:32 - mmengine - INFO - Iter(train) [ 19850/240000] lr: 9.2600e-03 eta: 1 day, 21:13:56 time: 0.7627 data_time: 0.0132 memory: 17392 loss: 0.2499 decode.loss_ce: 0.1682 decode.acc_seg: 91.5457 aux.loss_ce: 0.0818 aux.acc_seg: 91.3524 2023/06/07 12:27:10 - mmengine - INFO - Iter(train) [ 19900/240000] lr: 9.2581e-03 eta: 1 day, 21:13:34 time: 0.7690 data_time: 0.0197 memory: 17393 loss: 0.2469 decode.loss_ce: 0.1638 decode.acc_seg: 90.4898 aux.loss_ce: 0.0832 aux.acc_seg: 87.7635 2023/06/07 12:27:48 - mmengine - INFO - Iter(train) [ 19950/240000] lr: 9.2562e-03 eta: 1 day, 21:13:11 time: 0.7566 data_time: 0.1575 memory: 17392 loss: 0.2554 decode.loss_ce: 0.1692 decode.acc_seg: 91.7236 aux.loss_ce: 0.0862 aux.acc_seg: 88.3876 2023/06/07 12:28:26 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 12:28:26 - mmengine - INFO - Iter(train) [ 20000/240000] lr: 9.2543e-03 eta: 1 day, 21:12:40 time: 0.7541 data_time: 0.0587 memory: 17390 loss: 0.2594 decode.loss_ce: 0.1718 decode.acc_seg: 92.4983 aux.loss_ce: 0.0876 aux.acc_seg: 89.7326 2023/06/07 12:29:03 - mmengine - INFO - Iter(train) [ 20050/240000] lr: 9.2525e-03 eta: 1 day, 21:12:09 time: 0.7493 data_time: 0.1374 memory: 17396 loss: 0.2424 decode.loss_ce: 0.1605 decode.acc_seg: 93.2427 aux.loss_ce: 0.0819 aux.acc_seg: 90.6625 2023/06/07 12:29:41 - mmengine - INFO - Iter(train) [ 20100/240000] lr: 9.2506e-03 eta: 1 day, 21:11:37 time: 0.7486 data_time: 0.4221 memory: 17394 loss: 0.2604 decode.loss_ce: 0.1717 decode.acc_seg: 93.4229 aux.loss_ce: 0.0887 aux.acc_seg: 92.2189 2023/06/07 12:30:18 - mmengine - INFO - Iter(train) [ 20150/240000] lr: 9.2487e-03 eta: 1 day, 21:11:05 time: 0.7351 data_time: 0.4083 memory: 17393 loss: 0.2571 decode.loss_ce: 0.1719 decode.acc_seg: 92.2815 aux.loss_ce: 0.0852 aux.acc_seg: 89.4207 2023/06/07 12:30:56 - mmengine - INFO - Iter(train) [ 20200/240000] lr: 9.2469e-03 eta: 1 day, 21:10:34 time: 0.7559 data_time: 0.4303 memory: 17394 loss: 0.2420 decode.loss_ce: 0.1621 decode.acc_seg: 92.2354 aux.loss_ce: 0.0799 aux.acc_seg: 90.6759 2023/06/07 12:31:33 - mmengine - INFO - Iter(train) [ 20250/240000] lr: 9.2450e-03 eta: 1 day, 21:10:03 time: 0.7685 data_time: 0.4423 memory: 17393 loss: 0.2619 decode.loss_ce: 0.1734 decode.acc_seg: 92.4626 aux.loss_ce: 0.0884 aux.acc_seg: 91.6118 2023/06/07 12:32:11 - mmengine - INFO - Iter(train) [ 20300/240000] lr: 9.2431e-03 eta: 1 day, 21:09:31 time: 0.7388 data_time: 0.4127 memory: 17393 loss: 0.2569 decode.loss_ce: 0.1718 decode.acc_seg: 90.2656 aux.loss_ce: 0.0852 aux.acc_seg: 88.2304 2023/06/07 12:32:48 - mmengine - INFO - Iter(train) [ 20350/240000] lr: 9.2412e-03 eta: 1 day, 21:08:57 time: 0.7621 data_time: 0.3424 memory: 17394 loss: 0.2370 decode.loss_ce: 0.1599 decode.acc_seg: 93.5976 aux.loss_ce: 0.0771 aux.acc_seg: 91.7924 2023/06/07 12:33:25 - mmengine - INFO - Iter(train) [ 20400/240000] lr: 9.2394e-03 eta: 1 day, 21:08:23 time: 0.7457 data_time: 0.2774 memory: 17394 loss: 0.2413 decode.loss_ce: 0.1613 decode.acc_seg: 93.9621 aux.loss_ce: 0.0800 aux.acc_seg: 92.3271 2023/06/07 12:34:02 - mmengine - INFO - Iter(train) [ 20450/240000] lr: 9.2375e-03 eta: 1 day, 21:07:45 time: 0.7324 data_time: 0.4023 memory: 17395 loss: 0.2355 decode.loss_ce: 0.1560 decode.acc_seg: 93.9400 aux.loss_ce: 0.0795 aux.acc_seg: 91.1449 2023/06/07 12:34:39 - mmengine - INFO - Iter(train) [ 20500/240000] lr: 9.2356e-03 eta: 1 day, 21:07:09 time: 0.7418 data_time: 0.3506 memory: 17395 loss: 0.2675 decode.loss_ce: 0.1771 decode.acc_seg: 92.3340 aux.loss_ce: 0.0904 aux.acc_seg: 89.3448 2023/06/07 12:35:18 - mmengine - INFO - Iter(train) [ 20550/240000] lr: 9.2337e-03 eta: 1 day, 21:06:49 time: 0.7659 data_time: 0.0722 memory: 17393 loss: 0.2576 decode.loss_ce: 0.1674 decode.acc_seg: 92.6400 aux.loss_ce: 0.0902 aux.acc_seg: 91.0122 2023/06/07 12:35:54 - mmengine - INFO - Iter(train) [ 20600/240000] lr: 9.2319e-03 eta: 1 day, 21:06:08 time: 0.7235 data_time: 0.2081 memory: 17396 loss: 0.2725 decode.loss_ce: 0.1833 decode.acc_seg: 89.2054 aux.loss_ce: 0.0893 aux.acc_seg: 88.7396 2023/06/07 12:36:31 - mmengine - INFO - Iter(train) [ 20650/240000] lr: 9.2300e-03 eta: 1 day, 21:05:25 time: 0.7382 data_time: 0.4099 memory: 17394 loss: 0.2634 decode.loss_ce: 0.1751 decode.acc_seg: 94.5811 aux.loss_ce: 0.0882 aux.acc_seg: 93.0499 2023/06/07 12:37:07 - mmengine - INFO - Iter(train) [ 20700/240000] lr: 9.2281e-03 eta: 1 day, 21:04:42 time: 0.7018 data_time: 0.3759 memory: 17392 loss: 0.2444 decode.loss_ce: 0.1614 decode.acc_seg: 91.6203 aux.loss_ce: 0.0830 aux.acc_seg: 89.5629 2023/06/07 12:37:44 - mmengine - INFO - Iter(train) [ 20750/240000] lr: 9.2262e-03 eta: 1 day, 21:04:02 time: 0.7421 data_time: 0.3983 memory: 17392 loss: 0.2384 decode.loss_ce: 0.1585 decode.acc_seg: 89.9467 aux.loss_ce: 0.0799 aux.acc_seg: 92.4335 2023/06/07 12:38:20 - mmengine - INFO - Iter(train) [ 20800/240000] lr: 9.2244e-03 eta: 1 day, 21:03:19 time: 0.7234 data_time: 0.3018 memory: 17392 loss: 0.2501 decode.loss_ce: 0.1650 decode.acc_seg: 93.0907 aux.loss_ce: 0.0851 aux.acc_seg: 90.2920 2023/06/07 12:38:56 - mmengine - INFO - Iter(train) [ 20850/240000] lr: 9.2225e-03 eta: 1 day, 21:02:30 time: 0.7196 data_time: 0.0925 memory: 17395 loss: 0.2681 decode.loss_ce: 0.1800 decode.acc_seg: 93.1506 aux.loss_ce: 0.0881 aux.acc_seg: 90.6725 2023/06/07 12:39:32 - mmengine - INFO - Iter(train) [ 20900/240000] lr: 9.2206e-03 eta: 1 day, 21:01:38 time: 0.7248 data_time: 0.3768 memory: 17391 loss: 0.2714 decode.loss_ce: 0.1782 decode.acc_seg: 92.0120 aux.loss_ce: 0.0931 aux.acc_seg: 88.7779 2023/06/07 12:40:08 - mmengine - INFO - Iter(train) [ 20950/240000] lr: 9.2188e-03 eta: 1 day, 21:00:53 time: 0.7249 data_time: 0.1393 memory: 17393 loss: 0.2388 decode.loss_ce: 0.1621 decode.acc_seg: 94.2162 aux.loss_ce: 0.0768 aux.acc_seg: 92.8464 2023/06/07 12:40:44 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 12:40:44 - mmengine - INFO - Iter(train) [ 21000/240000] lr: 9.2169e-03 eta: 1 day, 21:00:08 time: 0.7412 data_time: 0.0575 memory: 17394 loss: 0.2598 decode.loss_ce: 0.1700 decode.acc_seg: 92.2350 aux.loss_ce: 0.0898 aux.acc_seg: 88.2561 2023/06/07 12:41:20 - mmengine - INFO - Iter(train) [ 21050/240000] lr: 9.2150e-03 eta: 1 day, 20:59:21 time: 0.7208 data_time: 0.0120 memory: 17393 loss: 0.2535 decode.loss_ce: 0.1678 decode.acc_seg: 90.8804 aux.loss_ce: 0.0857 aux.acc_seg: 88.6237 2023/06/07 12:41:57 - mmengine - INFO - Iter(train) [ 21100/240000] lr: 9.2131e-03 eta: 1 day, 20:58:36 time: 0.7360 data_time: 0.0123 memory: 17393 loss: 0.2475 decode.loss_ce: 0.1638 decode.acc_seg: 94.0315 aux.loss_ce: 0.0836 aux.acc_seg: 92.3730 2023/06/07 12:42:32 - mmengine - INFO - Iter(train) [ 21150/240000] lr: 9.2113e-03 eta: 1 day, 20:57:44 time: 0.7137 data_time: 0.3063 memory: 17392 loss: 0.2443 decode.loss_ce: 0.1645 decode.acc_seg: 93.8212 aux.loss_ce: 0.0798 aux.acc_seg: 91.5972 2023/06/07 12:43:08 - mmengine - INFO - Iter(train) [ 21200/240000] lr: 9.2094e-03 eta: 1 day, 20:56:54 time: 0.7218 data_time: 0.3975 memory: 17393 loss: 0.2680 decode.loss_ce: 0.1788 decode.acc_seg: 92.9270 aux.loss_ce: 0.0892 aux.acc_seg: 90.9581 2023/06/07 12:43:44 - mmengine - INFO - Iter(train) [ 21250/240000] lr: 9.2075e-03 eta: 1 day, 20:56:09 time: 0.7270 data_time: 0.3272 memory: 17394 loss: 0.2614 decode.loss_ce: 0.1739 decode.acc_seg: 88.5739 aux.loss_ce: 0.0875 aux.acc_seg: 84.8463 2023/06/07 12:44:20 - mmengine - INFO - Iter(train) [ 21300/240000] lr: 9.2056e-03 eta: 1 day, 20:55:24 time: 0.7354 data_time: 0.2016 memory: 17393 loss: 0.2423 decode.loss_ce: 0.1614 decode.acc_seg: 94.4320 aux.loss_ce: 0.0809 aux.acc_seg: 93.1206 2023/06/07 12:44:56 - mmengine - INFO - Iter(train) [ 21350/240000] lr: 9.2038e-03 eta: 1 day, 20:54:35 time: 0.7155 data_time: 0.2827 memory: 17391 loss: 0.2522 decode.loss_ce: 0.1672 decode.acc_seg: 92.6006 aux.loss_ce: 0.0851 aux.acc_seg: 90.5587 2023/06/07 12:45:32 - mmengine - INFO - Iter(train) [ 21400/240000] lr: 9.2019e-03 eta: 1 day, 20:53:49 time: 0.7242 data_time: 0.0264 memory: 17395 loss: 0.2760 decode.loss_ce: 0.1825 decode.acc_seg: 90.5095 aux.loss_ce: 0.0935 aux.acc_seg: 87.7390 2023/06/07 12:46:08 - mmengine - INFO - Iter(train) [ 21450/240000] lr: 9.2000e-03 eta: 1 day, 20:52:58 time: 0.7147 data_time: 0.0122 memory: 17394 loss: 0.2452 decode.loss_ce: 0.1637 decode.acc_seg: 92.5652 aux.loss_ce: 0.0816 aux.acc_seg: 91.6555 2023/06/07 12:46:44 - mmengine - INFO - Iter(train) [ 21500/240000] lr: 9.1981e-03 eta: 1 day, 20:52:18 time: 0.7407 data_time: 0.0501 memory: 17393 loss: 0.2506 decode.loss_ce: 0.1656 decode.acc_seg: 90.8298 aux.loss_ce: 0.0850 aux.acc_seg: 88.8057 2023/06/07 12:47:22 - mmengine - INFO - Iter(train) [ 21550/240000] lr: 9.1963e-03 eta: 1 day, 20:51:43 time: 0.7473 data_time: 0.0125 memory: 17391 loss: 0.2574 decode.loss_ce: 0.1712 decode.acc_seg: 91.5191 aux.loss_ce: 0.0862 aux.acc_seg: 89.1446 2023/06/07 12:47:58 - mmengine - INFO - Iter(train) [ 21600/240000] lr: 9.1944e-03 eta: 1 day, 20:51:05 time: 0.7305 data_time: 0.0120 memory: 17389 loss: 0.2482 decode.loss_ce: 0.1674 decode.acc_seg: 89.5290 aux.loss_ce: 0.0807 aux.acc_seg: 88.5962 2023/06/07 12:48:36 - mmengine - INFO - Iter(train) [ 21650/240000] lr: 9.1925e-03 eta: 1 day, 20:50:29 time: 0.7323 data_time: 0.0122 memory: 17394 loss: 0.2807 decode.loss_ce: 0.1877 decode.acc_seg: 89.7613 aux.loss_ce: 0.0930 aux.acc_seg: 88.7341 2023/06/07 12:49:13 - mmengine - INFO - Iter(train) [ 21700/240000] lr: 9.1907e-03 eta: 1 day, 20:49:59 time: 0.7587 data_time: 0.0120 memory: 17391 loss: 0.2326 decode.loss_ce: 0.1534 decode.acc_seg: 93.2160 aux.loss_ce: 0.0791 aux.acc_seg: 92.1218 2023/06/07 12:49:51 - mmengine - INFO - Iter(train) [ 21750/240000] lr: 9.1888e-03 eta: 1 day, 20:49:27 time: 0.7304 data_time: 0.0123 memory: 17395 loss: 0.2570 decode.loss_ce: 0.1700 decode.acc_seg: 91.7373 aux.loss_ce: 0.0869 aux.acc_seg: 88.9474 2023/06/07 12:50:27 - mmengine - INFO - Iter(train) [ 21800/240000] lr: 9.1869e-03 eta: 1 day, 20:48:44 time: 0.7275 data_time: 0.0120 memory: 17391 loss: 0.2535 decode.loss_ce: 0.1701 decode.acc_seg: 92.5434 aux.loss_ce: 0.0834 aux.acc_seg: 90.6217 2023/06/07 12:51:03 - mmengine - INFO - Iter(train) [ 21850/240000] lr: 9.1850e-03 eta: 1 day, 20:48:00 time: 0.7293 data_time: 0.0233 memory: 17392 loss: 0.2585 decode.loss_ce: 0.1723 decode.acc_seg: 91.7409 aux.loss_ce: 0.0862 aux.acc_seg: 90.7611 2023/06/07 12:51:40 - mmengine - INFO - Iter(train) [ 21900/240000] lr: 9.1832e-03 eta: 1 day, 20:47:20 time: 0.7394 data_time: 0.1218 memory: 17395 loss: 0.2453 decode.loss_ce: 0.1632 decode.acc_seg: 92.5742 aux.loss_ce: 0.0821 aux.acc_seg: 90.5386 2023/06/07 12:52:17 - mmengine - INFO - Iter(train) [ 21950/240000] lr: 9.1813e-03 eta: 1 day, 20:46:41 time: 0.7555 data_time: 0.1234 memory: 17393 loss: 0.2407 decode.loss_ce: 0.1574 decode.acc_seg: 94.3725 aux.loss_ce: 0.0833 aux.acc_seg: 92.5138 2023/06/07 12:52:53 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 12:52:53 - mmengine - INFO - Iter(train) [ 22000/240000] lr: 9.1794e-03 eta: 1 day, 20:46:02 time: 0.7299 data_time: 0.0426 memory: 17391 loss: 0.2632 decode.loss_ce: 0.1765 decode.acc_seg: 93.0883 aux.loss_ce: 0.0867 aux.acc_seg: 91.9848 2023/06/07 12:53:29 - mmengine - INFO - Iter(train) [ 22050/240000] lr: 9.1775e-03 eta: 1 day, 20:45:14 time: 0.7187 data_time: 0.3547 memory: 17390 loss: 0.2374 decode.loss_ce: 0.1592 decode.acc_seg: 93.4335 aux.loss_ce: 0.0782 aux.acc_seg: 92.3191 2023/06/07 12:54:06 - mmengine - INFO - Iter(train) [ 22100/240000] lr: 9.1757e-03 eta: 1 day, 20:44:34 time: 0.7278 data_time: 0.3926 memory: 17391 loss: 0.2573 decode.loss_ce: 0.1730 decode.acc_seg: 92.1571 aux.loss_ce: 0.0844 aux.acc_seg: 89.1223 2023/06/07 12:54:42 - mmengine - INFO - Iter(train) [ 22150/240000] lr: 9.1738e-03 eta: 1 day, 20:43:50 time: 0.7254 data_time: 0.3958 memory: 17392 loss: 0.2525 decode.loss_ce: 0.1690 decode.acc_seg: 94.0849 aux.loss_ce: 0.0835 aux.acc_seg: 91.3668 2023/06/07 12:55:19 - mmengine - INFO - Iter(train) [ 22200/240000] lr: 9.1719e-03 eta: 1 day, 20:43:15 time: 0.7675 data_time: 0.4074 memory: 17392 loss: 0.2467 decode.loss_ce: 0.1626 decode.acc_seg: 87.9329 aux.loss_ce: 0.0841 aux.acc_seg: 83.0014 2023/06/07 12:55:56 - mmengine - INFO - Iter(train) [ 22250/240000] lr: 9.1700e-03 eta: 1 day, 20:42:36 time: 0.7390 data_time: 0.3975 memory: 17390 loss: 0.2499 decode.loss_ce: 0.1675 decode.acc_seg: 91.2242 aux.loss_ce: 0.0824 aux.acc_seg: 90.2191 2023/06/07 12:56:33 - mmengine - INFO - Iter(train) [ 22300/240000] lr: 9.1682e-03 eta: 1 day, 20:41:56 time: 0.7142 data_time: 0.3775 memory: 17390 loss: 0.2510 decode.loss_ce: 0.1639 decode.acc_seg: 93.1557 aux.loss_ce: 0.0871 aux.acc_seg: 90.7123 2023/06/07 12:57:10 - mmengine - INFO - Iter(train) [ 22350/240000] lr: 9.1663e-03 eta: 1 day, 20:41:24 time: 0.7472 data_time: 0.3506 memory: 17393 loss: 0.2469 decode.loss_ce: 0.1646 decode.acc_seg: 90.3204 aux.loss_ce: 0.0823 aux.acc_seg: 87.9207 2023/06/07 12:57:47 - mmengine - INFO - Iter(train) [ 22400/240000] lr: 9.1644e-03 eta: 1 day, 20:40:42 time: 0.7171 data_time: 0.3890 memory: 17392 loss: 0.2636 decode.loss_ce: 0.1751 decode.acc_seg: 89.6155 aux.loss_ce: 0.0885 aux.acc_seg: 87.5723 2023/06/07 12:58:24 - mmengine - INFO - Iter(train) [ 22450/240000] lr: 9.1625e-03 eta: 1 day, 20:40:08 time: 0.7277 data_time: 0.3916 memory: 17393 loss: 0.2386 decode.loss_ce: 0.1582 decode.acc_seg: 92.4689 aux.loss_ce: 0.0804 aux.acc_seg: 90.0362 2023/06/07 12:59:01 - mmengine - INFO - Iter(train) [ 22500/240000] lr: 9.1607e-03 eta: 1 day, 20:39:33 time: 0.7604 data_time: 0.4180 memory: 17393 loss: 0.2569 decode.loss_ce: 0.1702 decode.acc_seg: 91.9608 aux.loss_ce: 0.0867 aux.acc_seg: 86.4299 2023/06/07 12:59:38 - mmengine - INFO - Iter(train) [ 22550/240000] lr: 9.1588e-03 eta: 1 day, 20:38:56 time: 0.7359 data_time: 0.3702 memory: 17391 loss: 0.2363 decode.loss_ce: 0.1550 decode.acc_seg: 94.0339 aux.loss_ce: 0.0813 aux.acc_seg: 92.0914 2023/06/07 13:00:14 - mmengine - INFO - Iter(train) [ 22600/240000] lr: 9.1569e-03 eta: 1 day, 20:38:12 time: 0.7183 data_time: 0.3887 memory: 17391 loss: 0.2652 decode.loss_ce: 0.1766 decode.acc_seg: 91.7228 aux.loss_ce: 0.0886 aux.acc_seg: 89.3492 2023/06/07 13:00:51 - mmengine - INFO - Iter(train) [ 22650/240000] lr: 9.1550e-03 eta: 1 day, 20:37:35 time: 0.7257 data_time: 0.3926 memory: 17392 loss: 0.2554 decode.loss_ce: 0.1677 decode.acc_seg: 92.5484 aux.loss_ce: 0.0877 aux.acc_seg: 90.3821 2023/06/07 13:01:28 - mmengine - INFO - Iter(train) [ 22700/240000] lr: 9.1532e-03 eta: 1 day, 20:36:54 time: 0.7338 data_time: 0.3426 memory: 17393 loss: 0.2642 decode.loss_ce: 0.1772 decode.acc_seg: 91.5797 aux.loss_ce: 0.0871 aux.acc_seg: 89.4811 2023/06/07 13:02:04 - mmengine - INFO - Iter(train) [ 22750/240000] lr: 9.1513e-03 eta: 1 day, 20:36:14 time: 0.7682 data_time: 0.1718 memory: 17391 loss: 0.2410 decode.loss_ce: 0.1612 decode.acc_seg: 93.5890 aux.loss_ce: 0.0798 aux.acc_seg: 91.7698 2023/06/07 13:02:41 - mmengine - INFO - Iter(train) [ 22800/240000] lr: 9.1494e-03 eta: 1 day, 20:35:38 time: 0.7285 data_time: 0.0114 memory: 17392 loss: 0.2677 decode.loss_ce: 0.1787 decode.acc_seg: 92.9682 aux.loss_ce: 0.0890 aux.acc_seg: 90.6951 2023/06/07 13:03:18 - mmengine - INFO - Iter(train) [ 22850/240000] lr: 9.1475e-03 eta: 1 day, 20:34:58 time: 0.7108 data_time: 0.0487 memory: 17392 loss: 0.2651 decode.loss_ce: 0.1770 decode.acc_seg: 92.4949 aux.loss_ce: 0.0881 aux.acc_seg: 89.6443 2023/06/07 13:03:55 - mmengine - INFO - Iter(train) [ 22900/240000] lr: 9.1457e-03 eta: 1 day, 20:34:21 time: 0.7431 data_time: 0.2144 memory: 17395 loss: 0.2462 decode.loss_ce: 0.1632 decode.acc_seg: 92.7618 aux.loss_ce: 0.0831 aux.acc_seg: 90.2565 2023/06/07 13:04:32 - mmengine - INFO - Iter(train) [ 22950/240000] lr: 9.1438e-03 eta: 1 day, 20:33:44 time: 0.7406 data_time: 0.0860 memory: 17393 loss: 0.2602 decode.loss_ce: 0.1751 decode.acc_seg: 92.1682 aux.loss_ce: 0.0851 aux.acc_seg: 90.6030 2023/06/07 13:05:09 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 13:05:09 - mmengine - INFO - Iter(train) [ 23000/240000] lr: 9.1419e-03 eta: 1 day, 20:33:09 time: 0.7359 data_time: 0.0418 memory: 17391 loss: 0.2537 decode.loss_ce: 0.1685 decode.acc_seg: 92.8432 aux.loss_ce: 0.0852 aux.acc_seg: 90.7910 2023/06/07 13:05:48 - mmengine - INFO - Iter(train) [ 23050/240000] lr: 9.1400e-03 eta: 1 day, 20:32:47 time: 0.7424 data_time: 0.1313 memory: 17394 loss: 0.2350 decode.loss_ce: 0.1568 decode.acc_seg: 90.5674 aux.loss_ce: 0.0782 aux.acc_seg: 86.9248 2023/06/07 13:06:24 - mmengine - INFO - Iter(train) [ 23100/240000] lr: 9.1382e-03 eta: 1 day, 20:32:04 time: 0.7192 data_time: 0.0120 memory: 17394 loss: 0.2708 decode.loss_ce: 0.1795 decode.acc_seg: 92.7599 aux.loss_ce: 0.0913 aux.acc_seg: 88.6254 2023/06/07 13:07:01 - mmengine - INFO - Iter(train) [ 23150/240000] lr: 9.1363e-03 eta: 1 day, 20:31:32 time: 0.7915 data_time: 0.0136 memory: 17396 loss: 0.2558 decode.loss_ce: 0.1698 decode.acc_seg: 91.5020 aux.loss_ce: 0.0860 aux.acc_seg: 88.8972 2023/06/07 13:07:39 - mmengine - INFO - Iter(train) [ 23200/240000] lr: 9.1344e-03 eta: 1 day, 20:30:57 time: 0.7593 data_time: 0.0126 memory: 17392 loss: 0.2508 decode.loss_ce: 0.1659 decode.acc_seg: 92.6473 aux.loss_ce: 0.0849 aux.acc_seg: 88.4473 2023/06/07 13:08:16 - mmengine - INFO - Iter(train) [ 23250/240000] lr: 9.1325e-03 eta: 1 day, 20:30:26 time: 0.7587 data_time: 0.0125 memory: 17390 loss: 0.2638 decode.loss_ce: 0.1742 decode.acc_seg: 92.8985 aux.loss_ce: 0.0897 aux.acc_seg: 90.2029 2023/06/07 13:08:53 - mmengine - INFO - Iter(train) [ 23300/240000] lr: 9.1307e-03 eta: 1 day, 20:29:50 time: 0.7217 data_time: 0.0124 memory: 17392 loss: 0.2589 decode.loss_ce: 0.1720 decode.acc_seg: 92.4637 aux.loss_ce: 0.0869 aux.acc_seg: 90.1496 2023/06/07 13:09:31 - mmengine - INFO - Iter(train) [ 23350/240000] lr: 9.1288e-03 eta: 1 day, 20:29:15 time: 0.7298 data_time: 0.0123 memory: 17392 loss: 0.2462 decode.loss_ce: 0.1658 decode.acc_seg: 93.5628 aux.loss_ce: 0.0804 aux.acc_seg: 91.6599 2023/06/07 13:10:08 - mmengine - INFO - Iter(train) [ 23400/240000] lr: 9.1269e-03 eta: 1 day, 20:28:39 time: 0.7402 data_time: 0.0127 memory: 17394 loss: 0.2269 decode.loss_ce: 0.1478 decode.acc_seg: 93.5428 aux.loss_ce: 0.0791 aux.acc_seg: 92.0951 2023/06/07 13:10:44 - mmengine - INFO - Iter(train) [ 23450/240000] lr: 9.1250e-03 eta: 1 day, 20:27:54 time: 0.7115 data_time: 0.0117 memory: 17394 loss: 0.2582 decode.loss_ce: 0.1715 decode.acc_seg: 91.2915 aux.loss_ce: 0.0867 aux.acc_seg: 89.3828 2023/06/07 13:11:20 - mmengine - INFO - Iter(train) [ 23500/240000] lr: 9.1232e-03 eta: 1 day, 20:27:12 time: 0.7086 data_time: 0.0120 memory: 17390 loss: 0.2482 decode.loss_ce: 0.1636 decode.acc_seg: 92.9007 aux.loss_ce: 0.0846 aux.acc_seg: 88.3590 2023/06/07 13:11:56 - mmengine - INFO - Iter(train) [ 23550/240000] lr: 9.1213e-03 eta: 1 day, 20:26:28 time: 0.7447 data_time: 0.0125 memory: 17394 loss: 0.2605 decode.loss_ce: 0.1749 decode.acc_seg: 92.9231 aux.loss_ce: 0.0856 aux.acc_seg: 91.0093 2023/06/07 13:12:32 - mmengine - INFO - Iter(train) [ 23600/240000] lr: 9.1194e-03 eta: 1 day, 20:25:41 time: 0.7294 data_time: 0.0126 memory: 17393 loss: 0.2657 decode.loss_ce: 0.1786 decode.acc_seg: 93.9246 aux.loss_ce: 0.0871 aux.acc_seg: 92.6640 2023/06/07 13:13:09 - mmengine - INFO - Iter(train) [ 23650/240000] lr: 9.1175e-03 eta: 1 day, 20:25:00 time: 0.7385 data_time: 0.0125 memory: 17391 loss: 0.2450 decode.loss_ce: 0.1647 decode.acc_seg: 94.0975 aux.loss_ce: 0.0802 aux.acc_seg: 93.2057 2023/06/07 13:13:45 - mmengine - INFO - Iter(train) [ 23700/240000] lr: 9.1157e-03 eta: 1 day, 20:24:15 time: 0.7112 data_time: 0.0123 memory: 17394 loss: 0.2543 decode.loss_ce: 0.1692 decode.acc_seg: 93.3167 aux.loss_ce: 0.0851 aux.acc_seg: 92.9478 2023/06/07 13:14:23 - mmengine - INFO - Iter(train) [ 23750/240000] lr: 9.1138e-03 eta: 1 day, 20:23:48 time: 0.7468 data_time: 0.0118 memory: 17392 loss: 0.2614 decode.loss_ce: 0.1733 decode.acc_seg: 91.7966 aux.loss_ce: 0.0881 aux.acc_seg: 89.6707 2023/06/07 13:15:00 - mmengine - INFO - Iter(train) [ 23800/240000] lr: 9.1119e-03 eta: 1 day, 20:23:11 time: 0.7184 data_time: 0.0117 memory: 17393 loss: 0.2430 decode.loss_ce: 0.1611 decode.acc_seg: 90.0299 aux.loss_ce: 0.0820 aux.acc_seg: 87.4874 2023/06/07 13:15:36 - mmengine - INFO - Iter(train) [ 23850/240000] lr: 9.1100e-03 eta: 1 day, 20:22:31 time: 0.7357 data_time: 0.2576 memory: 17393 loss: 0.2632 decode.loss_ce: 0.1746 decode.acc_seg: 92.9946 aux.loss_ce: 0.0886 aux.acc_seg: 89.8053 2023/06/07 13:16:12 - mmengine - INFO - Iter(train) [ 23900/240000] lr: 9.1082e-03 eta: 1 day, 20:21:43 time: 0.7177 data_time: 0.3929 memory: 17394 loss: 0.2605 decode.loss_ce: 0.1760 decode.acc_seg: 92.7959 aux.loss_ce: 0.0846 aux.acc_seg: 91.2320 2023/06/07 13:16:48 - mmengine - INFO - Iter(train) [ 23950/240000] lr: 9.1063e-03 eta: 1 day, 20:20:55 time: 0.7197 data_time: 0.3742 memory: 17393 loss: 0.2460 decode.loss_ce: 0.1639 decode.acc_seg: 93.6530 aux.loss_ce: 0.0821 aux.acc_seg: 92.0286 2023/06/07 13:17:24 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 13:17:24 - mmengine - INFO - Iter(train) [ 24000/240000] lr: 9.1044e-03 eta: 1 day, 20:20:09 time: 0.7347 data_time: 0.3580 memory: 17391 loss: 0.2403 decode.loss_ce: 0.1583 decode.acc_seg: 92.3639 aux.loss_ce: 0.0821 aux.acc_seg: 89.4253 2023/06/07 13:17:24 - mmengine - INFO - Saving checkpoint at 24000 iterations 2023/06/07 13:17:29 - mmengine - INFO - Iter(val) [ 50/1297] eta: 0:01:57 time: 0.0282 data_time: 0.0203 memory: 19590 2023/06/07 13:17:30 - mmengine - INFO - Iter(val) [ 100/1297] eta: 0:01:11 time: 0.0223 data_time: 0.0140 memory: 203 2023/06/07 13:17:31 - mmengine - INFO - Iter(val) [ 150/1297] eta: 0:00:55 time: 0.0306 data_time: 0.0225 memory: 203 2023/06/07 13:17:33 - mmengine - INFO - Iter(val) [ 200/1297] eta: 0:00:45 time: 0.0194 data_time: 0.0093 memory: 203 2023/06/07 13:17:34 - mmengine - INFO - Iter(val) [ 250/1297] eta: 0:00:40 time: 0.0254 data_time: 0.0174 memory: 203 2023/06/07 13:17:35 - mmengine - INFO - Iter(val) [ 300/1297] eta: 0:00:35 time: 0.0216 data_time: 0.0131 memory: 203 2023/06/07 13:17:36 - mmengine - INFO - Iter(val) [ 350/1297] eta: 0:00:32 time: 0.0273 data_time: 0.0191 memory: 203 2023/06/07 13:17:37 - mmengine - INFO - Iter(val) [ 400/1297] eta: 0:00:29 time: 0.0207 data_time: 0.0125 memory: 203 2023/06/07 13:17:39 - mmengine - INFO - Iter(val) [ 450/1297] eta: 0:00:27 time: 0.0274 data_time: 0.0193 memory: 203 2023/06/07 13:17:40 - mmengine - INFO - Iter(val) [ 500/1297] eta: 0:00:24 time: 0.0213 data_time: 0.0127 memory: 203 2023/06/07 13:17:41 - mmengine - INFO - Iter(val) [ 550/1297] eta: 0:00:22 time: 0.0305 data_time: 0.0204 memory: 203 2023/06/07 13:17:42 - mmengine - INFO - Iter(val) [ 600/1297] eta: 0:00:20 time: 0.0232 data_time: 0.0139 memory: 203 2023/06/07 13:17:44 - mmengine - INFO - Iter(val) [ 650/1297] eta: 0:00:19 time: 0.0248 data_time: 0.0169 memory: 203 2023/06/07 13:17:45 - mmengine - INFO - Iter(val) [ 700/1297] eta: 0:00:17 time: 0.0210 data_time: 0.0117 memory: 203 2023/06/07 13:17:46 - mmengine - INFO - Iter(val) [ 750/1297] eta: 0:00:15 time: 0.0263 data_time: 0.0177 memory: 203 2023/06/07 13:17:47 - mmengine - INFO - Iter(val) [ 800/1297] eta: 0:00:14 time: 0.0222 data_time: 0.0142 memory: 203 2023/06/07 13:17:49 - mmengine - INFO - Iter(val) [ 850/1297] eta: 0:00:12 time: 0.0237 data_time: 0.0147 memory: 203 2023/06/07 13:17:50 - mmengine - INFO - Iter(val) [ 900/1297] eta: 0:00:11 time: 0.0253 data_time: 0.0154 memory: 203 2023/06/07 13:17:51 - mmengine - INFO - Iter(val) [ 950/1297] eta: 0:00:09 time: 0.0265 data_time: 0.0177 memory: 203 2023/06/07 13:17:52 - mmengine - INFO - Iter(val) [1000/1297] eta: 0:00:08 time: 0.0244 data_time: 0.0162 memory: 203 2023/06/07 13:17:54 - mmengine - INFO - Iter(val) [1050/1297] eta: 0:00:06 time: 0.0301 data_time: 0.0174 memory: 203 2023/06/07 13:17:55 - mmengine - INFO - Iter(val) [1100/1297] eta: 0:00:05 time: 0.0266 data_time: 0.0183 memory: 203 2023/06/07 13:17:56 - mmengine - INFO - Iter(val) [1150/1297] eta: 0:00:04 time: 0.0237 data_time: 0.0150 memory: 203 2023/06/07 13:17:58 - mmengine - INFO - Iter(val) [1200/1297] eta: 0:00:02 time: 0.0233 data_time: 0.0145 memory: 203 2023/06/07 13:17:59 - mmengine - INFO - Iter(val) [1250/1297] eta: 0:00:01 time: 0.0204 data_time: 0.0116 memory: 203 2023/06/07 13:18:01 - mmengine - INFO - per class results: 2023/06/07 13:18:01 - mmengine - INFO - +------------+-------+-------+ | Class | IoU | Acc | +------------+-------+-------+ | background | 89.14 | 95.38 | | obstacle | 83.43 | 89.63 | | human | 48.91 | 59.38 | +------------+-------+-------+ 2023/06/07 13:18:01 - mmengine - INFO - Iter(val) [1297/1297] aAcc: 92.6600 mIoU: 73.8300 mAcc: 81.4600 data_time: 0.0164 time: 0.0275 2023/06/07 13:18:36 - mmengine - INFO - Iter(train) [ 24050/240000] lr: 9.1025e-03 eta: 1 day, 20:19:25 time: 0.7316 data_time: 0.0672 memory: 17394 loss: 0.2440 decode.loss_ce: 0.1612 decode.acc_seg: 94.8357 aux.loss_ce: 0.0828 aux.acc_seg: 90.2767 2023/06/07 13:19:12 - mmengine - INFO - Iter(train) [ 24100/240000] lr: 9.1007e-03 eta: 1 day, 20:18:41 time: 0.7334 data_time: 0.1833 memory: 17393 loss: 0.2527 decode.loss_ce: 0.1693 decode.acc_seg: 93.7256 aux.loss_ce: 0.0834 aux.acc_seg: 92.2458 2023/06/07 13:19:49 - mmengine - INFO - Iter(train) [ 24150/240000] lr: 9.0988e-03 eta: 1 day, 20:18:02 time: 0.7397 data_time: 0.1449 memory: 17397 loss: 0.2338 decode.loss_ce: 0.1555 decode.acc_seg: 92.6598 aux.loss_ce: 0.0783 aux.acc_seg: 89.7285 2023/06/07 13:20:27 - mmengine - INFO - Iter(train) [ 24200/240000] lr: 9.0969e-03 eta: 1 day, 20:17:35 time: 0.7418 data_time: 0.0120 memory: 17394 loss: 0.2777 decode.loss_ce: 0.1844 decode.acc_seg: 90.8619 aux.loss_ce: 0.0933 aux.acc_seg: 89.1594 2023/06/07 13:21:04 - mmengine - INFO - Iter(train) [ 24250/240000] lr: 9.0950e-03 eta: 1 day, 20:16:59 time: 0.7373 data_time: 0.0121 memory: 17396 loss: 0.2790 decode.loss_ce: 0.1870 decode.acc_seg: 92.7452 aux.loss_ce: 0.0921 aux.acc_seg: 88.4930 2023/06/07 13:21:42 - mmengine - INFO - Iter(train) [ 24300/240000] lr: 9.0931e-03 eta: 1 day, 20:16:34 time: 0.7883 data_time: 0.0127 memory: 17395 loss: 0.2689 decode.loss_ce: 0.1788 decode.acc_seg: 89.6271 aux.loss_ce: 0.0901 aux.acc_seg: 87.5587 2023/06/07 13:22:19 - mmengine - INFO - Iter(train) [ 24350/240000] lr: 9.0913e-03 eta: 1 day, 20:15:56 time: 0.7399 data_time: 0.0127 memory: 17393 loss: 0.2575 decode.loss_ce: 0.1747 decode.acc_seg: 91.8879 aux.loss_ce: 0.0828 aux.acc_seg: 90.4569 2023/06/07 13:22:56 - mmengine - INFO - Iter(train) [ 24400/240000] lr: 9.0894e-03 eta: 1 day, 20:15:15 time: 0.7232 data_time: 0.0123 memory: 17395 loss: 0.2508 decode.loss_ce: 0.1661 decode.acc_seg: 92.5318 aux.loss_ce: 0.0847 aux.acc_seg: 89.7879 2023/06/07 13:23:32 - mmengine - INFO - Iter(train) [ 24450/240000] lr: 9.0875e-03 eta: 1 day, 20:14:36 time: 0.7514 data_time: 0.0124 memory: 17393 loss: 0.2523 decode.loss_ce: 0.1636 decode.acc_seg: 90.3218 aux.loss_ce: 0.0887 aux.acc_seg: 87.9510 2023/06/07 13:24:09 - mmengine - INFO - Iter(train) [ 24500/240000] lr: 9.0856e-03 eta: 1 day, 20:13:56 time: 0.7375 data_time: 0.0125 memory: 17394 loss: 0.2485 decode.loss_ce: 0.1643 decode.acc_seg: 91.3423 aux.loss_ce: 0.0843 aux.acc_seg: 90.5931 2023/06/07 13:24:46 - mmengine - INFO - Iter(train) [ 24550/240000] lr: 9.0838e-03 eta: 1 day, 20:13:21 time: 0.7630 data_time: 0.0126 memory: 17396 loss: 0.2459 decode.loss_ce: 0.1635 decode.acc_seg: 94.5380 aux.loss_ce: 0.0824 aux.acc_seg: 92.7164 2023/06/07 13:25:23 - mmengine - INFO - Iter(train) [ 24600/240000] lr: 9.0819e-03 eta: 1 day, 20:12:43 time: 0.7705 data_time: 0.0126 memory: 17393 loss: 0.2535 decode.loss_ce: 0.1672 decode.acc_seg: 93.6914 aux.loss_ce: 0.0863 aux.acc_seg: 92.5390 2023/06/07 13:26:00 - mmengine - INFO - Iter(train) [ 24650/240000] lr: 9.0800e-03 eta: 1 day, 20:12:03 time: 0.7265 data_time: 0.0124 memory: 17395 loss: 0.2550 decode.loss_ce: 0.1668 decode.acc_seg: 91.9238 aux.loss_ce: 0.0882 aux.acc_seg: 89.8416 2023/06/07 13:26:37 - mmengine - INFO - Iter(train) [ 24700/240000] lr: 9.0781e-03 eta: 1 day, 20:11:29 time: 0.7278 data_time: 0.0112 memory: 17395 loss: 0.2444 decode.loss_ce: 0.1620 decode.acc_seg: 91.8073 aux.loss_ce: 0.0824 aux.acc_seg: 89.9232 2023/06/07 13:27:13 - mmengine - INFO - Iter(train) [ 24750/240000] lr: 9.0763e-03 eta: 1 day, 20:10:45 time: 0.7306 data_time: 0.0119 memory: 17396 loss: 0.2594 decode.loss_ce: 0.1713 decode.acc_seg: 90.6000 aux.loss_ce: 0.0881 aux.acc_seg: 89.6243 2023/06/07 13:27:50 - mmengine - INFO - Iter(train) [ 24800/240000] lr: 9.0744e-03 eta: 1 day, 20:10:11 time: 0.7539 data_time: 0.0157 memory: 17394 loss: 0.2585 decode.loss_ce: 0.1743 decode.acc_seg: 92.7865 aux.loss_ce: 0.0842 aux.acc_seg: 90.9626 2023/06/07 13:28:27 - mmengine - INFO - Iter(train) [ 24850/240000] lr: 9.0725e-03 eta: 1 day, 20:09:35 time: 0.7559 data_time: 0.0183 memory: 17394 loss: 0.2583 decode.loss_ce: 0.1707 decode.acc_seg: 91.5416 aux.loss_ce: 0.0877 aux.acc_seg: 87.1977 2023/06/07 13:29:04 - mmengine - INFO - Iter(train) [ 24900/240000] lr: 9.0706e-03 eta: 1 day, 20:08:58 time: 0.7369 data_time: 0.1123 memory: 17393 loss: 0.2375 decode.loss_ce: 0.1590 decode.acc_seg: 91.7439 aux.loss_ce: 0.0786 aux.acc_seg: 90.0973 2023/06/07 13:29:41 - mmengine - INFO - Iter(train) [ 24950/240000] lr: 9.0688e-03 eta: 1 day, 20:08:20 time: 0.7575 data_time: 0.4062 memory: 17394 loss: 0.2428 decode.loss_ce: 0.1623 decode.acc_seg: 92.4299 aux.loss_ce: 0.0805 aux.acc_seg: 90.5464 2023/06/07 13:30:17 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 13:30:17 - mmengine - INFO - Iter(train) [ 25000/240000] lr: 9.0669e-03 eta: 1 day, 20:07:36 time: 0.7078 data_time: 0.3816 memory: 17392 loss: 0.2394 decode.loss_ce: 0.1579 decode.acc_seg: 92.0593 aux.loss_ce: 0.0816 aux.acc_seg: 89.4890 2023/06/07 13:30:54 - mmengine - INFO - Iter(train) [ 25050/240000] lr: 9.0650e-03 eta: 1 day, 20:06:54 time: 0.7350 data_time: 0.3934 memory: 17392 loss: 0.2489 decode.loss_ce: 0.1665 decode.acc_seg: 93.7946 aux.loss_ce: 0.0824 aux.acc_seg: 93.1068 2023/06/07 13:31:30 - mmengine - INFO - Iter(train) [ 25100/240000] lr: 9.0631e-03 eta: 1 day, 20:06:13 time: 0.7116 data_time: 0.3846 memory: 17395 loss: 0.2479 decode.loss_ce: 0.1629 decode.acc_seg: 92.6537 aux.loss_ce: 0.0850 aux.acc_seg: 88.4858 2023/06/07 13:32:06 - mmengine - INFO - Iter(train) [ 25150/240000] lr: 9.0612e-03 eta: 1 day, 20:05:31 time: 0.7260 data_time: 0.4004 memory: 17395 loss: 0.2554 decode.loss_ce: 0.1683 decode.acc_seg: 93.6737 aux.loss_ce: 0.0871 aux.acc_seg: 91.9807 2023/06/07 13:32:43 - mmengine - INFO - Iter(train) [ 25200/240000] lr: 9.0594e-03 eta: 1 day, 20:04:48 time: 0.7290 data_time: 0.4023 memory: 17394 loss: 0.2325 decode.loss_ce: 0.1534 decode.acc_seg: 92.2649 aux.loss_ce: 0.0791 aux.acc_seg: 89.4812 2023/06/07 13:33:19 - mmengine - INFO - Iter(train) [ 25250/240000] lr: 9.0575e-03 eta: 1 day, 20:04:04 time: 0.7211 data_time: 0.3948 memory: 17395 loss: 0.2237 decode.loss_ce: 0.1490 decode.acc_seg: 93.1255 aux.loss_ce: 0.0747 aux.acc_seg: 91.5171 2023/06/07 13:33:56 - mmengine - INFO - Iter(train) [ 25300/240000] lr: 9.0556e-03 eta: 1 day, 20:03:27 time: 0.7291 data_time: 0.4014 memory: 17393 loss: 0.2853 decode.loss_ce: 0.1899 decode.acc_seg: 92.3865 aux.loss_ce: 0.0954 aux.acc_seg: 90.0421 2023/06/07 13:34:32 - mmengine - INFO - Iter(train) [ 25350/240000] lr: 9.0537e-03 eta: 1 day, 20:02:42 time: 0.7014 data_time: 0.3757 memory: 17394 loss: 0.2639 decode.loss_ce: 0.1756 decode.acc_seg: 92.9499 aux.loss_ce: 0.0883 aux.acc_seg: 91.3486 2023/06/07 13:35:08 - mmengine - INFO - Iter(train) [ 25400/240000] lr: 9.0519e-03 eta: 1 day, 20:02:03 time: 0.7153 data_time: 0.3758 memory: 17393 loss: 0.2504 decode.loss_ce: 0.1669 decode.acc_seg: 91.2559 aux.loss_ce: 0.0835 aux.acc_seg: 88.0469 2023/06/07 13:35:45 - mmengine - INFO - Iter(train) [ 25450/240000] lr: 9.0500e-03 eta: 1 day, 20:01:23 time: 0.7457 data_time: 0.4099 memory: 17395 loss: 0.2535 decode.loss_ce: 0.1691 decode.acc_seg: 90.8742 aux.loss_ce: 0.0844 aux.acc_seg: 88.9771 2023/06/07 13:36:22 - mmengine - INFO - Iter(train) [ 25500/240000] lr: 9.0481e-03 eta: 1 day, 20:00:50 time: 0.7631 data_time: 0.4125 memory: 17394 loss: 0.2578 decode.loss_ce: 0.1701 decode.acc_seg: 92.8967 aux.loss_ce: 0.0877 aux.acc_seg: 89.1443 2023/06/07 13:37:00 - mmengine - INFO - Iter(train) [ 25550/240000] lr: 9.0462e-03 eta: 1 day, 20:00:18 time: 0.7424 data_time: 0.3904 memory: 17396 loss: 0.2458 decode.loss_ce: 0.1626 decode.acc_seg: 93.4082 aux.loss_ce: 0.0832 aux.acc_seg: 90.4619 2023/06/07 13:37:37 - mmengine - INFO - Iter(train) [ 25600/240000] lr: 9.0444e-03 eta: 1 day, 19:59:38 time: 0.7348 data_time: 0.4070 memory: 17393 loss: 0.2626 decode.loss_ce: 0.1752 decode.acc_seg: 91.9738 aux.loss_ce: 0.0874 aux.acc_seg: 89.6350 2023/06/07 13:38:13 - mmengine - INFO - Iter(train) [ 25650/240000] lr: 9.0425e-03 eta: 1 day, 19:59:01 time: 0.7303 data_time: 0.3827 memory: 17396 loss: 0.2640 decode.loss_ce: 0.1774 decode.acc_seg: 88.5829 aux.loss_ce: 0.0866 aux.acc_seg: 86.5843 2023/06/07 13:38:51 - mmengine - INFO - Iter(train) [ 25700/240000] lr: 9.0406e-03 eta: 1 day, 19:58:26 time: 0.7210 data_time: 0.3805 memory: 17396 loss: 0.2571 decode.loss_ce: 0.1738 decode.acc_seg: 92.1808 aux.loss_ce: 0.0833 aux.acc_seg: 90.4999 2023/06/07 13:39:28 - mmengine - INFO - Iter(train) [ 25750/240000] lr: 9.0387e-03 eta: 1 day, 19:57:51 time: 0.7256 data_time: 0.3743 memory: 17395 loss: 0.2782 decode.loss_ce: 0.1835 decode.acc_seg: 89.9475 aux.loss_ce: 0.0948 aux.acc_seg: 87.5942 2023/06/07 13:40:05 - mmengine - INFO - Iter(train) [ 25800/240000] lr: 9.0368e-03 eta: 1 day, 19:57:13 time: 0.7483 data_time: 0.4220 memory: 17392 loss: 0.2566 decode.loss_ce: 0.1704 decode.acc_seg: 90.9064 aux.loss_ce: 0.0862 aux.acc_seg: 90.0803 2023/06/07 13:40:41 - mmengine - INFO - Iter(train) [ 25850/240000] lr: 9.0350e-03 eta: 1 day, 19:56:34 time: 0.7246 data_time: 0.3912 memory: 17393 loss: 0.2523 decode.loss_ce: 0.1688 decode.acc_seg: 92.3993 aux.loss_ce: 0.0835 aux.acc_seg: 90.0185 2023/06/07 13:41:18 - mmengine - INFO - Iter(train) [ 25900/240000] lr: 9.0331e-03 eta: 1 day, 19:55:56 time: 0.7313 data_time: 0.3945 memory: 17393 loss: 0.2382 decode.loss_ce: 0.1558 decode.acc_seg: 92.9136 aux.loss_ce: 0.0825 aux.acc_seg: 91.3235 2023/06/07 13:41:55 - mmengine - INFO - Iter(train) [ 25950/240000] lr: 9.0312e-03 eta: 1 day, 19:55:19 time: 0.7389 data_time: 0.3843 memory: 17395 loss: 0.2529 decode.loss_ce: 0.1676 decode.acc_seg: 93.5551 aux.loss_ce: 0.0853 aux.acc_seg: 91.3069 2023/06/07 13:42:32 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 13:42:32 - mmengine - INFO - Iter(train) [ 26000/240000] lr: 9.0293e-03 eta: 1 day, 19:54:40 time: 0.7147 data_time: 0.3617 memory: 17396 loss: 0.2377 decode.loss_ce: 0.1570 decode.acc_seg: 92.0213 aux.loss_ce: 0.0807 aux.acc_seg: 90.5715 2023/06/07 13:43:09 - mmengine - INFO - Iter(train) [ 26050/240000] lr: 9.0275e-03 eta: 1 day, 19:54:05 time: 0.7412 data_time: 0.3901 memory: 17392 loss: 0.2567 decode.loss_ce: 0.1683 decode.acc_seg: 92.4912 aux.loss_ce: 0.0883 aux.acc_seg: 90.5388 2023/06/07 13:43:46 - mmengine - INFO - Iter(train) [ 26100/240000] lr: 9.0256e-03 eta: 1 day, 19:53:29 time: 0.7418 data_time: 0.3963 memory: 17394 loss: 0.2655 decode.loss_ce: 0.1750 decode.acc_seg: 91.4004 aux.loss_ce: 0.0905 aux.acc_seg: 89.7993 2023/06/07 13:44:23 - mmengine - INFO - Iter(train) [ 26150/240000] lr: 9.0237e-03 eta: 1 day, 19:52:52 time: 0.7742 data_time: 0.4246 memory: 17394 loss: 0.2338 decode.loss_ce: 0.1532 decode.acc_seg: 91.6246 aux.loss_ce: 0.0806 aux.acc_seg: 90.1465 2023/06/07 13:45:00 - mmengine - INFO - Iter(train) [ 26200/240000] lr: 9.0218e-03 eta: 1 day, 19:52:14 time: 0.7301 data_time: 0.4044 memory: 17394 loss: 0.2308 decode.loss_ce: 0.1533 decode.acc_seg: 90.3757 aux.loss_ce: 0.0776 aux.acc_seg: 88.1576 2023/06/07 13:45:36 - mmengine - INFO - Iter(train) [ 26250/240000] lr: 9.0199e-03 eta: 1 day, 19:51:33 time: 0.7139 data_time: 0.3884 memory: 17396 loss: 0.2684 decode.loss_ce: 0.1788 decode.acc_seg: 92.4885 aux.loss_ce: 0.0896 aux.acc_seg: 89.6612 2023/06/07 13:46:12 - mmengine - INFO - Iter(train) [ 26300/240000] lr: 9.0181e-03 eta: 1 day, 19:50:50 time: 0.7222 data_time: 0.3924 memory: 17394 loss: 0.2435 decode.loss_ce: 0.1590 decode.acc_seg: 92.4721 aux.loss_ce: 0.0845 aux.acc_seg: 89.3246 2023/06/07 13:46:48 - mmengine - INFO - Iter(train) [ 26350/240000] lr: 9.0162e-03 eta: 1 day, 19:50:06 time: 0.7319 data_time: 0.4062 memory: 17395 loss: 0.2582 decode.loss_ce: 0.1695 decode.acc_seg: 92.7775 aux.loss_ce: 0.0887 aux.acc_seg: 89.1040 2023/06/07 13:47:25 - mmengine - INFO - Iter(train) [ 26400/240000] lr: 9.0143e-03 eta: 1 day, 19:49:23 time: 0.7283 data_time: 0.4022 memory: 17393 loss: 0.2558 decode.loss_ce: 0.1706 decode.acc_seg: 92.7050 aux.loss_ce: 0.0852 aux.acc_seg: 91.1223 2023/06/07 13:48:01 - mmengine - INFO - Iter(train) [ 26450/240000] lr: 9.0124e-03 eta: 1 day, 19:48:40 time: 0.7296 data_time: 0.4037 memory: 17398 loss: 0.2761 decode.loss_ce: 0.1889 decode.acc_seg: 94.1276 aux.loss_ce: 0.0871 aux.acc_seg: 92.1688 2023/06/07 13:48:37 - mmengine - INFO - Iter(train) [ 26500/240000] lr: 9.0106e-03 eta: 1 day, 19:47:59 time: 0.7280 data_time: 0.4020 memory: 17395 loss: 0.2560 decode.loss_ce: 0.1689 decode.acc_seg: 91.3051 aux.loss_ce: 0.0872 aux.acc_seg: 88.7321 2023/06/07 13:49:13 - mmengine - INFO - Iter(train) [ 26550/240000] lr: 9.0087e-03 eta: 1 day, 19:47:15 time: 0.7230 data_time: 0.3965 memory: 17395 loss: 0.2745 decode.loss_ce: 0.1837 decode.acc_seg: 92.0199 aux.loss_ce: 0.0908 aux.acc_seg: 88.3613 2023/06/07 13:49:49 - mmengine - INFO - Iter(train) [ 26600/240000] lr: 9.0068e-03 eta: 1 day, 19:46:31 time: 0.7161 data_time: 0.3905 memory: 17392 loss: 0.2529 decode.loss_ce: 0.1678 decode.acc_seg: 91.0380 aux.loss_ce: 0.0851 aux.acc_seg: 88.3776 2023/06/07 13:50:26 - mmengine - INFO - Iter(train) [ 26650/240000] lr: 9.0049e-03 eta: 1 day, 19:45:50 time: 0.7113 data_time: 0.3859 memory: 17393 loss: 0.2545 decode.loss_ce: 0.1693 decode.acc_seg: 91.0318 aux.loss_ce: 0.0852 aux.acc_seg: 87.0415 2023/06/07 13:51:02 - mmengine - INFO - Iter(train) [ 26700/240000] lr: 9.0030e-03 eta: 1 day, 19:45:07 time: 0.7081 data_time: 0.3825 memory: 17395 loss: 0.2520 decode.loss_ce: 0.1658 decode.acc_seg: 93.7250 aux.loss_ce: 0.0863 aux.acc_seg: 91.9634 2023/06/07 13:51:38 - mmengine - INFO - Iter(train) [ 26750/240000] lr: 9.0012e-03 eta: 1 day, 19:44:25 time: 0.7274 data_time: 0.4016 memory: 17395 loss: 0.2387 decode.loss_ce: 0.1581 decode.acc_seg: 91.8633 aux.loss_ce: 0.0807 aux.acc_seg: 90.0204 2023/06/07 13:52:14 - mmengine - INFO - Iter(train) [ 26800/240000] lr: 8.9993e-03 eta: 1 day, 19:43:44 time: 0.7261 data_time: 0.3990 memory: 17394 loss: 0.2703 decode.loss_ce: 0.1770 decode.acc_seg: 94.7173 aux.loss_ce: 0.0934 aux.acc_seg: 90.4220 2023/06/07 13:52:51 - mmengine - INFO - Iter(train) [ 26850/240000] lr: 8.9974e-03 eta: 1 day, 19:43:00 time: 0.7209 data_time: 0.3953 memory: 17392 loss: 0.2510 decode.loss_ce: 0.1657 decode.acc_seg: 91.7420 aux.loss_ce: 0.0853 aux.acc_seg: 89.0948 2023/06/07 13:53:27 - mmengine - INFO - Iter(train) [ 26900/240000] lr: 8.9955e-03 eta: 1 day, 19:42:18 time: 0.7199 data_time: 0.3938 memory: 17393 loss: 0.2579 decode.loss_ce: 0.1693 decode.acc_seg: 90.5548 aux.loss_ce: 0.0886 aux.acc_seg: 89.2900 2023/06/07 13:54:03 - mmengine - INFO - Iter(train) [ 26950/240000] lr: 8.9937e-03 eta: 1 day, 19:41:36 time: 0.7221 data_time: 0.3958 memory: 17399 loss: 0.2488 decode.loss_ce: 0.1650 decode.acc_seg: 93.6611 aux.loss_ce: 0.0839 aux.acc_seg: 92.3788 2023/06/07 13:54:39 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 13:54:39 - mmengine - INFO - Iter(train) [ 27000/240000] lr: 8.9918e-03 eta: 1 day, 19:40:51 time: 0.7244 data_time: 0.3987 memory: 17393 loss: 0.2550 decode.loss_ce: 0.1711 decode.acc_seg: 93.5499 aux.loss_ce: 0.0839 aux.acc_seg: 91.2869 2023/06/07 13:55:15 - mmengine - INFO - Iter(train) [ 27050/240000] lr: 8.9899e-03 eta: 1 day, 19:40:06 time: 0.7267 data_time: 0.4010 memory: 17393 loss: 0.2478 decode.loss_ce: 0.1630 decode.acc_seg: 92.7987 aux.loss_ce: 0.0848 aux.acc_seg: 90.6194 2023/06/07 13:55:51 - mmengine - INFO - Iter(train) [ 27100/240000] lr: 8.9880e-03 eta: 1 day, 19:39:21 time: 0.7238 data_time: 0.3933 memory: 17396 loss: 0.2373 decode.loss_ce: 0.1584 decode.acc_seg: 95.2436 aux.loss_ce: 0.0789 aux.acc_seg: 93.7909 2023/06/07 13:56:27 - mmengine - INFO - Iter(train) [ 27150/240000] lr: 8.9861e-03 eta: 1 day, 19:38:36 time: 0.7234 data_time: 0.3976 memory: 17395 loss: 0.2517 decode.loss_ce: 0.1650 decode.acc_seg: 93.9822 aux.loss_ce: 0.0867 aux.acc_seg: 92.5741 2023/06/07 13:57:03 - mmengine - INFO - Iter(train) [ 27200/240000] lr: 8.9843e-03 eta: 1 day, 19:37:53 time: 0.7207 data_time: 0.3945 memory: 17396 loss: 0.2630 decode.loss_ce: 0.1736 decode.acc_seg: 92.6898 aux.loss_ce: 0.0894 aux.acc_seg: 88.9744 2023/06/07 13:57:39 - mmengine - INFO - Iter(train) [ 27250/240000] lr: 8.9824e-03 eta: 1 day, 19:37:10 time: 0.7352 data_time: 0.4097 memory: 17393 loss: 0.2483 decode.loss_ce: 0.1644 decode.acc_seg: 92.7859 aux.loss_ce: 0.0839 aux.acc_seg: 90.7366 2023/06/07 13:58:15 - mmengine - INFO - Iter(train) [ 27300/240000] lr: 8.9805e-03 eta: 1 day, 19:36:25 time: 0.7176 data_time: 0.3918 memory: 17393 loss: 0.2379 decode.loss_ce: 0.1575 decode.acc_seg: 93.6164 aux.loss_ce: 0.0804 aux.acc_seg: 90.7093 2023/06/07 13:58:51 - mmengine - INFO - Iter(train) [ 27350/240000] lr: 8.9786e-03 eta: 1 day, 19:35:40 time: 0.7295 data_time: 0.4039 memory: 17393 loss: 0.2497 decode.loss_ce: 0.1647 decode.acc_seg: 92.9736 aux.loss_ce: 0.0849 aux.acc_seg: 90.2473 2023/06/07 13:59:27 - mmengine - INFO - Iter(train) [ 27400/240000] lr: 8.9767e-03 eta: 1 day, 19:34:58 time: 0.7167 data_time: 0.3905 memory: 17395 loss: 0.2531 decode.loss_ce: 0.1690 decode.acc_seg: 93.0162 aux.loss_ce: 0.0840 aux.acc_seg: 91.1150 2023/06/07 14:00:03 - mmengine - INFO - Iter(train) [ 27450/240000] lr: 8.9749e-03 eta: 1 day, 19:34:15 time: 0.7207 data_time: 0.3888 memory: 17394 loss: 0.2578 decode.loss_ce: 0.1720 decode.acc_seg: 92.2013 aux.loss_ce: 0.0858 aux.acc_seg: 90.0938 2023/06/07 14:00:38 - mmengine - INFO - Iter(train) [ 27500/240000] lr: 8.9730e-03 eta: 1 day, 19:33:26 time: 0.7093 data_time: 0.3839 memory: 17392 loss: 0.2499 decode.loss_ce: 0.1661 decode.acc_seg: 93.5416 aux.loss_ce: 0.0838 aux.acc_seg: 91.2041 2023/06/07 14:01:14 - mmengine - INFO - Iter(train) [ 27550/240000] lr: 8.9711e-03 eta: 1 day, 19:32:40 time: 0.7177 data_time: 0.3835 memory: 17395 loss: 0.2351 decode.loss_ce: 0.1541 decode.acc_seg: 94.6920 aux.loss_ce: 0.0810 aux.acc_seg: 93.6199 2023/06/07 14:01:50 - mmengine - INFO - Iter(train) [ 27600/240000] lr: 8.9692e-03 eta: 1 day, 19:31:57 time: 0.7118 data_time: 0.3858 memory: 17395 loss: 0.2788 decode.loss_ce: 0.1828 decode.acc_seg: 92.1112 aux.loss_ce: 0.0959 aux.acc_seg: 91.1556 2023/06/07 14:02:26 - mmengine - INFO - Iter(train) [ 27650/240000] lr: 8.9673e-03 eta: 1 day, 19:31:14 time: 0.7008 data_time: 0.3752 memory: 17394 loss: 0.2489 decode.loss_ce: 0.1694 decode.acc_seg: 93.9178 aux.loss_ce: 0.0795 aux.acc_seg: 92.2318 2023/06/07 14:03:02 - mmengine - INFO - Iter(train) [ 27700/240000] lr: 8.9655e-03 eta: 1 day, 19:30:30 time: 0.7190 data_time: 0.3930 memory: 17394 loss: 0.2477 decode.loss_ce: 0.1627 decode.acc_seg: 92.9116 aux.loss_ce: 0.0850 aux.acc_seg: 91.0428 2023/06/07 14:03:38 - mmengine - INFO - Iter(train) [ 27750/240000] lr: 8.9636e-03 eta: 1 day, 19:29:47 time: 0.7409 data_time: 0.2861 memory: 17393 loss: 0.2472 decode.loss_ce: 0.1591 decode.acc_seg: 91.3574 aux.loss_ce: 0.0880 aux.acc_seg: 87.8245 2023/06/07 14:04:15 - mmengine - INFO - Iter(train) [ 27800/240000] lr: 8.9617e-03 eta: 1 day, 19:29:07 time: 0.7319 data_time: 0.1207 memory: 17394 loss: 0.2597 decode.loss_ce: 0.1700 decode.acc_seg: 91.4507 aux.loss_ce: 0.0897 aux.acc_seg: 88.9394 2023/06/07 14:04:52 - mmengine - INFO - Iter(train) [ 27850/240000] lr: 8.9598e-03 eta: 1 day, 19:28:30 time: 0.7537 data_time: 0.1922 memory: 17394 loss: 0.2526 decode.loss_ce: 0.1697 decode.acc_seg: 92.4418 aux.loss_ce: 0.0829 aux.acc_seg: 90.3887 2023/06/07 14:05:29 - mmengine - INFO - Iter(train) [ 27900/240000] lr: 8.9580e-03 eta: 1 day, 19:27:58 time: 0.7308 data_time: 0.0123 memory: 17393 loss: 0.2430 decode.loss_ce: 0.1604 decode.acc_seg: 93.7343 aux.loss_ce: 0.0826 aux.acc_seg: 90.5235 2023/06/07 14:06:07 - mmengine - INFO - Iter(train) [ 27950/240000] lr: 8.9561e-03 eta: 1 day, 19:27:25 time: 0.7511 data_time: 0.0128 memory: 17393 loss: 0.2767 decode.loss_ce: 0.1837 decode.acc_seg: 92.1079 aux.loss_ce: 0.0931 aux.acc_seg: 89.1161 2023/06/07 14:06:44 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 14:06:44 - mmengine - INFO - Iter(train) [ 28000/240000] lr: 8.9542e-03 eta: 1 day, 19:26:53 time: 0.7593 data_time: 0.0113 memory: 17394 loss: 0.2636 decode.loss_ce: 0.1740 decode.acc_seg: 92.1674 aux.loss_ce: 0.0896 aux.acc_seg: 91.1289 2023/06/07 14:07:22 - mmengine - INFO - Iter(train) [ 28050/240000] lr: 8.9523e-03 eta: 1 day, 19:26:22 time: 0.7173 data_time: 0.0121 memory: 17393 loss: 0.2329 decode.loss_ce: 0.1527 decode.acc_seg: 91.4956 aux.loss_ce: 0.0801 aux.acc_seg: 88.8103 2023/06/07 14:07:58 - mmengine - INFO - Iter(train) [ 28100/240000] lr: 8.9504e-03 eta: 1 day, 19:25:40 time: 0.7073 data_time: 0.0123 memory: 17397 loss: 0.2548 decode.loss_ce: 0.1700 decode.acc_seg: 94.1687 aux.loss_ce: 0.0848 aux.acc_seg: 92.2452 2023/06/07 14:08:34 - mmengine - INFO - Iter(train) [ 28150/240000] lr: 8.9486e-03 eta: 1 day, 19:24:59 time: 0.7372 data_time: 0.0124 memory: 17392 loss: 0.2474 decode.loss_ce: 0.1617 decode.acc_seg: 92.0180 aux.loss_ce: 0.0857 aux.acc_seg: 89.9018 2023/06/07 14:09:11 - mmengine - INFO - Iter(train) [ 28200/240000] lr: 8.9467e-03 eta: 1 day, 19:24:17 time: 0.7270 data_time: 0.0125 memory: 17392 loss: 0.2549 decode.loss_ce: 0.1697 decode.acc_seg: 94.0334 aux.loss_ce: 0.0852 aux.acc_seg: 92.9265 2023/06/07 14:09:47 - mmengine - INFO - Iter(train) [ 28250/240000] lr: 8.9448e-03 eta: 1 day, 19:23:34 time: 0.7208 data_time: 0.0122 memory: 17394 loss: 0.2315 decode.loss_ce: 0.1546 decode.acc_seg: 93.4847 aux.loss_ce: 0.0769 aux.acc_seg: 92.1350 2023/06/07 14:10:23 - mmengine - INFO - Iter(train) [ 28300/240000] lr: 8.9429e-03 eta: 1 day, 19:22:57 time: 0.7516 data_time: 0.0136 memory: 17393 loss: 0.2599 decode.loss_ce: 0.1700 decode.acc_seg: 89.9784 aux.loss_ce: 0.0899 aux.acc_seg: 85.7090 2023/06/07 14:11:00 - mmengine - INFO - Iter(train) [ 28350/240000] lr: 8.9410e-03 eta: 1 day, 19:22:17 time: 0.7435 data_time: 0.0124 memory: 17398 loss: 0.2614 decode.loss_ce: 0.1740 decode.acc_seg: 94.5935 aux.loss_ce: 0.0875 aux.acc_seg: 93.3448 2023/06/07 14:11:37 - mmengine - INFO - Iter(train) [ 28400/240000] lr: 8.9392e-03 eta: 1 day, 19:21:40 time: 0.7381 data_time: 0.0126 memory: 17393 loss: 0.2578 decode.loss_ce: 0.1703 decode.acc_seg: 93.3132 aux.loss_ce: 0.0876 aux.acc_seg: 91.3337 2023/06/07 14:12:13 - mmengine - INFO - Iter(train) [ 28450/240000] lr: 8.9373e-03 eta: 1 day, 19:21:00 time: 0.7181 data_time: 0.3379 memory: 17395 loss: 0.2416 decode.loss_ce: 0.1586 decode.acc_seg: 93.9551 aux.loss_ce: 0.0830 aux.acc_seg: 89.7406 2023/06/07 14:12:50 - mmengine - INFO - Iter(train) [ 28500/240000] lr: 8.9354e-03 eta: 1 day, 19:20:24 time: 0.7291 data_time: 0.2615 memory: 17396 loss: 0.2289 decode.loss_ce: 0.1521 decode.acc_seg: 93.3882 aux.loss_ce: 0.0768 aux.acc_seg: 92.2740 2023/06/07 14:13:27 - mmengine - INFO - Iter(train) [ 28550/240000] lr: 8.9335e-03 eta: 1 day, 19:19:44 time: 0.7495 data_time: 0.4072 memory: 17397 loss: 0.2239 decode.loss_ce: 0.1484 decode.acc_seg: 94.5492 aux.loss_ce: 0.0755 aux.acc_seg: 93.0199 2023/06/07 14:14:04 - mmengine - INFO - Iter(train) [ 28600/240000] lr: 8.9316e-03 eta: 1 day, 19:19:08 time: 0.7465 data_time: 0.4090 memory: 17393 loss: 0.2564 decode.loss_ce: 0.1699 decode.acc_seg: 91.4635 aux.loss_ce: 0.0865 aux.acc_seg: 89.2141 2023/06/07 14:14:41 - mmengine - INFO - Iter(train) [ 28650/240000] lr: 8.9298e-03 eta: 1 day, 19:18:32 time: 0.7340 data_time: 0.3970 memory: 17394 loss: 0.2681 decode.loss_ce: 0.1802 decode.acc_seg: 91.4374 aux.loss_ce: 0.0879 aux.acc_seg: 88.0627 2023/06/07 14:15:18 - mmengine - INFO - Iter(train) [ 28700/240000] lr: 8.9279e-03 eta: 1 day, 19:17:57 time: 0.7437 data_time: 0.4050 memory: 17391 loss: 0.2477 decode.loss_ce: 0.1641 decode.acc_seg: 92.4394 aux.loss_ce: 0.0837 aux.acc_seg: 89.7657 2023/06/07 14:15:55 - mmengine - INFO - Iter(train) [ 28750/240000] lr: 8.9260e-03 eta: 1 day, 19:17:22 time: 0.7409 data_time: 0.3949 memory: 17393 loss: 0.2719 decode.loss_ce: 0.1835 decode.acc_seg: 92.0478 aux.loss_ce: 0.0884 aux.acc_seg: 90.4094 2023/06/07 14:16:33 - mmengine - INFO - Iter(train) [ 28800/240000] lr: 8.9241e-03 eta: 1 day, 19:16:53 time: 0.7501 data_time: 0.3899 memory: 17395 loss: 0.2506 decode.loss_ce: 0.1662 decode.acc_seg: 91.9269 aux.loss_ce: 0.0844 aux.acc_seg: 91.0399 2023/06/07 14:17:11 - mmengine - INFO - Iter(train) [ 28850/240000] lr: 8.9222e-03 eta: 1 day, 19:16:23 time: 0.7675 data_time: 0.4069 memory: 17393 loss: 0.2693 decode.loss_ce: 0.1775 decode.acc_seg: 93.1174 aux.loss_ce: 0.0918 aux.acc_seg: 91.0750 2023/06/07 14:17:48 - mmengine - INFO - Iter(train) [ 28900/240000] lr: 8.9204e-03 eta: 1 day, 19:15:51 time: 0.7611 data_time: 0.3988 memory: 17393 loss: 0.2445 decode.loss_ce: 0.1618 decode.acc_seg: 93.3414 aux.loss_ce: 0.0828 aux.acc_seg: 90.2624 2023/06/07 14:18:26 - mmengine - INFO - Iter(train) [ 28950/240000] lr: 8.9185e-03 eta: 1 day, 19:15:21 time: 0.7606 data_time: 0.4061 memory: 17394 loss: 0.2342 decode.loss_ce: 0.1546 decode.acc_seg: 94.2202 aux.loss_ce: 0.0796 aux.acc_seg: 92.4536 2023/06/07 14:19:02 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 14:19:02 - mmengine - INFO - Iter(train) [ 29000/240000] lr: 8.9166e-03 eta: 1 day, 19:14:38 time: 0.7199 data_time: 0.3956 memory: 17393 loss: 0.2704 decode.loss_ce: 0.1782 decode.acc_seg: 91.5266 aux.loss_ce: 0.0923 aux.acc_seg: 87.0255 2023/06/07 14:19:38 - mmengine - INFO - Iter(train) [ 29050/240000] lr: 8.9147e-03 eta: 1 day, 19:13:55 time: 0.7186 data_time: 0.3938 memory: 17392 loss: 0.2479 decode.loss_ce: 0.1659 decode.acc_seg: 90.8691 aux.loss_ce: 0.0820 aux.acc_seg: 89.1955 2023/06/07 14:20:15 - mmengine - INFO - Iter(train) [ 29100/240000] lr: 8.9128e-03 eta: 1 day, 19:13:14 time: 0.7111 data_time: 0.3871 memory: 17394 loss: 0.2381 decode.loss_ce: 0.1587 decode.acc_seg: 93.1350 aux.loss_ce: 0.0794 aux.acc_seg: 90.7134 2023/06/07 14:20:51 - mmengine - INFO - Iter(train) [ 29150/240000] lr: 8.9110e-03 eta: 1 day, 19:12:30 time: 0.7197 data_time: 0.3956 memory: 17393 loss: 0.2341 decode.loss_ce: 0.1558 decode.acc_seg: 92.8933 aux.loss_ce: 0.0783 aux.acc_seg: 91.2109 2023/06/07 14:21:27 - mmengine - INFO - Iter(train) [ 29200/240000] lr: 8.9091e-03 eta: 1 day, 19:11:47 time: 0.7136 data_time: 0.3888 memory: 17393 loss: 0.2374 decode.loss_ce: 0.1567 decode.acc_seg: 93.5314 aux.loss_ce: 0.0807 aux.acc_seg: 91.6435 2023/06/07 14:22:03 - mmengine - INFO - Iter(train) [ 29250/240000] lr: 8.9072e-03 eta: 1 day, 19:11:04 time: 0.7186 data_time: 0.3936 memory: 17395 loss: 0.2349 decode.loss_ce: 0.1544 decode.acc_seg: 94.0927 aux.loss_ce: 0.0805 aux.acc_seg: 92.6644 2023/06/07 14:22:39 - mmengine - INFO - Iter(train) [ 29300/240000] lr: 8.9053e-03 eta: 1 day, 19:10:23 time: 0.7200 data_time: 0.3952 memory: 17393 loss: 0.2526 decode.loss_ce: 0.1667 decode.acc_seg: 92.4357 aux.loss_ce: 0.0859 aux.acc_seg: 91.0957 2023/06/07 14:23:15 - mmengine - INFO - Iter(train) [ 29350/240000] lr: 8.9034e-03 eta: 1 day, 19:09:40 time: 0.7042 data_time: 0.3798 memory: 17394 loss: 0.2561 decode.loss_ce: 0.1707 decode.acc_seg: 92.1885 aux.loss_ce: 0.0853 aux.acc_seg: 89.6313 2023/06/07 14:23:51 - mmengine - INFO - Iter(train) [ 29400/240000] lr: 8.9016e-03 eta: 1 day, 19:08:57 time: 0.7163 data_time: 0.3915 memory: 17394 loss: 0.2502 decode.loss_ce: 0.1635 decode.acc_seg: 93.0662 aux.loss_ce: 0.0867 aux.acc_seg: 90.9737 2023/06/07 14:24:27 - mmengine - INFO - Iter(train) [ 29450/240000] lr: 8.8997e-03 eta: 1 day, 19:08:17 time: 0.7150 data_time: 0.3896 memory: 17393 loss: 0.2673 decode.loss_ce: 0.1774 decode.acc_seg: 92.9945 aux.loss_ce: 0.0898 aux.acc_seg: 87.5227 2023/06/07 14:25:04 - mmengine - INFO - Iter(train) [ 29500/240000] lr: 8.8978e-03 eta: 1 day, 19:07:36 time: 0.7207 data_time: 0.3956 memory: 17392 loss: 0.2449 decode.loss_ce: 0.1610 decode.acc_seg: 91.8368 aux.loss_ce: 0.0840 aux.acc_seg: 90.1547 2023/06/07 14:25:40 - mmengine - INFO - Iter(train) [ 29550/240000] lr: 8.8959e-03 eta: 1 day, 19:06:54 time: 0.7060 data_time: 0.3809 memory: 17393 loss: 0.2726 decode.loss_ce: 0.1807 decode.acc_seg: 90.8514 aux.loss_ce: 0.0919 aux.acc_seg: 88.5043 2023/06/07 14:26:16 - mmengine - INFO - Iter(train) [ 29600/240000] lr: 8.8940e-03 eta: 1 day, 19:06:12 time: 0.7267 data_time: 0.4017 memory: 17395 loss: 0.2676 decode.loss_ce: 0.1753 decode.acc_seg: 90.4274 aux.loss_ce: 0.0923 aux.acc_seg: 87.0334 2023/06/07 14:26:52 - mmengine - INFO - Iter(train) [ 29650/240000] lr: 8.8921e-03 eta: 1 day, 19:05:29 time: 0.7128 data_time: 0.3882 memory: 17394 loss: 0.2577 decode.loss_ce: 0.1705 decode.acc_seg: 89.6967 aux.loss_ce: 0.0872 aux.acc_seg: 87.3712 2023/06/07 14:27:28 - mmengine - INFO - Iter(train) [ 29700/240000] lr: 8.8903e-03 eta: 1 day, 19:04:48 time: 0.7253 data_time: 0.4003 memory: 17395 loss: 0.2448 decode.loss_ce: 0.1631 decode.acc_seg: 92.8189 aux.loss_ce: 0.0816 aux.acc_seg: 90.4115 2023/06/07 14:28:05 - mmengine - INFO - Iter(train) [ 29750/240000] lr: 8.8884e-03 eta: 1 day, 19:04:07 time: 0.7279 data_time: 0.3867 memory: 17394 loss: 0.2454 decode.loss_ce: 0.1612 decode.acc_seg: 91.5471 aux.loss_ce: 0.0842 aux.acc_seg: 89.0101 2023/06/07 14:28:41 - mmengine - INFO - Iter(train) [ 29800/240000] lr: 8.8865e-03 eta: 1 day, 19:03:25 time: 0.7189 data_time: 0.3939 memory: 17396 loss: 0.2364 decode.loss_ce: 0.1550 decode.acc_seg: 92.0665 aux.loss_ce: 0.0815 aux.acc_seg: 89.2455 2023/06/07 14:29:17 - mmengine - INFO - Iter(train) [ 29850/240000] lr: 8.8846e-03 eta: 1 day, 19:02:45 time: 0.7243 data_time: 0.3994 memory: 17395 loss: 0.2679 decode.loss_ce: 0.1781 decode.acc_seg: 92.1708 aux.loss_ce: 0.0898 aux.acc_seg: 91.3921 2023/06/07 14:29:53 - mmengine - INFO - Iter(train) [ 29900/240000] lr: 8.8827e-03 eta: 1 day, 19:02:03 time: 0.7172 data_time: 0.3923 memory: 17392 loss: 0.2385 decode.loss_ce: 0.1579 decode.acc_seg: 95.4115 aux.loss_ce: 0.0806 aux.acc_seg: 93.7532 2023/06/07 14:30:29 - mmengine - INFO - Iter(train) [ 29950/240000] lr: 8.8809e-03 eta: 1 day, 19:01:20 time: 0.7086 data_time: 0.3835 memory: 17393 loss: 0.2479 decode.loss_ce: 0.1658 decode.acc_seg: 92.9083 aux.loss_ce: 0.0821 aux.acc_seg: 91.3524 2023/06/07 14:31:06 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 14:31:06 - mmengine - INFO - Iter(train) [ 30000/240000] lr: 8.8790e-03 eta: 1 day, 19:00:38 time: 0.7234 data_time: 0.3973 memory: 17394 loss: 0.2462 decode.loss_ce: 0.1647 decode.acc_seg: 89.6054 aux.loss_ce: 0.0815 aux.acc_seg: 88.1040 2023/06/07 14:31:42 - mmengine - INFO - Iter(train) [ 30050/240000] lr: 8.8771e-03 eta: 1 day, 18:59:55 time: 0.7070 data_time: 0.3831 memory: 17395 loss: 0.2464 decode.loss_ce: 0.1619 decode.acc_seg: 92.8027 aux.loss_ce: 0.0845 aux.acc_seg: 91.1548 2023/06/07 14:32:17 - mmengine - INFO - Iter(train) [ 30100/240000] lr: 8.8752e-03 eta: 1 day, 18:59:11 time: 0.7280 data_time: 0.4046 memory: 17395 loss: 0.2400 decode.loss_ce: 0.1591 decode.acc_seg: 93.1337 aux.loss_ce: 0.0809 aux.acc_seg: 89.1891 2023/06/07 14:32:53 - mmengine - INFO - Iter(train) [ 30150/240000] lr: 8.8733e-03 eta: 1 day, 18:58:27 time: 0.7194 data_time: 0.3960 memory: 17392 loss: 0.2396 decode.loss_ce: 0.1575 decode.acc_seg: 93.4661 aux.loss_ce: 0.0821 aux.acc_seg: 91.6388 2023/06/07 14:33:29 - mmengine - INFO - Iter(train) [ 30200/240000] lr: 8.8715e-03 eta: 1 day, 18:57:43 time: 0.7148 data_time: 0.3910 memory: 17393 loss: 0.2342 decode.loss_ce: 0.1553 decode.acc_seg: 92.5039 aux.loss_ce: 0.0789 aux.acc_seg: 90.3965 2023/06/07 14:34:05 - mmengine - INFO - Iter(train) [ 30250/240000] lr: 8.8696e-03 eta: 1 day, 18:57:01 time: 0.7163 data_time: 0.3926 memory: 17392 loss: 0.2459 decode.loss_ce: 0.1628 decode.acc_seg: 92.4364 aux.loss_ce: 0.0831 aux.acc_seg: 90.0396 2023/06/07 14:34:41 - mmengine - INFO - Iter(train) [ 30300/240000] lr: 8.8677e-03 eta: 1 day, 18:56:18 time: 0.6986 data_time: 0.3750 memory: 17395 loss: 0.2420 decode.loss_ce: 0.1606 decode.acc_seg: 93.4289 aux.loss_ce: 0.0814 aux.acc_seg: 91.1875 2023/06/07 14:35:17 - mmengine - INFO - Iter(train) [ 30350/240000] lr: 8.8658e-03 eta: 1 day, 18:55:33 time: 0.7129 data_time: 0.3895 memory: 17392 loss: 0.2315 decode.loss_ce: 0.1533 decode.acc_seg: 92.3605 aux.loss_ce: 0.0782 aux.acc_seg: 90.4011 2023/06/07 14:35:53 - mmengine - INFO - Iter(train) [ 30400/240000] lr: 8.8639e-03 eta: 1 day, 18:54:48 time: 0.7291 data_time: 0.4054 memory: 17395 loss: 0.2572 decode.loss_ce: 0.1686 decode.acc_seg: 90.3566 aux.loss_ce: 0.0886 aux.acc_seg: 88.3714 2023/06/07 14:36:28 - mmengine - INFO - Iter(train) [ 30450/240000] lr: 8.8620e-03 eta: 1 day, 18:54:04 time: 0.7239 data_time: 0.4001 memory: 17395 loss: 0.2574 decode.loss_ce: 0.1677 decode.acc_seg: 93.1430 aux.loss_ce: 0.0897 aux.acc_seg: 90.8481 2023/06/07 14:37:04 - mmengine - INFO - Iter(train) [ 30500/240000] lr: 8.8602e-03 eta: 1 day, 18:53:19 time: 0.7224 data_time: 0.3977 memory: 17394 loss: 0.2415 decode.loss_ce: 0.1587 decode.acc_seg: 92.7096 aux.loss_ce: 0.0828 aux.acc_seg: 89.2585 2023/06/07 14:37:40 - mmengine - INFO - Iter(train) [ 30550/240000] lr: 8.8583e-03 eta: 1 day, 18:52:39 time: 0.7227 data_time: 0.3991 memory: 17394 loss: 0.2459 decode.loss_ce: 0.1634 decode.acc_seg: 92.7605 aux.loss_ce: 0.0825 aux.acc_seg: 90.3090 2023/06/07 14:38:16 - mmengine - INFO - Iter(train) [ 30600/240000] lr: 8.8564e-03 eta: 1 day, 18:51:57 time: 0.7079 data_time: 0.3841 memory: 17395 loss: 0.2314 decode.loss_ce: 0.1514 decode.acc_seg: 93.2221 aux.loss_ce: 0.0799 aux.acc_seg: 91.4174 2023/06/07 14:38:52 - mmengine - INFO - Iter(train) [ 30650/240000] lr: 8.8545e-03 eta: 1 day, 18:51:13 time: 0.7255 data_time: 0.4024 memory: 17396 loss: 0.2256 decode.loss_ce: 0.1471 decode.acc_seg: 93.5459 aux.loss_ce: 0.0785 aux.acc_seg: 90.3856 2023/06/07 14:39:28 - mmengine - INFO - Iter(train) [ 30700/240000] lr: 8.8526e-03 eta: 1 day, 18:50:31 time: 0.7244 data_time: 0.4011 memory: 17393 loss: 0.2366 decode.loss_ce: 0.1576 decode.acc_seg: 92.2146 aux.loss_ce: 0.0789 aux.acc_seg: 90.8282 2023/06/07 14:40:04 - mmengine - INFO - Iter(train) [ 30750/240000] lr: 8.8508e-03 eta: 1 day, 18:49:45 time: 0.7171 data_time: 0.3924 memory: 17394 loss: 0.2558 decode.loss_ce: 0.1683 decode.acc_seg: 90.9582 aux.loss_ce: 0.0875 aux.acc_seg: 88.8858 2023/06/07 14:40:40 - mmengine - INFO - Iter(train) [ 30800/240000] lr: 8.8489e-03 eta: 1 day, 18:49:02 time: 0.7165 data_time: 0.3931 memory: 17392 loss: 0.2240 decode.loss_ce: 0.1462 decode.acc_seg: 93.4301 aux.loss_ce: 0.0778 aux.acc_seg: 90.3254 2023/06/07 14:41:16 - mmengine - INFO - Iter(train) [ 30850/240000] lr: 8.8470e-03 eta: 1 day, 18:48:17 time: 0.7110 data_time: 0.3879 memory: 17394 loss: 0.2387 decode.loss_ce: 0.1573 decode.acc_seg: 91.7918 aux.loss_ce: 0.0814 aux.acc_seg: 90.5863 2023/06/07 14:41:51 - mmengine - INFO - Iter(train) [ 30900/240000] lr: 8.8451e-03 eta: 1 day, 18:47:33 time: 0.7196 data_time: 0.3964 memory: 17393 loss: 0.2321 decode.loss_ce: 0.1530 decode.acc_seg: 93.2406 aux.loss_ce: 0.0792 aux.acc_seg: 88.8569 2023/06/07 14:42:27 - mmengine - INFO - Iter(train) [ 30950/240000] lr: 8.8432e-03 eta: 1 day, 18:46:51 time: 0.7200 data_time: 0.3967 memory: 17395 loss: 0.2462 decode.loss_ce: 0.1620 decode.acc_seg: 93.3395 aux.loss_ce: 0.0843 aux.acc_seg: 91.8967 2023/06/07 14:43:03 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 14:43:03 - mmengine - INFO - Iter(train) [ 31000/240000] lr: 8.8413e-03 eta: 1 day, 18:46:09 time: 0.7212 data_time: 0.3977 memory: 17393 loss: 0.2672 decode.loss_ce: 0.1768 decode.acc_seg: 91.5134 aux.loss_ce: 0.0904 aux.acc_seg: 89.7805 2023/06/07 14:43:39 - mmengine - INFO - Iter(train) [ 31050/240000] lr: 8.8395e-03 eta: 1 day, 18:45:25 time: 0.7212 data_time: 0.3491 memory: 17396 loss: 0.2268 decode.loss_ce: 0.1500 decode.acc_seg: 93.5149 aux.loss_ce: 0.0768 aux.acc_seg: 90.0902 2023/06/07 14:44:15 - mmengine - INFO - Iter(train) [ 31100/240000] lr: 8.8376e-03 eta: 1 day, 18:44:41 time: 0.7154 data_time: 0.3695 memory: 17393 loss: 0.2356 decode.loss_ce: 0.1563 decode.acc_seg: 92.9552 aux.loss_ce: 0.0793 aux.acc_seg: 89.5149 2023/06/07 14:44:51 - mmengine - INFO - Iter(train) [ 31150/240000] lr: 8.8357e-03 eta: 1 day, 18:44:00 time: 0.7237 data_time: 0.0225 memory: 17396 loss: 0.2276 decode.loss_ce: 0.1501 decode.acc_seg: 92.6514 aux.loss_ce: 0.0775 aux.acc_seg: 90.4578 2023/06/07 14:45:28 - mmengine - INFO - Iter(train) [ 31200/240000] lr: 8.8338e-03 eta: 1 day, 18:43:23 time: 0.7221 data_time: 0.1322 memory: 17396 loss: 0.2409 decode.loss_ce: 0.1604 decode.acc_seg: 91.0357 aux.loss_ce: 0.0805 aux.acc_seg: 88.3167 2023/06/07 14:46:04 - mmengine - INFO - Iter(train) [ 31250/240000] lr: 8.8319e-03 eta: 1 day, 18:42:39 time: 0.7271 data_time: 0.1608 memory: 17393 loss: 0.2694 decode.loss_ce: 0.1789 decode.acc_seg: 93.0294 aux.loss_ce: 0.0905 aux.acc_seg: 91.6357 2023/06/07 14:46:40 - mmengine - INFO - Iter(train) [ 31300/240000] lr: 8.8301e-03 eta: 1 day, 18:41:59 time: 0.7183 data_time: 0.0121 memory: 17396 loss: 0.2388 decode.loss_ce: 0.1574 decode.acc_seg: 93.9438 aux.loss_ce: 0.0814 aux.acc_seg: 92.4827 2023/06/07 14:47:16 - mmengine - INFO - Iter(train) [ 31350/240000] lr: 8.8282e-03 eta: 1 day, 18:41:14 time: 0.7083 data_time: 0.0121 memory: 17395 loss: 0.2476 decode.loss_ce: 0.1658 decode.acc_seg: 93.2779 aux.loss_ce: 0.0818 aux.acc_seg: 91.9102 2023/06/07 14:47:51 - mmengine - INFO - Iter(train) [ 31400/240000] lr: 8.8263e-03 eta: 1 day, 18:40:30 time: 0.7082 data_time: 0.0123 memory: 17394 loss: 0.2437 decode.loss_ce: 0.1617 decode.acc_seg: 93.1893 aux.loss_ce: 0.0820 aux.acc_seg: 89.2021 2023/06/07 14:48:27 - mmengine - INFO - Iter(train) [ 31450/240000] lr: 8.8244e-03 eta: 1 day, 18:39:47 time: 0.7296 data_time: 0.0912 memory: 17395 loss: 0.2373 decode.loss_ce: 0.1593 decode.acc_seg: 93.0214 aux.loss_ce: 0.0781 aux.acc_seg: 90.9821 2023/06/07 14:49:03 - mmengine - INFO - Iter(train) [ 31500/240000] lr: 8.8225e-03 eta: 1 day, 18:39:03 time: 0.6987 data_time: 0.0715 memory: 17393 loss: 0.2663 decode.loss_ce: 0.1750 decode.acc_seg: 89.8909 aux.loss_ce: 0.0913 aux.acc_seg: 88.9820 2023/06/07 14:49:40 - mmengine - INFO - Iter(train) [ 31550/240000] lr: 8.8206e-03 eta: 1 day, 18:38:24 time: 0.7418 data_time: 0.0843 memory: 17395 loss: 0.2615 decode.loss_ce: 0.1729 decode.acc_seg: 94.1918 aux.loss_ce: 0.0886 aux.acc_seg: 90.4578 2023/06/07 14:50:15 - mmengine - INFO - Iter(train) [ 31600/240000] lr: 8.8188e-03 eta: 1 day, 18:37:41 time: 0.7316 data_time: 0.1326 memory: 17391 loss: 0.2509 decode.loss_ce: 0.1651 decode.acc_seg: 93.3142 aux.loss_ce: 0.0858 aux.acc_seg: 91.0456 2023/06/07 14:50:52 - mmengine - INFO - Iter(train) [ 31650/240000] lr: 8.8169e-03 eta: 1 day, 18:37:00 time: 0.7133 data_time: 0.0121 memory: 17396 loss: 0.2744 decode.loss_ce: 0.1801 decode.acc_seg: 93.2442 aux.loss_ce: 0.0943 aux.acc_seg: 90.9874 2023/06/07 14:51:28 - mmengine - INFO - Iter(train) [ 31700/240000] lr: 8.8150e-03 eta: 1 day, 18:36:18 time: 0.7286 data_time: 0.0314 memory: 17393 loss: 0.2546 decode.loss_ce: 0.1695 decode.acc_seg: 94.1020 aux.loss_ce: 0.0851 aux.acc_seg: 92.3808 2023/06/07 14:52:03 - mmengine - INFO - Iter(train) [ 31750/240000] lr: 8.8131e-03 eta: 1 day, 18:35:33 time: 0.6941 data_time: 0.0245 memory: 17395 loss: 0.2428 decode.loss_ce: 0.1615 decode.acc_seg: 89.0458 aux.loss_ce: 0.0813 aux.acc_seg: 89.6254 2023/06/07 14:52:39 - mmengine - INFO - Iter(train) [ 31800/240000] lr: 8.8112e-03 eta: 1 day, 18:34:48 time: 0.7231 data_time: 0.3112 memory: 17395 loss: 0.2413 decode.loss_ce: 0.1596 decode.acc_seg: 90.1198 aux.loss_ce: 0.0817 aux.acc_seg: 87.8180 2023/06/07 14:53:15 - mmengine - INFO - Iter(train) [ 31850/240000] lr: 8.8093e-03 eta: 1 day, 18:34:06 time: 0.7207 data_time: 0.2522 memory: 17393 loss: 0.2549 decode.loss_ce: 0.1706 decode.acc_seg: 91.5850 aux.loss_ce: 0.0844 aux.acc_seg: 88.3416 2023/06/07 14:53:51 - mmengine - INFO - Iter(train) [ 31900/240000] lr: 8.8075e-03 eta: 1 day, 18:33:23 time: 0.7213 data_time: 0.3702 memory: 17393 loss: 0.2409 decode.loss_ce: 0.1599 decode.acc_seg: 93.9287 aux.loss_ce: 0.0811 aux.acc_seg: 91.8658 2023/06/07 14:54:27 - mmengine - INFO - Iter(train) [ 31950/240000] lr: 8.8056e-03 eta: 1 day, 18:32:40 time: 0.7066 data_time: 0.1849 memory: 17391 loss: 0.2606 decode.loss_ce: 0.1734 decode.acc_seg: 89.6986 aux.loss_ce: 0.0872 aux.acc_seg: 88.1118 2023/06/07 14:55:03 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 14:55:03 - mmengine - INFO - Iter(train) [ 32000/240000] lr: 8.8037e-03 eta: 1 day, 18:32:00 time: 0.7352 data_time: 0.0121 memory: 17394 loss: 0.2409 decode.loss_ce: 0.1601 decode.acc_seg: 91.6080 aux.loss_ce: 0.0808 aux.acc_seg: 86.6749 2023/06/07 14:55:39 - mmengine - INFO - Iter(train) [ 32050/240000] lr: 8.8018e-03 eta: 1 day, 18:31:17 time: 0.7136 data_time: 0.0125 memory: 17396 loss: 0.2495 decode.loss_ce: 0.1646 decode.acc_seg: 93.9920 aux.loss_ce: 0.0849 aux.acc_seg: 90.9286 2023/06/07 14:56:15 - mmengine - INFO - Iter(train) [ 32100/240000] lr: 8.7999e-03 eta: 1 day, 18:30:34 time: 0.7246 data_time: 0.0871 memory: 17394 loss: 0.2459 decode.loss_ce: 0.1635 decode.acc_seg: 89.3973 aux.loss_ce: 0.0825 aux.acc_seg: 85.5498 2023/06/07 14:56:51 - mmengine - INFO - Iter(train) [ 32150/240000] lr: 8.7980e-03 eta: 1 day, 18:29:52 time: 0.7064 data_time: 0.0782 memory: 17395 loss: 0.2258 decode.loss_ce: 0.1495 decode.acc_seg: 94.1601 aux.loss_ce: 0.0763 aux.acc_seg: 91.7546 2023/06/07 14:57:26 - mmengine - INFO - Iter(train) [ 32200/240000] lr: 8.7962e-03 eta: 1 day, 18:29:07 time: 0.7020 data_time: 0.0689 memory: 17393 loss: 0.2515 decode.loss_ce: 0.1673 decode.acc_seg: 92.0874 aux.loss_ce: 0.0842 aux.acc_seg: 88.3992 2023/06/07 14:58:02 - mmengine - INFO - Iter(train) [ 32250/240000] lr: 8.7943e-03 eta: 1 day, 18:28:27 time: 0.7199 data_time: 0.0426 memory: 17399 loss: 0.2505 decode.loss_ce: 0.1646 decode.acc_seg: 88.7733 aux.loss_ce: 0.0859 aux.acc_seg: 85.1063 2023/06/07 14:58:38 - mmengine - INFO - Iter(train) [ 32300/240000] lr: 8.7924e-03 eta: 1 day, 18:27:43 time: 0.7019 data_time: 0.1303 memory: 17394 loss: 0.2540 decode.loss_ce: 0.1685 decode.acc_seg: 92.6563 aux.loss_ce: 0.0855 aux.acc_seg: 86.8067 2023/06/07 14:59:14 - mmengine - INFO - Iter(train) [ 32350/240000] lr: 8.7905e-03 eta: 1 day, 18:26:58 time: 0.7147 data_time: 0.3841 memory: 17395 loss: 0.2529 decode.loss_ce: 0.1675 decode.acc_seg: 91.9678 aux.loss_ce: 0.0853 aux.acc_seg: 90.5042 2023/06/07 14:59:49 - mmengine - INFO - Iter(train) [ 32400/240000] lr: 8.7886e-03 eta: 1 day, 18:26:15 time: 0.7060 data_time: 0.3728 memory: 17392 loss: 0.2244 decode.loss_ce: 0.1478 decode.acc_seg: 92.5940 aux.loss_ce: 0.0766 aux.acc_seg: 91.5904 2023/06/07 15:00:25 - mmengine - INFO - Iter(train) [ 32450/240000] lr: 8.7867e-03 eta: 1 day, 18:25:33 time: 0.7273 data_time: 0.0915 memory: 17393 loss: 0.2385 decode.loss_ce: 0.1604 decode.acc_seg: 93.8856 aux.loss_ce: 0.0781 aux.acc_seg: 92.3306 2023/06/07 15:01:01 - mmengine - INFO - Iter(train) [ 32500/240000] lr: 8.7849e-03 eta: 1 day, 18:24:49 time: 0.7043 data_time: 0.0909 memory: 17393 loss: 0.2520 decode.loss_ce: 0.1683 decode.acc_seg: 91.5367 aux.loss_ce: 0.0836 aux.acc_seg: 91.0750 2023/06/07 15:01:37 - mmengine - INFO - Iter(train) [ 32550/240000] lr: 8.7830e-03 eta: 1 day, 18:24:08 time: 0.7151 data_time: 0.0412 memory: 17394 loss: 0.2504 decode.loss_ce: 0.1695 decode.acc_seg: 93.1736 aux.loss_ce: 0.0809 aux.acc_seg: 91.8324 2023/06/07 15:02:13 - mmengine - INFO - Iter(train) [ 32600/240000] lr: 8.7811e-03 eta: 1 day, 18:23:27 time: 0.7247 data_time: 0.0281 memory: 17394 loss: 0.2362 decode.loss_ce: 0.1554 decode.acc_seg: 92.9272 aux.loss_ce: 0.0807 aux.acc_seg: 90.9329 2023/06/07 15:02:49 - mmengine - INFO - Iter(train) [ 32650/240000] lr: 8.7792e-03 eta: 1 day, 18:22:44 time: 0.7200 data_time: 0.1490 memory: 17396 loss: 0.2448 decode.loss_ce: 0.1629 decode.acc_seg: 92.6718 aux.loss_ce: 0.0819 aux.acc_seg: 90.6921 2023/06/07 15:03:25 - mmengine - INFO - Iter(train) [ 32700/240000] lr: 8.7773e-03 eta: 1 day, 18:22:00 time: 0.7082 data_time: 0.3846 memory: 17392 loss: 0.2503 decode.loss_ce: 0.1635 decode.acc_seg: 93.9475 aux.loss_ce: 0.0868 aux.acc_seg: 91.4325 2023/06/07 15:04:01 - mmengine - INFO - Iter(train) [ 32750/240000] lr: 8.7754e-03 eta: 1 day, 18:21:17 time: 0.6952 data_time: 0.3340 memory: 17394 loss: 0.2759 decode.loss_ce: 0.1818 decode.acc_seg: 93.3225 aux.loss_ce: 0.0941 aux.acc_seg: 92.0481 2023/06/07 15:04:36 - mmengine - INFO - Iter(train) [ 32800/240000] lr: 8.7736e-03 eta: 1 day, 18:20:31 time: 0.7018 data_time: 0.3472 memory: 17392 loss: 0.2661 decode.loss_ce: 0.1751 decode.acc_seg: 93.0252 aux.loss_ce: 0.0909 aux.acc_seg: 90.7066 2023/06/07 15:05:12 - mmengine - INFO - Iter(train) [ 32850/240000] lr: 8.7717e-03 eta: 1 day, 18:19:49 time: 0.7177 data_time: 0.3944 memory: 17392 loss: 0.2506 decode.loss_ce: 0.1633 decode.acc_seg: 93.7008 aux.loss_ce: 0.0873 aux.acc_seg: 92.3064 2023/06/07 15:05:48 - mmengine - INFO - Iter(train) [ 32900/240000] lr: 8.7698e-03 eta: 1 day, 18:19:06 time: 0.7319 data_time: 0.4055 memory: 17395 loss: 0.2409 decode.loss_ce: 0.1584 decode.acc_seg: 92.7054 aux.loss_ce: 0.0825 aux.acc_seg: 90.9241 2023/06/07 15:06:24 - mmengine - INFO - Iter(train) [ 32950/240000] lr: 8.7679e-03 eta: 1 day, 18:18:25 time: 0.7148 data_time: 0.3906 memory: 17395 loss: 0.2665 decode.loss_ce: 0.1782 decode.acc_seg: 90.5495 aux.loss_ce: 0.0883 aux.acc_seg: 89.1159 2023/06/07 15:07:00 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 15:07:00 - mmengine - INFO - Iter(train) [ 33000/240000] lr: 8.7660e-03 eta: 1 day, 18:17:44 time: 0.7174 data_time: 0.3939 memory: 17392 loss: 0.2458 decode.loss_ce: 0.1616 decode.acc_seg: 93.6363 aux.loss_ce: 0.0842 aux.acc_seg: 90.7972 2023/06/07 15:07:36 - mmengine - INFO - Iter(train) [ 33050/240000] lr: 8.7641e-03 eta: 1 day, 18:17:04 time: 0.7377 data_time: 0.4137 memory: 17392 loss: 0.2659 decode.loss_ce: 0.1783 decode.acc_seg: 90.6833 aux.loss_ce: 0.0876 aux.acc_seg: 88.5138 2023/06/07 15:08:12 - mmengine - INFO - Iter(train) [ 33100/240000] lr: 8.7623e-03 eta: 1 day, 18:16:21 time: 0.7166 data_time: 0.3928 memory: 17394 loss: 0.2532 decode.loss_ce: 0.1678 decode.acc_seg: 91.3271 aux.loss_ce: 0.0854 aux.acc_seg: 87.7581 2023/06/07 15:08:48 - mmengine - INFO - Iter(train) [ 33150/240000] lr: 8.7604e-03 eta: 1 day, 18:15:38 time: 0.7212 data_time: 0.3920 memory: 17396 loss: 0.2632 decode.loss_ce: 0.1754 decode.acc_seg: 91.3250 aux.loss_ce: 0.0878 aux.acc_seg: 87.1881 2023/06/07 15:09:24 - mmengine - INFO - Iter(train) [ 33200/240000] lr: 8.7585e-03 eta: 1 day, 18:14:58 time: 0.7098 data_time: 0.3862 memory: 17396 loss: 0.2918 decode.loss_ce: 0.1935 decode.acc_seg: 89.7126 aux.loss_ce: 0.0982 aux.acc_seg: 87.9260 2023/06/07 15:10:00 - mmengine - INFO - Iter(train) [ 33250/240000] lr: 8.7566e-03 eta: 1 day, 18:14:16 time: 0.7412 data_time: 0.4168 memory: 17393 loss: 0.2408 decode.loss_ce: 0.1602 decode.acc_seg: 93.9319 aux.loss_ce: 0.0806 aux.acc_seg: 92.1132 2023/06/07 15:10:36 - mmengine - INFO - Iter(train) [ 33300/240000] lr: 8.7547e-03 eta: 1 day, 18:13:32 time: 0.7134 data_time: 0.3900 memory: 17393 loss: 0.2567 decode.loss_ce: 0.1712 decode.acc_seg: 93.1362 aux.loss_ce: 0.0856 aux.acc_seg: 89.5642 2023/06/07 15:11:12 - mmengine - INFO - Iter(train) [ 33350/240000] lr: 8.7528e-03 eta: 1 day, 18:12:51 time: 0.7327 data_time: 0.4086 memory: 17394 loss: 0.2425 decode.loss_ce: 0.1599 decode.acc_seg: 92.2133 aux.loss_ce: 0.0826 aux.acc_seg: 90.4750 2023/06/07 15:11:47 - mmengine - INFO - Iter(train) [ 33400/240000] lr: 8.7510e-03 eta: 1 day, 18:12:08 time: 0.7027 data_time: 0.3788 memory: 17392 loss: 0.2526 decode.loss_ce: 0.1704 decode.acc_seg: 92.4732 aux.loss_ce: 0.0822 aux.acc_seg: 90.5375 2023/06/07 15:12:24 - mmengine - INFO - Iter(train) [ 33450/240000] lr: 8.7491e-03 eta: 1 day, 18:11:27 time: 0.7309 data_time: 0.4073 memory: 17394 loss: 0.2386 decode.loss_ce: 0.1583 decode.acc_seg: 93.5596 aux.loss_ce: 0.0804 aux.acc_seg: 92.4138 2023/06/07 15:13:00 - mmengine - INFO - Iter(train) [ 33500/240000] lr: 8.7472e-03 eta: 1 day, 18:10:48 time: 0.7255 data_time: 0.4018 memory: 17392 loss: 0.2385 decode.loss_ce: 0.1569 decode.acc_seg: 94.1236 aux.loss_ce: 0.0816 aux.acc_seg: 91.7913 2023/06/07 15:13:36 - mmengine - INFO - Iter(train) [ 33550/240000] lr: 8.7453e-03 eta: 1 day, 18:10:05 time: 0.7164 data_time: 0.3928 memory: 17395 loss: 0.2428 decode.loss_ce: 0.1594 decode.acc_seg: 93.8013 aux.loss_ce: 0.0834 aux.acc_seg: 91.9586 2023/06/07 15:14:12 - mmengine - INFO - Iter(train) [ 33600/240000] lr: 8.7434e-03 eta: 1 day, 18:09:24 time: 0.7300 data_time: 0.4057 memory: 17398 loss: 0.2441 decode.loss_ce: 0.1626 decode.acc_seg: 92.6464 aux.loss_ce: 0.0815 aux.acc_seg: 91.0252 2023/06/07 15:14:48 - mmengine - INFO - Iter(train) [ 33650/240000] lr: 8.7415e-03 eta: 1 day, 18:08:41 time: 0.7005 data_time: 0.3719 memory: 17393 loss: 0.2473 decode.loss_ce: 0.1618 decode.acc_seg: 95.4773 aux.loss_ce: 0.0855 aux.acc_seg: 94.1685 2023/06/07 15:15:23 - mmengine - INFO - Iter(train) [ 33700/240000] lr: 8.7396e-03 eta: 1 day, 18:07:58 time: 0.7115 data_time: 0.3803 memory: 17395 loss: 0.2249 decode.loss_ce: 0.1472 decode.acc_seg: 93.8198 aux.loss_ce: 0.0777 aux.acc_seg: 92.6988 2023/06/07 15:15:59 - mmengine - INFO - Iter(train) [ 33750/240000] lr: 8.7378e-03 eta: 1 day, 18:07:15 time: 0.7221 data_time: 0.3984 memory: 17395 loss: 0.2420 decode.loss_ce: 0.1594 decode.acc_seg: 93.6756 aux.loss_ce: 0.0826 aux.acc_seg: 91.6286 2023/06/07 15:16:35 - mmengine - INFO - Iter(train) [ 33800/240000] lr: 8.7359e-03 eta: 1 day, 18:06:32 time: 0.7212 data_time: 0.3983 memory: 17395 loss: 0.2361 decode.loss_ce: 0.1553 decode.acc_seg: 91.9611 aux.loss_ce: 0.0807 aux.acc_seg: 89.4147 2023/06/07 15:17:11 - mmengine - INFO - Iter(train) [ 33850/240000] lr: 8.7340e-03 eta: 1 day, 18:05:52 time: 0.7181 data_time: 0.3944 memory: 17394 loss: 0.2503 decode.loss_ce: 0.1632 decode.acc_seg: 94.4280 aux.loss_ce: 0.0871 aux.acc_seg: 90.9792 2023/06/07 15:17:47 - mmengine - INFO - Iter(train) [ 33900/240000] lr: 8.7321e-03 eta: 1 day, 18:05:11 time: 0.7320 data_time: 0.4034 memory: 17395 loss: 0.2409 decode.loss_ce: 0.1592 decode.acc_seg: 92.9283 aux.loss_ce: 0.0817 aux.acc_seg: 91.3234 2023/06/07 15:18:23 - mmengine - INFO - Iter(train) [ 33950/240000] lr: 8.7302e-03 eta: 1 day, 18:04:31 time: 0.7158 data_time: 0.3926 memory: 17395 loss: 0.2458 decode.loss_ce: 0.1655 decode.acc_seg: 93.3312 aux.loss_ce: 0.0803 aux.acc_seg: 91.3155 2023/06/07 15:18:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 15:18:59 - mmengine - INFO - Iter(train) [ 34000/240000] lr: 8.7283e-03 eta: 1 day, 18:03:47 time: 0.7073 data_time: 0.3836 memory: 17393 loss: 0.2444 decode.loss_ce: 0.1597 decode.acc_seg: 91.6437 aux.loss_ce: 0.0847 aux.acc_seg: 89.1477 2023/06/07 15:19:34 - mmengine - INFO - Iter(train) [ 34050/240000] lr: 8.7265e-03 eta: 1 day, 18:03:03 time: 0.7100 data_time: 0.3861 memory: 17395 loss: 0.2610 decode.loss_ce: 0.1731 decode.acc_seg: 92.3308 aux.loss_ce: 0.0880 aux.acc_seg: 90.2613 2023/06/07 15:20:10 - mmengine - INFO - Iter(train) [ 34100/240000] lr: 8.7246e-03 eta: 1 day, 18:02:20 time: 0.7113 data_time: 0.2824 memory: 17397 loss: 0.2444 decode.loss_ce: 0.1603 decode.acc_seg: 91.2403 aux.loss_ce: 0.0841 aux.acc_seg: 88.3564 2023/06/07 15:20:46 - mmengine - INFO - Iter(train) [ 34150/240000] lr: 8.7227e-03 eta: 1 day, 18:01:39 time: 0.7228 data_time: 0.3992 memory: 17393 loss: 0.2618 decode.loss_ce: 0.1717 decode.acc_seg: 93.0125 aux.loss_ce: 0.0901 aux.acc_seg: 90.3177 2023/06/07 15:21:22 - mmengine - INFO - Iter(train) [ 34200/240000] lr: 8.7208e-03 eta: 1 day, 18:00:59 time: 0.7246 data_time: 0.4008 memory: 17392 loss: 0.2544 decode.loss_ce: 0.1698 decode.acc_seg: 92.1755 aux.loss_ce: 0.0846 aux.acc_seg: 90.2892 2023/06/07 15:21:58 - mmengine - INFO - Iter(train) [ 34250/240000] lr: 8.7189e-03 eta: 1 day, 18:00:18 time: 0.7187 data_time: 0.3954 memory: 17392 loss: 0.2491 decode.loss_ce: 0.1656 decode.acc_seg: 92.9606 aux.loss_ce: 0.0834 aux.acc_seg: 90.1959 2023/06/07 15:22:35 - mmengine - INFO - Iter(train) [ 34300/240000] lr: 8.7170e-03 eta: 1 day, 17:59:37 time: 0.7374 data_time: 0.4140 memory: 17394 loss: 0.2469 decode.loss_ce: 0.1642 decode.acc_seg: 95.2473 aux.loss_ce: 0.0828 aux.acc_seg: 92.6065 2023/06/07 15:23:11 - mmengine - INFO - Iter(train) [ 34350/240000] lr: 8.7151e-03 eta: 1 day, 17:58:57 time: 0.7294 data_time: 0.4056 memory: 17393 loss: 0.2516 decode.loss_ce: 0.1662 decode.acc_seg: 93.5220 aux.loss_ce: 0.0854 aux.acc_seg: 91.8009 2023/06/07 15:23:46 - mmengine - INFO - Iter(train) [ 34400/240000] lr: 8.7133e-03 eta: 1 day, 17:58:13 time: 0.7289 data_time: 0.4053 memory: 17394 loss: 0.2376 decode.loss_ce: 0.1555 decode.acc_seg: 91.8925 aux.loss_ce: 0.0821 aux.acc_seg: 89.2578 2023/06/07 15:24:22 - mmengine - INFO - Iter(train) [ 34450/240000] lr: 8.7114e-03 eta: 1 day, 17:57:31 time: 0.7339 data_time: 0.4099 memory: 17393 loss: 0.2496 decode.loss_ce: 0.1666 decode.acc_seg: 91.1189 aux.loss_ce: 0.0830 aux.acc_seg: 87.6663 2023/06/07 15:24:58 - mmengine - INFO - Iter(train) [ 34500/240000] lr: 8.7095e-03 eta: 1 day, 17:56:47 time: 0.7258 data_time: 0.1542 memory: 17394 loss: 0.2489 decode.loss_ce: 0.1642 decode.acc_seg: 91.8288 aux.loss_ce: 0.0847 aux.acc_seg: 90.4008 2023/06/07 15:25:34 - mmengine - INFO - Iter(train) [ 34550/240000] lr: 8.7076e-03 eta: 1 day, 17:56:06 time: 0.7161 data_time: 0.0120 memory: 17394 loss: 0.2557 decode.loss_ce: 0.1687 decode.acc_seg: 90.2167 aux.loss_ce: 0.0869 aux.acc_seg: 87.6058 2023/06/07 15:26:10 - mmengine - INFO - Iter(train) [ 34600/240000] lr: 8.7057e-03 eta: 1 day, 17:55:25 time: 0.7152 data_time: 0.0121 memory: 17392 loss: 0.2930 decode.loss_ce: 0.1946 decode.acc_seg: 90.7158 aux.loss_ce: 0.0984 aux.acc_seg: 87.5925 2023/06/07 15:26:45 - mmengine - INFO - Iter(train) [ 34650/240000] lr: 8.7038e-03 eta: 1 day, 17:54:42 time: 0.7290 data_time: 0.1034 memory: 17398 loss: 0.2356 decode.loss_ce: 0.1541 decode.acc_seg: 92.3714 aux.loss_ce: 0.0815 aux.acc_seg: 90.4444 2023/06/07 15:27:22 - mmengine - INFO - Iter(train) [ 34700/240000] lr: 8.7019e-03 eta: 1 day, 17:54:02 time: 0.7145 data_time: 0.0121 memory: 17393 loss: 0.2537 decode.loss_ce: 0.1678 decode.acc_seg: 90.5464 aux.loss_ce: 0.0859 aux.acc_seg: 88.7994 2023/06/07 15:27:57 - mmengine - INFO - Iter(train) [ 34750/240000] lr: 8.7001e-03 eta: 1 day, 17:53:19 time: 0.7237 data_time: 0.0118 memory: 17393 loss: 0.2555 decode.loss_ce: 0.1689 decode.acc_seg: 88.6507 aux.loss_ce: 0.0866 aux.acc_seg: 84.5037 2023/06/07 15:28:34 - mmengine - INFO - Iter(train) [ 34800/240000] lr: 8.6982e-03 eta: 1 day, 17:52:43 time: 0.7210 data_time: 0.0122 memory: 17395 loss: 0.2679 decode.loss_ce: 0.1796 decode.acc_seg: 93.8518 aux.loss_ce: 0.0883 aux.acc_seg: 92.2494 2023/06/07 15:29:10 - mmengine - INFO - Iter(train) [ 34850/240000] lr: 8.6963e-03 eta: 1 day, 17:52:02 time: 0.7125 data_time: 0.0120 memory: 17395 loss: 0.2446 decode.loss_ce: 0.1588 decode.acc_seg: 93.4221 aux.loss_ce: 0.0859 aux.acc_seg: 89.7275 2023/06/07 15:29:47 - mmengine - INFO - Iter(train) [ 34900/240000] lr: 8.6944e-03 eta: 1 day, 17:51:24 time: 0.7235 data_time: 0.0120 memory: 17395 loss: 0.2320 decode.loss_ce: 0.1540 decode.acc_seg: 93.5348 aux.loss_ce: 0.0780 aux.acc_seg: 91.9728 2023/06/07 15:30:23 - mmengine - INFO - Iter(train) [ 34950/240000] lr: 8.6925e-03 eta: 1 day, 17:50:45 time: 0.7331 data_time: 0.0301 memory: 17394 loss: 0.2308 decode.loss_ce: 0.1521 decode.acc_seg: 93.9506 aux.loss_ce: 0.0787 aux.acc_seg: 92.1257 2023/06/07 15:30:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 15:30:59 - mmengine - INFO - Iter(train) [ 35000/240000] lr: 8.6906e-03 eta: 1 day, 17:50:05 time: 0.7285 data_time: 0.0121 memory: 17394 loss: 0.2486 decode.loss_ce: 0.1654 decode.acc_seg: 89.9290 aux.loss_ce: 0.0831 aux.acc_seg: 86.9521 2023/06/07 15:31:35 - mmengine - INFO - Iter(train) [ 35050/240000] lr: 8.6887e-03 eta: 1 day, 17:49:25 time: 0.7277 data_time: 0.0124 memory: 17395 loss: 0.2515 decode.loss_ce: 0.1639 decode.acc_seg: 93.4978 aux.loss_ce: 0.0876 aux.acc_seg: 89.3493 2023/06/07 15:32:11 - mmengine - INFO - Iter(train) [ 35100/240000] lr: 8.6869e-03 eta: 1 day, 17:48:42 time: 0.7072 data_time: 0.0119 memory: 17395 loss: 0.2443 decode.loss_ce: 0.1597 decode.acc_seg: 91.3708 aux.loss_ce: 0.0846 aux.acc_seg: 88.7942 2023/06/07 15:32:47 - mmengine - INFO - Iter(train) [ 35150/240000] lr: 8.6850e-03 eta: 1 day, 17:47:59 time: 0.7217 data_time: 0.1751 memory: 17393 loss: 0.2426 decode.loss_ce: 0.1597 decode.acc_seg: 92.0134 aux.loss_ce: 0.0830 aux.acc_seg: 88.7097 2023/06/07 15:33:23 - mmengine - INFO - Iter(train) [ 35200/240000] lr: 8.6831e-03 eta: 1 day, 17:47:17 time: 0.7047 data_time: 0.0124 memory: 17396 loss: 0.2473 decode.loss_ce: 0.1649 decode.acc_seg: 93.3153 aux.loss_ce: 0.0823 aux.acc_seg: 91.5030 2023/06/07 15:33:58 - mmengine - INFO - Iter(train) [ 35250/240000] lr: 8.6812e-03 eta: 1 day, 17:46:36 time: 0.7268 data_time: 0.0451 memory: 17393 loss: 0.2460 decode.loss_ce: 0.1627 decode.acc_seg: 93.1710 aux.loss_ce: 0.0833 aux.acc_seg: 89.9924 2023/06/07 15:34:34 - mmengine - INFO - Iter(train) [ 35300/240000] lr: 8.6793e-03 eta: 1 day, 17:45:55 time: 0.7113 data_time: 0.0119 memory: 17395 loss: 0.2308 decode.loss_ce: 0.1511 decode.acc_seg: 93.4042 aux.loss_ce: 0.0797 aux.acc_seg: 91.9958 2023/06/07 15:35:11 - mmengine - INFO - Iter(train) [ 35350/240000] lr: 8.6774e-03 eta: 1 day, 17:45:16 time: 0.7285 data_time: 0.0120 memory: 17395 loss: 0.2718 decode.loss_ce: 0.1798 decode.acc_seg: 90.6784 aux.loss_ce: 0.0920 aux.acc_seg: 88.8258 2023/06/07 15:35:47 - mmengine - INFO - Iter(train) [ 35400/240000] lr: 8.6755e-03 eta: 1 day, 17:44:34 time: 0.7198 data_time: 0.2200 memory: 17395 loss: 0.2396 decode.loss_ce: 0.1563 decode.acc_seg: 92.5418 aux.loss_ce: 0.0833 aux.acc_seg: 91.4481 2023/06/07 15:36:22 - mmengine - INFO - Iter(train) [ 35450/240000] lr: 8.6737e-03 eta: 1 day, 17:43:50 time: 0.7045 data_time: 0.1742 memory: 17392 loss: 0.2679 decode.loss_ce: 0.1778 decode.acc_seg: 93.1751 aux.loss_ce: 0.0901 aux.acc_seg: 90.6819 2023/06/07 15:36:58 - mmengine - INFO - Iter(train) [ 35500/240000] lr: 8.6718e-03 eta: 1 day, 17:43:08 time: 0.7249 data_time: 0.0425 memory: 17395 loss: 0.2354 decode.loss_ce: 0.1543 decode.acc_seg: 93.7466 aux.loss_ce: 0.0811 aux.acc_seg: 90.5553 2023/06/07 15:37:34 - mmengine - INFO - Iter(train) [ 35550/240000] lr: 8.6699e-03 eta: 1 day, 17:42:28 time: 0.7284 data_time: 0.0120 memory: 17396 loss: 0.2474 decode.loss_ce: 0.1646 decode.acc_seg: 93.8953 aux.loss_ce: 0.0827 aux.acc_seg: 92.3701 2023/06/07 15:38:10 - mmengine - INFO - Iter(train) [ 35600/240000] lr: 8.6680e-03 eta: 1 day, 17:41:48 time: 0.7285 data_time: 0.0137 memory: 17394 loss: 0.2218 decode.loss_ce: 0.1448 decode.acc_seg: 94.2799 aux.loss_ce: 0.0770 aux.acc_seg: 91.7854 2023/06/07 15:38:46 - mmengine - INFO - Iter(train) [ 35650/240000] lr: 8.6661e-03 eta: 1 day, 17:41:07 time: 0.7206 data_time: 0.0120 memory: 17394 loss: 0.2454 decode.loss_ce: 0.1620 decode.acc_seg: 93.0833 aux.loss_ce: 0.0834 aux.acc_seg: 90.2156 2023/06/07 15:39:23 - mmengine - INFO - Iter(train) [ 35700/240000] lr: 8.6642e-03 eta: 1 day, 17:40:29 time: 0.7125 data_time: 0.0124 memory: 17392 loss: 0.2334 decode.loss_ce: 0.1542 decode.acc_seg: 92.5386 aux.loss_ce: 0.0792 aux.acc_seg: 91.3236 2023/06/07 15:39:58 - mmengine - INFO - Iter(train) [ 35750/240000] lr: 8.6623e-03 eta: 1 day, 17:39:46 time: 0.7173 data_time: 0.0120 memory: 17393 loss: 0.2504 decode.loss_ce: 0.1657 decode.acc_seg: 92.5233 aux.loss_ce: 0.0847 aux.acc_seg: 91.2134 2023/06/07 15:40:34 - mmengine - INFO - Iter(train) [ 35800/240000] lr: 8.6605e-03 eta: 1 day, 17:39:03 time: 0.7088 data_time: 0.0121 memory: 17391 loss: 0.2354 decode.loss_ce: 0.1559 decode.acc_seg: 93.7925 aux.loss_ce: 0.0796 aux.acc_seg: 92.1424 2023/06/07 15:41:10 - mmengine - INFO - Iter(train) [ 35850/240000] lr: 8.6586e-03 eta: 1 day, 17:38:22 time: 0.7265 data_time: 0.0125 memory: 17394 loss: 0.2193 decode.loss_ce: 0.1452 decode.acc_seg: 93.7326 aux.loss_ce: 0.0741 aux.acc_seg: 91.8234 2023/06/07 15:41:46 - mmengine - INFO - Iter(train) [ 35900/240000] lr: 8.6567e-03 eta: 1 day, 17:37:42 time: 0.7243 data_time: 0.0121 memory: 17394 loss: 0.2404 decode.loss_ce: 0.1570 decode.acc_seg: 91.7561 aux.loss_ce: 0.0834 aux.acc_seg: 90.2029 2023/06/07 15:42:22 - mmengine - INFO - Iter(train) [ 35950/240000] lr: 8.6548e-03 eta: 1 day, 17:37:02 time: 0.7312 data_time: 0.0122 memory: 17396 loss: 0.2435 decode.loss_ce: 0.1604 decode.acc_seg: 92.2726 aux.loss_ce: 0.0830 aux.acc_seg: 91.1647 2023/06/07 15:42:58 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 15:42:58 - mmengine - INFO - Iter(train) [ 36000/240000] lr: 8.6529e-03 eta: 1 day, 17:36:22 time: 0.7221 data_time: 0.0121 memory: 17394 loss: 0.2413 decode.loss_ce: 0.1607 decode.acc_seg: 93.5419 aux.loss_ce: 0.0805 aux.acc_seg: 92.0631 2023/06/07 15:43:34 - mmengine - INFO - Iter(train) [ 36050/240000] lr: 8.6510e-03 eta: 1 day, 17:35:42 time: 0.7242 data_time: 0.0121 memory: 17393 loss: 0.2306 decode.loss_ce: 0.1522 decode.acc_seg: 92.4748 aux.loss_ce: 0.0784 aux.acc_seg: 89.8348 2023/06/07 15:44:11 - mmengine - INFO - Iter(train) [ 36100/240000] lr: 8.6491e-03 eta: 1 day, 17:35:02 time: 0.6946 data_time: 0.0119 memory: 17398 loss: 0.2474 decode.loss_ce: 0.1660 decode.acc_seg: 93.1864 aux.loss_ce: 0.0814 aux.acc_seg: 90.8418 2023/06/07 15:44:46 - mmengine - INFO - Iter(train) [ 36150/240000] lr: 8.6472e-03 eta: 1 day, 17:34:20 time: 0.7286 data_time: 0.0121 memory: 17393 loss: 0.2441 decode.loss_ce: 0.1616 decode.acc_seg: 90.9452 aux.loss_ce: 0.0825 aux.acc_seg: 88.5699 2023/06/07 15:45:22 - mmengine - INFO - Iter(train) [ 36200/240000] lr: 8.6454e-03 eta: 1 day, 17:33:40 time: 0.7216 data_time: 0.0122 memory: 17393 loss: 0.2377 decode.loss_ce: 0.1564 decode.acc_seg: 91.7605 aux.loss_ce: 0.0813 aux.acc_seg: 89.3365 2023/06/07 15:45:58 - mmengine - INFO - Iter(train) [ 36250/240000] lr: 8.6435e-03 eta: 1 day, 17:32:59 time: 0.7227 data_time: 0.0123 memory: 17393 loss: 0.2304 decode.loss_ce: 0.1520 decode.acc_seg: 94.3729 aux.loss_ce: 0.0784 aux.acc_seg: 92.5578 2023/06/07 15:46:35 - mmengine - INFO - Iter(train) [ 36300/240000] lr: 8.6416e-03 eta: 1 day, 17:32:21 time: 0.7252 data_time: 0.0124 memory: 17394 loss: 0.2616 decode.loss_ce: 0.1760 decode.acc_seg: 87.0959 aux.loss_ce: 0.0856 aux.acc_seg: 85.5039 2023/06/07 15:47:11 - mmengine - INFO - Iter(train) [ 36350/240000] lr: 8.6397e-03 eta: 1 day, 17:31:41 time: 0.7219 data_time: 0.0121 memory: 17396 loss: 0.2310 decode.loss_ce: 0.1520 decode.acc_seg: 93.8221 aux.loss_ce: 0.0790 aux.acc_seg: 90.5797 2023/06/07 15:47:47 - mmengine - INFO - Iter(train) [ 36400/240000] lr: 8.6378e-03 eta: 1 day, 17:31:00 time: 0.7183 data_time: 0.0122 memory: 17390 loss: 0.2363 decode.loss_ce: 0.1560 decode.acc_seg: 94.4352 aux.loss_ce: 0.0803 aux.acc_seg: 93.5721 2023/06/07 15:48:23 - mmengine - INFO - Iter(train) [ 36450/240000] lr: 8.6359e-03 eta: 1 day, 17:30:20 time: 0.7114 data_time: 0.0200 memory: 17396 loss: 0.2485 decode.loss_ce: 0.1587 decode.acc_seg: 94.0932 aux.loss_ce: 0.0897 aux.acc_seg: 93.0747 2023/06/07 15:48:59 - mmengine - INFO - Iter(train) [ 36500/240000] lr: 8.6340e-03 eta: 1 day, 17:29:40 time: 0.7405 data_time: 0.0124 memory: 17394 loss: 0.2451 decode.loss_ce: 0.1605 decode.acc_seg: 92.7339 aux.loss_ce: 0.0846 aux.acc_seg: 89.9224 2023/06/07 15:49:36 - mmengine - INFO - Iter(train) [ 36550/240000] lr: 8.6322e-03 eta: 1 day, 17:29:03 time: 0.7308 data_time: 0.0122 memory: 17395 loss: 0.2484 decode.loss_ce: 0.1639 decode.acc_seg: 92.7259 aux.loss_ce: 0.0845 aux.acc_seg: 90.9797 2023/06/07 15:50:12 - mmengine - INFO - Iter(train) [ 36600/240000] lr: 8.6303e-03 eta: 1 day, 17:28:23 time: 0.7185 data_time: 0.0119 memory: 17395 loss: 0.2425 decode.loss_ce: 0.1585 decode.acc_seg: 92.4153 aux.loss_ce: 0.0840 aux.acc_seg: 91.2541 2023/06/07 15:50:48 - mmengine - INFO - Iter(train) [ 36650/240000] lr: 8.6284e-03 eta: 1 day, 17:27:43 time: 0.7089 data_time: 0.0123 memory: 17394 loss: 0.2571 decode.loss_ce: 0.1708 decode.acc_seg: 93.4836 aux.loss_ce: 0.0863 aux.acc_seg: 90.5556 2023/06/07 15:51:24 - mmengine - INFO - Iter(train) [ 36700/240000] lr: 8.6265e-03 eta: 1 day, 17:27:03 time: 0.7144 data_time: 0.0121 memory: 17393 loss: 0.2248 decode.loss_ce: 0.1492 decode.acc_seg: 92.5584 aux.loss_ce: 0.0757 aux.acc_seg: 90.8959 2023/06/07 15:52:00 - mmengine - INFO - Iter(train) [ 36750/240000] lr: 8.6246e-03 eta: 1 day, 17:26:23 time: 0.7235 data_time: 0.0122 memory: 17394 loss: 0.2499 decode.loss_ce: 0.1655 decode.acc_seg: 92.2517 aux.loss_ce: 0.0844 aux.acc_seg: 89.2207 2023/06/07 15:52:36 - mmengine - INFO - Iter(train) [ 36800/240000] lr: 8.6227e-03 eta: 1 day, 17:25:43 time: 0.7376 data_time: 0.0124 memory: 17395 loss: 0.2531 decode.loss_ce: 0.1666 decode.acc_seg: 91.4526 aux.loss_ce: 0.0866 aux.acc_seg: 86.8876 2023/06/07 15:53:12 - mmengine - INFO - Iter(train) [ 36850/240000] lr: 8.6208e-03 eta: 1 day, 17:25:02 time: 0.7248 data_time: 0.0123 memory: 17396 loss: 0.2548 decode.loss_ce: 0.1692 decode.acc_seg: 90.5414 aux.loss_ce: 0.0856 aux.acc_seg: 89.7851 2023/06/07 15:53:48 - mmengine - INFO - Iter(train) [ 36900/240000] lr: 8.6189e-03 eta: 1 day, 17:24:21 time: 0.7082 data_time: 0.0121 memory: 17393 loss: 0.2438 decode.loss_ce: 0.1614 decode.acc_seg: 93.6153 aux.loss_ce: 0.0823 aux.acc_seg: 92.6999 2023/06/07 15:54:24 - mmengine - INFO - Iter(train) [ 36950/240000] lr: 8.6171e-03 eta: 1 day, 17:23:40 time: 0.7048 data_time: 0.0119 memory: 17395 loss: 0.2278 decode.loss_ce: 0.1505 decode.acc_seg: 92.1749 aux.loss_ce: 0.0773 aux.acc_seg: 88.9852 2023/06/07 15:55:00 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 15:55:00 - mmengine - INFO - Iter(train) [ 37000/240000] lr: 8.6152e-03 eta: 1 day, 17:22:59 time: 0.7076 data_time: 0.0121 memory: 17393 loss: 0.2216 decode.loss_ce: 0.1478 decode.acc_seg: 93.2561 aux.loss_ce: 0.0738 aux.acc_seg: 91.6654 2023/06/07 15:55:36 - mmengine - INFO - Iter(train) [ 37050/240000] lr: 8.6133e-03 eta: 1 day, 17:22:19 time: 0.7316 data_time: 0.0124 memory: 17394 loss: 0.2337 decode.loss_ce: 0.1527 decode.acc_seg: 93.9872 aux.loss_ce: 0.0809 aux.acc_seg: 89.8676 2023/06/07 15:56:12 - mmengine - INFO - Iter(train) [ 37100/240000] lr: 8.6114e-03 eta: 1 day, 17:21:38 time: 0.7257 data_time: 0.0122 memory: 17394 loss: 0.2354 decode.loss_ce: 0.1540 decode.acc_seg: 93.0302 aux.loss_ce: 0.0814 aux.acc_seg: 91.1376 2023/06/07 15:56:48 - mmengine - INFO - Iter(train) [ 37150/240000] lr: 8.6095e-03 eta: 1 day, 17:20:58 time: 0.7284 data_time: 0.0121 memory: 17393 loss: 0.2508 decode.loss_ce: 0.1653 decode.acc_seg: 93.8198 aux.loss_ce: 0.0855 aux.acc_seg: 91.4574 2023/06/07 15:57:24 - mmengine - INFO - Iter(train) [ 37200/240000] lr: 8.6076e-03 eta: 1 day, 17:20:17 time: 0.7264 data_time: 0.0123 memory: 17395 loss: 0.2412 decode.loss_ce: 0.1592 decode.acc_seg: 91.6386 aux.loss_ce: 0.0820 aux.acc_seg: 89.6689 2023/06/07 15:58:00 - mmengine - INFO - Iter(train) [ 37250/240000] lr: 8.6057e-03 eta: 1 day, 17:19:37 time: 0.7164 data_time: 0.0126 memory: 17394 loss: 0.2538 decode.loss_ce: 0.1673 decode.acc_seg: 93.9474 aux.loss_ce: 0.0865 aux.acc_seg: 92.7574 2023/06/07 15:58:36 - mmengine - INFO - Iter(train) [ 37300/240000] lr: 8.6038e-03 eta: 1 day, 17:18:55 time: 0.7130 data_time: 0.0121 memory: 17396 loss: 0.2514 decode.loss_ce: 0.1666 decode.acc_seg: 94.0389 aux.loss_ce: 0.0849 aux.acc_seg: 92.3020 2023/06/07 15:59:12 - mmengine - INFO - Iter(train) [ 37350/240000] lr: 8.6020e-03 eta: 1 day, 17:18:14 time: 0.7298 data_time: 0.0124 memory: 17395 loss: 0.2535 decode.loss_ce: 0.1667 decode.acc_seg: 90.8001 aux.loss_ce: 0.0868 aux.acc_seg: 88.4210 2023/06/07 15:59:48 - mmengine - INFO - Iter(train) [ 37400/240000] lr: 8.6001e-03 eta: 1 day, 17:17:36 time: 0.7156 data_time: 0.0121 memory: 17392 loss: 0.2494 decode.loss_ce: 0.1648 decode.acc_seg: 93.9244 aux.loss_ce: 0.0846 aux.acc_seg: 92.5903 2023/06/07 16:00:24 - mmengine - INFO - Iter(train) [ 37450/240000] lr: 8.5982e-03 eta: 1 day, 17:16:55 time: 0.7217 data_time: 0.0124 memory: 17395 loss: 0.2365 decode.loss_ce: 0.1552 decode.acc_seg: 94.5221 aux.loss_ce: 0.0813 aux.acc_seg: 92.2364 2023/06/07 16:01:00 - mmengine - INFO - Iter(train) [ 37500/240000] lr: 8.5963e-03 eta: 1 day, 17:16:16 time: 0.7264 data_time: 0.0122 memory: 17393 loss: 0.2663 decode.loss_ce: 0.1772 decode.acc_seg: 93.4653 aux.loss_ce: 0.0891 aux.acc_seg: 90.7836 2023/06/07 16:01:36 - mmengine - INFO - Iter(train) [ 37550/240000] lr: 8.5944e-03 eta: 1 day, 17:15:34 time: 0.7071 data_time: 0.0122 memory: 17393 loss: 0.2721 decode.loss_ce: 0.1811 decode.acc_seg: 88.3208 aux.loss_ce: 0.0910 aux.acc_seg: 85.9607 2023/06/07 16:02:12 - mmengine - INFO - Iter(train) [ 37600/240000] lr: 8.5925e-03 eta: 1 day, 17:14:51 time: 0.7051 data_time: 0.0122 memory: 17397 loss: 0.2401 decode.loss_ce: 0.1586 decode.acc_seg: 92.0788 aux.loss_ce: 0.0814 aux.acc_seg: 89.6546 2023/06/07 16:02:48 - mmengine - INFO - Iter(train) [ 37650/240000] lr: 8.5906e-03 eta: 1 day, 17:14:11 time: 0.7162 data_time: 0.0121 memory: 17396 loss: 0.2227 decode.loss_ce: 0.1448 decode.acc_seg: 93.1364 aux.loss_ce: 0.0779 aux.acc_seg: 90.2971 2023/06/07 16:03:24 - mmengine - INFO - Iter(train) [ 37700/240000] lr: 8.5887e-03 eta: 1 day, 17:13:32 time: 0.7269 data_time: 0.0125 memory: 17395 loss: 0.2440 decode.loss_ce: 0.1607 decode.acc_seg: 91.7288 aux.loss_ce: 0.0833 aux.acc_seg: 90.4133 2023/06/07 16:04:00 - mmengine - INFO - Iter(train) [ 37750/240000] lr: 8.5868e-03 eta: 1 day, 17:12:51 time: 0.7162 data_time: 0.0128 memory: 17393 loss: 0.2324 decode.loss_ce: 0.1523 decode.acc_seg: 92.5370 aux.loss_ce: 0.0802 aux.acc_seg: 90.6380 2023/06/07 16:04:36 - mmengine - INFO - Iter(train) [ 37800/240000] lr: 8.5850e-03 eta: 1 day, 17:12:12 time: 0.7117 data_time: 0.0122 memory: 17394 loss: 0.2463 decode.loss_ce: 0.1621 decode.acc_seg: 93.3573 aux.loss_ce: 0.0842 aux.acc_seg: 89.9342 2023/06/07 16:05:12 - mmengine - INFO - Iter(train) [ 37850/240000] lr: 8.5831e-03 eta: 1 day, 17:11:30 time: 0.7259 data_time: 0.0120 memory: 17396 loss: 0.2439 decode.loss_ce: 0.1614 decode.acc_seg: 93.8641 aux.loss_ce: 0.0824 aux.acc_seg: 90.1103 2023/06/07 16:05:48 - mmengine - INFO - Iter(train) [ 37900/240000] lr: 8.5812e-03 eta: 1 day, 17:10:49 time: 0.6953 data_time: 0.0122 memory: 17392 loss: 0.2598 decode.loss_ce: 0.1694 decode.acc_seg: 90.6040 aux.loss_ce: 0.0904 aux.acc_seg: 88.0003 2023/06/07 16:06:24 - mmengine - INFO - Iter(train) [ 37950/240000] lr: 8.5793e-03 eta: 1 day, 17:10:11 time: 0.7266 data_time: 0.0123 memory: 17395 loss: 0.2142 decode.loss_ce: 0.1407 decode.acc_seg: 94.1064 aux.loss_ce: 0.0735 aux.acc_seg: 93.1149 2023/06/07 16:07:00 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 16:07:00 - mmengine - INFO - Iter(train) [ 38000/240000] lr: 8.5774e-03 eta: 1 day, 17:09:32 time: 0.7179 data_time: 0.0119 memory: 17396 loss: 0.3232 decode.loss_ce: 0.2162 decode.acc_seg: 86.3895 aux.loss_ce: 0.1070 aux.acc_seg: 83.5679 2023/06/07 16:07:36 - mmengine - INFO - Iter(train) [ 38050/240000] lr: 8.5755e-03 eta: 1 day, 17:08:53 time: 0.7332 data_time: 0.0122 memory: 17393 loss: 0.2606 decode.loss_ce: 0.1693 decode.acc_seg: 93.3470 aux.loss_ce: 0.0913 aux.acc_seg: 88.5853 2023/06/07 16:08:12 - mmengine - INFO - Iter(train) [ 38100/240000] lr: 8.5736e-03 eta: 1 day, 17:08:12 time: 0.7143 data_time: 0.0121 memory: 17393 loss: 0.2386 decode.loss_ce: 0.1574 decode.acc_seg: 92.9823 aux.loss_ce: 0.0812 aux.acc_seg: 90.2603 2023/06/07 16:08:48 - mmengine - INFO - Iter(train) [ 38150/240000] lr: 8.5717e-03 eta: 1 day, 17:07:32 time: 0.7296 data_time: 0.0181 memory: 17394 loss: 0.2401 decode.loss_ce: 0.1557 decode.acc_seg: 92.8506 aux.loss_ce: 0.0844 aux.acc_seg: 90.7514 2023/06/07 16:09:24 - mmengine - INFO - Iter(train) [ 38200/240000] lr: 8.5699e-03 eta: 1 day, 17:06:52 time: 0.7219 data_time: 0.0121 memory: 17396 loss: 0.2388 decode.loss_ce: 0.1592 decode.acc_seg: 91.6418 aux.loss_ce: 0.0795 aux.acc_seg: 88.9386 2023/06/07 16:10:00 - mmengine - INFO - Iter(train) [ 38250/240000] lr: 8.5680e-03 eta: 1 day, 17:06:11 time: 0.7124 data_time: 0.0124 memory: 17395 loss: 0.2399 decode.loss_ce: 0.1558 decode.acc_seg: 92.6585 aux.loss_ce: 0.0841 aux.acc_seg: 90.0604 2023/06/07 16:10:36 - mmengine - INFO - Iter(train) [ 38300/240000] lr: 8.5661e-03 eta: 1 day, 17:05:31 time: 0.7232 data_time: 0.0121 memory: 17395 loss: 0.2561 decode.loss_ce: 0.1670 decode.acc_seg: 92.8425 aux.loss_ce: 0.0891 aux.acc_seg: 90.7819 2023/06/07 16:11:12 - mmengine - INFO - Iter(train) [ 38350/240000] lr: 8.5642e-03 eta: 1 day, 17:04:51 time: 0.7239 data_time: 0.0121 memory: 17393 loss: 0.2411 decode.loss_ce: 0.1597 decode.acc_seg: 93.1502 aux.loss_ce: 0.0815 aux.acc_seg: 90.3757 2023/06/07 16:11:48 - mmengine - INFO - Iter(train) [ 38400/240000] lr: 8.5623e-03 eta: 1 day, 17:04:09 time: 0.7162 data_time: 0.0121 memory: 17394 loss: 0.2467 decode.loss_ce: 0.1621 decode.acc_seg: 92.7509 aux.loss_ce: 0.0847 aux.acc_seg: 89.8474 2023/06/07 16:12:24 - mmengine - INFO - Iter(train) [ 38450/240000] lr: 8.5604e-03 eta: 1 day, 17:03:29 time: 0.7246 data_time: 0.0124 memory: 17395 loss: 0.2387 decode.loss_ce: 0.1549 decode.acc_seg: 94.6623 aux.loss_ce: 0.0838 aux.acc_seg: 92.8141 2023/06/07 16:13:00 - mmengine - INFO - Iter(train) [ 38500/240000] lr: 8.5585e-03 eta: 1 day, 17:02:50 time: 0.7223 data_time: 0.0125 memory: 17395 loss: 0.2712 decode.loss_ce: 0.1792 decode.acc_seg: 93.7493 aux.loss_ce: 0.0920 aux.acc_seg: 89.4597 2023/06/07 16:13:36 - mmengine - INFO - Iter(train) [ 38550/240000] lr: 8.5566e-03 eta: 1 day, 17:02:08 time: 0.7164 data_time: 0.0121 memory: 17392 loss: 0.2448 decode.loss_ce: 0.1609 decode.acc_seg: 93.5797 aux.loss_ce: 0.0839 aux.acc_seg: 89.8674 2023/06/07 16:14:12 - mmengine - INFO - Iter(train) [ 38600/240000] lr: 8.5547e-03 eta: 1 day, 17:01:28 time: 0.7218 data_time: 0.0124 memory: 17394 loss: 0.2404 decode.loss_ce: 0.1584 decode.acc_seg: 94.9461 aux.loss_ce: 0.0820 aux.acc_seg: 93.9093 2023/06/07 16:14:48 - mmengine - INFO - Iter(train) [ 38650/240000] lr: 8.5529e-03 eta: 1 day, 17:00:48 time: 0.7303 data_time: 0.0124 memory: 17394 loss: 0.2540 decode.loss_ce: 0.1694 decode.acc_seg: 91.7958 aux.loss_ce: 0.0846 aux.acc_seg: 89.1034 2023/06/07 16:15:24 - mmengine - INFO - Iter(train) [ 38700/240000] lr: 8.5510e-03 eta: 1 day, 17:00:08 time: 0.7122 data_time: 0.0122 memory: 17394 loss: 0.2277 decode.loss_ce: 0.1505 decode.acc_seg: 92.7841 aux.loss_ce: 0.0772 aux.acc_seg: 90.8521 2023/06/07 16:16:00 - mmengine - INFO - Iter(train) [ 38750/240000] lr: 8.5491e-03 eta: 1 day, 16:59:26 time: 0.7164 data_time: 0.0120 memory: 17394 loss: 0.2270 decode.loss_ce: 0.1498 decode.acc_seg: 92.4242 aux.loss_ce: 0.0772 aux.acc_seg: 91.5499 2023/06/07 16:16:35 - mmengine - INFO - Iter(train) [ 38800/240000] lr: 8.5472e-03 eta: 1 day, 16:58:45 time: 0.7076 data_time: 0.0121 memory: 17394 loss: 0.2537 decode.loss_ce: 0.1679 decode.acc_seg: 92.6783 aux.loss_ce: 0.0857 aux.acc_seg: 87.6446 2023/06/07 16:17:11 - mmengine - INFO - Iter(train) [ 38850/240000] lr: 8.5453e-03 eta: 1 day, 16:58:03 time: 0.7128 data_time: 0.1053 memory: 17392 loss: 0.2267 decode.loss_ce: 0.1497 decode.acc_seg: 93.0725 aux.loss_ce: 0.0770 aux.acc_seg: 91.0923 2023/06/07 16:17:47 - mmengine - INFO - Iter(train) [ 38900/240000] lr: 8.5434e-03 eta: 1 day, 16:57:22 time: 0.7133 data_time: 0.0123 memory: 17395 loss: 0.2366 decode.loss_ce: 0.1554 decode.acc_seg: 93.4375 aux.loss_ce: 0.0811 aux.acc_seg: 91.4428 2023/06/07 16:18:23 - mmengine - INFO - Iter(train) [ 38950/240000] lr: 8.5415e-03 eta: 1 day, 16:56:43 time: 0.7383 data_time: 0.0122 memory: 17396 loss: 0.2291 decode.loss_ce: 0.1499 decode.acc_seg: 93.6802 aux.loss_ce: 0.0792 aux.acc_seg: 92.0995 2023/06/07 16:18:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 16:18:59 - mmengine - INFO - Iter(train) [ 39000/240000] lr: 8.5396e-03 eta: 1 day, 16:56:04 time: 0.7182 data_time: 0.0122 memory: 17393 loss: 0.2449 decode.loss_ce: 0.1620 decode.acc_seg: 94.0157 aux.loss_ce: 0.0829 aux.acc_seg: 91.4959 2023/06/07 16:19:35 - mmengine - INFO - Iter(train) [ 39050/240000] lr: 8.5377e-03 eta: 1 day, 16:55:23 time: 0.7354 data_time: 0.0122 memory: 17391 loss: 0.2368 decode.loss_ce: 0.1558 decode.acc_seg: 90.3290 aux.loss_ce: 0.0810 aux.acc_seg: 89.1542 2023/06/07 16:20:11 - mmengine - INFO - Iter(train) [ 39100/240000] lr: 8.5358e-03 eta: 1 day, 16:54:43 time: 0.7307 data_time: 0.0124 memory: 17395 loss: 0.2212 decode.loss_ce: 0.1441 decode.acc_seg: 92.1972 aux.loss_ce: 0.0771 aux.acc_seg: 89.6163 2023/06/07 16:20:47 - mmengine - INFO - Iter(train) [ 39150/240000] lr: 8.5340e-03 eta: 1 day, 16:54:00 time: 0.7134 data_time: 0.0121 memory: 17395 loss: 0.2451 decode.loss_ce: 0.1630 decode.acc_seg: 93.1967 aux.loss_ce: 0.0821 aux.acc_seg: 90.5926 2023/06/07 16:21:23 - mmengine - INFO - Iter(train) [ 39200/240000] lr: 8.5321e-03 eta: 1 day, 16:53:21 time: 0.7069 data_time: 0.0123 memory: 17394 loss: 0.2248 decode.loss_ce: 0.1480 decode.acc_seg: 94.5767 aux.loss_ce: 0.0768 aux.acc_seg: 93.4138 2023/06/07 16:21:59 - mmengine - INFO - Iter(train) [ 39250/240000] lr: 8.5302e-03 eta: 1 day, 16:52:42 time: 0.7294 data_time: 0.0122 memory: 17396 loss: 0.2350 decode.loss_ce: 0.1537 decode.acc_seg: 93.5507 aux.loss_ce: 0.0813 aux.acc_seg: 91.4076 2023/06/07 16:22:35 - mmengine - INFO - Iter(train) [ 39300/240000] lr: 8.5283e-03 eta: 1 day, 16:52:01 time: 0.7062 data_time: 0.0123 memory: 17393 loss: 0.2458 decode.loss_ce: 0.1604 decode.acc_seg: 90.8440 aux.loss_ce: 0.0854 aux.acc_seg: 88.2454 2023/06/07 16:23:11 - mmengine - INFO - Iter(train) [ 39350/240000] lr: 8.5264e-03 eta: 1 day, 16:51:20 time: 0.7075 data_time: 0.0122 memory: 17393 loss: 0.2468 decode.loss_ce: 0.1590 decode.acc_seg: 94.0369 aux.loss_ce: 0.0878 aux.acc_seg: 90.4126 2023/06/07 16:23:47 - mmengine - INFO - Iter(train) [ 39400/240000] lr: 8.5245e-03 eta: 1 day, 16:50:40 time: 0.7148 data_time: 0.0124 memory: 17396 loss: 0.2197 decode.loss_ce: 0.1444 decode.acc_seg: 93.9624 aux.loss_ce: 0.0753 aux.acc_seg: 92.5103 2023/06/07 16:24:23 - mmengine - INFO - Iter(train) [ 39450/240000] lr: 8.5226e-03 eta: 1 day, 16:50:00 time: 0.7236 data_time: 0.0121 memory: 17393 loss: 0.2713 decode.loss_ce: 0.1793 decode.acc_seg: 92.2196 aux.loss_ce: 0.0920 aux.acc_seg: 88.8172 2023/06/07 16:24:59 - mmengine - INFO - Iter(train) [ 39500/240000] lr: 8.5207e-03 eta: 1 day, 16:49:20 time: 0.7335 data_time: 0.0123 memory: 17393 loss: 0.2722 decode.loss_ce: 0.1813 decode.acc_seg: 91.5827 aux.loss_ce: 0.0910 aux.acc_seg: 89.3566 2023/06/07 16:25:35 - mmengine - INFO - Iter(train) [ 39550/240000] lr: 8.5188e-03 eta: 1 day, 16:48:41 time: 0.7135 data_time: 0.0122 memory: 17395 loss: 0.2319 decode.loss_ce: 0.1509 decode.acc_seg: 92.4035 aux.loss_ce: 0.0810 aux.acc_seg: 86.2309 2023/06/07 16:26:11 - mmengine - INFO - Iter(train) [ 39600/240000] lr: 8.5169e-03 eta: 1 day, 16:48:03 time: 0.7187 data_time: 0.0123 memory: 17393 loss: 0.2503 decode.loss_ce: 0.1648 decode.acc_seg: 90.8937 aux.loss_ce: 0.0855 aux.acc_seg: 88.8162 2023/06/07 16:26:47 - mmengine - INFO - Iter(train) [ 39650/240000] lr: 8.5151e-03 eta: 1 day, 16:47:23 time: 0.7155 data_time: 0.0122 memory: 17394 loss: 0.2515 decode.loss_ce: 0.1680 decode.acc_seg: 90.0978 aux.loss_ce: 0.0835 aux.acc_seg: 87.5398 2023/06/07 16:27:23 - mmengine - INFO - Iter(train) [ 39700/240000] lr: 8.5132e-03 eta: 1 day, 16:46:43 time: 0.7111 data_time: 0.0127 memory: 17394 loss: 0.2519 decode.loss_ce: 0.1660 decode.acc_seg: 92.7264 aux.loss_ce: 0.0859 aux.acc_seg: 89.3768 2023/06/07 16:27:59 - mmengine - INFO - Iter(train) [ 39750/240000] lr: 8.5113e-03 eta: 1 day, 16:46:03 time: 0.7073 data_time: 0.0119 memory: 17392 loss: 0.2375 decode.loss_ce: 0.1557 decode.acc_seg: 94.2650 aux.loss_ce: 0.0817 aux.acc_seg: 91.8508 2023/06/07 16:28:35 - mmengine - INFO - Iter(train) [ 39800/240000] lr: 8.5094e-03 eta: 1 day, 16:45:22 time: 0.7158 data_time: 0.0120 memory: 17395 loss: 0.2426 decode.loss_ce: 0.1573 decode.acc_seg: 91.9364 aux.loss_ce: 0.0853 aux.acc_seg: 87.1517 2023/06/07 16:29:11 - mmengine - INFO - Iter(train) [ 39850/240000] lr: 8.5075e-03 eta: 1 day, 16:44:44 time: 0.7179 data_time: 0.0123 memory: 17393 loss: 0.2563 decode.loss_ce: 0.1739 decode.acc_seg: 91.3859 aux.loss_ce: 0.0824 aux.acc_seg: 90.7501 2023/06/07 16:29:47 - mmengine - INFO - Iter(train) [ 39900/240000] lr: 8.5056e-03 eta: 1 day, 16:44:03 time: 0.7163 data_time: 0.0122 memory: 17397 loss: 0.2314 decode.loss_ce: 0.1520 decode.acc_seg: 94.0664 aux.loss_ce: 0.0794 aux.acc_seg: 91.9228 2023/06/07 16:30:23 - mmengine - INFO - Iter(train) [ 39950/240000] lr: 8.5037e-03 eta: 1 day, 16:43:23 time: 0.7237 data_time: 0.0123 memory: 17396 loss: 0.2362 decode.loss_ce: 0.1550 decode.acc_seg: 92.7813 aux.loss_ce: 0.0812 aux.acc_seg: 90.7323 2023/06/07 16:30:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 16:30:59 - mmengine - INFO - Iter(train) [ 40000/240000] lr: 8.5018e-03 eta: 1 day, 16:42:45 time: 0.7138 data_time: 0.0122 memory: 17395 loss: 0.2478 decode.loss_ce: 0.1632 decode.acc_seg: 89.3285 aux.loss_ce: 0.0846 aux.acc_seg: 87.7683 2023/06/07 16:31:35 - mmengine - INFO - Iter(train) [ 40050/240000] lr: 8.4999e-03 eta: 1 day, 16:42:04 time: 0.7225 data_time: 0.0122 memory: 17393 loss: 0.2330 decode.loss_ce: 0.1536 decode.acc_seg: 91.3262 aux.loss_ce: 0.0794 aux.acc_seg: 87.6189 2023/06/07 16:32:11 - mmengine - INFO - Iter(train) [ 40100/240000] lr: 8.4980e-03 eta: 1 day, 16:41:23 time: 0.7082 data_time: 0.0118 memory: 17393 loss: 0.2458 decode.loss_ce: 0.1626 decode.acc_seg: 94.0058 aux.loss_ce: 0.0832 aux.acc_seg: 92.1283 2023/06/07 16:32:47 - mmengine - INFO - Iter(train) [ 40150/240000] lr: 8.4962e-03 eta: 1 day, 16:40:45 time: 0.7172 data_time: 0.0121 memory: 17391 loss: 0.2219 decode.loss_ce: 0.1472 decode.acc_seg: 94.1757 aux.loss_ce: 0.0747 aux.acc_seg: 91.9762 2023/06/07 16:33:23 - mmengine - INFO - Iter(train) [ 40200/240000] lr: 8.4943e-03 eta: 1 day, 16:40:04 time: 0.7114 data_time: 0.1981 memory: 17395 loss: 0.2544 decode.loss_ce: 0.1668 decode.acc_seg: 93.2800 aux.loss_ce: 0.0876 aux.acc_seg: 89.5869 2023/06/07 16:33:59 - mmengine - INFO - Iter(train) [ 40250/240000] lr: 8.4924e-03 eta: 1 day, 16:39:22 time: 0.7156 data_time: 0.2312 memory: 17394 loss: 0.2297 decode.loss_ce: 0.1489 decode.acc_seg: 95.3138 aux.loss_ce: 0.0808 aux.acc_seg: 93.4070 2023/06/07 16:34:34 - mmengine - INFO - Iter(train) [ 40300/240000] lr: 8.4905e-03 eta: 1 day, 16:38:41 time: 0.7138 data_time: 0.1705 memory: 17395 loss: 0.2666 decode.loss_ce: 0.1765 decode.acc_seg: 94.0239 aux.loss_ce: 0.0901 aux.acc_seg: 92.0698 2023/06/07 16:35:10 - mmengine - INFO - Iter(train) [ 40350/240000] lr: 8.4886e-03 eta: 1 day, 16:37:59 time: 0.7163 data_time: 0.1355 memory: 17392 loss: 0.2635 decode.loss_ce: 0.1728 decode.acc_seg: 91.1380 aux.loss_ce: 0.0908 aux.acc_seg: 87.5001 2023/06/07 16:35:45 - mmengine - INFO - Iter(train) [ 40400/240000] lr: 8.4867e-03 eta: 1 day, 16:37:16 time: 0.7074 data_time: 0.3146 memory: 17392 loss: 0.2168 decode.loss_ce: 0.1427 decode.acc_seg: 92.6491 aux.loss_ce: 0.0741 aux.acc_seg: 90.4243 2023/06/07 16:36:21 - mmengine - INFO - Iter(train) [ 40450/240000] lr: 8.4848e-03 eta: 1 day, 16:36:34 time: 0.7166 data_time: 0.0333 memory: 17391 loss: 0.2586 decode.loss_ce: 0.1683 decode.acc_seg: 90.7431 aux.loss_ce: 0.0902 aux.acc_seg: 87.3761 2023/06/07 16:36:57 - mmengine - INFO - Iter(train) [ 40500/240000] lr: 8.4829e-03 eta: 1 day, 16:35:53 time: 0.7189 data_time: 0.0120 memory: 17394 loss: 0.2207 decode.loss_ce: 0.1449 decode.acc_seg: 92.3361 aux.loss_ce: 0.0758 aux.acc_seg: 90.2892 2023/06/07 16:37:33 - mmengine - INFO - Iter(train) [ 40550/240000] lr: 8.4810e-03 eta: 1 day, 16:35:14 time: 0.7158 data_time: 0.0123 memory: 17393 loss: 0.2356 decode.loss_ce: 0.1540 decode.acc_seg: 93.2990 aux.loss_ce: 0.0816 aux.acc_seg: 91.1414 2023/06/07 16:38:09 - mmengine - INFO - Iter(train) [ 40600/240000] lr: 8.4791e-03 eta: 1 day, 16:34:34 time: 0.7162 data_time: 0.0121 memory: 17390 loss: 0.2165 decode.loss_ce: 0.1428 decode.acc_seg: 92.2350 aux.loss_ce: 0.0737 aux.acc_seg: 90.5834 2023/06/07 16:38:45 - mmengine - INFO - Iter(train) [ 40650/240000] lr: 8.4772e-03 eta: 1 day, 16:33:56 time: 0.7371 data_time: 0.0123 memory: 17392 loss: 0.2303 decode.loss_ce: 0.1500 decode.acc_seg: 93.6593 aux.loss_ce: 0.0803 aux.acc_seg: 91.5445 2023/06/07 16:39:21 - mmengine - INFO - Iter(train) [ 40700/240000] lr: 8.4754e-03 eta: 1 day, 16:33:17 time: 0.7249 data_time: 0.0122 memory: 17394 loss: 0.2420 decode.loss_ce: 0.1583 decode.acc_seg: 93.6939 aux.loss_ce: 0.0837 aux.acc_seg: 92.0078 2023/06/07 16:39:57 - mmengine - INFO - Iter(train) [ 40750/240000] lr: 8.4735e-03 eta: 1 day, 16:32:37 time: 0.6946 data_time: 0.0123 memory: 17394 loss: 0.2221 decode.loss_ce: 0.1452 decode.acc_seg: 95.3428 aux.loss_ce: 0.0769 aux.acc_seg: 93.6187 2023/06/07 16:40:33 - mmengine - INFO - Iter(train) [ 40800/240000] lr: 8.4716e-03 eta: 1 day, 16:31:57 time: 0.7277 data_time: 0.0123 memory: 17393 loss: 0.2296 decode.loss_ce: 0.1528 decode.acc_seg: 94.1083 aux.loss_ce: 0.0768 aux.acc_seg: 91.2792 2023/06/07 16:41:09 - mmengine - INFO - Iter(train) [ 40850/240000] lr: 8.4697e-03 eta: 1 day, 16:31:19 time: 0.7118 data_time: 0.0122 memory: 17395 loss: 0.2634 decode.loss_ce: 0.1773 decode.acc_seg: 93.6527 aux.loss_ce: 0.0861 aux.acc_seg: 91.6849 2023/06/07 16:41:45 - mmengine - INFO - Iter(train) [ 40900/240000] lr: 8.4678e-03 eta: 1 day, 16:30:39 time: 0.7061 data_time: 0.0122 memory: 17394 loss: 0.2467 decode.loss_ce: 0.1601 decode.acc_seg: 93.6000 aux.loss_ce: 0.0866 aux.acc_seg: 90.0997 2023/06/07 16:42:21 - mmengine - INFO - Iter(train) [ 40950/240000] lr: 8.4659e-03 eta: 1 day, 16:29:59 time: 0.6972 data_time: 0.0120 memory: 17395 loss: 0.2429 decode.loss_ce: 0.1612 decode.acc_seg: 91.5969 aux.loss_ce: 0.0816 aux.acc_seg: 89.8753 2023/06/07 16:42:57 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 16:42:57 - mmengine - INFO - Iter(train) [ 41000/240000] lr: 8.4640e-03 eta: 1 day, 16:29:19 time: 0.7254 data_time: 0.0122 memory: 17391 loss: 0.2293 decode.loss_ce: 0.1511 decode.acc_seg: 94.2505 aux.loss_ce: 0.0781 aux.acc_seg: 92.8479 2023/06/07 16:43:33 - mmengine - INFO - Iter(train) [ 41050/240000] lr: 8.4621e-03 eta: 1 day, 16:28:39 time: 0.7108 data_time: 0.0121 memory: 17392 loss: 0.2601 decode.loss_ce: 0.1734 decode.acc_seg: 90.7803 aux.loss_ce: 0.0867 aux.acc_seg: 89.1170 2023/06/07 16:44:09 - mmengine - INFO - Iter(train) [ 41100/240000] lr: 8.4602e-03 eta: 1 day, 16:27:58 time: 0.6992 data_time: 0.0122 memory: 17393 loss: 0.2320 decode.loss_ce: 0.1520 decode.acc_seg: 93.4734 aux.loss_ce: 0.0800 aux.acc_seg: 90.9299 2023/06/07 16:44:45 - mmengine - INFO - Iter(train) [ 41150/240000] lr: 8.4583e-03 eta: 1 day, 16:27:18 time: 0.7190 data_time: 0.0635 memory: 17393 loss: 0.2409 decode.loss_ce: 0.1601 decode.acc_seg: 93.0934 aux.loss_ce: 0.0808 aux.acc_seg: 92.6235 2023/06/07 16:45:20 - mmengine - INFO - Iter(train) [ 41200/240000] lr: 8.4564e-03 eta: 1 day, 16:26:37 time: 0.7068 data_time: 0.2802 memory: 17394 loss: 0.2301 decode.loss_ce: 0.1501 decode.acc_seg: 93.0640 aux.loss_ce: 0.0799 aux.acc_seg: 90.7364 2023/06/07 16:45:56 - mmengine - INFO - Iter(train) [ 41250/240000] lr: 8.4546e-03 eta: 1 day, 16:25:57 time: 0.7139 data_time: 0.1403 memory: 17396 loss: 0.2700 decode.loss_ce: 0.1776 decode.acc_seg: 89.5998 aux.loss_ce: 0.0924 aux.acc_seg: 86.8708 2023/06/07 16:46:32 - mmengine - INFO - Iter(train) [ 41300/240000] lr: 8.4527e-03 eta: 1 day, 16:25:14 time: 0.7185 data_time: 0.3491 memory: 17394 loss: 0.2624 decode.loss_ce: 0.1719 decode.acc_seg: 93.0754 aux.loss_ce: 0.0905 aux.acc_seg: 91.0029 2023/06/07 16:47:08 - mmengine - INFO - Iter(train) [ 41350/240000] lr: 8.4508e-03 eta: 1 day, 16:24:35 time: 0.7183 data_time: 0.3956 memory: 17395 loss: 0.2398 decode.loss_ce: 0.1586 decode.acc_seg: 94.1281 aux.loss_ce: 0.0813 aux.acc_seg: 92.8549 2023/06/07 16:47:43 - mmengine - INFO - Iter(train) [ 41400/240000] lr: 8.4489e-03 eta: 1 day, 16:23:53 time: 0.7128 data_time: 0.3885 memory: 17393 loss: 0.2428 decode.loss_ce: 0.1606 decode.acc_seg: 92.1308 aux.loss_ce: 0.0821 aux.acc_seg: 89.0109 2023/06/07 16:48:19 - mmengine - INFO - Iter(train) [ 41450/240000] lr: 8.4470e-03 eta: 1 day, 16:23:14 time: 0.7206 data_time: 0.3926 memory: 17393 loss: 0.2219 decode.loss_ce: 0.1467 decode.acc_seg: 91.6821 aux.loss_ce: 0.0752 aux.acc_seg: 88.9026 2023/06/07 16:48:55 - mmengine - INFO - Iter(train) [ 41500/240000] lr: 8.4451e-03 eta: 1 day, 16:22:34 time: 0.7214 data_time: 0.3982 memory: 17393 loss: 0.2325 decode.loss_ce: 0.1556 decode.acc_seg: 93.2939 aux.loss_ce: 0.0769 aux.acc_seg: 90.9497 2023/06/07 16:49:31 - mmengine - INFO - Iter(train) [ 41550/240000] lr: 8.4432e-03 eta: 1 day, 16:21:52 time: 0.7179 data_time: 0.3935 memory: 17395 loss: 0.2447 decode.loss_ce: 0.1616 decode.acc_seg: 92.3499 aux.loss_ce: 0.0831 aux.acc_seg: 89.3128 2023/06/07 16:50:07 - mmengine - INFO - Iter(train) [ 41600/240000] lr: 8.4413e-03 eta: 1 day, 16:21:14 time: 0.7288 data_time: 0.4052 memory: 17391 loss: 0.2453 decode.loss_ce: 0.1632 decode.acc_seg: 95.1475 aux.loss_ce: 0.0821 aux.acc_seg: 93.0009 2023/06/07 16:50:43 - mmengine - INFO - Iter(train) [ 41650/240000] lr: 8.4394e-03 eta: 1 day, 16:20:33 time: 0.7161 data_time: 0.3888 memory: 17394 loss: 0.2569 decode.loss_ce: 0.1696 decode.acc_seg: 92.6528 aux.loss_ce: 0.0873 aux.acc_seg: 89.9590 2023/06/07 16:51:19 - mmengine - INFO - Iter(train) [ 41700/240000] lr: 8.4375e-03 eta: 1 day, 16:19:53 time: 0.7301 data_time: 0.1343 memory: 17393 loss: 0.2414 decode.loss_ce: 0.1585 decode.acc_seg: 91.8767 aux.loss_ce: 0.0829 aux.acc_seg: 89.4899 2023/06/07 16:51:55 - mmengine - INFO - Iter(train) [ 41750/240000] lr: 8.4356e-03 eta: 1 day, 16:19:13 time: 0.7012 data_time: 0.0153 memory: 17395 loss: 0.2509 decode.loss_ce: 0.1649 decode.acc_seg: 93.5597 aux.loss_ce: 0.0860 aux.acc_seg: 91.3509 2023/06/07 16:52:30 - mmengine - INFO - Iter(train) [ 41800/240000] lr: 8.4337e-03 eta: 1 day, 16:18:32 time: 0.7118 data_time: 0.0123 memory: 17394 loss: 0.2352 decode.loss_ce: 0.1571 decode.acc_seg: 93.7609 aux.loss_ce: 0.0782 aux.acc_seg: 92.2372 2023/06/07 16:53:06 - mmengine - INFO - Iter(train) [ 41850/240000] lr: 8.4318e-03 eta: 1 day, 16:17:52 time: 0.7160 data_time: 0.0120 memory: 17394 loss: 0.2755 decode.loss_ce: 0.1831 decode.acc_seg: 91.3210 aux.loss_ce: 0.0924 aux.acc_seg: 89.0355 2023/06/07 16:53:43 - mmengine - INFO - Iter(train) [ 41900/240000] lr: 8.4300e-03 eta: 1 day, 16:17:15 time: 0.7139 data_time: 0.0121 memory: 17394 loss: 0.2534 decode.loss_ce: 0.1662 decode.acc_seg: 93.1352 aux.loss_ce: 0.0872 aux.acc_seg: 91.2057 2023/06/07 16:54:18 - mmengine - INFO - Iter(train) [ 41950/240000] lr: 8.4281e-03 eta: 1 day, 16:16:34 time: 0.7123 data_time: 0.0123 memory: 17392 loss: 0.2466 decode.loss_ce: 0.1624 decode.acc_seg: 93.6569 aux.loss_ce: 0.0842 aux.acc_seg: 91.4168 2023/06/07 16:54:54 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 16:54:54 - mmengine - INFO - Iter(train) [ 42000/240000] lr: 8.4262e-03 eta: 1 day, 16:15:53 time: 0.7115 data_time: 0.1196 memory: 17393 loss: 0.2298 decode.loss_ce: 0.1502 decode.acc_seg: 95.2833 aux.loss_ce: 0.0796 aux.acc_seg: 93.5661 2023/06/07 16:55:30 - mmengine - INFO - Iter(train) [ 42050/240000] lr: 8.4243e-03 eta: 1 day, 16:15:13 time: 0.7089 data_time: 0.2215 memory: 17396 loss: 0.2727 decode.loss_ce: 0.1817 decode.acc_seg: 93.0926 aux.loss_ce: 0.0909 aux.acc_seg: 88.1370 2023/06/07 16:56:06 - mmengine - INFO - Iter(train) [ 42100/240000] lr: 8.4224e-03 eta: 1 day, 16:14:33 time: 0.7121 data_time: 0.3080 memory: 17394 loss: 0.2451 decode.loss_ce: 0.1598 decode.acc_seg: 93.1010 aux.loss_ce: 0.0854 aux.acc_seg: 89.7697 2023/06/07 16:56:41 - mmengine - INFO - Iter(train) [ 42150/240000] lr: 8.4205e-03 eta: 1 day, 16:13:49 time: 0.7025 data_time: 0.3770 memory: 17396 loss: 0.2332 decode.loss_ce: 0.1554 decode.acc_seg: 93.8940 aux.loss_ce: 0.0778 aux.acc_seg: 90.6550 2023/06/07 16:57:16 - mmengine - INFO - Iter(train) [ 42200/240000] lr: 8.4186e-03 eta: 1 day, 16:13:07 time: 0.7073 data_time: 0.3838 memory: 17396 loss: 0.2405 decode.loss_ce: 0.1565 decode.acc_seg: 93.8150 aux.loss_ce: 0.0839 aux.acc_seg: 88.6006 2023/06/07 16:57:52 - mmengine - INFO - Iter(train) [ 42250/240000] lr: 8.4167e-03 eta: 1 day, 16:12:27 time: 0.7162 data_time: 0.0321 memory: 17395 loss: 0.2419 decode.loss_ce: 0.1586 decode.acc_seg: 91.7010 aux.loss_ce: 0.0834 aux.acc_seg: 89.6034 2023/06/07 16:58:28 - mmengine - INFO - Iter(train) [ 42300/240000] lr: 8.4148e-03 eta: 1 day, 16:11:47 time: 0.7244 data_time: 0.0121 memory: 17392 loss: 0.2147 decode.loss_ce: 0.1414 decode.acc_seg: 93.5604 aux.loss_ce: 0.0733 aux.acc_seg: 91.6581 2023/06/07 16:59:04 - mmengine - INFO - Iter(train) [ 42350/240000] lr: 8.4129e-03 eta: 1 day, 16:11:07 time: 0.7237 data_time: 0.0120 memory: 17394 loss: 0.2625 decode.loss_ce: 0.1742 decode.acc_seg: 89.8963 aux.loss_ce: 0.0884 aux.acc_seg: 86.6390 2023/06/07 16:59:40 - mmengine - INFO - Iter(train) [ 42400/240000] lr: 8.4110e-03 eta: 1 day, 16:10:27 time: 0.7055 data_time: 0.0154 memory: 17395 loss: 0.2272 decode.loss_ce: 0.1496 decode.acc_seg: 93.4183 aux.loss_ce: 0.0775 aux.acc_seg: 90.9168 2023/06/07 17:00:15 - mmengine - INFO - Iter(train) [ 42450/240000] lr: 8.4091e-03 eta: 1 day, 16:09:46 time: 0.7133 data_time: 0.1250 memory: 17392 loss: 0.2416 decode.loss_ce: 0.1594 decode.acc_seg: 90.9874 aux.loss_ce: 0.0822 aux.acc_seg: 88.5790 2023/06/07 17:00:51 - mmengine - INFO - Iter(train) [ 42500/240000] lr: 8.4072e-03 eta: 1 day, 16:09:05 time: 0.7222 data_time: 0.2734 memory: 17395 loss: 0.2326 decode.loss_ce: 0.1488 decode.acc_seg: 93.1231 aux.loss_ce: 0.0838 aux.acc_seg: 91.0895 2023/06/07 17:01:27 - mmengine - INFO - Iter(train) [ 42550/240000] lr: 8.4054e-03 eta: 1 day, 16:08:24 time: 0.7015 data_time: 0.1628 memory: 17394 loss: 0.2342 decode.loss_ce: 0.1544 decode.acc_seg: 92.6005 aux.loss_ce: 0.0798 aux.acc_seg: 90.0780 2023/06/07 17:02:03 - mmengine - INFO - Iter(train) [ 42600/240000] lr: 8.4035e-03 eta: 1 day, 16:07:44 time: 0.7198 data_time: 0.1481 memory: 17392 loss: 0.2383 decode.loss_ce: 0.1580 decode.acc_seg: 93.5665 aux.loss_ce: 0.0802 aux.acc_seg: 92.3751 2023/06/07 17:02:38 - mmengine - INFO - Iter(train) [ 42650/240000] lr: 8.4016e-03 eta: 1 day, 16:07:03 time: 0.7053 data_time: 0.1320 memory: 17395 loss: 0.2635 decode.loss_ce: 0.1734 decode.acc_seg: 93.4801 aux.loss_ce: 0.0901 aux.acc_seg: 90.9046 2023/06/07 17:03:14 - mmengine - INFO - Iter(train) [ 42700/240000] lr: 8.3997e-03 eta: 1 day, 16:06:21 time: 0.7087 data_time: 0.1514 memory: 17393 loss: 0.2443 decode.loss_ce: 0.1582 decode.acc_seg: 91.6280 aux.loss_ce: 0.0861 aux.acc_seg: 85.4313 2023/06/07 17:03:49 - mmengine - INFO - Iter(train) [ 42750/240000] lr: 8.3978e-03 eta: 1 day, 16:05:39 time: 0.7150 data_time: 0.2875 memory: 17393 loss: 0.2237 decode.loss_ce: 0.1482 decode.acc_seg: 91.9053 aux.loss_ce: 0.0755 aux.acc_seg: 89.2839 2023/06/07 17:04:24 - mmengine - INFO - Iter(train) [ 42800/240000] lr: 8.3959e-03 eta: 1 day, 16:04:57 time: 0.7103 data_time: 0.3490 memory: 17396 loss: 0.2446 decode.loss_ce: 0.1617 decode.acc_seg: 92.4063 aux.loss_ce: 0.0829 aux.acc_seg: 90.5354 2023/06/07 17:05:00 - mmengine - INFO - Iter(train) [ 42850/240000] lr: 8.3940e-03 eta: 1 day, 16:04:17 time: 0.7264 data_time: 0.0335 memory: 17393 loss: 0.2417 decode.loss_ce: 0.1592 decode.acc_seg: 93.7351 aux.loss_ce: 0.0825 aux.acc_seg: 91.7033 2023/06/07 17:05:36 - mmengine - INFO - Iter(train) [ 42900/240000] lr: 8.3921e-03 eta: 1 day, 16:03:35 time: 0.7077 data_time: 0.2748 memory: 17396 loss: 0.2371 decode.loss_ce: 0.1582 decode.acc_seg: 89.7041 aux.loss_ce: 0.0789 aux.acc_seg: 88.3257 2023/06/07 17:06:11 - mmengine - INFO - Iter(train) [ 42950/240000] lr: 8.3902e-03 eta: 1 day, 16:02:54 time: 0.7071 data_time: 0.3805 memory: 17394 loss: 0.2293 decode.loss_ce: 0.1498 decode.acc_seg: 92.8784 aux.loss_ce: 0.0794 aux.acc_seg: 91.1456 2023/06/07 17:06:47 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 17:06:47 - mmengine - INFO - Iter(train) [ 43000/240000] lr: 8.3883e-03 eta: 1 day, 16:02:12 time: 0.7199 data_time: 0.3676 memory: 17393 loss: 0.2492 decode.loss_ce: 0.1635 decode.acc_seg: 94.8092 aux.loss_ce: 0.0857 aux.acc_seg: 93.0292 2023/06/07 17:07:23 - mmengine - INFO - Iter(train) [ 43050/240000] lr: 8.3864e-03 eta: 1 day, 16:01:32 time: 0.7111 data_time: 0.3875 memory: 17393 loss: 0.2351 decode.loss_ce: 0.1574 decode.acc_seg: 90.3533 aux.loss_ce: 0.0777 aux.acc_seg: 87.7896 2023/06/07 17:07:58 - mmengine - INFO - Iter(train) [ 43100/240000] lr: 8.3845e-03 eta: 1 day, 16:00:49 time: 0.7036 data_time: 0.3191 memory: 17395 loss: 0.2302 decode.loss_ce: 0.1516 decode.acc_seg: 90.1786 aux.loss_ce: 0.0787 aux.acc_seg: 87.5333 2023/06/07 17:08:33 - mmengine - INFO - Iter(train) [ 43150/240000] lr: 8.3826e-03 eta: 1 day, 16:00:08 time: 0.7026 data_time: 0.3791 memory: 17396 loss: 0.2359 decode.loss_ce: 0.1543 decode.acc_seg: 92.3645 aux.loss_ce: 0.0815 aux.acc_seg: 90.1604 2023/06/07 17:09:09 - mmengine - INFO - Iter(train) [ 43200/240000] lr: 8.3807e-03 eta: 1 day, 15:59:27 time: 0.7237 data_time: 0.3998 memory: 17393 loss: 0.2241 decode.loss_ce: 0.1459 decode.acc_seg: 93.1898 aux.loss_ce: 0.0782 aux.acc_seg: 90.3336 2023/06/07 17:09:45 - mmengine - INFO - Iter(train) [ 43250/240000] lr: 8.3788e-03 eta: 1 day, 15:58:47 time: 0.7152 data_time: 0.3903 memory: 17395 loss: 0.2255 decode.loss_ce: 0.1469 decode.acc_seg: 94.4948 aux.loss_ce: 0.0786 aux.acc_seg: 92.3444 2023/06/07 17:10:21 - mmengine - INFO - Iter(train) [ 43300/240000] lr: 8.3770e-03 eta: 1 day, 15:58:07 time: 0.7315 data_time: 0.4077 memory: 17393 loss: 0.2265 decode.loss_ce: 0.1500 decode.acc_seg: 94.9041 aux.loss_ce: 0.0765 aux.acc_seg: 93.9356 2023/06/07 17:10:57 - mmengine - INFO - Iter(train) [ 43350/240000] lr: 8.3751e-03 eta: 1 day, 15:57:27 time: 0.7116 data_time: 0.3880 memory: 17392 loss: 0.2372 decode.loss_ce: 0.1567 decode.acc_seg: 93.1125 aux.loss_ce: 0.0805 aux.acc_seg: 91.1646 2023/06/07 17:11:32 - mmengine - INFO - Iter(train) [ 43400/240000] lr: 8.3732e-03 eta: 1 day, 15:56:46 time: 0.7281 data_time: 0.4044 memory: 17394 loss: 0.2415 decode.loss_ce: 0.1620 decode.acc_seg: 91.6875 aux.loss_ce: 0.0794 aux.acc_seg: 90.8884 2023/06/07 17:12:08 - mmengine - INFO - Iter(train) [ 43450/240000] lr: 8.3713e-03 eta: 1 day, 15:56:07 time: 0.7079 data_time: 0.3844 memory: 17393 loss: 0.2481 decode.loss_ce: 0.1607 decode.acc_seg: 94.5396 aux.loss_ce: 0.0874 aux.acc_seg: 91.7643 2023/06/07 17:12:44 - mmengine - INFO - Iter(train) [ 43500/240000] lr: 8.3694e-03 eta: 1 day, 15:55:25 time: 0.7039 data_time: 0.2195 memory: 17393 loss: 0.2661 decode.loss_ce: 0.1756 decode.acc_seg: 92.5622 aux.loss_ce: 0.0904 aux.acc_seg: 89.3399 2023/06/07 17:13:19 - mmengine - INFO - Iter(train) [ 43550/240000] lr: 8.3675e-03 eta: 1 day, 15:54:45 time: 0.7134 data_time: 0.0130 memory: 17395 loss: 0.2393 decode.loss_ce: 0.1572 decode.acc_seg: 92.4871 aux.loss_ce: 0.0822 aux.acc_seg: 90.7341 2023/06/07 17:13:56 - mmengine - INFO - Iter(train) [ 43600/240000] lr: 8.3656e-03 eta: 1 day, 15:54:07 time: 0.7207 data_time: 0.0123 memory: 17393 loss: 0.2327 decode.loss_ce: 0.1529 decode.acc_seg: 94.4788 aux.loss_ce: 0.0798 aux.acc_seg: 92.3160 2023/06/07 17:14:32 - mmengine - INFO - Iter(train) [ 43650/240000] lr: 8.3637e-03 eta: 1 day, 15:53:29 time: 0.7142 data_time: 0.0119 memory: 17396 loss: 0.2413 decode.loss_ce: 0.1599 decode.acc_seg: 93.6849 aux.loss_ce: 0.0814 aux.acc_seg: 90.7478 2023/06/07 17:15:07 - mmengine - INFO - Iter(train) [ 43700/240000] lr: 8.3618e-03 eta: 1 day, 15:52:48 time: 0.7109 data_time: 0.0118 memory: 17396 loss: 0.2405 decode.loss_ce: 0.1600 decode.acc_seg: 92.8043 aux.loss_ce: 0.0805 aux.acc_seg: 91.9206 2023/06/07 17:15:44 - mmengine - INFO - Iter(train) [ 43750/240000] lr: 8.3599e-03 eta: 1 day, 15:52:09 time: 0.7260 data_time: 0.0120 memory: 17394 loss: 0.2185 decode.loss_ce: 0.1431 decode.acc_seg: 94.1339 aux.loss_ce: 0.0754 aux.acc_seg: 91.5340 2023/06/07 17:16:19 - mmengine - INFO - Iter(train) [ 43800/240000] lr: 8.3580e-03 eta: 1 day, 15:51:29 time: 0.7022 data_time: 0.0121 memory: 17396 loss: 0.2525 decode.loss_ce: 0.1706 decode.acc_seg: 94.2299 aux.loss_ce: 0.0819 aux.acc_seg: 92.9119 2023/06/07 17:16:55 - mmengine - INFO - Iter(train) [ 43850/240000] lr: 8.3561e-03 eta: 1 day, 15:50:47 time: 0.7167 data_time: 0.1267 memory: 17393 loss: 0.2862 decode.loss_ce: 0.1894 decode.acc_seg: 90.6884 aux.loss_ce: 0.0968 aux.acc_seg: 87.4945 2023/06/07 17:17:30 - mmengine - INFO - Iter(train) [ 43900/240000] lr: 8.3542e-03 eta: 1 day, 15:50:05 time: 0.7111 data_time: 0.3169 memory: 17393 loss: 0.2412 decode.loss_ce: 0.1590 decode.acc_seg: 92.9622 aux.loss_ce: 0.0822 aux.acc_seg: 91.6928 2023/06/07 17:18:06 - mmengine - INFO - Iter(train) [ 43950/240000] lr: 8.3523e-03 eta: 1 day, 15:49:24 time: 0.7196 data_time: 0.1322 memory: 17392 loss: 0.2351 decode.loss_ce: 0.1566 decode.acc_seg: 93.6890 aux.loss_ce: 0.0785 aux.acc_seg: 90.9511 2023/06/07 17:18:42 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 17:18:42 - mmengine - INFO - Iter(train) [ 44000/240000] lr: 8.3504e-03 eta: 1 day, 15:48:45 time: 0.7121 data_time: 0.2443 memory: 17393 loss: 0.2340 decode.loss_ce: 0.1555 decode.acc_seg: 93.6161 aux.loss_ce: 0.0785 aux.acc_seg: 91.2429 2023/06/07 17:19:17 - mmengine - INFO - Iter(train) [ 44050/240000] lr: 8.3485e-03 eta: 1 day, 15:48:04 time: 0.7107 data_time: 0.3408 memory: 17393 loss: 0.2512 decode.loss_ce: 0.1668 decode.acc_seg: 93.4376 aux.loss_ce: 0.0844 aux.acc_seg: 91.5038 2023/06/07 17:19:53 - mmengine - INFO - Iter(train) [ 44100/240000] lr: 8.3467e-03 eta: 1 day, 15:47:23 time: 0.7109 data_time: 0.1005 memory: 17396 loss: 0.2285 decode.loss_ce: 0.1515 decode.acc_seg: 93.2597 aux.loss_ce: 0.0770 aux.acc_seg: 91.3729 2023/06/07 17:20:29 - mmengine - INFO - Iter(train) [ 44150/240000] lr: 8.3448e-03 eta: 1 day, 15:46:45 time: 0.7170 data_time: 0.0121 memory: 17392 loss: 0.2287 decode.loss_ce: 0.1504 decode.acc_seg: 92.1529 aux.loss_ce: 0.0783 aux.acc_seg: 90.5413 2023/06/07 17:21:05 - mmengine - INFO - Iter(train) [ 44200/240000] lr: 8.3429e-03 eta: 1 day, 15:46:05 time: 0.7195 data_time: 0.0121 memory: 17392 loss: 0.2227 decode.loss_ce: 0.1454 decode.acc_seg: 94.5431 aux.loss_ce: 0.0773 aux.acc_seg: 92.6905 2023/06/07 17:21:41 - mmengine - INFO - Iter(train) [ 44250/240000] lr: 8.3410e-03 eta: 1 day, 15:45:25 time: 0.7105 data_time: 0.0121 memory: 17394 loss: 0.2401 decode.loss_ce: 0.1582 decode.acc_seg: 90.8975 aux.loss_ce: 0.0818 aux.acc_seg: 88.7182 2023/06/07 17:22:16 - mmengine - INFO - Iter(train) [ 44300/240000] lr: 8.3391e-03 eta: 1 day, 15:44:45 time: 0.7273 data_time: 0.1515 memory: 17392 loss: 0.2277 decode.loss_ce: 0.1514 decode.acc_seg: 92.6545 aux.loss_ce: 0.0763 aux.acc_seg: 90.3210 2023/06/07 17:22:52 - mmengine - INFO - Iter(train) [ 44350/240000] lr: 8.3372e-03 eta: 1 day, 15:44:06 time: 0.7153 data_time: 0.0122 memory: 17394 loss: 0.2354 decode.loss_ce: 0.1539 decode.acc_seg: 93.2724 aux.loss_ce: 0.0815 aux.acc_seg: 90.7089 2023/06/07 17:23:28 - mmengine - INFO - Iter(train) [ 44400/240000] lr: 8.3353e-03 eta: 1 day, 15:43:26 time: 0.7114 data_time: 0.0119 memory: 17392 loss: 0.2377 decode.loss_ce: 0.1561 decode.acc_seg: 94.4452 aux.loss_ce: 0.0816 aux.acc_seg: 91.8367 2023/06/07 17:24:04 - mmengine - INFO - Iter(train) [ 44450/240000] lr: 8.3334e-03 eta: 1 day, 15:42:47 time: 0.7429 data_time: 0.0571 memory: 17395 loss: 0.2383 decode.loss_ce: 0.1566 decode.acc_seg: 90.7982 aux.loss_ce: 0.0817 aux.acc_seg: 87.6384 2023/06/07 17:24:40 - mmengine - INFO - Iter(train) [ 44500/240000] lr: 8.3315e-03 eta: 1 day, 15:42:08 time: 0.7226 data_time: 0.0123 memory: 17391 loss: 0.2471 decode.loss_ce: 0.1636 decode.acc_seg: 92.4889 aux.loss_ce: 0.0835 aux.acc_seg: 91.6814 2023/06/07 17:25:16 - mmengine - INFO - Iter(train) [ 44550/240000] lr: 8.3296e-03 eta: 1 day, 15:41:29 time: 0.7265 data_time: 0.0120 memory: 17396 loss: 0.2212 decode.loss_ce: 0.1449 decode.acc_seg: 91.7917 aux.loss_ce: 0.0763 aux.acc_seg: 87.5672 2023/06/07 17:25:52 - mmengine - INFO - Iter(train) [ 44600/240000] lr: 8.3277e-03 eta: 1 day, 15:40:49 time: 0.7151 data_time: 0.0120 memory: 17393 loss: 0.2220 decode.loss_ce: 0.1460 decode.acc_seg: 92.7632 aux.loss_ce: 0.0760 aux.acc_seg: 90.3269 2023/06/07 17:26:28 - mmengine - INFO - Iter(train) [ 44650/240000] lr: 8.3258e-03 eta: 1 day, 15:40:10 time: 0.7141 data_time: 0.0121 memory: 17393 loss: 0.2149 decode.loss_ce: 0.1411 decode.acc_seg: 93.6373 aux.loss_ce: 0.0738 aux.acc_seg: 91.9837 2023/06/07 17:27:04 - mmengine - INFO - Iter(train) [ 44700/240000] lr: 8.3239e-03 eta: 1 day, 15:39:30 time: 0.7090 data_time: 0.0405 memory: 17396 loss: 0.2608 decode.loss_ce: 0.1738 decode.acc_seg: 93.9969 aux.loss_ce: 0.0870 aux.acc_seg: 91.6337 2023/06/07 17:27:40 - mmengine - INFO - Iter(train) [ 44750/240000] lr: 8.3220e-03 eta: 1 day, 15:38:51 time: 0.7304 data_time: 0.2377 memory: 17393 loss: 0.2281 decode.loss_ce: 0.1487 decode.acc_seg: 92.1485 aux.loss_ce: 0.0794 aux.acc_seg: 89.3863 2023/06/07 17:28:16 - mmengine - INFO - Iter(train) [ 44800/240000] lr: 8.3201e-03 eta: 1 day, 15:38:14 time: 0.7232 data_time: 0.0119 memory: 17394 loss: 0.2377 decode.loss_ce: 0.1558 decode.acc_seg: 91.5309 aux.loss_ce: 0.0819 aux.acc_seg: 90.6755 2023/06/07 17:28:52 - mmengine - INFO - Iter(train) [ 44850/240000] lr: 8.3182e-03 eta: 1 day, 15:37:34 time: 0.7165 data_time: 0.0123 memory: 17396 loss: 0.2568 decode.loss_ce: 0.1691 decode.acc_seg: 93.6776 aux.loss_ce: 0.0877 aux.acc_seg: 91.4283 2023/06/07 17:29:28 - mmengine - INFO - Iter(train) [ 44900/240000] lr: 8.3163e-03 eta: 1 day, 15:36:54 time: 0.7261 data_time: 0.0723 memory: 17394 loss: 0.2361 decode.loss_ce: 0.1551 decode.acc_seg: 91.5562 aux.loss_ce: 0.0810 aux.acc_seg: 90.5500 2023/06/07 17:30:04 - mmengine - INFO - Iter(train) [ 44950/240000] lr: 8.3144e-03 eta: 1 day, 15:36:16 time: 0.7288 data_time: 0.0119 memory: 17395 loss: 0.2347 decode.loss_ce: 0.1532 decode.acc_seg: 92.5561 aux.loss_ce: 0.0815 aux.acc_seg: 91.2677 2023/06/07 17:30:40 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 17:30:40 - mmengine - INFO - Iter(train) [ 45000/240000] lr: 8.3125e-03 eta: 1 day, 15:35:38 time: 0.7383 data_time: 0.0121 memory: 17392 loss: 0.2589 decode.loss_ce: 0.1725 decode.acc_seg: 93.4377 aux.loss_ce: 0.0864 aux.acc_seg: 90.7191 2023/06/07 17:31:16 - mmengine - INFO - Iter(train) [ 45050/240000] lr: 8.3107e-03 eta: 1 day, 15:34:59 time: 0.7096 data_time: 0.0119 memory: 17393 loss: 0.2431 decode.loss_ce: 0.1593 decode.acc_seg: 93.0950 aux.loss_ce: 0.0837 aux.acc_seg: 90.2288 2023/06/07 17:31:52 - mmengine - INFO - Iter(train) [ 45100/240000] lr: 8.3088e-03 eta: 1 day, 15:34:19 time: 0.7047 data_time: 0.0127 memory: 17395 loss: 0.2184 decode.loss_ce: 0.1437 decode.acc_seg: 93.7317 aux.loss_ce: 0.0746 aux.acc_seg: 92.1045 2023/06/07 17:32:28 - mmengine - INFO - Iter(train) [ 45150/240000] lr: 8.3069e-03 eta: 1 day, 15:33:40 time: 0.7386 data_time: 0.1131 memory: 17393 loss: 0.2254 decode.loss_ce: 0.1501 decode.acc_seg: 93.4186 aux.loss_ce: 0.0753 aux.acc_seg: 91.8738 2023/06/07 17:33:04 - mmengine - INFO - Iter(train) [ 45200/240000] lr: 8.3050e-03 eta: 1 day, 15:33:01 time: 0.7226 data_time: 0.2282 memory: 17393 loss: 0.2250 decode.loss_ce: 0.1471 decode.acc_seg: 94.0969 aux.loss_ce: 0.0780 aux.acc_seg: 87.8331 2023/06/07 17:33:40 - mmengine - INFO - Iter(train) [ 45250/240000] lr: 8.3031e-03 eta: 1 day, 15:32:22 time: 0.7245 data_time: 0.0900 memory: 17397 loss: 0.2320 decode.loss_ce: 0.1504 decode.acc_seg: 93.8381 aux.loss_ce: 0.0817 aux.acc_seg: 89.8636 2023/06/07 17:34:15 - mmengine - INFO - Iter(train) [ 45300/240000] lr: 8.3012e-03 eta: 1 day, 15:31:41 time: 0.7016 data_time: 0.2114 memory: 17392 loss: 0.2444 decode.loss_ce: 0.1638 decode.acc_seg: 92.9165 aux.loss_ce: 0.0806 aux.acc_seg: 91.0577 2023/06/07 17:34:51 - mmengine - INFO - Iter(train) [ 45350/240000] lr: 8.2993e-03 eta: 1 day, 15:31:00 time: 0.7124 data_time: 0.3441 memory: 17395 loss: 0.2567 decode.loss_ce: 0.1688 decode.acc_seg: 92.0797 aux.loss_ce: 0.0879 aux.acc_seg: 88.6780 2023/06/07 17:35:26 - mmengine - INFO - Iter(train) [ 45400/240000] lr: 8.2974e-03 eta: 1 day, 15:30:19 time: 0.7070 data_time: 0.3822 memory: 17394 loss: 0.2563 decode.loss_ce: 0.1700 decode.acc_seg: 94.2072 aux.loss_ce: 0.0862 aux.acc_seg: 92.1543 2023/06/07 17:36:02 - mmengine - INFO - Iter(train) [ 45450/240000] lr: 8.2955e-03 eta: 1 day, 15:29:38 time: 0.7059 data_time: 0.0977 memory: 17395 loss: 0.2418 decode.loss_ce: 0.1601 decode.acc_seg: 94.0062 aux.loss_ce: 0.0816 aux.acc_seg: 91.9921 2023/06/07 17:36:38 - mmengine - INFO - Iter(train) [ 45500/240000] lr: 8.2936e-03 eta: 1 day, 15:28:59 time: 0.7166 data_time: 0.1611 memory: 17398 loss: 0.2242 decode.loss_ce: 0.1473 decode.acc_seg: 91.8228 aux.loss_ce: 0.0769 aux.acc_seg: 89.2636 2023/06/07 17:37:14 - mmengine - INFO - Iter(train) [ 45550/240000] lr: 8.2917e-03 eta: 1 day, 15:28:20 time: 0.7356 data_time: 0.2185 memory: 17394 loss: 0.2422 decode.loss_ce: 0.1592 decode.acc_seg: 94.1457 aux.loss_ce: 0.0830 aux.acc_seg: 92.8181 2023/06/07 17:37:50 - mmengine - INFO - Iter(train) [ 45600/240000] lr: 8.2898e-03 eta: 1 day, 15:27:43 time: 0.7077 data_time: 0.0398 memory: 17395 loss: 0.2794 decode.loss_ce: 0.1821 decode.acc_seg: 92.2936 aux.loss_ce: 0.0973 aux.acc_seg: 89.2913 2023/06/07 17:38:25 - mmengine - INFO - Iter(train) [ 45650/240000] lr: 8.2879e-03 eta: 1 day, 15:27:01 time: 0.7125 data_time: 0.3024 memory: 17393 loss: 0.2774 decode.loss_ce: 0.1826 decode.acc_seg: 93.1565 aux.loss_ce: 0.0948 aux.acc_seg: 91.4412 2023/06/07 17:39:01 - mmengine - INFO - Iter(train) [ 45700/240000] lr: 8.2860e-03 eta: 1 day, 15:26:21 time: 0.7125 data_time: 0.3402 memory: 17395 loss: 0.2408 decode.loss_ce: 0.1591 decode.acc_seg: 91.4570 aux.loss_ce: 0.0816 aux.acc_seg: 90.2929 2023/06/07 17:39:37 - mmengine - INFO - Iter(train) [ 45750/240000] lr: 8.2841e-03 eta: 1 day, 15:25:44 time: 0.7208 data_time: 0.0120 memory: 17393 loss: 0.2366 decode.loss_ce: 0.1565 decode.acc_seg: 91.8517 aux.loss_ce: 0.0802 aux.acc_seg: 90.5111 2023/06/07 17:40:13 - mmengine - INFO - Iter(train) [ 45800/240000] lr: 8.2822e-03 eta: 1 day, 15:25:03 time: 0.7199 data_time: 0.1205 memory: 17393 loss: 0.2400 decode.loss_ce: 0.1566 decode.acc_seg: 93.6045 aux.loss_ce: 0.0834 aux.acc_seg: 90.9337 2023/06/07 17:40:48 - mmengine - INFO - Iter(train) [ 45850/240000] lr: 8.2803e-03 eta: 1 day, 15:24:22 time: 0.7138 data_time: 0.3907 memory: 17395 loss: 0.2384 decode.loss_ce: 0.1557 decode.acc_seg: 92.9162 aux.loss_ce: 0.0827 aux.acc_seg: 90.5594 2023/06/07 17:41:24 - mmengine - INFO - Iter(train) [ 45900/240000] lr: 8.2784e-03 eta: 1 day, 15:23:42 time: 0.7047 data_time: 0.1131 memory: 17395 loss: 0.2370 decode.loss_ce: 0.1562 decode.acc_seg: 91.4406 aux.loss_ce: 0.0808 aux.acc_seg: 89.7922 2023/06/07 17:42:00 - mmengine - INFO - Iter(train) [ 45950/240000] lr: 8.2765e-03 eta: 1 day, 15:23:01 time: 0.6955 data_time: 0.1742 memory: 17394 loss: 0.2467 decode.loss_ce: 0.1622 decode.acc_seg: 93.4854 aux.loss_ce: 0.0846 aux.acc_seg: 90.9874 2023/06/07 17:42:35 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 17:42:35 - mmengine - INFO - Iter(train) [ 46000/240000] lr: 8.2746e-03 eta: 1 day, 15:22:21 time: 0.7204 data_time: 0.1888 memory: 17393 loss: 0.2386 decode.loss_ce: 0.1573 decode.acc_seg: 91.3399 aux.loss_ce: 0.0813 aux.acc_seg: 89.4393 2023/06/07 17:43:12 - mmengine - INFO - Iter(train) [ 46050/240000] lr: 8.2727e-03 eta: 1 day, 15:21:43 time: 0.7173 data_time: 0.0119 memory: 17392 loss: 0.2463 decode.loss_ce: 0.1622 decode.acc_seg: 93.6709 aux.loss_ce: 0.0840 aux.acc_seg: 89.6469 2023/06/07 17:43:47 - mmengine - INFO - Iter(train) [ 46100/240000] lr: 8.2708e-03 eta: 1 day, 15:21:03 time: 0.7121 data_time: 0.0119 memory: 17395 loss: 0.2500 decode.loss_ce: 0.1625 decode.acc_seg: 92.7784 aux.loss_ce: 0.0875 aux.acc_seg: 89.7458 2023/06/07 17:44:24 - mmengine - INFO - Iter(train) [ 46150/240000] lr: 8.2689e-03 eta: 1 day, 15:20:25 time: 0.7190 data_time: 0.0122 memory: 17396 loss: 0.2388 decode.loss_ce: 0.1566 decode.acc_seg: 90.7138 aux.loss_ce: 0.0822 aux.acc_seg: 87.8749 2023/06/07 17:45:00 - mmengine - INFO - Iter(train) [ 46200/240000] lr: 8.2670e-03 eta: 1 day, 15:19:47 time: 0.7293 data_time: 0.0123 memory: 17393 loss: 0.2154 decode.loss_ce: 0.1410 decode.acc_seg: 93.7669 aux.loss_ce: 0.0745 aux.acc_seg: 91.0424 2023/06/07 17:45:36 - mmengine - INFO - Iter(train) [ 46250/240000] lr: 8.2652e-03 eta: 1 day, 15:19:10 time: 0.7244 data_time: 0.0125 memory: 17396 loss: 0.2337 decode.loss_ce: 0.1532 decode.acc_seg: 92.3456 aux.loss_ce: 0.0805 aux.acc_seg: 91.0702 2023/06/07 17:46:12 - mmengine - INFO - Iter(train) [ 46300/240000] lr: 8.2633e-03 eta: 1 day, 15:18:32 time: 0.7212 data_time: 0.0125 memory: 17393 loss: 0.2457 decode.loss_ce: 0.1607 decode.acc_seg: 92.8748 aux.loss_ce: 0.0851 aux.acc_seg: 89.2426 2023/06/07 17:46:48 - mmengine - INFO - Iter(train) [ 46350/240000] lr: 8.2614e-03 eta: 1 day, 15:17:52 time: 0.6988 data_time: 0.0122 memory: 17395 loss: 0.2439 decode.loss_ce: 0.1626 decode.acc_seg: 92.7833 aux.loss_ce: 0.0812 aux.acc_seg: 90.3698 2023/06/07 17:47:24 - mmengine - INFO - Iter(train) [ 46400/240000] lr: 8.2595e-03 eta: 1 day, 15:17:13 time: 0.7107 data_time: 0.0121 memory: 17395 loss: 0.2393 decode.loss_ce: 0.1587 decode.acc_seg: 93.5549 aux.loss_ce: 0.0805 aux.acc_seg: 92.0634 2023/06/07 17:48:00 - mmengine - INFO - Iter(train) [ 46450/240000] lr: 8.2576e-03 eta: 1 day, 15:16:34 time: 0.7245 data_time: 0.0123 memory: 17393 loss: 0.2450 decode.loss_ce: 0.1605 decode.acc_seg: 92.6042 aux.loss_ce: 0.0845 aux.acc_seg: 89.4798 2023/06/07 17:48:35 - mmengine - INFO - Iter(train) [ 46500/240000] lr: 8.2557e-03 eta: 1 day, 15:15:53 time: 0.7124 data_time: 0.0119 memory: 17391 loss: 0.2260 decode.loss_ce: 0.1507 decode.acc_seg: 94.0481 aux.loss_ce: 0.0753 aux.acc_seg: 92.4734 2023/06/07 17:49:11 - mmengine - INFO - Iter(train) [ 46550/240000] lr: 8.2538e-03 eta: 1 day, 15:15:13 time: 0.7069 data_time: 0.0120 memory: 17393 loss: 0.2340 decode.loss_ce: 0.1548 decode.acc_seg: 91.1125 aux.loss_ce: 0.0791 aux.acc_seg: 89.0475 2023/06/07 17:49:47 - mmengine - INFO - Iter(train) [ 46600/240000] lr: 8.2519e-03 eta: 1 day, 15:14:34 time: 0.7198 data_time: 0.1407 memory: 17392 loss: 0.2148 decode.loss_ce: 0.1381 decode.acc_seg: 93.7136 aux.loss_ce: 0.0767 aux.acc_seg: 91.6296 2023/06/07 17:50:23 - mmengine - INFO - Iter(train) [ 46650/240000] lr: 8.2500e-03 eta: 1 day, 15:13:54 time: 0.7081 data_time: 0.1525 memory: 17396 loss: 0.2394 decode.loss_ce: 0.1605 decode.acc_seg: 93.3756 aux.loss_ce: 0.0788 aux.acc_seg: 92.0261 2023/06/07 17:50:58 - mmengine - INFO - Iter(train) [ 46700/240000] lr: 8.2481e-03 eta: 1 day, 15:13:15 time: 0.7140 data_time: 0.1153 memory: 17395 loss: 0.2342 decode.loss_ce: 0.1526 decode.acc_seg: 93.2007 aux.loss_ce: 0.0815 aux.acc_seg: 90.8827 2023/06/07 17:51:34 - mmengine - INFO - Iter(train) [ 46750/240000] lr: 8.2462e-03 eta: 1 day, 15:12:35 time: 0.7266 data_time: 0.0194 memory: 17392 loss: 0.2676 decode.loss_ce: 0.1803 decode.acc_seg: 92.6971 aux.loss_ce: 0.0874 aux.acc_seg: 90.5057 2023/06/07 17:52:10 - mmengine - INFO - Iter(train) [ 46800/240000] lr: 8.2443e-03 eta: 1 day, 15:11:57 time: 0.7174 data_time: 0.0115 memory: 17396 loss: 0.2156 decode.loss_ce: 0.1414 decode.acc_seg: 93.5086 aux.loss_ce: 0.0742 aux.acc_seg: 91.7190 2023/06/07 17:52:46 - mmengine - INFO - Iter(train) [ 46850/240000] lr: 8.2424e-03 eta: 1 day, 15:11:17 time: 0.7213 data_time: 0.0121 memory: 17393 loss: 0.2305 decode.loss_ce: 0.1516 decode.acc_seg: 95.3120 aux.loss_ce: 0.0789 aux.acc_seg: 92.6358 2023/06/07 17:53:22 - mmengine - INFO - Iter(train) [ 46900/240000] lr: 8.2405e-03 eta: 1 day, 15:10:38 time: 0.7140 data_time: 0.0122 memory: 17393 loss: 0.2949 decode.loss_ce: 0.1955 decode.acc_seg: 90.4626 aux.loss_ce: 0.0994 aux.acc_seg: 86.7344 2023/06/07 17:53:58 - mmengine - INFO - Iter(train) [ 46950/240000] lr: 8.2386e-03 eta: 1 day, 15:10:00 time: 0.7191 data_time: 0.0122 memory: 17395 loss: 0.2466 decode.loss_ce: 0.1618 decode.acc_seg: 91.9322 aux.loss_ce: 0.0848 aux.acc_seg: 90.2325 2023/06/07 17:54:34 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 17:54:34 - mmengine - INFO - Iter(train) [ 47000/240000] lr: 8.2367e-03 eta: 1 day, 15:09:21 time: 0.7190 data_time: 0.1125 memory: 17392 loss: 0.2419 decode.loss_ce: 0.1596 decode.acc_seg: 91.9489 aux.loss_ce: 0.0823 aux.acc_seg: 89.7151 2023/06/07 17:55:10 - mmengine - INFO - Iter(train) [ 47050/240000] lr: 8.2348e-03 eta: 1 day, 15:08:41 time: 0.7192 data_time: 0.3961 memory: 17393 loss: 0.2497 decode.loss_ce: 0.1643 decode.acc_seg: 93.8165 aux.loss_ce: 0.0853 aux.acc_seg: 91.3683 2023/06/07 17:55:45 - mmengine - INFO - Iter(train) [ 47100/240000] lr: 8.2329e-03 eta: 1 day, 15:08:00 time: 0.7059 data_time: 0.3823 memory: 17394 loss: 0.2487 decode.loss_ce: 0.1652 decode.acc_seg: 94.3318 aux.loss_ce: 0.0835 aux.acc_seg: 92.0709 2023/06/07 17:56:21 - mmengine - INFO - Iter(train) [ 47150/240000] lr: 8.2310e-03 eta: 1 day, 15:07:19 time: 0.6967 data_time: 0.3735 memory: 17393 loss: 0.2469 decode.loss_ce: 0.1604 decode.acc_seg: 89.8530 aux.loss_ce: 0.0865 aux.acc_seg: 86.0668 2023/06/07 17:56:56 - mmengine - INFO - Iter(train) [ 47200/240000] lr: 8.2291e-03 eta: 1 day, 15:06:39 time: 0.7062 data_time: 0.2902 memory: 17392 loss: 0.2455 decode.loss_ce: 0.1627 decode.acc_seg: 90.9853 aux.loss_ce: 0.0828 aux.acc_seg: 89.7672 2023/06/07 17:57:32 - mmengine - INFO - Iter(train) [ 47250/240000] lr: 8.2272e-03 eta: 1 day, 15:05:59 time: 0.7123 data_time: 0.3882 memory: 17397 loss: 0.2553 decode.loss_ce: 0.1696 decode.acc_seg: 94.5175 aux.loss_ce: 0.0857 aux.acc_seg: 92.8796 2023/06/07 17:58:08 - mmengine - INFO - Iter(train) [ 47300/240000] lr: 8.2253e-03 eta: 1 day, 15:05:20 time: 0.7122 data_time: 0.3350 memory: 17395 loss: 0.2305 decode.loss_ce: 0.1497 decode.acc_seg: 93.0746 aux.loss_ce: 0.0808 aux.acc_seg: 89.3791 2023/06/07 17:58:43 - mmengine - INFO - Iter(train) [ 47350/240000] lr: 8.2234e-03 eta: 1 day, 15:04:40 time: 0.7148 data_time: 0.2712 memory: 17394 loss: 0.2266 decode.loss_ce: 0.1484 decode.acc_seg: 94.3988 aux.loss_ce: 0.0781 aux.acc_seg: 92.7952 2023/06/07 17:59:19 - mmengine - INFO - Iter(train) [ 47400/240000] lr: 8.2215e-03 eta: 1 day, 15:04:00 time: 0.7022 data_time: 0.1093 memory: 17396 loss: 0.2582 decode.loss_ce: 0.1711 decode.acc_seg: 93.2385 aux.loss_ce: 0.0871 aux.acc_seg: 91.7800 2023/06/07 17:59:54 - mmengine - INFO - Iter(train) [ 47450/240000] lr: 8.2196e-03 eta: 1 day, 15:03:19 time: 0.7138 data_time: 0.2600 memory: 17391 loss: 0.2151 decode.loss_ce: 0.1405 decode.acc_seg: 93.9844 aux.loss_ce: 0.0746 aux.acc_seg: 92.0899 2023/06/07 18:00:30 - mmengine - INFO - Iter(train) [ 47500/240000] lr: 8.2177e-03 eta: 1 day, 15:02:38 time: 0.7078 data_time: 0.1945 memory: 17392 loss: 0.2176 decode.loss_ce: 0.1430 decode.acc_seg: 94.2028 aux.loss_ce: 0.0745 aux.acc_seg: 91.0621 2023/06/07 18:01:06 - mmengine - INFO - Iter(train) [ 47550/240000] lr: 8.2158e-03 eta: 1 day, 15:01:59 time: 0.7201 data_time: 0.1392 memory: 17396 loss: 0.2320 decode.loss_ce: 0.1533 decode.acc_seg: 93.4988 aux.loss_ce: 0.0787 aux.acc_seg: 91.4095 2023/06/07 18:01:42 - mmengine - INFO - Iter(train) [ 47600/240000] lr: 8.2139e-03 eta: 1 day, 15:01:20 time: 0.7066 data_time: 0.3831 memory: 17395 loss: 0.2443 decode.loss_ce: 0.1593 decode.acc_seg: 93.9537 aux.loss_ce: 0.0850 aux.acc_seg: 90.8640 2023/06/07 18:02:18 - mmengine - INFO - Iter(train) [ 47650/240000] lr: 8.2120e-03 eta: 1 day, 15:00:43 time: 0.7209 data_time: 0.3973 memory: 17393 loss: 0.2253 decode.loss_ce: 0.1468 decode.acc_seg: 91.7764 aux.loss_ce: 0.0785 aux.acc_seg: 86.7280 2023/06/07 18:02:54 - mmengine - INFO - Iter(train) [ 47700/240000] lr: 8.2101e-03 eta: 1 day, 15:00:04 time: 0.7174 data_time: 0.3938 memory: 17391 loss: 0.2239 decode.loss_ce: 0.1475 decode.acc_seg: 93.7667 aux.loss_ce: 0.0764 aux.acc_seg: 90.4087 2023/06/07 18:03:29 - mmengine - INFO - Iter(train) [ 47750/240000] lr: 8.2082e-03 eta: 1 day, 14:59:23 time: 0.7084 data_time: 0.3848 memory: 17394 loss: 0.2285 decode.loss_ce: 0.1488 decode.acc_seg: 92.8948 aux.loss_ce: 0.0796 aux.acc_seg: 90.2853 2023/06/07 18:04:05 - mmengine - INFO - Iter(train) [ 47800/240000] lr: 8.2063e-03 eta: 1 day, 14:58:44 time: 0.7218 data_time: 0.0120 memory: 17393 loss: 0.2449 decode.loss_ce: 0.1618 decode.acc_seg: 92.1858 aux.loss_ce: 0.0831 aux.acc_seg: 91.7292 2023/06/07 18:04:41 - mmengine - INFO - Iter(train) [ 47850/240000] lr: 8.2044e-03 eta: 1 day, 14:58:05 time: 0.7211 data_time: 0.0120 memory: 17395 loss: 0.2371 decode.loss_ce: 0.1518 decode.acc_seg: 93.5527 aux.loss_ce: 0.0852 aux.acc_seg: 90.2192 2023/06/07 18:05:17 - mmengine - INFO - Iter(train) [ 47900/240000] lr: 8.2025e-03 eta: 1 day, 14:57:25 time: 0.7061 data_time: 0.0489 memory: 17394 loss: 0.2142 decode.loss_ce: 0.1398 decode.acc_seg: 92.0527 aux.loss_ce: 0.0745 aux.acc_seg: 89.1024 2023/06/07 18:05:53 - mmengine - INFO - Iter(train) [ 47950/240000] lr: 8.2006e-03 eta: 1 day, 14:56:46 time: 0.7216 data_time: 0.0120 memory: 17392 loss: 0.2397 decode.loss_ce: 0.1582 decode.acc_seg: 94.5160 aux.loss_ce: 0.0815 aux.acc_seg: 92.9717 2023/06/07 18:06:29 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 18:06:29 - mmengine - INFO - Iter(train) [ 48000/240000] lr: 8.1987e-03 eta: 1 day, 14:56:08 time: 0.7214 data_time: 0.0123 memory: 17397 loss: 0.2334 decode.loss_ce: 0.1532 decode.acc_seg: 91.9039 aux.loss_ce: 0.0802 aux.acc_seg: 90.1641 2023/06/07 18:06:29 - mmengine - INFO - Saving checkpoint at 48000 iterations 2023/06/07 18:06:31 - mmengine - INFO - Iter(val) [ 50/1297] eta: 0:00:38 time: 0.0338 data_time: 0.0259 memory: 203 2023/06/07 18:06:32 - mmengine - INFO - Iter(val) [ 100/1297] eta: 0:00:34 time: 0.0246 data_time: 0.0164 memory: 203 2023/06/07 18:06:34 - mmengine - INFO - Iter(val) [ 150/1297] eta: 0:00:33 time: 0.0349 data_time: 0.0267 memory: 203 2023/06/07 18:06:35 - mmengine - INFO - Iter(val) [ 200/1297] eta: 0:00:29 time: 0.0186 data_time: 0.0107 memory: 203 2023/06/07 18:06:36 - mmengine - INFO - Iter(val) [ 250/1297] eta: 0:00:28 time: 0.0301 data_time: 0.0222 memory: 203 2023/06/07 18:06:37 - mmengine - INFO - Iter(val) [ 300/1297] eta: 0:00:26 time: 0.0192 data_time: 0.0112 memory: 203 2023/06/07 18:06:38 - mmengine - INFO - Iter(val) [ 350/1297] eta: 0:00:24 time: 0.0256 data_time: 0.0175 memory: 203 2023/06/07 18:06:40 - mmengine - INFO - Iter(val) [ 400/1297] eta: 0:00:23 time: 0.0203 data_time: 0.0122 memory: 203 2023/06/07 18:06:41 - mmengine - INFO - Iter(val) [ 450/1297] eta: 0:00:21 time: 0.0262 data_time: 0.0181 memory: 203 2023/06/07 18:06:42 - mmengine - INFO - Iter(val) [ 500/1297] eta: 0:00:20 time: 0.0204 data_time: 0.0122 memory: 203 2023/06/07 18:06:43 - mmengine - INFO - Iter(val) [ 550/1297] eta: 0:00:18 time: 0.0273 data_time: 0.0192 memory: 203 2023/06/07 18:06:44 - mmengine - INFO - Iter(val) [ 600/1297] eta: 0:00:17 time: 0.0180 data_time: 0.0100 memory: 203 2023/06/07 18:06:46 - mmengine - INFO - Iter(val) [ 650/1297] eta: 0:00:16 time: 0.0236 data_time: 0.0155 memory: 203 2023/06/07 18:06:47 - mmengine - INFO - Iter(val) [ 700/1297] eta: 0:00:14 time: 0.0203 data_time: 0.0123 memory: 203 2023/06/07 18:06:48 - mmengine - INFO - Iter(val) [ 750/1297] eta: 0:00:13 time: 0.0244 data_time: 0.0164 memory: 203 2023/06/07 18:06:49 - mmengine - INFO - Iter(val) [ 800/1297] eta: 0:00:12 time: 0.0199 data_time: 0.0118 memory: 203 2023/06/07 18:06:50 - mmengine - INFO - Iter(val) [ 850/1297] eta: 0:00:11 time: 0.0268 data_time: 0.0187 memory: 203 2023/06/07 18:06:51 - mmengine - INFO - Iter(val) [ 900/1297] eta: 0:00:09 time: 0.0198 data_time: 0.0118 memory: 203 2023/06/07 18:06:53 - mmengine - INFO - Iter(val) [ 950/1297] eta: 0:00:08 time: 0.0256 data_time: 0.0175 memory: 203 2023/06/07 18:06:54 - mmengine - INFO - Iter(val) [1000/1297] eta: 0:00:07 time: 0.0221 data_time: 0.0140 memory: 203 2023/06/07 18:06:55 - mmengine - INFO - Iter(val) [1050/1297] eta: 0:00:06 time: 0.0299 data_time: 0.0218 memory: 203 2023/06/07 18:06:56 - mmengine - INFO - Iter(val) [1100/1297] eta: 0:00:04 time: 0.0210 data_time: 0.0129 memory: 203 2023/06/07 18:06:57 - mmengine - INFO - Iter(val) [1150/1297] eta: 0:00:03 time: 0.0283 data_time: 0.0205 memory: 203 2023/06/07 18:06:59 - mmengine - INFO - Iter(val) [1200/1297] eta: 0:00:02 time: 0.0192 data_time: 0.0111 memory: 203 2023/06/07 18:07:00 - mmengine - INFO - Iter(val) [1250/1297] eta: 0:00:01 time: 0.0274 data_time: 0.0191 memory: 203 2023/06/07 18:07:01 - mmengine - INFO - per class results: 2023/06/07 18:07:01 - mmengine - INFO - +------------+-------+-------+ | Class | IoU | Acc | +------------+-------+-------+ | background | 90.19 | 95.4 | | obstacle | 85.12 | 91.43 | | human | 52.21 | 61.67 | +------------+-------+-------+ 2023/06/07 18:07:01 - mmengine - INFO - Iter(val) [1297/1297] aAcc: 93.4300 mIoU: 75.8400 mAcc: 82.8300 data_time: 0.0160 time: 0.0241 2023/06/07 18:07:36 - mmengine - INFO - Iter(train) [ 48050/240000] lr: 8.1968e-03 eta: 1 day, 14:55:26 time: 0.7109 data_time: 0.2612 memory: 17394 loss: 0.2323 decode.loss_ce: 0.1511 decode.acc_seg: 92.5065 aux.loss_ce: 0.0812 aux.acc_seg: 89.4914 2023/06/07 18:08:11 - mmengine - INFO - Iter(train) [ 48100/240000] lr: 8.1950e-03 eta: 1 day, 14:54:46 time: 0.7091 data_time: 0.3432 memory: 17393 loss: 0.2157 decode.loss_ce: 0.1423 decode.acc_seg: 94.2007 aux.loss_ce: 0.0734 aux.acc_seg: 93.1193 2023/06/07 18:08:47 - mmengine - INFO - Iter(train) [ 48150/240000] lr: 8.1931e-03 eta: 1 day, 14:54:06 time: 0.7246 data_time: 0.2500 memory: 17393 loss: 0.2306 decode.loss_ce: 0.1517 decode.acc_seg: 93.2236 aux.loss_ce: 0.0789 aux.acc_seg: 90.3246 2023/06/07 18:09:23 - mmengine - INFO - Iter(train) [ 48200/240000] lr: 8.1912e-03 eta: 1 day, 14:53:26 time: 0.7036 data_time: 0.2637 memory: 17396 loss: 0.2188 decode.loss_ce: 0.1432 decode.acc_seg: 94.4861 aux.loss_ce: 0.0756 aux.acc_seg: 92.5390 2023/06/07 18:09:58 - mmengine - INFO - Iter(train) [ 48250/240000] lr: 8.1893e-03 eta: 1 day, 14:52:45 time: 0.7026 data_time: 0.1943 memory: 17394 loss: 0.2278 decode.loss_ce: 0.1465 decode.acc_seg: 93.7628 aux.loss_ce: 0.0813 aux.acc_seg: 91.7714 2023/06/07 18:10:33 - mmengine - INFO - Iter(train) [ 48300/240000] lr: 8.1874e-03 eta: 1 day, 14:52:05 time: 0.7206 data_time: 0.2586 memory: 17396 loss: 0.2134 decode.loss_ce: 0.1403 decode.acc_seg: 91.3639 aux.loss_ce: 0.0731 aux.acc_seg: 89.2542 2023/06/07 18:11:09 - mmengine - INFO - Iter(train) [ 48350/240000] lr: 8.1855e-03 eta: 1 day, 14:51:25 time: 0.7283 data_time: 0.2671 memory: 17395 loss: 0.2292 decode.loss_ce: 0.1509 decode.acc_seg: 94.3698 aux.loss_ce: 0.0783 aux.acc_seg: 89.3724 2023/06/07 18:11:45 - mmengine - INFO - Iter(train) [ 48400/240000] lr: 8.1836e-03 eta: 1 day, 14:50:47 time: 0.7204 data_time: 0.1200 memory: 17393 loss: 0.2323 decode.loss_ce: 0.1500 decode.acc_seg: 92.3087 aux.loss_ce: 0.0824 aux.acc_seg: 88.8721 2023/06/07 18:12:21 - mmengine - INFO - Iter(train) [ 48450/240000] lr: 8.1817e-03 eta: 1 day, 14:50:06 time: 0.7245 data_time: 0.3330 memory: 17395 loss: 0.2427 decode.loss_ce: 0.1585 decode.acc_seg: 92.6336 aux.loss_ce: 0.0842 aux.acc_seg: 90.5520 2023/06/07 18:12:56 - mmengine - INFO - Iter(train) [ 48500/240000] lr: 8.1798e-03 eta: 1 day, 14:49:27 time: 0.7174 data_time: 0.2251 memory: 17393 loss: 0.2440 decode.loss_ce: 0.1600 decode.acc_seg: 92.7499 aux.loss_ce: 0.0840 aux.acc_seg: 89.7932 2023/06/07 18:13:33 - mmengine - INFO - Iter(train) [ 48550/240000] lr: 8.1779e-03 eta: 1 day, 14:48:50 time: 0.7236 data_time: 0.1043 memory: 17392 loss: 0.2289 decode.loss_ce: 0.1528 decode.acc_seg: 93.4081 aux.loss_ce: 0.0761 aux.acc_seg: 92.0606 2023/06/07 18:14:08 - mmengine - INFO - Iter(train) [ 48600/240000] lr: 8.1760e-03 eta: 1 day, 14:48:09 time: 0.7098 data_time: 0.3838 memory: 17396 loss: 0.2351 decode.loss_ce: 0.1552 decode.acc_seg: 93.8636 aux.loss_ce: 0.0798 aux.acc_seg: 92.3794 2023/06/07 18:14:44 - mmengine - INFO - Iter(train) [ 48650/240000] lr: 8.1741e-03 eta: 1 day, 14:47:29 time: 0.7192 data_time: 0.3955 memory: 17393 loss: 0.2345 decode.loss_ce: 0.1526 decode.acc_seg: 93.7956 aux.loss_ce: 0.0818 aux.acc_seg: 89.9976 2023/06/07 18:15:19 - mmengine - INFO - Iter(train) [ 48700/240000] lr: 8.1722e-03 eta: 1 day, 14:46:49 time: 0.7061 data_time: 0.0121 memory: 17393 loss: 0.2492 decode.loss_ce: 0.1628 decode.acc_seg: 91.1646 aux.loss_ce: 0.0865 aux.acc_seg: 88.7377 2023/06/07 18:15:55 - mmengine - INFO - Iter(train) [ 48750/240000] lr: 8.1703e-03 eta: 1 day, 14:46:11 time: 0.7023 data_time: 0.0120 memory: 17395 loss: 0.2516 decode.loss_ce: 0.1663 decode.acc_seg: 93.7362 aux.loss_ce: 0.0854 aux.acc_seg: 89.7930 2023/06/07 18:16:31 - mmengine - INFO - Iter(train) [ 48800/240000] lr: 8.1684e-03 eta: 1 day, 14:45:32 time: 0.7217 data_time: 0.0122 memory: 17393 loss: 0.2261 decode.loss_ce: 0.1505 decode.acc_seg: 91.5720 aux.loss_ce: 0.0755 aux.acc_seg: 90.0110 2023/06/07 18:17:07 - mmengine - INFO - Iter(train) [ 48850/240000] lr: 8.1665e-03 eta: 1 day, 14:44:53 time: 0.7190 data_time: 0.0122 memory: 17396 loss: 0.2340 decode.loss_ce: 0.1519 decode.acc_seg: 93.5565 aux.loss_ce: 0.0821 aux.acc_seg: 91.7102 2023/06/07 18:17:43 - mmengine - INFO - Iter(train) [ 48900/240000] lr: 8.1646e-03 eta: 1 day, 14:44:15 time: 0.7140 data_time: 0.0123 memory: 17392 loss: 0.2410 decode.loss_ce: 0.1574 decode.acc_seg: 91.3922 aux.loss_ce: 0.0836 aux.acc_seg: 87.5735 2023/06/07 18:18:20 - mmengine - INFO - Iter(train) [ 48950/240000] lr: 8.1627e-03 eta: 1 day, 14:43:38 time: 0.7214 data_time: 0.0124 memory: 17394 loss: 0.2487 decode.loss_ce: 0.1632 decode.acc_seg: 92.5281 aux.loss_ce: 0.0855 aux.acc_seg: 91.4228 2023/06/07 18:18:55 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 18:18:55 - mmengine - INFO - Iter(train) [ 49000/240000] lr: 8.1608e-03 eta: 1 day, 14:42:58 time: 0.7075 data_time: 0.0123 memory: 17395 loss: 0.2305 decode.loss_ce: 0.1509 decode.acc_seg: 94.0692 aux.loss_ce: 0.0795 aux.acc_seg: 92.0249 2023/06/07 18:19:31 - mmengine - INFO - Iter(train) [ 49050/240000] lr: 8.1589e-03 eta: 1 day, 14:42:20 time: 0.7017 data_time: 0.0122 memory: 17394 loss: 0.2411 decode.loss_ce: 0.1593 decode.acc_seg: 92.6440 aux.loss_ce: 0.0818 aux.acc_seg: 89.8292 2023/06/07 18:20:07 - mmengine - INFO - Iter(train) [ 49100/240000] lr: 8.1570e-03 eta: 1 day, 14:41:41 time: 0.7258 data_time: 0.0123 memory: 17393 loss: 0.2141 decode.loss_ce: 0.1387 decode.acc_seg: 94.8997 aux.loss_ce: 0.0754 aux.acc_seg: 92.2188 2023/06/07 18:20:44 - mmengine - INFO - Iter(train) [ 49150/240000] lr: 8.1551e-03 eta: 1 day, 14:41:05 time: 0.7392 data_time: 0.0126 memory: 17397 loss: 0.2471 decode.loss_ce: 0.1620 decode.acc_seg: 93.9312 aux.loss_ce: 0.0851 aux.acc_seg: 91.8020 2023/06/07 18:21:20 - mmengine - INFO - Iter(train) [ 49200/240000] lr: 8.1532e-03 eta: 1 day, 14:40:28 time: 0.7163 data_time: 0.0124 memory: 17394 loss: 0.2390 decode.loss_ce: 0.1580 decode.acc_seg: 92.2727 aux.loss_ce: 0.0811 aux.acc_seg: 90.2864 2023/06/07 18:21:56 - mmengine - INFO - Iter(train) [ 49250/240000] lr: 8.1513e-03 eta: 1 day, 14:39:48 time: 0.7031 data_time: 0.0120 memory: 17392 loss: 0.2335 decode.loss_ce: 0.1535 decode.acc_seg: 93.6054 aux.loss_ce: 0.0799 aux.acc_seg: 91.2894 2023/06/07 18:22:31 - mmengine - INFO - Iter(train) [ 49300/240000] lr: 8.1494e-03 eta: 1 day, 14:39:07 time: 0.7174 data_time: 0.0120 memory: 17393 loss: 0.2263 decode.loss_ce: 0.1476 decode.acc_seg: 94.4039 aux.loss_ce: 0.0786 aux.acc_seg: 92.4969 2023/06/07 18:23:07 - mmengine - INFO - Iter(train) [ 49350/240000] lr: 8.1475e-03 eta: 1 day, 14:38:29 time: 0.7062 data_time: 0.0118 memory: 17392 loss: 0.2294 decode.loss_ce: 0.1502 decode.acc_seg: 93.0837 aux.loss_ce: 0.0792 aux.acc_seg: 90.8349 2023/06/07 18:23:43 - mmengine - INFO - Iter(train) [ 49400/240000] lr: 8.1456e-03 eta: 1 day, 14:37:49 time: 0.7063 data_time: 0.0119 memory: 17393 loss: 0.2269 decode.loss_ce: 0.1486 decode.acc_seg: 93.9509 aux.loss_ce: 0.0783 aux.acc_seg: 92.1387 2023/06/07 18:24:18 - mmengine - INFO - Iter(train) [ 49450/240000] lr: 8.1437e-03 eta: 1 day, 14:37:09 time: 0.7068 data_time: 0.0117 memory: 17395 loss: 0.2122 decode.loss_ce: 0.1378 decode.acc_seg: 94.0124 aux.loss_ce: 0.0744 aux.acc_seg: 92.3645 2023/06/07 18:24:54 - mmengine - INFO - Iter(train) [ 49500/240000] lr: 8.1418e-03 eta: 1 day, 14:36:31 time: 0.7045 data_time: 0.0652 memory: 17395 loss: 0.2194 decode.loss_ce: 0.1448 decode.acc_seg: 92.9019 aux.loss_ce: 0.0746 aux.acc_seg: 89.9888 2023/06/07 18:25:30 - mmengine - INFO - Iter(train) [ 49550/240000] lr: 8.1399e-03 eta: 1 day, 14:35:53 time: 0.7048 data_time: 0.0118 memory: 17393 loss: 0.2291 decode.loss_ce: 0.1515 decode.acc_seg: 94.1234 aux.loss_ce: 0.0776 aux.acc_seg: 92.3045 2023/06/07 18:26:07 - mmengine - INFO - Iter(train) [ 49600/240000] lr: 8.1380e-03 eta: 1 day, 14:35:15 time: 0.7198 data_time: 0.0118 memory: 17394 loss: 0.2405 decode.loss_ce: 0.1586 decode.acc_seg: 92.9777 aux.loss_ce: 0.0819 aux.acc_seg: 92.1264 2023/06/07 18:26:43 - mmengine - INFO - Iter(train) [ 49650/240000] lr: 8.1361e-03 eta: 1 day, 14:34:37 time: 0.7172 data_time: 0.0122 memory: 17392 loss: 0.2251 decode.loss_ce: 0.1462 decode.acc_seg: 94.4052 aux.loss_ce: 0.0789 aux.acc_seg: 91.9964 2023/06/07 18:27:18 - mmengine - INFO - Iter(train) [ 49700/240000] lr: 8.1342e-03 eta: 1 day, 14:33:57 time: 0.7213 data_time: 0.0173 memory: 17393 loss: 0.2387 decode.loss_ce: 0.1576 decode.acc_seg: 93.1688 aux.loss_ce: 0.0810 aux.acc_seg: 91.2762 2023/06/07 18:27:54 - mmengine - INFO - Iter(train) [ 49750/240000] lr: 8.1323e-03 eta: 1 day, 14:33:18 time: 0.7046 data_time: 0.0119 memory: 17396 loss: 0.2415 decode.loss_ce: 0.1608 decode.acc_seg: 94.1765 aux.loss_ce: 0.0807 aux.acc_seg: 92.5397 2023/06/07 18:28:30 - mmengine - INFO - Iter(train) [ 49800/240000] lr: 8.1304e-03 eta: 1 day, 14:32:40 time: 0.7216 data_time: 0.0120 memory: 17397 loss: 0.2666 decode.loss_ce: 0.1732 decode.acc_seg: 91.1891 aux.loss_ce: 0.0933 aux.acc_seg: 88.7164 2023/06/07 18:29:06 - mmengine - INFO - Iter(train) [ 49850/240000] lr: 8.1285e-03 eta: 1 day, 14:32:02 time: 0.7206 data_time: 0.0120 memory: 17394 loss: 0.2394 decode.loss_ce: 0.1581 decode.acc_seg: 90.1087 aux.loss_ce: 0.0813 aux.acc_seg: 87.4896 2023/06/07 18:29:42 - mmengine - INFO - Iter(train) [ 49900/240000] lr: 8.1266e-03 eta: 1 day, 14:31:23 time: 0.7052 data_time: 0.0121 memory: 17396 loss: 0.2240 decode.loss_ce: 0.1464 decode.acc_seg: 93.8244 aux.loss_ce: 0.0776 aux.acc_seg: 91.2912 2023/06/07 18:30:18 - mmengine - INFO - Iter(train) [ 49950/240000] lr: 8.1247e-03 eta: 1 day, 14:30:44 time: 0.7206 data_time: 0.0125 memory: 17395 loss: 0.2474 decode.loss_ce: 0.1597 decode.acc_seg: 92.4540 aux.loss_ce: 0.0877 aux.acc_seg: 86.9706 2023/06/07 18:30:53 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 18:30:53 - mmengine - INFO - Iter(train) [ 50000/240000] lr: 8.1228e-03 eta: 1 day, 14:30:03 time: 0.7085 data_time: 0.0120 memory: 17395 loss: 0.2434 decode.loss_ce: 0.1601 decode.acc_seg: 90.6632 aux.loss_ce: 0.0833 aux.acc_seg: 87.7993 2023/06/07 18:31:30 - mmengine - INFO - Iter(train) [ 50050/240000] lr: 8.1209e-03 eta: 1 day, 14:29:26 time: 0.7150 data_time: 0.0119 memory: 17394 loss: 0.2311 decode.loss_ce: 0.1510 decode.acc_seg: 94.5096 aux.loss_ce: 0.0801 aux.acc_seg: 92.0802 2023/06/07 18:32:06 - mmengine - INFO - Iter(train) [ 50100/240000] lr: 8.1190e-03 eta: 1 day, 14:28:48 time: 0.7219 data_time: 0.0119 memory: 17395 loss: 0.2091 decode.loss_ce: 0.1348 decode.acc_seg: 94.6821 aux.loss_ce: 0.0742 aux.acc_seg: 91.7356 2023/06/07 18:32:41 - mmengine - INFO - Iter(train) [ 50150/240000] lr: 8.1171e-03 eta: 1 day, 14:28:09 time: 0.7215 data_time: 0.0126 memory: 17394 loss: 0.2504 decode.loss_ce: 0.1636 decode.acc_seg: 91.2172 aux.loss_ce: 0.0868 aux.acc_seg: 88.6724 2023/06/07 18:33:18 - mmengine - INFO - Iter(train) [ 50200/240000] lr: 8.1152e-03 eta: 1 day, 14:27:31 time: 0.7138 data_time: 0.0124 memory: 17394 loss: 0.2282 decode.loss_ce: 0.1514 decode.acc_seg: 93.0000 aux.loss_ce: 0.0767 aux.acc_seg: 90.1356 2023/06/07 18:33:53 - mmengine - INFO - Iter(train) [ 50250/240000] lr: 8.1133e-03 eta: 1 day, 14:26:52 time: 0.6969 data_time: 0.0119 memory: 17393 loss: 0.2457 decode.loss_ce: 0.1637 decode.acc_seg: 95.2594 aux.loss_ce: 0.0820 aux.acc_seg: 92.9852 2023/06/07 18:34:29 - mmengine - INFO - Iter(train) [ 50300/240000] lr: 8.1114e-03 eta: 1 day, 14:26:13 time: 0.7330 data_time: 0.0121 memory: 17392 loss: 0.2205 decode.loss_ce: 0.1440 decode.acc_seg: 94.4753 aux.loss_ce: 0.0764 aux.acc_seg: 93.0184 2023/06/07 18:35:05 - mmengine - INFO - Iter(train) [ 50350/240000] lr: 8.1095e-03 eta: 1 day, 14:25:34 time: 0.7147 data_time: 0.0122 memory: 17395 loss: 0.2432 decode.loss_ce: 0.1610 decode.acc_seg: 93.3346 aux.loss_ce: 0.0822 aux.acc_seg: 92.1270 2023/06/07 18:35:40 - mmengine - INFO - Iter(train) [ 50400/240000] lr: 8.1076e-03 eta: 1 day, 14:24:54 time: 0.7249 data_time: 0.0120 memory: 17392 loss: 0.2248 decode.loss_ce: 0.1480 decode.acc_seg: 94.0435 aux.loss_ce: 0.0768 aux.acc_seg: 92.1900 2023/06/07 18:36:16 - mmengine - INFO - Iter(train) [ 50450/240000] lr: 8.1057e-03 eta: 1 day, 14:24:15 time: 0.7127 data_time: 0.0122 memory: 17395 loss: 0.2368 decode.loss_ce: 0.1546 decode.acc_seg: 93.5669 aux.loss_ce: 0.0822 aux.acc_seg: 91.8334 2023/06/07 18:36:52 - mmengine - INFO - Iter(train) [ 50500/240000] lr: 8.1038e-03 eta: 1 day, 14:23:36 time: 0.7218 data_time: 0.0124 memory: 17395 loss: 0.2284 decode.loss_ce: 0.1504 decode.acc_seg: 93.5369 aux.loss_ce: 0.0780 aux.acc_seg: 91.0618 2023/06/07 18:37:28 - mmengine - INFO - Iter(train) [ 50550/240000] lr: 8.1019e-03 eta: 1 day, 14:22:57 time: 0.7074 data_time: 0.0126 memory: 17394 loss: 0.2501 decode.loss_ce: 0.1646 decode.acc_seg: 92.0232 aux.loss_ce: 0.0855 aux.acc_seg: 91.1298 2023/06/07 18:38:04 - mmengine - INFO - Iter(train) [ 50600/240000] lr: 8.1000e-03 eta: 1 day, 14:22:19 time: 0.7083 data_time: 0.0120 memory: 17392 loss: 0.2322 decode.loss_ce: 0.1505 decode.acc_seg: 93.4237 aux.loss_ce: 0.0817 aux.acc_seg: 90.7560 2023/06/07 18:38:40 - mmengine - INFO - Iter(train) [ 50650/240000] lr: 8.0981e-03 eta: 1 day, 14:21:42 time: 0.7314 data_time: 0.0123 memory: 17395 loss: 0.2314 decode.loss_ce: 0.1500 decode.acc_seg: 93.5632 aux.loss_ce: 0.0814 aux.acc_seg: 91.7162 2023/06/07 18:39:16 - mmengine - INFO - Iter(train) [ 50700/240000] lr: 8.0962e-03 eta: 1 day, 14:21:03 time: 0.7173 data_time: 0.0121 memory: 17396 loss: 0.2187 decode.loss_ce: 0.1440 decode.acc_seg: 92.2975 aux.loss_ce: 0.0747 aux.acc_seg: 91.6376 2023/06/07 18:39:52 - mmengine - INFO - Iter(train) [ 50750/240000] lr: 8.0943e-03 eta: 1 day, 14:20:26 time: 0.7284 data_time: 0.0122 memory: 17392 loss: 0.2273 decode.loss_ce: 0.1494 decode.acc_seg: 93.7088 aux.loss_ce: 0.0779 aux.acc_seg: 92.2886 2023/06/07 18:40:29 - mmengine - INFO - Iter(train) [ 50800/240000] lr: 8.0924e-03 eta: 1 day, 14:19:49 time: 0.7056 data_time: 0.0122 memory: 17392 loss: 0.2365 decode.loss_ce: 0.1528 decode.acc_seg: 92.3666 aux.loss_ce: 0.0837 aux.acc_seg: 89.3937 2023/06/07 18:41:05 - mmengine - INFO - Iter(train) [ 50850/240000] lr: 8.0905e-03 eta: 1 day, 14:19:10 time: 0.7259 data_time: 0.0120 memory: 17393 loss: 0.2426 decode.loss_ce: 0.1582 decode.acc_seg: 94.0895 aux.loss_ce: 0.0844 aux.acc_seg: 91.9258 2023/06/07 18:41:41 - mmengine - INFO - Iter(train) [ 50900/240000] lr: 8.0886e-03 eta: 1 day, 14:18:34 time: 0.7238 data_time: 0.0123 memory: 17395 loss: 0.2361 decode.loss_ce: 0.1555 decode.acc_seg: 94.9785 aux.loss_ce: 0.0806 aux.acc_seg: 93.5006 2023/06/07 18:42:17 - mmengine - INFO - Iter(train) [ 50950/240000] lr: 8.0867e-03 eta: 1 day, 14:17:55 time: 0.7256 data_time: 0.0121 memory: 17392 loss: 0.2353 decode.loss_ce: 0.1557 decode.acc_seg: 93.6791 aux.loss_ce: 0.0796 aux.acc_seg: 90.6053 2023/06/07 18:42:53 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 18:42:53 - mmengine - INFO - Iter(train) [ 51000/240000] lr: 8.0848e-03 eta: 1 day, 14:17:16 time: 0.7236 data_time: 0.0122 memory: 17393 loss: 0.2329 decode.loss_ce: 0.1556 decode.acc_seg: 95.1855 aux.loss_ce: 0.0773 aux.acc_seg: 93.1757 2023/06/07 18:43:29 - mmengine - INFO - Iter(train) [ 51050/240000] lr: 8.0829e-03 eta: 1 day, 14:16:40 time: 0.7301 data_time: 0.0122 memory: 17393 loss: 0.2263 decode.loss_ce: 0.1474 decode.acc_seg: 93.2599 aux.loss_ce: 0.0788 aux.acc_seg: 91.2983 2023/06/07 18:44:05 - mmengine - INFO - Iter(train) [ 51100/240000] lr: 8.0810e-03 eta: 1 day, 14:16:02 time: 0.7126 data_time: 0.0119 memory: 17394 loss: 0.2463 decode.loss_ce: 0.1619 decode.acc_seg: 94.4068 aux.loss_ce: 0.0844 aux.acc_seg: 92.5631 2023/06/07 18:44:41 - mmengine - INFO - Iter(train) [ 51150/240000] lr: 8.0791e-03 eta: 1 day, 14:15:22 time: 0.6929 data_time: 0.0117 memory: 17392 loss: 0.2237 decode.loss_ce: 0.1482 decode.acc_seg: 93.9000 aux.loss_ce: 0.0756 aux.acc_seg: 90.8667 2023/06/07 18:45:17 - mmengine - INFO - Iter(train) [ 51200/240000] lr: 8.0772e-03 eta: 1 day, 14:14:44 time: 0.7095 data_time: 0.0119 memory: 17393 loss: 0.2229 decode.loss_ce: 0.1456 decode.acc_seg: 92.9391 aux.loss_ce: 0.0773 aux.acc_seg: 90.7473 2023/06/07 18:45:53 - mmengine - INFO - Iter(train) [ 51250/240000] lr: 8.0753e-03 eta: 1 day, 14:14:06 time: 0.7071 data_time: 0.0118 memory: 17395 loss: 0.2169 decode.loss_ce: 0.1440 decode.acc_seg: 93.7549 aux.loss_ce: 0.0729 aux.acc_seg: 91.3364 2023/06/07 18:46:29 - mmengine - INFO - Iter(train) [ 51300/240000] lr: 8.0734e-03 eta: 1 day, 14:13:28 time: 0.7270 data_time: 0.0126 memory: 17395 loss: 0.2097 decode.loss_ce: 0.1360 decode.acc_seg: 92.8312 aux.loss_ce: 0.0737 aux.acc_seg: 90.6244 2023/06/07 18:47:05 - mmengine - INFO - Iter(train) [ 51350/240000] lr: 8.0715e-03 eta: 1 day, 14:12:50 time: 0.7208 data_time: 0.0116 memory: 17396 loss: 0.2071 decode.loss_ce: 0.1351 decode.acc_seg: 94.9963 aux.loss_ce: 0.0720 aux.acc_seg: 93.5715 2023/06/07 18:47:41 - mmengine - INFO - Iter(train) [ 51400/240000] lr: 8.0696e-03 eta: 1 day, 14:12:13 time: 0.7273 data_time: 0.0121 memory: 17393 loss: 0.2347 decode.loss_ce: 0.1557 decode.acc_seg: 94.1963 aux.loss_ce: 0.0790 aux.acc_seg: 92.5677 2023/06/07 18:48:17 - mmengine - INFO - Iter(train) [ 51450/240000] lr: 8.0677e-03 eta: 1 day, 14:11:34 time: 0.7169 data_time: 0.0128 memory: 17393 loss: 0.2723 decode.loss_ce: 0.1812 decode.acc_seg: 90.1624 aux.loss_ce: 0.0911 aux.acc_seg: 88.1903 2023/06/07 18:48:53 - mmengine - INFO - Iter(train) [ 51500/240000] lr: 8.0658e-03 eta: 1 day, 14:10:55 time: 0.7057 data_time: 0.0121 memory: 17394 loss: 0.2399 decode.loss_ce: 0.1574 decode.acc_seg: 92.3751 aux.loss_ce: 0.0825 aux.acc_seg: 89.7767 2023/06/07 18:49:29 - mmengine - INFO - Iter(train) [ 51550/240000] lr: 8.0639e-03 eta: 1 day, 14:10:17 time: 0.7136 data_time: 0.0122 memory: 17396 loss: 0.2270 decode.loss_ce: 0.1475 decode.acc_seg: 92.4924 aux.loss_ce: 0.0795 aux.acc_seg: 90.8785 2023/06/07 18:50:05 - mmengine - INFO - Iter(train) [ 51600/240000] lr: 8.0620e-03 eta: 1 day, 14:09:39 time: 0.7375 data_time: 0.0119 memory: 17394 loss: 0.2429 decode.loss_ce: 0.1602 decode.acc_seg: 93.9933 aux.loss_ce: 0.0828 aux.acc_seg: 91.2543 2023/06/07 18:50:41 - mmengine - INFO - Iter(train) [ 51650/240000] lr: 8.0600e-03 eta: 1 day, 14:09:00 time: 0.6977 data_time: 0.0121 memory: 17395 loss: 0.2375 decode.loss_ce: 0.1562 decode.acc_seg: 89.7767 aux.loss_ce: 0.0813 aux.acc_seg: 88.1369 2023/06/07 18:51:16 - mmengine - INFO - Iter(train) [ 51700/240000] lr: 8.0581e-03 eta: 1 day, 14:08:20 time: 0.7066 data_time: 0.0121 memory: 17394 loss: 0.2414 decode.loss_ce: 0.1597 decode.acc_seg: 93.0973 aux.loss_ce: 0.0817 aux.acc_seg: 89.9617 2023/06/07 18:51:52 - mmengine - INFO - Iter(train) [ 51750/240000] lr: 8.0562e-03 eta: 1 day, 14:07:42 time: 0.7173 data_time: 0.0123 memory: 17393 loss: 0.2096 decode.loss_ce: 0.1387 decode.acc_seg: 93.5763 aux.loss_ce: 0.0709 aux.acc_seg: 92.7455 2023/06/07 18:52:28 - mmengine - INFO - Iter(train) [ 51800/240000] lr: 8.0543e-03 eta: 1 day, 14:07:03 time: 0.7068 data_time: 0.0121 memory: 17393 loss: 0.2211 decode.loss_ce: 0.1440 decode.acc_seg: 94.2836 aux.loss_ce: 0.0771 aux.acc_seg: 89.3293 2023/06/07 18:53:04 - mmengine - INFO - Iter(train) [ 51850/240000] lr: 8.0524e-03 eta: 1 day, 14:06:25 time: 0.7277 data_time: 0.0123 memory: 17394 loss: 0.2225 decode.loss_ce: 0.1465 decode.acc_seg: 93.3395 aux.loss_ce: 0.0760 aux.acc_seg: 92.2789 2023/06/07 18:53:40 - mmengine - INFO - Iter(train) [ 51900/240000] lr: 8.0505e-03 eta: 1 day, 14:05:47 time: 0.7092 data_time: 0.0121 memory: 17395 loss: 0.2071 decode.loss_ce: 0.1355 decode.acc_seg: 93.4771 aux.loss_ce: 0.0716 aux.acc_seg: 89.6178 2023/06/07 18:54:15 - mmengine - INFO - Iter(train) [ 51950/240000] lr: 8.0486e-03 eta: 1 day, 14:05:07 time: 0.7059 data_time: 0.2802 memory: 17393 loss: 0.2181 decode.loss_ce: 0.1429 decode.acc_seg: 93.2525 aux.loss_ce: 0.0752 aux.acc_seg: 90.9710 2023/06/07 18:54:51 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 18:54:51 - mmengine - INFO - Iter(train) [ 52000/240000] lr: 8.0467e-03 eta: 1 day, 14:04:28 time: 0.7076 data_time: 0.3389 memory: 17394 loss: 0.2338 decode.loss_ce: 0.1514 decode.acc_seg: 93.5482 aux.loss_ce: 0.0824 aux.acc_seg: 91.5898 2023/06/07 18:55:27 - mmengine - INFO - Iter(train) [ 52050/240000] lr: 8.0448e-03 eta: 1 day, 14:03:48 time: 0.7142 data_time: 0.0690 memory: 17393 loss: 0.2584 decode.loss_ce: 0.1690 decode.acc_seg: 93.6242 aux.loss_ce: 0.0894 aux.acc_seg: 88.9485 2023/06/07 18:56:02 - mmengine - INFO - Iter(train) [ 52100/240000] lr: 8.0429e-03 eta: 1 day, 14:03:08 time: 0.7177 data_time: 0.2589 memory: 17393 loss: 0.2493 decode.loss_ce: 0.1603 decode.acc_seg: 92.7673 aux.loss_ce: 0.0890 aux.acc_seg: 87.8968 2023/06/07 18:56:38 - mmengine - INFO - Iter(train) [ 52150/240000] lr: 8.0410e-03 eta: 1 day, 14:02:29 time: 0.7208 data_time: 0.0145 memory: 17393 loss: 0.2349 decode.loss_ce: 0.1537 decode.acc_seg: 89.6507 aux.loss_ce: 0.0813 aux.acc_seg: 86.9831 2023/06/07 18:57:14 - mmengine - INFO - Iter(train) [ 52200/240000] lr: 8.0391e-03 eta: 1 day, 14:01:50 time: 0.6947 data_time: 0.0184 memory: 17394 loss: 0.2405 decode.loss_ce: 0.1577 decode.acc_seg: 93.8952 aux.loss_ce: 0.0828 aux.acc_seg: 92.6859 2023/06/07 18:57:49 - mmengine - INFO - Iter(train) [ 52250/240000] lr: 8.0372e-03 eta: 1 day, 14:01:11 time: 0.7126 data_time: 0.0155 memory: 17393 loss: 0.2369 decode.loss_ce: 0.1547 decode.acc_seg: 94.0716 aux.loss_ce: 0.0822 aux.acc_seg: 91.3459 2023/06/07 18:58:25 - mmengine - INFO - Iter(train) [ 52300/240000] lr: 8.0353e-03 eta: 1 day, 14:00:32 time: 0.7267 data_time: 0.0119 memory: 17395 loss: 0.2301 decode.loss_ce: 0.1475 decode.acc_seg: 93.2581 aux.loss_ce: 0.0826 aux.acc_seg: 89.8445 2023/06/07 18:59:01 - mmengine - INFO - Iter(train) [ 52350/240000] lr: 8.0334e-03 eta: 1 day, 13:59:55 time: 0.7263 data_time: 0.0123 memory: 17392 loss: 0.2458 decode.loss_ce: 0.1631 decode.acc_seg: 90.4400 aux.loss_ce: 0.0827 aux.acc_seg: 88.3589 2023/06/07 18:59:37 - mmengine - INFO - Iter(train) [ 52400/240000] lr: 8.0315e-03 eta: 1 day, 13:59:15 time: 0.7243 data_time: 0.0119 memory: 17393 loss: 0.2493 decode.loss_ce: 0.1643 decode.acc_seg: 92.7230 aux.loss_ce: 0.0850 aux.acc_seg: 90.7449 2023/06/07 19:00:13 - mmengine - INFO - Iter(train) [ 52450/240000] lr: 8.0296e-03 eta: 1 day, 13:58:37 time: 0.7217 data_time: 0.0124 memory: 17395 loss: 0.2222 decode.loss_ce: 0.1473 decode.acc_seg: 92.8250 aux.loss_ce: 0.0748 aux.acc_seg: 90.7559 2023/06/07 19:00:49 - mmengine - INFO - Iter(train) [ 52500/240000] lr: 8.0277e-03 eta: 1 day, 13:57:57 time: 0.7220 data_time: 0.0121 memory: 17392 loss: 0.2401 decode.loss_ce: 0.1578 decode.acc_seg: 93.2892 aux.loss_ce: 0.0823 aux.acc_seg: 90.2018 2023/06/07 19:01:25 - mmengine - INFO - Iter(train) [ 52550/240000] lr: 8.0258e-03 eta: 1 day, 13:57:20 time: 0.7216 data_time: 0.0124 memory: 17394 loss: 0.2405 decode.loss_ce: 0.1595 decode.acc_seg: 92.1559 aux.loss_ce: 0.0810 aux.acc_seg: 90.2712 2023/06/07 19:02:01 - mmengine - INFO - Iter(train) [ 52600/240000] lr: 8.0239e-03 eta: 1 day, 13:56:42 time: 0.7163 data_time: 0.0121 memory: 17393 loss: 0.2362 decode.loss_ce: 0.1519 decode.acc_seg: 94.7117 aux.loss_ce: 0.0843 aux.acc_seg: 92.5751 2023/06/07 19:02:36 - mmengine - INFO - Iter(train) [ 52650/240000] lr: 8.0220e-03 eta: 1 day, 13:56:02 time: 0.6999 data_time: 0.0119 memory: 17394 loss: 0.2238 decode.loss_ce: 0.1465 decode.acc_seg: 93.3751 aux.loss_ce: 0.0773 aux.acc_seg: 91.2124 2023/06/07 19:03:11 - mmengine - INFO - Iter(train) [ 52700/240000] lr: 8.0201e-03 eta: 1 day, 13:55:21 time: 0.7124 data_time: 0.2671 memory: 17395 loss: 0.2245 decode.loss_ce: 0.1476 decode.acc_seg: 93.4696 aux.loss_ce: 0.0768 aux.acc_seg: 91.6413 2023/06/07 19:03:47 - mmengine - INFO - Iter(train) [ 52750/240000] lr: 8.0182e-03 eta: 1 day, 13:54:41 time: 0.7122 data_time: 0.1994 memory: 17395 loss: 0.2589 decode.loss_ce: 0.1692 decode.acc_seg: 92.1371 aux.loss_ce: 0.0896 aux.acc_seg: 90.0667 2023/06/07 19:04:23 - mmengine - INFO - Iter(train) [ 52800/240000] lr: 8.0163e-03 eta: 1 day, 13:54:03 time: 0.7251 data_time: 0.0119 memory: 17393 loss: 0.2335 decode.loss_ce: 0.1518 decode.acc_seg: 93.4283 aux.loss_ce: 0.0817 aux.acc_seg: 90.2466 2023/06/07 19:04:58 - mmengine - INFO - Iter(train) [ 52850/240000] lr: 8.0144e-03 eta: 1 day, 13:53:22 time: 0.7037 data_time: 0.2533 memory: 17394 loss: 0.2211 decode.loss_ce: 0.1456 decode.acc_seg: 92.4387 aux.loss_ce: 0.0754 aux.acc_seg: 90.5766 2023/06/07 19:05:34 - mmengine - INFO - Iter(train) [ 52900/240000] lr: 8.0125e-03 eta: 1 day, 13:52:43 time: 0.7073 data_time: 0.1531 memory: 17391 loss: 0.2372 decode.loss_ce: 0.1533 decode.acc_seg: 94.2579 aux.loss_ce: 0.0839 aux.acc_seg: 92.7642 2023/06/07 19:06:10 - mmengine - INFO - Iter(train) [ 52950/240000] lr: 8.0106e-03 eta: 1 day, 13:52:04 time: 0.7125 data_time: 0.0118 memory: 17392 loss: 0.2101 decode.loss_ce: 0.1351 decode.acc_seg: 94.1616 aux.loss_ce: 0.0750 aux.acc_seg: 92.0780 2023/06/07 19:06:45 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 19:06:45 - mmengine - INFO - Iter(train) [ 53000/240000] lr: 8.0087e-03 eta: 1 day, 13:51:25 time: 0.7149 data_time: 0.2859 memory: 17392 loss: 0.2278 decode.loss_ce: 0.1478 decode.acc_seg: 94.5565 aux.loss_ce: 0.0800 aux.acc_seg: 92.9632 2023/06/07 19:07:21 - mmengine - INFO - Iter(train) [ 53050/240000] lr: 8.0068e-03 eta: 1 day, 13:50:46 time: 0.7077 data_time: 0.2055 memory: 17394 loss: 0.2371 decode.loss_ce: 0.1544 decode.acc_seg: 92.8409 aux.loss_ce: 0.0827 aux.acc_seg: 90.6156 2023/06/07 19:07:56 - mmengine - INFO - Iter(train) [ 53100/240000] lr: 8.0049e-03 eta: 1 day, 13:50:06 time: 0.7150 data_time: 0.2180 memory: 17396 loss: 0.2155 decode.loss_ce: 0.1412 decode.acc_seg: 95.4806 aux.loss_ce: 0.0742 aux.acc_seg: 93.9809 2023/06/07 19:08:32 - mmengine - INFO - Iter(train) [ 53150/240000] lr: 8.0030e-03 eta: 1 day, 13:49:26 time: 0.7101 data_time: 0.3865 memory: 17395 loss: 0.2206 decode.loss_ce: 0.1440 decode.acc_seg: 90.3893 aux.loss_ce: 0.0766 aux.acc_seg: 89.4441 2023/06/07 19:09:07 - mmengine - INFO - Iter(train) [ 53200/240000] lr: 8.0011e-03 eta: 1 day, 13:48:47 time: 0.7244 data_time: 0.4010 memory: 17397 loss: 0.2110 decode.loss_ce: 0.1372 decode.acc_seg: 93.0510 aux.loss_ce: 0.0738 aux.acc_seg: 91.7199 2023/06/07 19:09:43 - mmengine - INFO - Iter(train) [ 53250/240000] lr: 7.9992e-03 eta: 1 day, 13:48:07 time: 0.7164 data_time: 0.3925 memory: 17394 loss: 0.2252 decode.loss_ce: 0.1445 decode.acc_seg: 92.9299 aux.loss_ce: 0.0807 aux.acc_seg: 90.4658 2023/06/07 19:10:19 - mmengine - INFO - Iter(train) [ 53300/240000] lr: 7.9973e-03 eta: 1 day, 13:47:29 time: 0.7253 data_time: 0.4019 memory: 17395 loss: 0.2249 decode.loss_ce: 0.1489 decode.acc_seg: 92.0996 aux.loss_ce: 0.0760 aux.acc_seg: 90.4480 2023/06/07 19:10:55 - mmengine - INFO - Iter(train) [ 53350/240000] lr: 7.9954e-03 eta: 1 day, 13:46:50 time: 0.7097 data_time: 0.3862 memory: 17395 loss: 0.2576 decode.loss_ce: 0.1703 decode.acc_seg: 92.8673 aux.loss_ce: 0.0872 aux.acc_seg: 91.5660 2023/06/07 19:11:30 - mmengine - INFO - Iter(train) [ 53400/240000] lr: 7.9935e-03 eta: 1 day, 13:46:11 time: 0.6964 data_time: 0.3723 memory: 17393 loss: 0.2434 decode.loss_ce: 0.1596 decode.acc_seg: 93.7686 aux.loss_ce: 0.0838 aux.acc_seg: 90.8586 2023/06/07 19:12:06 - mmengine - INFO - Iter(train) [ 53450/240000] lr: 7.9916e-03 eta: 1 day, 13:45:31 time: 0.7147 data_time: 0.3908 memory: 17394 loss: 0.2307 decode.loss_ce: 0.1525 decode.acc_seg: 92.4440 aux.loss_ce: 0.0782 aux.acc_seg: 90.4363 2023/06/07 19:12:42 - mmengine - INFO - Iter(train) [ 53500/240000] lr: 7.9896e-03 eta: 1 day, 13:44:54 time: 0.7306 data_time: 0.4070 memory: 17393 loss: 0.2145 decode.loss_ce: 0.1395 decode.acc_seg: 94.3019 aux.loss_ce: 0.0750 aux.acc_seg: 91.4871 2023/06/07 19:13:17 - mmengine - INFO - Iter(train) [ 53550/240000] lr: 7.9877e-03 eta: 1 day, 13:44:14 time: 0.7244 data_time: 0.3995 memory: 17393 loss: 0.2537 decode.loss_ce: 0.1677 decode.acc_seg: 92.7232 aux.loss_ce: 0.0860 aux.acc_seg: 89.3410 2023/06/07 19:13:53 - mmengine - INFO - Iter(train) [ 53600/240000] lr: 7.9858e-03 eta: 1 day, 13:43:35 time: 0.7199 data_time: 0.3964 memory: 17394 loss: 0.2258 decode.loss_ce: 0.1473 decode.acc_seg: 93.4855 aux.loss_ce: 0.0785 aux.acc_seg: 89.1140 2023/06/07 19:14:29 - mmengine - INFO - Iter(train) [ 53650/240000] lr: 7.9839e-03 eta: 1 day, 13:42:56 time: 0.7299 data_time: 0.4059 memory: 17394 loss: 0.2370 decode.loss_ce: 0.1554 decode.acc_seg: 93.6733 aux.loss_ce: 0.0817 aux.acc_seg: 92.4292 2023/06/07 19:15:05 - mmengine - INFO - Iter(train) [ 53700/240000] lr: 7.9820e-03 eta: 1 day, 13:42:19 time: 0.7212 data_time: 0.3972 memory: 17392 loss: 0.2339 decode.loss_ce: 0.1536 decode.acc_seg: 94.0509 aux.loss_ce: 0.0803 aux.acc_seg: 91.9032 2023/06/07 19:15:41 - mmengine - INFO - Iter(train) [ 53750/240000] lr: 7.9801e-03 eta: 1 day, 13:41:40 time: 0.7217 data_time: 0.3988 memory: 17394 loss: 0.2375 decode.loss_ce: 0.1558 decode.acc_seg: 93.9709 aux.loss_ce: 0.0817 aux.acc_seg: 92.0614 2023/06/07 19:16:16 - mmengine - INFO - Iter(train) [ 53800/240000] lr: 7.9782e-03 eta: 1 day, 13:41:01 time: 0.7195 data_time: 0.3959 memory: 17395 loss: 0.2152 decode.loss_ce: 0.1412 decode.acc_seg: 92.6350 aux.loss_ce: 0.0740 aux.acc_seg: 89.5883 2023/06/07 19:16:52 - mmengine - INFO - Iter(train) [ 53850/240000] lr: 7.9763e-03 eta: 1 day, 13:40:22 time: 0.7153 data_time: 0.3920 memory: 17395 loss: 0.2134 decode.loss_ce: 0.1400 decode.acc_seg: 93.8016 aux.loss_ce: 0.0734 aux.acc_seg: 91.5707 2023/06/07 19:17:28 - mmengine - INFO - Iter(train) [ 53900/240000] lr: 7.9744e-03 eta: 1 day, 13:39:43 time: 0.7124 data_time: 0.3889 memory: 17395 loss: 0.2116 decode.loss_ce: 0.1374 decode.acc_seg: 94.3249 aux.loss_ce: 0.0742 aux.acc_seg: 92.2866 2023/06/07 19:18:04 - mmengine - INFO - Iter(train) [ 53950/240000] lr: 7.9725e-03 eta: 1 day, 13:39:05 time: 0.7041 data_time: 0.3810 memory: 17391 loss: 0.2430 decode.loss_ce: 0.1595 decode.acc_seg: 90.1879 aux.loss_ce: 0.0835 aux.acc_seg: 88.5755 2023/06/07 19:18:39 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 19:18:39 - mmengine - INFO - Iter(train) [ 54000/240000] lr: 7.9706e-03 eta: 1 day, 13:38:25 time: 0.7197 data_time: 0.3959 memory: 17394 loss: 0.2055 decode.loss_ce: 0.1335 decode.acc_seg: 93.5034 aux.loss_ce: 0.0720 aux.acc_seg: 91.5900 2023/06/07 19:19:15 - mmengine - INFO - Iter(train) [ 54050/240000] lr: 7.9687e-03 eta: 1 day, 13:37:48 time: 0.7233 data_time: 0.3997 memory: 17392 loss: 0.2204 decode.loss_ce: 0.1427 decode.acc_seg: 93.6982 aux.loss_ce: 0.0777 aux.acc_seg: 90.4897 2023/06/07 19:19:51 - mmengine - INFO - Iter(train) [ 54100/240000] lr: 7.9668e-03 eta: 1 day, 13:37:10 time: 0.7159 data_time: 0.3918 memory: 17395 loss: 0.2496 decode.loss_ce: 0.1634 decode.acc_seg: 90.5179 aux.loss_ce: 0.0862 aux.acc_seg: 85.5469 2023/06/07 19:20:28 - mmengine - INFO - Iter(train) [ 54150/240000] lr: 7.9649e-03 eta: 1 day, 13:36:33 time: 0.7154 data_time: 0.3920 memory: 17394 loss: 0.2505 decode.loss_ce: 0.1643 decode.acc_seg: 91.9121 aux.loss_ce: 0.0862 aux.acc_seg: 91.0249 2023/06/07 19:21:03 - mmengine - INFO - Iter(train) [ 54200/240000] lr: 7.9630e-03 eta: 1 day, 13:35:54 time: 0.7174 data_time: 0.3942 memory: 17394 loss: 0.2211 decode.loss_ce: 0.1466 decode.acc_seg: 94.0896 aux.loss_ce: 0.0745 aux.acc_seg: 91.7763 2023/06/07 19:21:40 - mmengine - INFO - Iter(train) [ 54250/240000] lr: 7.9611e-03 eta: 1 day, 13:35:17 time: 0.7003 data_time: 0.3767 memory: 17393 loss: 0.2141 decode.loss_ce: 0.1393 decode.acc_seg: 93.4432 aux.loss_ce: 0.0748 aux.acc_seg: 88.7697 2023/06/07 19:22:16 - mmengine - INFO - Iter(train) [ 54300/240000] lr: 7.9592e-03 eta: 1 day, 13:34:41 time: 0.7200 data_time: 0.3966 memory: 17394 loss: 0.2829 decode.loss_ce: 0.1952 decode.acc_seg: 91.8211 aux.loss_ce: 0.0878 aux.acc_seg: 89.5442 2023/06/07 19:22:52 - mmengine - INFO - Iter(train) [ 54350/240000] lr: 7.9573e-03 eta: 1 day, 13:34:01 time: 0.7246 data_time: 0.4013 memory: 17394 loss: 0.2634 decode.loss_ce: 0.1715 decode.acc_seg: 88.3444 aux.loss_ce: 0.0919 aux.acc_seg: 86.1773 2023/06/07 19:23:27 - mmengine - INFO - Iter(train) [ 54400/240000] lr: 7.9554e-03 eta: 1 day, 13:33:21 time: 0.6886 data_time: 0.3645 memory: 17394 loss: 0.2270 decode.loss_ce: 0.1479 decode.acc_seg: 93.5398 aux.loss_ce: 0.0791 aux.acc_seg: 92.2986 2023/06/07 19:24:03 - mmengine - INFO - Iter(train) [ 54450/240000] lr: 7.9535e-03 eta: 1 day, 13:32:42 time: 0.7182 data_time: 0.3941 memory: 17396 loss: 0.2283 decode.loss_ce: 0.1498 decode.acc_seg: 92.3600 aux.loss_ce: 0.0785 aux.acc_seg: 90.2248 2023/06/07 19:24:38 - mmengine - INFO - Iter(train) [ 54500/240000] lr: 7.9516e-03 eta: 1 day, 13:32:03 time: 0.7165 data_time: 0.3932 memory: 17394 loss: 0.2226 decode.loss_ce: 0.1453 decode.acc_seg: 90.7254 aux.loss_ce: 0.0773 aux.acc_seg: 88.4979 2023/06/07 19:25:14 - mmengine - INFO - Iter(train) [ 54550/240000] lr: 7.9497e-03 eta: 1 day, 13:31:24 time: 0.7096 data_time: 0.3857 memory: 17395 loss: 0.2397 decode.loss_ce: 0.1550 decode.acc_seg: 92.4572 aux.loss_ce: 0.0846 aux.acc_seg: 90.0352 2023/06/07 19:25:49 - mmengine - INFO - Iter(train) [ 54600/240000] lr: 7.9478e-03 eta: 1 day, 13:30:43 time: 0.7062 data_time: 0.3778 memory: 17392 loss: 0.2170 decode.loss_ce: 0.1438 decode.acc_seg: 93.7148 aux.loss_ce: 0.0733 aux.acc_seg: 92.1086 2023/06/07 19:26:25 - mmengine - INFO - Iter(train) [ 54650/240000] lr: 7.9459e-03 eta: 1 day, 13:30:04 time: 0.7020 data_time: 0.3637 memory: 17393 loss: 0.2358 decode.loss_ce: 0.1542 decode.acc_seg: 92.4092 aux.loss_ce: 0.0817 aux.acc_seg: 91.2782 2023/06/07 19:27:00 - mmengine - INFO - Iter(train) [ 54700/240000] lr: 7.9439e-03 eta: 1 day, 13:29:25 time: 0.7114 data_time: 0.1218 memory: 17393 loss: 0.2743 decode.loss_ce: 0.1824 decode.acc_seg: 93.9410 aux.loss_ce: 0.0919 aux.acc_seg: 92.2082 2023/06/07 19:27:36 - mmengine - INFO - Iter(train) [ 54750/240000] lr: 7.9420e-03 eta: 1 day, 13:28:47 time: 0.7222 data_time: 0.0550 memory: 17392 loss: 0.2364 decode.loss_ce: 0.1545 decode.acc_seg: 91.9485 aux.loss_ce: 0.0820 aux.acc_seg: 89.8117 2023/06/07 19:28:11 - mmengine - INFO - Iter(train) [ 54800/240000] lr: 7.9401e-03 eta: 1 day, 13:28:07 time: 0.7006 data_time: 0.2010 memory: 17394 loss: 0.2260 decode.loss_ce: 0.1471 decode.acc_seg: 93.5804 aux.loss_ce: 0.0789 aux.acc_seg: 90.9961 2023/06/07 19:28:47 - mmengine - INFO - Iter(train) [ 54850/240000] lr: 7.9382e-03 eta: 1 day, 13:27:28 time: 0.7093 data_time: 0.2822 memory: 17394 loss: 0.2234 decode.loss_ce: 0.1468 decode.acc_seg: 90.8707 aux.loss_ce: 0.0766 aux.acc_seg: 89.0143 2023/06/07 19:29:23 - mmengine - INFO - Iter(train) [ 54900/240000] lr: 7.9363e-03 eta: 1 day, 13:26:48 time: 0.7265 data_time: 0.2979 memory: 17394 loss: 0.2263 decode.loss_ce: 0.1467 decode.acc_seg: 92.7711 aux.loss_ce: 0.0796 aux.acc_seg: 90.3663 2023/06/07 19:29:58 - mmengine - INFO - Iter(train) [ 54950/240000] lr: 7.9344e-03 eta: 1 day, 13:26:08 time: 0.7039 data_time: 0.0800 memory: 17392 loss: 0.2193 decode.loss_ce: 0.1441 decode.acc_seg: 93.0949 aux.loss_ce: 0.0752 aux.acc_seg: 91.9717 2023/06/07 19:30:34 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 19:30:34 - mmengine - INFO - Iter(train) [ 55000/240000] lr: 7.9325e-03 eta: 1 day, 13:25:29 time: 0.7202 data_time: 0.2339 memory: 17392 loss: 0.2368 decode.loss_ce: 0.1583 decode.acc_seg: 93.2713 aux.loss_ce: 0.0785 aux.acc_seg: 91.2509 2023/06/07 19:31:10 - mmengine - INFO - Iter(train) [ 55050/240000] lr: 7.9306e-03 eta: 1 day, 13:24:51 time: 0.7153 data_time: 0.0121 memory: 17392 loss: 0.2340 decode.loss_ce: 0.1541 decode.acc_seg: 93.0969 aux.loss_ce: 0.0798 aux.acc_seg: 90.8025 2023/06/07 19:31:45 - mmengine - INFO - Iter(train) [ 55100/240000] lr: 7.9287e-03 eta: 1 day, 13:24:12 time: 0.7173 data_time: 0.0119 memory: 17397 loss: 0.2384 decode.loss_ce: 0.1553 decode.acc_seg: 92.5729 aux.loss_ce: 0.0831 aux.acc_seg: 88.2839 2023/06/07 19:32:21 - mmengine - INFO - Iter(train) [ 55150/240000] lr: 7.9268e-03 eta: 1 day, 13:23:32 time: 0.6954 data_time: 0.1773 memory: 17394 loss: 0.2160 decode.loss_ce: 0.1386 decode.acc_seg: 93.9018 aux.loss_ce: 0.0774 aux.acc_seg: 92.1510 2023/06/07 19:32:56 - mmengine - INFO - Iter(train) [ 55200/240000] lr: 7.9249e-03 eta: 1 day, 13:22:53 time: 0.7157 data_time: 0.3925 memory: 17394 loss: 0.2157 decode.loss_ce: 0.1425 decode.acc_seg: 93.3427 aux.loss_ce: 0.0732 aux.acc_seg: 91.9318 2023/06/07 19:33:32 - mmengine - INFO - Iter(train) [ 55250/240000] lr: 7.9230e-03 eta: 1 day, 13:22:14 time: 0.7105 data_time: 0.3869 memory: 17395 loss: 0.2208 decode.loss_ce: 0.1462 decode.acc_seg: 92.8032 aux.loss_ce: 0.0747 aux.acc_seg: 91.4996 2023/06/07 19:34:08 - mmengine - INFO - Iter(train) [ 55300/240000] lr: 7.9211e-03 eta: 1 day, 13:21:36 time: 0.7254 data_time: 0.4022 memory: 17394 loss: 0.2264 decode.loss_ce: 0.1469 decode.acc_seg: 92.8607 aux.loss_ce: 0.0795 aux.acc_seg: 90.7551 2023/06/07 19:34:44 - mmengine - INFO - Iter(train) [ 55350/240000] lr: 7.9192e-03 eta: 1 day, 13:20:58 time: 0.7300 data_time: 0.4065 memory: 17393 loss: 0.2290 decode.loss_ce: 0.1500 decode.acc_seg: 93.1821 aux.loss_ce: 0.0790 aux.acc_seg: 90.7394 2023/06/07 19:35:20 - mmengine - INFO - Iter(train) [ 55400/240000] lr: 7.9173e-03 eta: 1 day, 13:20:21 time: 0.7129 data_time: 0.3889 memory: 17394 loss: 0.2486 decode.loss_ce: 0.1642 decode.acc_seg: 92.7305 aux.loss_ce: 0.0844 aux.acc_seg: 90.6681 2023/06/07 19:35:56 - mmengine - INFO - Iter(train) [ 55450/240000] lr: 7.9154e-03 eta: 1 day, 13:19:42 time: 0.7018 data_time: 0.3782 memory: 17395 loss: 0.2074 decode.loss_ce: 0.1360 decode.acc_seg: 94.1456 aux.loss_ce: 0.0714 aux.acc_seg: 92.0735 2023/06/07 19:36:31 - mmengine - INFO - Iter(train) [ 55500/240000] lr: 7.9135e-03 eta: 1 day, 13:19:04 time: 0.7101 data_time: 0.3861 memory: 17397 loss: 0.2512 decode.loss_ce: 0.1608 decode.acc_seg: 93.5210 aux.loss_ce: 0.0904 aux.acc_seg: 88.1997 2023/06/07 19:37:07 - mmengine - INFO - Iter(train) [ 55550/240000] lr: 7.9116e-03 eta: 1 day, 13:18:26 time: 0.7252 data_time: 0.4019 memory: 17393 loss: 0.2272 decode.loss_ce: 0.1480 decode.acc_seg: 91.2968 aux.loss_ce: 0.0793 aux.acc_seg: 89.1183 2023/06/07 19:37:43 - mmengine - INFO - Iter(train) [ 55600/240000] lr: 7.9096e-03 eta: 1 day, 13:17:48 time: 0.7312 data_time: 0.4075 memory: 17393 loss: 0.2399 decode.loss_ce: 0.1574 decode.acc_seg: 91.6253 aux.loss_ce: 0.0825 aux.acc_seg: 89.3727 2023/06/07 19:38:19 - mmengine - INFO - Iter(train) [ 55650/240000] lr: 7.9077e-03 eta: 1 day, 13:17:10 time: 0.7204 data_time: 0.3968 memory: 17397 loss: 0.2444 decode.loss_ce: 0.1587 decode.acc_seg: 90.0946 aux.loss_ce: 0.0857 aux.acc_seg: 84.4350 2023/06/07 19:38:55 - mmengine - INFO - Iter(train) [ 55700/240000] lr: 7.9058e-03 eta: 1 day, 13:16:32 time: 0.7191 data_time: 0.3956 memory: 17392 loss: 0.2367 decode.loss_ce: 0.1555 decode.acc_seg: 92.3086 aux.loss_ce: 0.0812 aux.acc_seg: 90.9528 2023/06/07 19:39:31 - mmengine - INFO - Iter(train) [ 55750/240000] lr: 7.9039e-03 eta: 1 day, 13:15:53 time: 0.7072 data_time: 0.3838 memory: 17393 loss: 0.2395 decode.loss_ce: 0.1583 decode.acc_seg: 95.7410 aux.loss_ce: 0.0812 aux.acc_seg: 94.4289 2023/06/07 19:40:06 - mmengine - INFO - Iter(train) [ 55800/240000] lr: 7.9020e-03 eta: 1 day, 13:15:14 time: 0.7016 data_time: 0.3782 memory: 17394 loss: 0.2341 decode.loss_ce: 0.1542 decode.acc_seg: 90.5867 aux.loss_ce: 0.0799 aux.acc_seg: 89.0205 2023/06/07 19:40:42 - mmengine - INFO - Iter(train) [ 55850/240000] lr: 7.9001e-03 eta: 1 day, 13:14:36 time: 0.7393 data_time: 0.4155 memory: 17395 loss: 0.2003 decode.loss_ce: 0.1291 decode.acc_seg: 95.3832 aux.loss_ce: 0.0712 aux.acc_seg: 91.6658 2023/06/07 19:41:18 - mmengine - INFO - Iter(train) [ 55900/240000] lr: 7.8982e-03 eta: 1 day, 13:13:57 time: 0.7158 data_time: 0.3925 memory: 17395 loss: 0.2389 decode.loss_ce: 0.1574 decode.acc_seg: 86.4796 aux.loss_ce: 0.0815 aux.acc_seg: 84.1025 2023/06/07 19:41:54 - mmengine - INFO - Iter(train) [ 55950/240000] lr: 7.8963e-03 eta: 1 day, 13:13:18 time: 0.7240 data_time: 0.3857 memory: 17396 loss: 0.2164 decode.loss_ce: 0.1413 decode.acc_seg: 94.7086 aux.loss_ce: 0.0751 aux.acc_seg: 91.5613 2023/06/07 19:42:30 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 19:42:30 - mmengine - INFO - Iter(train) [ 56000/240000] lr: 7.8944e-03 eta: 1 day, 13:12:40 time: 0.7236 data_time: 0.3997 memory: 17395 loss: 0.2438 decode.loss_ce: 0.1624 decode.acc_seg: 94.9688 aux.loss_ce: 0.0814 aux.acc_seg: 93.1696 2023/06/07 19:43:06 - mmengine - INFO - Iter(train) [ 56050/240000] lr: 7.8925e-03 eta: 1 day, 13:12:03 time: 0.7031 data_time: 0.3795 memory: 17394 loss: 0.2374 decode.loss_ce: 0.1568 decode.acc_seg: 92.9160 aux.loss_ce: 0.0806 aux.acc_seg: 90.4022 2023/06/07 19:43:41 - mmengine - INFO - Iter(train) [ 56100/240000] lr: 7.8906e-03 eta: 1 day, 13:11:24 time: 0.7110 data_time: 0.3883 memory: 17393 loss: 0.2232 decode.loss_ce: 0.1457 decode.acc_seg: 92.9733 aux.loss_ce: 0.0776 aux.acc_seg: 91.2902 2023/06/07 19:44:17 - mmengine - INFO - Iter(train) [ 56150/240000] lr: 7.8887e-03 eta: 1 day, 13:10:46 time: 0.7318 data_time: 0.4083 memory: 17395 loss: 0.2275 decode.loss_ce: 0.1482 decode.acc_seg: 92.6168 aux.loss_ce: 0.0793 aux.acc_seg: 89.7523 2023/06/07 19:44:53 - mmengine - INFO - Iter(train) [ 56200/240000] lr: 7.8868e-03 eta: 1 day, 13:10:08 time: 0.7139 data_time: 0.3801 memory: 17394 loss: 0.2319 decode.loss_ce: 0.1510 decode.acc_seg: 92.1127 aux.loss_ce: 0.0809 aux.acc_seg: 87.6631 2023/06/07 19:45:29 - mmengine - INFO - Iter(train) [ 56250/240000] lr: 7.8849e-03 eta: 1 day, 13:09:30 time: 0.7214 data_time: 0.3978 memory: 17393 loss: 0.2397 decode.loss_ce: 0.1566 decode.acc_seg: 94.8287 aux.loss_ce: 0.0831 aux.acc_seg: 93.2261 2023/06/07 19:46:05 - mmengine - INFO - Iter(train) [ 56300/240000] lr: 7.8830e-03 eta: 1 day, 13:08:52 time: 0.7057 data_time: 0.3816 memory: 17393 loss: 0.2628 decode.loss_ce: 0.1737 decode.acc_seg: 92.6098 aux.loss_ce: 0.0891 aux.acc_seg: 90.8726 2023/06/07 19:46:41 - mmengine - INFO - Iter(train) [ 56350/240000] lr: 7.8811e-03 eta: 1 day, 13:08:14 time: 0.7188 data_time: 0.3944 memory: 17397 loss: 0.2176 decode.loss_ce: 0.1426 decode.acc_seg: 93.9119 aux.loss_ce: 0.0751 aux.acc_seg: 92.9061 2023/06/07 19:47:17 - mmengine - INFO - Iter(train) [ 56400/240000] lr: 7.8791e-03 eta: 1 day, 13:07:36 time: 0.7133 data_time: 0.3896 memory: 17392 loss: 0.2307 decode.loss_ce: 0.1511 decode.acc_seg: 91.4423 aux.loss_ce: 0.0796 aux.acc_seg: 88.8333 2023/06/07 19:47:53 - mmengine - INFO - Iter(train) [ 56450/240000] lr: 7.8772e-03 eta: 1 day, 13:06:57 time: 0.7124 data_time: 0.3881 memory: 17393 loss: 0.2349 decode.loss_ce: 0.1564 decode.acc_seg: 94.1448 aux.loss_ce: 0.0785 aux.acc_seg: 91.8895 2023/06/07 19:48:28 - mmengine - INFO - Iter(train) [ 56500/240000] lr: 7.8753e-03 eta: 1 day, 13:06:18 time: 0.7094 data_time: 0.3863 memory: 17395 loss: 0.2227 decode.loss_ce: 0.1467 decode.acc_seg: 91.5539 aux.loss_ce: 0.0760 aux.acc_seg: 89.8996 2023/06/07 19:49:04 - mmengine - INFO - Iter(train) [ 56550/240000] lr: 7.8734e-03 eta: 1 day, 13:05:39 time: 0.7098 data_time: 0.3868 memory: 17393 loss: 0.2258 decode.loss_ce: 0.1467 decode.acc_seg: 93.0195 aux.loss_ce: 0.0791 aux.acc_seg: 91.5000 2023/06/07 19:49:39 - mmengine - INFO - Iter(train) [ 56600/240000] lr: 7.8715e-03 eta: 1 day, 13:05:00 time: 0.7138 data_time: 0.3901 memory: 17394 loss: 0.2320 decode.loss_ce: 0.1527 decode.acc_seg: 94.6298 aux.loss_ce: 0.0793 aux.acc_seg: 92.5991 2023/06/07 19:50:15 - mmengine - INFO - Iter(train) [ 56650/240000] lr: 7.8696e-03 eta: 1 day, 13:04:22 time: 0.7238 data_time: 0.3994 memory: 17392 loss: 0.2408 decode.loss_ce: 0.1556 decode.acc_seg: 94.6346 aux.loss_ce: 0.0852 aux.acc_seg: 92.1493 2023/06/07 19:50:51 - mmengine - INFO - Iter(train) [ 56700/240000] lr: 7.8677e-03 eta: 1 day, 13:03:43 time: 0.7139 data_time: 0.3900 memory: 17394 loss: 0.2483 decode.loss_ce: 0.1617 decode.acc_seg: 93.2805 aux.loss_ce: 0.0866 aux.acc_seg: 89.7121 2023/06/07 19:51:26 - mmengine - INFO - Iter(train) [ 56750/240000] lr: 7.8658e-03 eta: 1 day, 13:03:04 time: 0.7129 data_time: 0.3889 memory: 17397 loss: 0.2438 decode.loss_ce: 0.1594 decode.acc_seg: 94.3078 aux.loss_ce: 0.0844 aux.acc_seg: 91.3091 2023/06/07 19:52:01 - mmengine - INFO - Iter(train) [ 56800/240000] lr: 7.8639e-03 eta: 1 day, 13:02:24 time: 0.6966 data_time: 0.3054 memory: 17396 loss: 0.2435 decode.loss_ce: 0.1602 decode.acc_seg: 93.3248 aux.loss_ce: 0.0833 aux.acc_seg: 90.3596 2023/06/07 19:52:37 - mmengine - INFO - Iter(train) [ 56850/240000] lr: 7.8620e-03 eta: 1 day, 13:01:44 time: 0.7080 data_time: 0.3332 memory: 17394 loss: 0.2105 decode.loss_ce: 0.1353 decode.acc_seg: 93.1838 aux.loss_ce: 0.0752 aux.acc_seg: 90.8176 2023/06/07 19:53:13 - mmengine - INFO - Iter(train) [ 56900/240000] lr: 7.8601e-03 eta: 1 day, 13:01:05 time: 0.7155 data_time: 0.2432 memory: 17394 loss: 0.2151 decode.loss_ce: 0.1396 decode.acc_seg: 94.1449 aux.loss_ce: 0.0755 aux.acc_seg: 91.4758 2023/06/07 19:53:48 - mmengine - INFO - Iter(train) [ 56950/240000] lr: 7.8582e-03 eta: 1 day, 13:00:26 time: 0.7009 data_time: 0.1990 memory: 17396 loss: 0.3412 decode.loss_ce: 0.2257 decode.acc_seg: 91.5106 aux.loss_ce: 0.1155 aux.acc_seg: 88.3876 2023/06/07 19:54:24 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 19:54:24 - mmengine - INFO - Iter(train) [ 57000/240000] lr: 7.8563e-03 eta: 1 day, 12:59:48 time: 0.7347 data_time: 0.0163 memory: 17395 loss: 0.2760 decode.loss_ce: 0.1802 decode.acc_seg: 92.1803 aux.loss_ce: 0.0958 aux.acc_seg: 88.2332 2023/06/07 19:55:00 - mmengine - INFO - Iter(train) [ 57050/240000] lr: 7.8544e-03 eta: 1 day, 12:59:09 time: 0.7177 data_time: 0.0159 memory: 17391 loss: 0.2326 decode.loss_ce: 0.1499 decode.acc_seg: 93.2803 aux.loss_ce: 0.0827 aux.acc_seg: 91.3956 2023/06/07 19:55:36 - mmengine - INFO - Iter(train) [ 57100/240000] lr: 7.8525e-03 eta: 1 day, 12:58:32 time: 0.7120 data_time: 0.0119 memory: 17394 loss: 0.2784 decode.loss_ce: 0.1874 decode.acc_seg: 91.0654 aux.loss_ce: 0.0909 aux.acc_seg: 89.1246 2023/06/07 19:56:11 - mmengine - INFO - Iter(train) [ 57150/240000] lr: 7.8505e-03 eta: 1 day, 12:57:53 time: 0.7118 data_time: 0.0123 memory: 17393 loss: 0.2407 decode.loss_ce: 0.1595 decode.acc_seg: 91.3398 aux.loss_ce: 0.0813 aux.acc_seg: 91.1959 2023/06/07 19:56:47 - mmengine - INFO - Iter(train) [ 57200/240000] lr: 7.8486e-03 eta: 1 day, 12:57:15 time: 0.7049 data_time: 0.1549 memory: 17395 loss: 0.2615 decode.loss_ce: 0.1715 decode.acc_seg: 90.0658 aux.loss_ce: 0.0900 aux.acc_seg: 86.6607 2023/06/07 19:57:22 - mmengine - INFO - Iter(train) [ 57250/240000] lr: 7.8467e-03 eta: 1 day, 12:56:35 time: 0.7141 data_time: 0.3891 memory: 17393 loss: 0.2363 decode.loss_ce: 0.1534 decode.acc_seg: 92.3477 aux.loss_ce: 0.0829 aux.acc_seg: 87.0338 2023/06/07 19:57:58 - mmengine - INFO - Iter(train) [ 57300/240000] lr: 7.8448e-03 eta: 1 day, 12:55:55 time: 0.7102 data_time: 0.3758 memory: 17394 loss: 0.2323 decode.loss_ce: 0.1486 decode.acc_seg: 93.1656 aux.loss_ce: 0.0837 aux.acc_seg: 88.4159 2023/06/07 19:58:34 - mmengine - INFO - Iter(train) [ 57350/240000] lr: 7.8429e-03 eta: 1 day, 12:55:17 time: 0.7166 data_time: 0.0268 memory: 17395 loss: 0.2347 decode.loss_ce: 0.1536 decode.acc_seg: 92.5488 aux.loss_ce: 0.0811 aux.acc_seg: 90.2569 2023/06/07 19:59:10 - mmengine - INFO - Iter(train) [ 57400/240000] lr: 7.8410e-03 eta: 1 day, 12:54:40 time: 0.7209 data_time: 0.0123 memory: 17397 loss: 0.2398 decode.loss_ce: 0.1567 decode.acc_seg: 92.9342 aux.loss_ce: 0.0831 aux.acc_seg: 89.4578 2023/06/07 19:59:46 - mmengine - INFO - Iter(train) [ 57450/240000] lr: 7.8391e-03 eta: 1 day, 12:54:02 time: 0.7037 data_time: 0.0119 memory: 17393 loss: 0.2219 decode.loss_ce: 0.1437 decode.acc_seg: 93.4696 aux.loss_ce: 0.0782 aux.acc_seg: 90.2996 2023/06/07 20:00:21 - mmengine - INFO - Iter(train) [ 57500/240000] lr: 7.8372e-03 eta: 1 day, 12:53:24 time: 0.7267 data_time: 0.0122 memory: 17391 loss: 0.2276 decode.loss_ce: 0.1459 decode.acc_seg: 91.4828 aux.loss_ce: 0.0817 aux.acc_seg: 88.1018 2023/06/07 20:00:57 - mmengine - INFO - Iter(train) [ 57550/240000] lr: 7.8353e-03 eta: 1 day, 12:52:46 time: 0.7221 data_time: 0.0122 memory: 17393 loss: 0.2065 decode.loss_ce: 0.1359 decode.acc_seg: 94.1806 aux.loss_ce: 0.0705 aux.acc_seg: 92.1613 2023/06/07 20:01:34 - mmengine - INFO - Iter(train) [ 57600/240000] lr: 7.8334e-03 eta: 1 day, 12:52:09 time: 0.7276 data_time: 0.0121 memory: 17396 loss: 0.2318 decode.loss_ce: 0.1499 decode.acc_seg: 93.3624 aux.loss_ce: 0.0819 aux.acc_seg: 92.0395 2023/06/07 20:02:09 - mmengine - INFO - Iter(train) [ 57650/240000] lr: 7.8315e-03 eta: 1 day, 12:51:30 time: 0.7189 data_time: 0.0123 memory: 17394 loss: 0.2304 decode.loss_ce: 0.1532 decode.acc_seg: 94.4715 aux.loss_ce: 0.0772 aux.acc_seg: 92.9247 2023/06/07 20:02:45 - mmengine - INFO - Iter(train) [ 57700/240000] lr: 7.8296e-03 eta: 1 day, 12:50:52 time: 0.7157 data_time: 0.0122 memory: 17396 loss: 0.2333 decode.loss_ce: 0.1536 decode.acc_seg: 91.4923 aux.loss_ce: 0.0797 aux.acc_seg: 89.8771 2023/06/07 20:03:21 - mmengine - INFO - Iter(train) [ 57750/240000] lr: 7.8277e-03 eta: 1 day, 12:50:14 time: 0.7151 data_time: 0.0123 memory: 17393 loss: 0.2329 decode.loss_ce: 0.1551 decode.acc_seg: 93.4386 aux.loss_ce: 0.0779 aux.acc_seg: 91.9728 2023/06/07 20:03:57 - mmengine - INFO - Iter(train) [ 57800/240000] lr: 7.8257e-03 eta: 1 day, 12:49:36 time: 0.7179 data_time: 0.0124 memory: 17393 loss: 0.2412 decode.loss_ce: 0.1597 decode.acc_seg: 93.6484 aux.loss_ce: 0.0815 aux.acc_seg: 92.0790 2023/06/07 20:04:33 - mmengine - INFO - Iter(train) [ 57850/240000] lr: 7.8238e-03 eta: 1 day, 12:48:58 time: 0.7133 data_time: 0.1047 memory: 17396 loss: 0.2660 decode.loss_ce: 0.1705 decode.acc_seg: 94.3816 aux.loss_ce: 0.0954 aux.acc_seg: 91.1625 2023/06/07 20:05:09 - mmengine - INFO - Iter(train) [ 57900/240000] lr: 7.8219e-03 eta: 1 day, 12:48:20 time: 0.7309 data_time: 0.0151 memory: 17393 loss: 0.2440 decode.loss_ce: 0.1606 decode.acc_seg: 91.7978 aux.loss_ce: 0.0835 aux.acc_seg: 90.0722 2023/06/07 20:05:45 - mmengine - INFO - Iter(train) [ 57950/240000] lr: 7.8200e-03 eta: 1 day, 12:47:43 time: 0.7200 data_time: 0.0124 memory: 17393 loss: 0.2519 decode.loss_ce: 0.1645 decode.acc_seg: 92.4614 aux.loss_ce: 0.0873 aux.acc_seg: 90.3351 2023/06/07 20:06:20 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 20:06:20 - mmengine - INFO - Iter(train) [ 58000/240000] lr: 7.8181e-03 eta: 1 day, 12:47:04 time: 0.7241 data_time: 0.1460 memory: 17394 loss: 0.2291 decode.loss_ce: 0.1494 decode.acc_seg: 95.6328 aux.loss_ce: 0.0796 aux.acc_seg: 94.6788 2023/06/07 20:06:56 - mmengine - INFO - Iter(train) [ 58050/240000] lr: 7.8162e-03 eta: 1 day, 12:46:25 time: 0.7108 data_time: 0.1956 memory: 17392 loss: 0.2219 decode.loss_ce: 0.1449 decode.acc_seg: 92.1377 aux.loss_ce: 0.0770 aux.acc_seg: 88.3935 2023/06/07 20:07:31 - mmengine - INFO - Iter(train) [ 58100/240000] lr: 7.8143e-03 eta: 1 day, 12:45:45 time: 0.7098 data_time: 0.3242 memory: 17393 loss: 0.2268 decode.loss_ce: 0.1464 decode.acc_seg: 94.2307 aux.loss_ce: 0.0805 aux.acc_seg: 91.3422 2023/06/07 20:08:07 - mmengine - INFO - Iter(train) [ 58150/240000] lr: 7.8124e-03 eta: 1 day, 12:45:06 time: 0.7082 data_time: 0.1345 memory: 17394 loss: 0.2280 decode.loss_ce: 0.1471 decode.acc_seg: 93.6715 aux.loss_ce: 0.0809 aux.acc_seg: 91.6401 2023/06/07 20:08:42 - mmengine - INFO - Iter(train) [ 58200/240000] lr: 7.8105e-03 eta: 1 day, 12:44:27 time: 0.7138 data_time: 0.2040 memory: 17395 loss: 0.2691 decode.loss_ce: 0.1788 decode.acc_seg: 92.7850 aux.loss_ce: 0.0903 aux.acc_seg: 91.5093 2023/06/07 20:09:18 - mmengine - INFO - Iter(train) [ 58250/240000] lr: 7.8086e-03 eta: 1 day, 12:43:50 time: 0.7339 data_time: 0.0122 memory: 17393 loss: 0.2309 decode.loss_ce: 0.1508 decode.acc_seg: 93.1165 aux.loss_ce: 0.0801 aux.acc_seg: 91.0009 2023/06/07 20:09:54 - mmengine - INFO - Iter(train) [ 58300/240000] lr: 7.8067e-03 eta: 1 day, 12:43:11 time: 0.7147 data_time: 0.0120 memory: 17391 loss: 0.2295 decode.loss_ce: 0.1497 decode.acc_seg: 93.9734 aux.loss_ce: 0.0798 aux.acc_seg: 92.2336 2023/06/07 20:10:30 - mmengine - INFO - Iter(train) [ 58350/240000] lr: 7.8048e-03 eta: 1 day, 12:42:34 time: 0.7118 data_time: 0.0121 memory: 17393 loss: 0.2280 decode.loss_ce: 0.1481 decode.acc_seg: 93.2526 aux.loss_ce: 0.0799 aux.acc_seg: 91.2862 2023/06/07 20:11:06 - mmengine - INFO - Iter(train) [ 58400/240000] lr: 7.8028e-03 eta: 1 day, 12:41:56 time: 0.7200 data_time: 0.0120 memory: 17392 loss: 0.2388 decode.loss_ce: 0.1580 decode.acc_seg: 93.7199 aux.loss_ce: 0.0809 aux.acc_seg: 91.8901 2023/06/07 20:11:42 - mmengine - INFO - Iter(train) [ 58450/240000] lr: 7.8009e-03 eta: 1 day, 12:41:17 time: 0.7191 data_time: 0.0123 memory: 17393 loss: 0.2179 decode.loss_ce: 0.1424 decode.acc_seg: 94.2774 aux.loss_ce: 0.0756 aux.acc_seg: 92.0160 2023/06/07 20:12:17 - mmengine - INFO - Iter(train) [ 58500/240000] lr: 7.7990e-03 eta: 1 day, 12:40:39 time: 0.7110 data_time: 0.0123 memory: 17395 loss: 0.2261 decode.loss_ce: 0.1475 decode.acc_seg: 94.2638 aux.loss_ce: 0.0786 aux.acc_seg: 92.9253 2023/06/07 20:12:53 - mmengine - INFO - Iter(train) [ 58550/240000] lr: 7.7971e-03 eta: 1 day, 12:40:00 time: 0.7167 data_time: 0.0124 memory: 17393 loss: 0.2312 decode.loss_ce: 0.1486 decode.acc_seg: 93.5900 aux.loss_ce: 0.0826 aux.acc_seg: 91.6134 2023/06/07 20:13:28 - mmengine - INFO - Iter(train) [ 58600/240000] lr: 7.7952e-03 eta: 1 day, 12:39:20 time: 0.6944 data_time: 0.0230 memory: 17395 loss: 0.2561 decode.loss_ce: 0.1700 decode.acc_seg: 92.9125 aux.loss_ce: 0.0861 aux.acc_seg: 90.0244 2023/06/07 20:14:04 - mmengine - INFO - Iter(train) [ 58650/240000] lr: 7.7933e-03 eta: 1 day, 12:38:42 time: 0.7219 data_time: 0.1645 memory: 17395 loss: 0.2363 decode.loss_ce: 0.1535 decode.acc_seg: 92.6909 aux.loss_ce: 0.0828 aux.acc_seg: 89.9009 2023/06/07 20:14:40 - mmengine - INFO - Iter(train) [ 58700/240000] lr: 7.7914e-03 eta: 1 day, 12:38:04 time: 0.7217 data_time: 0.0494 memory: 17393 loss: 0.2266 decode.loss_ce: 0.1460 decode.acc_seg: 94.6712 aux.loss_ce: 0.0805 aux.acc_seg: 93.0422 2023/06/07 20:15:15 - mmengine - INFO - Iter(train) [ 58750/240000] lr: 7.7895e-03 eta: 1 day, 12:37:26 time: 0.6993 data_time: 0.0186 memory: 17395 loss: 0.2297 decode.loss_ce: 0.1516 decode.acc_seg: 91.5762 aux.loss_ce: 0.0781 aux.acc_seg: 90.9819 2023/06/07 20:15:51 - mmengine - INFO - Iter(train) [ 58800/240000] lr: 7.7876e-03 eta: 1 day, 12:36:47 time: 0.7161 data_time: 0.1173 memory: 17394 loss: 0.2317 decode.loss_ce: 0.1535 decode.acc_seg: 92.0826 aux.loss_ce: 0.0782 aux.acc_seg: 89.5180 2023/06/07 20:16:27 - mmengine - INFO - Iter(train) [ 58850/240000] lr: 7.7857e-03 eta: 1 day, 12:36:08 time: 0.7013 data_time: 0.0122 memory: 17391 loss: 0.2279 decode.loss_ce: 0.1481 decode.acc_seg: 94.5791 aux.loss_ce: 0.0799 aux.acc_seg: 92.9462 2023/06/07 20:17:03 - mmengine - INFO - Iter(train) [ 58900/240000] lr: 7.7838e-03 eta: 1 day, 12:35:30 time: 0.7142 data_time: 0.0120 memory: 17395 loss: 0.2233 decode.loss_ce: 0.1440 decode.acc_seg: 94.6884 aux.loss_ce: 0.0793 aux.acc_seg: 92.0558 2023/06/07 20:17:39 - mmengine - INFO - Iter(train) [ 58950/240000] lr: 7.7818e-03 eta: 1 day, 12:34:53 time: 0.7296 data_time: 0.0122 memory: 17393 loss: 0.2303 decode.loss_ce: 0.1512 decode.acc_seg: 92.4091 aux.loss_ce: 0.0790 aux.acc_seg: 89.6115 2023/06/07 20:18:14 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 20:18:15 - mmengine - INFO - Iter(train) [ 59000/240000] lr: 7.7799e-03 eta: 1 day, 12:34:15 time: 0.7263 data_time: 0.0123 memory: 17395 loss: 0.2217 decode.loss_ce: 0.1447 decode.acc_seg: 92.1068 aux.loss_ce: 0.0770 aux.acc_seg: 88.6561 2023/06/07 20:18:51 - mmengine - INFO - Iter(train) [ 59050/240000] lr: 7.7780e-03 eta: 1 day, 12:33:38 time: 0.7220 data_time: 0.0123 memory: 17395 loss: 0.2363 decode.loss_ce: 0.1530 decode.acc_seg: 91.3836 aux.loss_ce: 0.0833 aux.acc_seg: 89.9967 2023/06/07 20:19:26 - mmengine - INFO - Iter(train) [ 59100/240000] lr: 7.7761e-03 eta: 1 day, 12:32:59 time: 0.7172 data_time: 0.0121 memory: 17394 loss: 0.2285 decode.loss_ce: 0.1487 decode.acc_seg: 93.9221 aux.loss_ce: 0.0798 aux.acc_seg: 91.1430 2023/06/07 20:20:02 - mmengine - INFO - Iter(train) [ 59150/240000] lr: 7.7742e-03 eta: 1 day, 12:32:22 time: 0.7425 data_time: 0.0121 memory: 17394 loss: 0.2328 decode.loss_ce: 0.1528 decode.acc_seg: 93.0089 aux.loss_ce: 0.0800 aux.acc_seg: 89.9225 2023/06/07 20:20:38 - mmengine - INFO - Iter(train) [ 59200/240000] lr: 7.7723e-03 eta: 1 day, 12:31:44 time: 0.7156 data_time: 0.0122 memory: 17392 loss: 0.2110 decode.loss_ce: 0.1374 decode.acc_seg: 94.2504 aux.loss_ce: 0.0736 aux.acc_seg: 89.7824 2023/06/07 20:21:14 - mmengine - INFO - Iter(train) [ 59250/240000] lr: 7.7704e-03 eta: 1 day, 12:31:05 time: 0.7089 data_time: 0.0121 memory: 17394 loss: 0.2222 decode.loss_ce: 0.1452 decode.acc_seg: 92.2618 aux.loss_ce: 0.0771 aux.acc_seg: 91.0697 2023/06/07 20:21:49 - mmengine - INFO - Iter(train) [ 59300/240000] lr: 7.7685e-03 eta: 1 day, 12:30:27 time: 0.7230 data_time: 0.0119 memory: 17392 loss: 0.2333 decode.loss_ce: 0.1512 decode.acc_seg: 93.8545 aux.loss_ce: 0.0821 aux.acc_seg: 91.7015 2023/06/07 20:22:25 - mmengine - INFO - Iter(train) [ 59350/240000] lr: 7.7666e-03 eta: 1 day, 12:29:48 time: 0.7232 data_time: 0.0124 memory: 17393 loss: 0.2119 decode.loss_ce: 0.1390 decode.acc_seg: 93.8290 aux.loss_ce: 0.0730 aux.acc_seg: 92.8106 2023/06/07 20:23:01 - mmengine - INFO - Iter(train) [ 59400/240000] lr: 7.7647e-03 eta: 1 day, 12:29:10 time: 0.7259 data_time: 0.0121 memory: 17394 loss: 0.2306 decode.loss_ce: 0.1502 decode.acc_seg: 94.1418 aux.loss_ce: 0.0804 aux.acc_seg: 92.0386 2023/06/07 20:23:36 - mmengine - INFO - Iter(train) [ 59450/240000] lr: 7.7627e-03 eta: 1 day, 12:28:31 time: 0.7141 data_time: 0.0121 memory: 17395 loss: 0.2348 decode.loss_ce: 0.1536 decode.acc_seg: 92.4901 aux.loss_ce: 0.0812 aux.acc_seg: 89.9692 2023/06/07 20:24:13 - mmengine - INFO - Iter(train) [ 59500/240000] lr: 7.7608e-03 eta: 1 day, 12:27:54 time: 0.7060 data_time: 0.0124 memory: 17396 loss: 0.2166 decode.loss_ce: 0.1403 decode.acc_seg: 95.0923 aux.loss_ce: 0.0762 aux.acc_seg: 93.3055 2023/06/07 20:24:48 - mmengine - INFO - Iter(train) [ 59550/240000] lr: 7.7589e-03 eta: 1 day, 12:27:16 time: 0.7201 data_time: 0.0124 memory: 17396 loss: 0.2114 decode.loss_ce: 0.1353 decode.acc_seg: 94.3359 aux.loss_ce: 0.0761 aux.acc_seg: 91.6528 2023/06/07 20:25:24 - mmengine - INFO - Iter(train) [ 59600/240000] lr: 7.7570e-03 eta: 1 day, 12:26:37 time: 0.7214 data_time: 0.0121 memory: 17393 loss: 0.2266 decode.loss_ce: 0.1476 decode.acc_seg: 95.0736 aux.loss_ce: 0.0790 aux.acc_seg: 92.8765 2023/06/07 20:26:00 - mmengine - INFO - Iter(train) [ 59650/240000] lr: 7.7551e-03 eta: 1 day, 12:25:59 time: 0.7090 data_time: 0.0123 memory: 17395 loss: 0.2344 decode.loss_ce: 0.1558 decode.acc_seg: 92.7145 aux.loss_ce: 0.0787 aux.acc_seg: 91.9150 2023/06/07 20:26:36 - mmengine - INFO - Iter(train) [ 59700/240000] lr: 7.7532e-03 eta: 1 day, 12:25:22 time: 0.7303 data_time: 0.0122 memory: 17393 loss: 0.2321 decode.loss_ce: 0.1521 decode.acc_seg: 94.0827 aux.loss_ce: 0.0800 aux.acc_seg: 91.0982 2023/06/07 20:27:12 - mmengine - INFO - Iter(train) [ 59750/240000] lr: 7.7513e-03 eta: 1 day, 12:24:44 time: 0.7108 data_time: 0.0122 memory: 17396 loss: 0.2311 decode.loss_ce: 0.1484 decode.acc_seg: 94.6563 aux.loss_ce: 0.0827 aux.acc_seg: 92.2068 2023/06/07 20:27:47 - mmengine - INFO - Iter(train) [ 59800/240000] lr: 7.7494e-03 eta: 1 day, 12:24:06 time: 0.7117 data_time: 0.0124 memory: 17395 loss: 0.2380 decode.loss_ce: 0.1556 decode.acc_seg: 92.5669 aux.loss_ce: 0.0824 aux.acc_seg: 90.8442 2023/06/07 20:28:23 - mmengine - INFO - Iter(train) [ 59850/240000] lr: 7.7475e-03 eta: 1 day, 12:23:27 time: 0.7261 data_time: 0.0122 memory: 17392 loss: 0.2394 decode.loss_ce: 0.1541 decode.acc_seg: 93.3312 aux.loss_ce: 0.0853 aux.acc_seg: 91.2759 2023/06/07 20:28:59 - mmengine - INFO - Iter(train) [ 59900/240000] lr: 7.7456e-03 eta: 1 day, 12:22:49 time: 0.7070 data_time: 0.0121 memory: 17393 loss: 0.2090 decode.loss_ce: 0.1367 decode.acc_seg: 91.7802 aux.loss_ce: 0.0723 aux.acc_seg: 89.6720 2023/06/07 20:29:34 - mmengine - INFO - Iter(train) [ 59950/240000] lr: 7.7436e-03 eta: 1 day, 12:22:10 time: 0.7082 data_time: 0.0125 memory: 17393 loss: 0.2160 decode.loss_ce: 0.1401 decode.acc_seg: 95.3283 aux.loss_ce: 0.0760 aux.acc_seg: 92.3666 2023/06/07 20:30:10 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 20:30:10 - mmengine - INFO - Iter(train) [ 60000/240000] lr: 7.7417e-03 eta: 1 day, 12:21:32 time: 0.7101 data_time: 0.0122 memory: 17392 loss: 0.2181 decode.loss_ce: 0.1430 decode.acc_seg: 94.7795 aux.loss_ce: 0.0751 aux.acc_seg: 92.3963 2023/06/07 20:30:46 - mmengine - INFO - Iter(train) [ 60050/240000] lr: 7.7398e-03 eta: 1 day, 12:20:55 time: 0.7066 data_time: 0.0125 memory: 17394 loss: 0.2210 decode.loss_ce: 0.1431 decode.acc_seg: 92.9396 aux.loss_ce: 0.0779 aux.acc_seg: 90.3765 2023/06/07 20:31:22 - mmengine - INFO - Iter(train) [ 60100/240000] lr: 7.7379e-03 eta: 1 day, 12:20:17 time: 0.7193 data_time: 0.0124 memory: 17394 loss: 0.2382 decode.loss_ce: 0.1566 decode.acc_seg: 90.7626 aux.loss_ce: 0.0816 aux.acc_seg: 88.2411 2023/06/07 20:31:57 - mmengine - INFO - Iter(train) [ 60150/240000] lr: 7.7360e-03 eta: 1 day, 12:19:38 time: 0.7117 data_time: 0.0120 memory: 17394 loss: 0.2265 decode.loss_ce: 0.1498 decode.acc_seg: 94.2057 aux.loss_ce: 0.0767 aux.acc_seg: 92.4171 2023/06/07 20:32:33 - mmengine - INFO - Iter(train) [ 60200/240000] lr: 7.7341e-03 eta: 1 day, 12:19:00 time: 0.7175 data_time: 0.0121 memory: 17394 loss: 0.2448 decode.loss_ce: 0.1604 decode.acc_seg: 93.4918 aux.loss_ce: 0.0844 aux.acc_seg: 90.8747 2023/06/07 20:33:09 - mmengine - INFO - Iter(train) [ 60250/240000] lr: 7.7322e-03 eta: 1 day, 12:18:21 time: 0.7097 data_time: 0.0124 memory: 17395 loss: 0.2465 decode.loss_ce: 0.1618 decode.acc_seg: 93.4159 aux.loss_ce: 0.0847 aux.acc_seg: 91.0307 2023/06/07 20:33:45 - mmengine - INFO - Iter(train) [ 60300/240000] lr: 7.7303e-03 eta: 1 day, 12:17:43 time: 0.7097 data_time: 0.0122 memory: 17394 loss: 0.2573 decode.loss_ce: 0.1700 decode.acc_seg: 94.0981 aux.loss_ce: 0.0873 aux.acc_seg: 91.8390 2023/06/07 20:34:21 - mmengine - INFO - Iter(train) [ 60350/240000] lr: 7.7284e-03 eta: 1 day, 12:17:06 time: 0.7325 data_time: 0.0125 memory: 17394 loss: 0.2239 decode.loss_ce: 0.1457 decode.acc_seg: 93.1280 aux.loss_ce: 0.0782 aux.acc_seg: 90.3441 2023/06/07 20:34:56 - mmengine - INFO - Iter(train) [ 60400/240000] lr: 7.7264e-03 eta: 1 day, 12:16:27 time: 0.7182 data_time: 0.0121 memory: 17394 loss: 0.2302 decode.loss_ce: 0.1517 decode.acc_seg: 93.6839 aux.loss_ce: 0.0786 aux.acc_seg: 91.3174 2023/06/07 20:35:32 - mmengine - INFO - Iter(train) [ 60450/240000] lr: 7.7245e-03 eta: 1 day, 12:15:49 time: 0.7086 data_time: 0.0121 memory: 17393 loss: 0.2014 decode.loss_ce: 0.1307 decode.acc_seg: 92.8693 aux.loss_ce: 0.0707 aux.acc_seg: 90.6012 2023/06/07 20:36:08 - mmengine - INFO - Iter(train) [ 60500/240000] lr: 7.7226e-03 eta: 1 day, 12:15:11 time: 0.7361 data_time: 0.0122 memory: 17394 loss: 0.2281 decode.loss_ce: 0.1497 decode.acc_seg: 92.8467 aux.loss_ce: 0.0784 aux.acc_seg: 90.3186 2023/06/07 20:36:44 - mmengine - INFO - Iter(train) [ 60550/240000] lr: 7.7207e-03 eta: 1 day, 12:14:33 time: 0.7201 data_time: 0.0122 memory: 17394 loss: 0.2229 decode.loss_ce: 0.1449 decode.acc_seg: 93.1092 aux.loss_ce: 0.0780 aux.acc_seg: 89.6282 2023/06/07 20:37:19 - mmengine - INFO - Iter(train) [ 60600/240000] lr: 7.7188e-03 eta: 1 day, 12:13:55 time: 0.7134 data_time: 0.0122 memory: 17394 loss: 0.2628 decode.loss_ce: 0.1703 decode.acc_seg: 93.2884 aux.loss_ce: 0.0926 aux.acc_seg: 90.5773 2023/06/07 20:37:55 - mmengine - INFO - Iter(train) [ 60650/240000] lr: 7.7169e-03 eta: 1 day, 12:13:17 time: 0.7216 data_time: 0.0124 memory: 17392 loss: 0.2514 decode.loss_ce: 0.1659 decode.acc_seg: 90.9720 aux.loss_ce: 0.0856 aux.acc_seg: 88.1282 2023/06/07 20:38:31 - mmengine - INFO - Iter(train) [ 60700/240000] lr: 7.7150e-03 eta: 1 day, 12:12:38 time: 0.7176 data_time: 0.0123 memory: 17394 loss: 0.2267 decode.loss_ce: 0.1461 decode.acc_seg: 90.8618 aux.loss_ce: 0.0806 aux.acc_seg: 89.7246 2023/06/07 20:39:07 - mmengine - INFO - Iter(train) [ 60750/240000] lr: 7.7131e-03 eta: 1 day, 12:12:01 time: 0.7300 data_time: 0.0122 memory: 17393 loss: 0.2341 decode.loss_ce: 0.1522 decode.acc_seg: 95.4545 aux.loss_ce: 0.0819 aux.acc_seg: 92.9577 2023/06/07 20:39:43 - mmengine - INFO - Iter(train) [ 60800/240000] lr: 7.7112e-03 eta: 1 day, 12:11:23 time: 0.7123 data_time: 0.0123 memory: 17396 loss: 0.2292 decode.loss_ce: 0.1480 decode.acc_seg: 92.4204 aux.loss_ce: 0.0812 aux.acc_seg: 90.1521 2023/06/07 20:40:18 - mmengine - INFO - Iter(train) [ 60850/240000] lr: 7.7092e-03 eta: 1 day, 12:10:44 time: 0.7097 data_time: 0.0122 memory: 17392 loss: 0.2298 decode.loss_ce: 0.1491 decode.acc_seg: 94.1978 aux.loss_ce: 0.0807 aux.acc_seg: 92.5274 2023/06/07 20:40:54 - mmengine - INFO - Iter(train) [ 60900/240000] lr: 7.7073e-03 eta: 1 day, 12:10:06 time: 0.7069 data_time: 0.0123 memory: 17394 loss: 0.2257 decode.loss_ce: 0.1462 decode.acc_seg: 94.9384 aux.loss_ce: 0.0795 aux.acc_seg: 91.7495 2023/06/07 20:41:30 - mmengine - INFO - Iter(train) [ 60950/240000] lr: 7.7054e-03 eta: 1 day, 12:09:28 time: 0.7081 data_time: 0.0121 memory: 17391 loss: 0.2256 decode.loss_ce: 0.1488 decode.acc_seg: 91.8707 aux.loss_ce: 0.0768 aux.acc_seg: 87.9183 2023/06/07 20:42:05 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 20:42:05 - mmengine - INFO - Iter(train) [ 61000/240000] lr: 7.7035e-03 eta: 1 day, 12:08:50 time: 0.7228 data_time: 0.0121 memory: 17394 loss: 0.2472 decode.loss_ce: 0.1614 decode.acc_seg: 93.6662 aux.loss_ce: 0.0858 aux.acc_seg: 90.3070 2023/06/07 20:42:42 - mmengine - INFO - Iter(train) [ 61050/240000] lr: 7.7016e-03 eta: 1 day, 12:08:13 time: 0.7319 data_time: 0.0123 memory: 17392 loss: 0.3233 decode.loss_ce: 0.2148 decode.acc_seg: 89.5005 aux.loss_ce: 0.1084 aux.acc_seg: 87.7319 2023/06/07 20:43:17 - mmengine - INFO - Iter(train) [ 61100/240000] lr: 7.6997e-03 eta: 1 day, 12:07:35 time: 0.7234 data_time: 0.0123 memory: 17394 loss: 0.2794 decode.loss_ce: 0.1845 decode.acc_seg: 90.5602 aux.loss_ce: 0.0949 aux.acc_seg: 86.2733 2023/06/07 20:43:53 - mmengine - INFO - Iter(train) [ 61150/240000] lr: 7.6978e-03 eta: 1 day, 12:06:58 time: 0.7144 data_time: 0.0122 memory: 17394 loss: 0.2517 decode.loss_ce: 0.1659 decode.acc_seg: 93.8854 aux.loss_ce: 0.0858 aux.acc_seg: 91.7468 2023/06/07 20:44:29 - mmengine - INFO - Iter(train) [ 61200/240000] lr: 7.6959e-03 eta: 1 day, 12:06:21 time: 0.7133 data_time: 0.0121 memory: 17393 loss: 0.2319 decode.loss_ce: 0.1506 decode.acc_seg: 93.0624 aux.loss_ce: 0.0813 aux.acc_seg: 90.8405 2023/06/07 20:45:05 - mmengine - INFO - Iter(train) [ 61250/240000] lr: 7.6940e-03 eta: 1 day, 12:05:43 time: 0.7009 data_time: 0.0120 memory: 17393 loss: 0.2492 decode.loss_ce: 0.1619 decode.acc_seg: 93.9884 aux.loss_ce: 0.0873 aux.acc_seg: 91.3056 2023/06/07 20:45:41 - mmengine - INFO - Iter(train) [ 61300/240000] lr: 7.6920e-03 eta: 1 day, 12:05:05 time: 0.7348 data_time: 0.0122 memory: 17394 loss: 0.2295 decode.loss_ce: 0.1487 decode.acc_seg: 91.7463 aux.loss_ce: 0.0808 aux.acc_seg: 89.1793 2023/06/07 20:46:17 - mmengine - INFO - Iter(train) [ 61350/240000] lr: 7.6901e-03 eta: 1 day, 12:04:27 time: 0.7154 data_time: 0.0122 memory: 17394 loss: 0.2210 decode.loss_ce: 0.1432 decode.acc_seg: 92.0312 aux.loss_ce: 0.0778 aux.acc_seg: 89.3868 2023/06/07 20:46:53 - mmengine - INFO - Iter(train) [ 61400/240000] lr: 7.6882e-03 eta: 1 day, 12:03:50 time: 0.7327 data_time: 0.0125 memory: 17393 loss: 0.2282 decode.loss_ce: 0.1457 decode.acc_seg: 94.9841 aux.loss_ce: 0.0825 aux.acc_seg: 91.4743 2023/06/07 20:47:28 - mmengine - INFO - Iter(train) [ 61450/240000] lr: 7.6863e-03 eta: 1 day, 12:03:10 time: 0.7211 data_time: 0.0469 memory: 17394 loss: 0.2417 decode.loss_ce: 0.1577 decode.acc_seg: 92.8058 aux.loss_ce: 0.0839 aux.acc_seg: 90.2925 2023/06/07 20:48:04 - mmengine - INFO - Iter(train) [ 61500/240000] lr: 7.6844e-03 eta: 1 day, 12:02:32 time: 0.6984 data_time: 0.0616 memory: 17394 loss: 0.2527 decode.loss_ce: 0.1632 decode.acc_seg: 91.4166 aux.loss_ce: 0.0894 aux.acc_seg: 89.1867 2023/06/07 20:48:39 - mmengine - INFO - Iter(train) [ 61550/240000] lr: 7.6825e-03 eta: 1 day, 12:01:53 time: 0.7102 data_time: 0.2408 memory: 17393 loss: 0.2072 decode.loss_ce: 0.1353 decode.acc_seg: 93.9051 aux.loss_ce: 0.0719 aux.acc_seg: 91.5752 2023/06/07 20:49:15 - mmengine - INFO - Iter(train) [ 61600/240000] lr: 7.6806e-03 eta: 1 day, 12:01:14 time: 0.7148 data_time: 0.1323 memory: 17396 loss: 0.2451 decode.loss_ce: 0.1592 decode.acc_seg: 94.8491 aux.loss_ce: 0.0859 aux.acc_seg: 91.2177 2023/06/07 20:49:51 - mmengine - INFO - Iter(train) [ 61650/240000] lr: 7.6787e-03 eta: 1 day, 12:00:36 time: 0.7191 data_time: 0.0359 memory: 17398 loss: 0.2292 decode.loss_ce: 0.1512 decode.acc_seg: 91.9794 aux.loss_ce: 0.0780 aux.acc_seg: 89.3815 2023/06/07 20:50:26 - mmengine - INFO - Iter(train) [ 61700/240000] lr: 7.6767e-03 eta: 1 day, 11:59:58 time: 0.7084 data_time: 0.1017 memory: 17395 loss: 0.2399 decode.loss_ce: 0.1580 decode.acc_seg: 91.7013 aux.loss_ce: 0.0819 aux.acc_seg: 89.5700 2023/06/07 20:51:02 - mmengine - INFO - Iter(train) [ 61750/240000] lr: 7.6748e-03 eta: 1 day, 11:59:19 time: 0.6956 data_time: 0.2477 memory: 17395 loss: 0.2255 decode.loss_ce: 0.1469 decode.acc_seg: 90.0445 aux.loss_ce: 0.0786 aux.acc_seg: 87.2631 2023/06/07 20:51:37 - mmengine - INFO - Iter(train) [ 61800/240000] lr: 7.6729e-03 eta: 1 day, 11:58:39 time: 0.7052 data_time: 0.2999 memory: 17394 loss: 0.2200 decode.loss_ce: 0.1429 decode.acc_seg: 93.4817 aux.loss_ce: 0.0771 aux.acc_seg: 92.4201 2023/06/07 20:52:13 - mmengine - INFO - Iter(train) [ 61850/240000] lr: 7.6710e-03 eta: 1 day, 11:58:01 time: 0.7150 data_time: 0.0988 memory: 17393 loss: 0.2214 decode.loss_ce: 0.1440 decode.acc_seg: 93.9719 aux.loss_ce: 0.0773 aux.acc_seg: 91.3745 2023/06/07 20:52:48 - mmengine - INFO - Iter(train) [ 61900/240000] lr: 7.6691e-03 eta: 1 day, 11:57:22 time: 0.7104 data_time: 0.0152 memory: 17392 loss: 0.2329 decode.loss_ce: 0.1524 decode.acc_seg: 93.0775 aux.loss_ce: 0.0805 aux.acc_seg: 89.2081 2023/06/07 20:53:24 - mmengine - INFO - Iter(train) [ 61950/240000] lr: 7.6672e-03 eta: 1 day, 11:56:43 time: 0.6944 data_time: 0.2420 memory: 17396 loss: 0.2413 decode.loss_ce: 0.1604 decode.acc_seg: 92.2147 aux.loss_ce: 0.0808 aux.acc_seg: 90.4912 2023/06/07 20:53:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 20:53:59 - mmengine - INFO - Iter(train) [ 62000/240000] lr: 7.6653e-03 eta: 1 day, 11:56:05 time: 0.7134 data_time: 0.3747 memory: 17393 loss: 0.2106 decode.loss_ce: 0.1371 decode.acc_seg: 94.5689 aux.loss_ce: 0.0735 aux.acc_seg: 93.2761 2023/06/07 20:54:35 - mmengine - INFO - Iter(train) [ 62050/240000] lr: 7.6634e-03 eta: 1 day, 11:55:26 time: 0.7149 data_time: 0.0119 memory: 17392 loss: 0.2319 decode.loss_ce: 0.1520 decode.acc_seg: 93.6864 aux.loss_ce: 0.0800 aux.acc_seg: 91.6273 2023/06/07 20:55:10 - mmengine - INFO - Iter(train) [ 62100/240000] lr: 7.6614e-03 eta: 1 day, 11:54:47 time: 0.7078 data_time: 0.0122 memory: 17395 loss: 0.2050 decode.loss_ce: 0.1317 decode.acc_seg: 93.9758 aux.loss_ce: 0.0733 aux.acc_seg: 92.1350 2023/06/07 20:55:45 - mmengine - INFO - Iter(train) [ 62150/240000] lr: 7.6595e-03 eta: 1 day, 11:54:08 time: 0.7148 data_time: 0.1860 memory: 17395 loss: 0.2336 decode.loss_ce: 0.1495 decode.acc_seg: 93.0435 aux.loss_ce: 0.0842 aux.acc_seg: 89.9941 2023/06/07 20:56:21 - mmengine - INFO - Iter(train) [ 62200/240000] lr: 7.6576e-03 eta: 1 day, 11:53:30 time: 0.7015 data_time: 0.1236 memory: 17394 loss: 0.2041 decode.loss_ce: 0.1330 decode.acc_seg: 91.7540 aux.loss_ce: 0.0712 aux.acc_seg: 89.0564 2023/06/07 20:56:57 - mmengine - INFO - Iter(train) [ 62250/240000] lr: 7.6557e-03 eta: 1 day, 11:52:52 time: 0.7160 data_time: 0.0365 memory: 17393 loss: 0.2275 decode.loss_ce: 0.1490 decode.acc_seg: 90.6301 aux.loss_ce: 0.0785 aux.acc_seg: 89.7251 2023/06/07 20:57:32 - mmengine - INFO - Iter(train) [ 62300/240000] lr: 7.6538e-03 eta: 1 day, 11:52:12 time: 0.7037 data_time: 0.0145 memory: 17397 loss: 0.2130 decode.loss_ce: 0.1386 decode.acc_seg: 94.4920 aux.loss_ce: 0.0745 aux.acc_seg: 91.6855 2023/06/07 20:58:09 - mmengine - INFO - Iter(train) [ 62350/240000] lr: 7.6519e-03 eta: 1 day, 11:51:36 time: 0.7191 data_time: 0.0121 memory: 17393 loss: 0.2151 decode.loss_ce: 0.1404 decode.acc_seg: 89.8205 aux.loss_ce: 0.0747 aux.acc_seg: 87.7475 2023/06/07 20:58:44 - mmengine - INFO - Iter(train) [ 62400/240000] lr: 7.6500e-03 eta: 1 day, 11:50:58 time: 0.7151 data_time: 0.0122 memory: 17396 loss: 0.2167 decode.loss_ce: 0.1418 decode.acc_seg: 92.7141 aux.loss_ce: 0.0749 aux.acc_seg: 91.9435 2023/06/07 20:59:20 - mmengine - INFO - Iter(train) [ 62450/240000] lr: 7.6481e-03 eta: 1 day, 11:50:19 time: 0.7115 data_time: 0.0121 memory: 17394 loss: 0.2272 decode.loss_ce: 0.1494 decode.acc_seg: 94.3828 aux.loss_ce: 0.0777 aux.acc_seg: 91.8440 2023/06/07 20:59:56 - mmengine - INFO - Iter(train) [ 62500/240000] lr: 7.6461e-03 eta: 1 day, 11:49:41 time: 0.7320 data_time: 0.0123 memory: 17394 loss: 0.2441 decode.loss_ce: 0.1566 decode.acc_seg: 93.2130 aux.loss_ce: 0.0875 aux.acc_seg: 90.8373 2023/06/07 21:00:31 - mmengine - INFO - Iter(train) [ 62550/240000] lr: 7.6442e-03 eta: 1 day, 11:49:03 time: 0.6955 data_time: 0.0120 memory: 17395 loss: 0.2165 decode.loss_ce: 0.1425 decode.acc_seg: 92.8731 aux.loss_ce: 0.0740 aux.acc_seg: 90.9699 2023/06/07 21:01:07 - mmengine - INFO - Iter(train) [ 62600/240000] lr: 7.6423e-03 eta: 1 day, 11:48:24 time: 0.7057 data_time: 0.0126 memory: 17393 loss: 0.2605 decode.loss_ce: 0.1731 decode.acc_seg: 92.5423 aux.loss_ce: 0.0873 aux.acc_seg: 88.6397 2023/06/07 21:01:42 - mmengine - INFO - Iter(train) [ 62650/240000] lr: 7.6404e-03 eta: 1 day, 11:47:46 time: 0.7075 data_time: 0.0239 memory: 17394 loss: 0.2386 decode.loss_ce: 0.1559 decode.acc_seg: 94.2700 aux.loss_ce: 0.0827 aux.acc_seg: 92.4334 2023/06/07 21:02:18 - mmengine - INFO - Iter(train) [ 62700/240000] lr: 7.6385e-03 eta: 1 day, 11:47:08 time: 0.7304 data_time: 0.0769 memory: 17396 loss: 0.2456 decode.loss_ce: 0.1612 decode.acc_seg: 93.2966 aux.loss_ce: 0.0844 aux.acc_seg: 91.5699 2023/06/07 21:02:54 - mmengine - INFO - Iter(train) [ 62750/240000] lr: 7.6366e-03 eta: 1 day, 11:46:30 time: 0.7102 data_time: 0.0620 memory: 17396 loss: 0.2272 decode.loss_ce: 0.1457 decode.acc_seg: 94.1916 aux.loss_ce: 0.0815 aux.acc_seg: 91.8798 2023/06/07 21:03:29 - mmengine - INFO - Iter(train) [ 62800/240000] lr: 7.6347e-03 eta: 1 day, 11:45:52 time: 0.6956 data_time: 0.2102 memory: 17396 loss: 0.2267 decode.loss_ce: 0.1507 decode.acc_seg: 87.9901 aux.loss_ce: 0.0759 aux.acc_seg: 87.4628 2023/06/07 21:04:05 - mmengine - INFO - Iter(train) [ 62850/240000] lr: 7.6328e-03 eta: 1 day, 11:45:13 time: 0.7109 data_time: 0.2645 memory: 17396 loss: 0.2411 decode.loss_ce: 0.1562 decode.acc_seg: 92.5632 aux.loss_ce: 0.0850 aux.acc_seg: 91.3868 2023/06/07 21:04:41 - mmengine - INFO - Iter(train) [ 62900/240000] lr: 7.6308e-03 eta: 1 day, 11:44:35 time: 0.7195 data_time: 0.3956 memory: 17393 loss: 0.2298 decode.loss_ce: 0.1510 decode.acc_seg: 91.3244 aux.loss_ce: 0.0788 aux.acc_seg: 88.8661 2023/06/07 21:05:16 - mmengine - INFO - Iter(train) [ 62950/240000] lr: 7.6289e-03 eta: 1 day, 11:43:56 time: 0.7102 data_time: 0.1753 memory: 17394 loss: 0.2205 decode.loss_ce: 0.1463 decode.acc_seg: 94.6299 aux.loss_ce: 0.0742 aux.acc_seg: 92.0920 2023/06/07 21:05:51 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 21:05:51 - mmengine - INFO - Iter(train) [ 63000/240000] lr: 7.6270e-03 eta: 1 day, 11:43:17 time: 0.7040 data_time: 0.3809 memory: 17394 loss: 0.2333 decode.loss_ce: 0.1524 decode.acc_seg: 91.7670 aux.loss_ce: 0.0809 aux.acc_seg: 90.9380 2023/06/07 21:06:27 - mmengine - INFO - Iter(train) [ 63050/240000] lr: 7.6251e-03 eta: 1 day, 11:42:39 time: 0.7082 data_time: 0.3847 memory: 17396 loss: 0.2143 decode.loss_ce: 0.1388 decode.acc_seg: 94.4051 aux.loss_ce: 0.0756 aux.acc_seg: 92.5581 2023/06/07 21:07:03 - mmengine - INFO - Iter(train) [ 63100/240000] lr: 7.6232e-03 eta: 1 day, 11:42:01 time: 0.7218 data_time: 0.3986 memory: 17394 loss: 0.2335 decode.loss_ce: 0.1523 decode.acc_seg: 94.2465 aux.loss_ce: 0.0812 aux.acc_seg: 91.2044 2023/06/07 21:07:38 - mmengine - INFO - Iter(train) [ 63150/240000] lr: 7.6213e-03 eta: 1 day, 11:41:22 time: 0.7047 data_time: 0.3813 memory: 17396 loss: 0.2187 decode.loss_ce: 0.1427 decode.acc_seg: 93.3402 aux.loss_ce: 0.0760 aux.acc_seg: 90.8634 2023/06/07 21:08:14 - mmengine - INFO - Iter(train) [ 63200/240000] lr: 7.6194e-03 eta: 1 day, 11:40:43 time: 0.7210 data_time: 0.3982 memory: 17394 loss: 0.2246 decode.loss_ce: 0.1458 decode.acc_seg: 94.3280 aux.loss_ce: 0.0788 aux.acc_seg: 92.5545 2023/06/07 21:08:50 - mmengine - INFO - Iter(train) [ 63250/240000] lr: 7.6174e-03 eta: 1 day, 11:40:05 time: 0.7092 data_time: 0.3857 memory: 17394 loss: 0.2288 decode.loss_ce: 0.1493 decode.acc_seg: 91.2587 aux.loss_ce: 0.0795 aux.acc_seg: 90.6302 2023/06/07 21:09:25 - mmengine - INFO - Iter(train) [ 63300/240000] lr: 7.6155e-03 eta: 1 day, 11:39:27 time: 0.7067 data_time: 0.3830 memory: 17394 loss: 0.2070 decode.loss_ce: 0.1328 decode.acc_seg: 94.1727 aux.loss_ce: 0.0742 aux.acc_seg: 92.4633 2023/06/07 21:10:01 - mmengine - INFO - Iter(train) [ 63350/240000] lr: 7.6136e-03 eta: 1 day, 11:38:48 time: 0.7092 data_time: 0.1980 memory: 17393 loss: 0.2234 decode.loss_ce: 0.1450 decode.acc_seg: 94.3133 aux.loss_ce: 0.0784 aux.acc_seg: 91.3857 2023/06/07 21:10:36 - mmengine - INFO - Iter(train) [ 63400/240000] lr: 7.6117e-03 eta: 1 day, 11:38:10 time: 0.7127 data_time: 0.3895 memory: 17395 loss: 0.2232 decode.loss_ce: 0.1446 decode.acc_seg: 91.9941 aux.loss_ce: 0.0787 aux.acc_seg: 90.1056 2023/06/07 21:11:12 - mmengine - INFO - Iter(train) [ 63450/240000] lr: 7.6098e-03 eta: 1 day, 11:37:32 time: 0.7038 data_time: 0.3801 memory: 17393 loss: 0.2126 decode.loss_ce: 0.1378 decode.acc_seg: 94.8421 aux.loss_ce: 0.0747 aux.acc_seg: 93.3493 2023/06/07 21:11:47 - mmengine - INFO - Iter(train) [ 63500/240000] lr: 7.6079e-03 eta: 1 day, 11:36:53 time: 0.6959 data_time: 0.2944 memory: 17393 loss: 0.2150 decode.loss_ce: 0.1388 decode.acc_seg: 93.4699 aux.loss_ce: 0.0762 aux.acc_seg: 91.6766 2023/06/07 21:12:23 - mmengine - INFO - Iter(train) [ 63550/240000] lr: 7.6060e-03 eta: 1 day, 11:36:14 time: 0.7163 data_time: 0.2814 memory: 17392 loss: 0.2131 decode.loss_ce: 0.1365 decode.acc_seg: 92.9300 aux.loss_ce: 0.0766 aux.acc_seg: 90.7148 2023/06/07 21:12:58 - mmengine - INFO - Iter(train) [ 63600/240000] lr: 7.6040e-03 eta: 1 day, 11:35:35 time: 0.7099 data_time: 0.3720 memory: 17395 loss: 0.2250 decode.loss_ce: 0.1471 decode.acc_seg: 92.5120 aux.loss_ce: 0.0779 aux.acc_seg: 91.1125 2023/06/07 21:13:33 - mmengine - INFO - Iter(train) [ 63650/240000] lr: 7.6021e-03 eta: 1 day, 11:34:55 time: 0.7036 data_time: 0.3521 memory: 17394 loss: 0.2171 decode.loss_ce: 0.1417 decode.acc_seg: 94.8617 aux.loss_ce: 0.0754 aux.acc_seg: 93.6036 2023/06/07 21:14:09 - mmengine - INFO - Iter(train) [ 63700/240000] lr: 7.6002e-03 eta: 1 day, 11:34:17 time: 0.7202 data_time: 0.0161 memory: 17397 loss: 0.2175 decode.loss_ce: 0.1429 decode.acc_seg: 95.1864 aux.loss_ce: 0.0746 aux.acc_seg: 93.4849 2023/06/07 21:14:45 - mmengine - INFO - Iter(train) [ 63750/240000] lr: 7.5983e-03 eta: 1 day, 11:33:39 time: 0.7184 data_time: 0.0120 memory: 17394 loss: 0.2153 decode.loss_ce: 0.1402 decode.acc_seg: 94.4130 aux.loss_ce: 0.0751 aux.acc_seg: 91.9722 2023/06/07 21:15:20 - mmengine - INFO - Iter(train) [ 63800/240000] lr: 7.5964e-03 eta: 1 day, 11:33:02 time: 0.7395 data_time: 0.0124 memory: 17394 loss: 0.2271 decode.loss_ce: 0.1467 decode.acc_seg: 95.3486 aux.loss_ce: 0.0804 aux.acc_seg: 92.6308 2023/06/07 21:15:56 - mmengine - INFO - Iter(train) [ 63850/240000] lr: 7.5945e-03 eta: 1 day, 11:32:24 time: 0.7074 data_time: 0.0121 memory: 17394 loss: 0.2273 decode.loss_ce: 0.1484 decode.acc_seg: 94.0659 aux.loss_ce: 0.0789 aux.acc_seg: 91.1351 2023/06/07 21:16:32 - mmengine - INFO - Iter(train) [ 63900/240000] lr: 7.5926e-03 eta: 1 day, 11:31:47 time: 0.7251 data_time: 0.0124 memory: 17393 loss: 0.2300 decode.loss_ce: 0.1506 decode.acc_seg: 90.9896 aux.loss_ce: 0.0795 aux.acc_seg: 88.5860 2023/06/07 21:17:08 - mmengine - INFO - Iter(train) [ 63950/240000] lr: 7.5906e-03 eta: 1 day, 11:31:09 time: 0.7179 data_time: 0.0123 memory: 17394 loss: 0.2284 decode.loss_ce: 0.1510 decode.acc_seg: 94.5256 aux.loss_ce: 0.0774 aux.acc_seg: 92.9811 2023/06/07 21:17:44 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 21:17:44 - mmengine - INFO - Iter(train) [ 64000/240000] lr: 7.5887e-03 eta: 1 day, 11:30:31 time: 0.7168 data_time: 0.0121 memory: 17393 loss: 0.2273 decode.loss_ce: 0.1464 decode.acc_seg: 94.0551 aux.loss_ce: 0.0809 aux.acc_seg: 90.8601 2023/06/07 21:18:19 - mmengine - INFO - Iter(train) [ 64050/240000] lr: 7.5868e-03 eta: 1 day, 11:29:53 time: 0.7137 data_time: 0.0120 memory: 17394 loss: 0.2321 decode.loss_ce: 0.1502 decode.acc_seg: 93.9455 aux.loss_ce: 0.0819 aux.acc_seg: 90.7309 2023/06/07 21:18:55 - mmengine - INFO - Iter(train) [ 64100/240000] lr: 7.5849e-03 eta: 1 day, 11:29:16 time: 0.7234 data_time: 0.0121 memory: 17394 loss: 0.2311 decode.loss_ce: 0.1509 decode.acc_seg: 93.7291 aux.loss_ce: 0.0802 aux.acc_seg: 90.7827 2023/06/07 21:19:30 - mmengine - INFO - Iter(train) [ 64150/240000] lr: 7.5830e-03 eta: 1 day, 11:28:36 time: 0.6982 data_time: 0.1264 memory: 17395 loss: 0.2348 decode.loss_ce: 0.1532 decode.acc_seg: 93.8484 aux.loss_ce: 0.0816 aux.acc_seg: 91.2449 2023/06/07 21:20:06 - mmengine - INFO - Iter(train) [ 64200/240000] lr: 7.5811e-03 eta: 1 day, 11:27:58 time: 0.7080 data_time: 0.0322 memory: 17392 loss: 0.2215 decode.loss_ce: 0.1450 decode.acc_seg: 93.0697 aux.loss_ce: 0.0764 aux.acc_seg: 88.9501 2023/06/07 21:20:42 - mmengine - INFO - Iter(train) [ 64250/240000] lr: 7.5792e-03 eta: 1 day, 11:27:19 time: 0.7134 data_time: 0.0122 memory: 17393 loss: 0.2207 decode.loss_ce: 0.1433 decode.acc_seg: 93.7753 aux.loss_ce: 0.0774 aux.acc_seg: 91.8596 2023/06/07 21:21:18 - mmengine - INFO - Iter(train) [ 64300/240000] lr: 7.5772e-03 eta: 1 day, 11:26:42 time: 0.7154 data_time: 0.0121 memory: 17397 loss: 0.2114 decode.loss_ce: 0.1369 decode.acc_seg: 93.6308 aux.loss_ce: 0.0744 aux.acc_seg: 91.2517 2023/06/07 21:21:53 - mmengine - INFO - Iter(train) [ 64350/240000] lr: 7.5753e-03 eta: 1 day, 11:26:04 time: 0.7105 data_time: 0.0119 memory: 17393 loss: 0.2298 decode.loss_ce: 0.1487 decode.acc_seg: 93.6537 aux.loss_ce: 0.0811 aux.acc_seg: 90.7300 2023/06/07 21:22:29 - mmengine - INFO - Iter(train) [ 64400/240000] lr: 7.5734e-03 eta: 1 day, 11:25:27 time: 0.7162 data_time: 0.0123 memory: 17393 loss: 0.2168 decode.loss_ce: 0.1410 decode.acc_seg: 91.9035 aux.loss_ce: 0.0758 aux.acc_seg: 88.1795 2023/06/07 21:23:05 - mmengine - INFO - Iter(train) [ 64450/240000] lr: 7.5715e-03 eta: 1 day, 11:24:48 time: 0.7083 data_time: 0.0119 memory: 17397 loss: 0.2458 decode.loss_ce: 0.1581 decode.acc_seg: 93.2177 aux.loss_ce: 0.0877 aux.acc_seg: 88.0917 2023/06/07 21:23:40 - mmengine - INFO - Iter(train) [ 64500/240000] lr: 7.5696e-03 eta: 1 day, 11:24:09 time: 0.7042 data_time: 0.0906 memory: 17395 loss: 0.2522 decode.loss_ce: 0.1637 decode.acc_seg: 92.1872 aux.loss_ce: 0.0885 aux.acc_seg: 89.6760 2023/06/07 21:24:16 - mmengine - INFO - Iter(train) [ 64550/240000] lr: 7.5677e-03 eta: 1 day, 11:23:31 time: 0.7174 data_time: 0.2057 memory: 17396 loss: 0.2492 decode.loss_ce: 0.1637 decode.acc_seg: 91.9924 aux.loss_ce: 0.0855 aux.acc_seg: 89.6925 2023/06/07 21:24:51 - mmengine - INFO - Iter(train) [ 64600/240000] lr: 7.5657e-03 eta: 1 day, 11:22:52 time: 0.7009 data_time: 0.3619 memory: 17396 loss: 0.2310 decode.loss_ce: 0.1504 decode.acc_seg: 92.0633 aux.loss_ce: 0.0806 aux.acc_seg: 89.2820 2023/06/07 21:25:27 - mmengine - INFO - Iter(train) [ 64650/240000] lr: 7.5638e-03 eta: 1 day, 11:22:15 time: 0.7166 data_time: 0.2105 memory: 17396 loss: 0.2234 decode.loss_ce: 0.1445 decode.acc_seg: 93.5544 aux.loss_ce: 0.0790 aux.acc_seg: 90.1214 2023/06/07 21:26:02 - mmengine - INFO - Iter(train) [ 64700/240000] lr: 7.5619e-03 eta: 1 day, 11:21:36 time: 0.7212 data_time: 0.0558 memory: 17395 loss: 0.2265 decode.loss_ce: 0.1472 decode.acc_seg: 93.8105 aux.loss_ce: 0.0793 aux.acc_seg: 90.6452 2023/06/07 21:26:38 - mmengine - INFO - Iter(train) [ 64750/240000] lr: 7.5600e-03 eta: 1 day, 11:20:59 time: 0.7178 data_time: 0.0120 memory: 17393 loss: 0.2235 decode.loss_ce: 0.1466 decode.acc_seg: 92.2298 aux.loss_ce: 0.0769 aux.acc_seg: 90.4283 2023/06/07 21:27:14 - mmengine - INFO - Iter(train) [ 64800/240000] lr: 7.5581e-03 eta: 1 day, 11:20:22 time: 0.7238 data_time: 0.0123 memory: 17392 loss: 0.2159 decode.loss_ce: 0.1405 decode.acc_seg: 95.2878 aux.loss_ce: 0.0754 aux.acc_seg: 93.6223 2023/06/07 21:27:50 - mmengine - INFO - Iter(train) [ 64850/240000] lr: 7.5562e-03 eta: 1 day, 11:19:44 time: 0.7101 data_time: 0.0123 memory: 17395 loss: 0.2392 decode.loss_ce: 0.1539 decode.acc_seg: 92.6301 aux.loss_ce: 0.0853 aux.acc_seg: 91.5303 2023/06/07 21:28:26 - mmengine - INFO - Iter(train) [ 64900/240000] lr: 7.5543e-03 eta: 1 day, 11:19:06 time: 0.7090 data_time: 0.0122 memory: 17393 loss: 0.2397 decode.loss_ce: 0.1570 decode.acc_seg: 93.1395 aux.loss_ce: 0.0828 aux.acc_seg: 91.9437 2023/06/07 21:29:01 - mmengine - INFO - Iter(train) [ 64950/240000] lr: 7.5523e-03 eta: 1 day, 11:18:27 time: 0.7083 data_time: 0.0121 memory: 17395 loss: 0.2575 decode.loss_ce: 0.1697 decode.acc_seg: 92.6501 aux.loss_ce: 0.0878 aux.acc_seg: 90.6639 2023/06/07 21:29:37 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 21:29:37 - mmengine - INFO - Iter(train) [ 65000/240000] lr: 7.5504e-03 eta: 1 day, 11:17:50 time: 0.7331 data_time: 0.0120 memory: 17393 loss: 0.2099 decode.loss_ce: 0.1368 decode.acc_seg: 94.4983 aux.loss_ce: 0.0730 aux.acc_seg: 91.0307 2023/06/07 21:30:13 - mmengine - INFO - Iter(train) [ 65050/240000] lr: 7.5485e-03 eta: 1 day, 11:17:13 time: 0.7384 data_time: 0.0125 memory: 17395 loss: 0.2323 decode.loss_ce: 0.1526 decode.acc_seg: 93.4820 aux.loss_ce: 0.0797 aux.acc_seg: 91.8941 2023/06/07 21:30:49 - mmengine - INFO - Iter(train) [ 65100/240000] lr: 7.5466e-03 eta: 1 day, 11:16:35 time: 0.7195 data_time: 0.0122 memory: 17397 loss: 0.2252 decode.loss_ce: 0.1471 decode.acc_seg: 93.6232 aux.loss_ce: 0.0782 aux.acc_seg: 91.7498 2023/06/07 21:31:25 - mmengine - INFO - Iter(train) [ 65150/240000] lr: 7.5447e-03 eta: 1 day, 11:15:57 time: 0.7220 data_time: 0.0811 memory: 17392 loss: 0.2321 decode.loss_ce: 0.1507 decode.acc_seg: 90.8713 aux.loss_ce: 0.0814 aux.acc_seg: 88.7854 2023/06/07 21:32:00 - mmengine - INFO - Iter(train) [ 65200/240000] lr: 7.5428e-03 eta: 1 day, 11:15:18 time: 0.7087 data_time: 0.1888 memory: 17393 loss: 0.2422 decode.loss_ce: 0.1586 decode.acc_seg: 92.5146 aux.loss_ce: 0.0836 aux.acc_seg: 89.1114 2023/06/07 21:32:35 - mmengine - INFO - Iter(train) [ 65250/240000] lr: 7.5408e-03 eta: 1 day, 11:14:39 time: 0.7151 data_time: 0.3917 memory: 17391 loss: 0.2290 decode.loss_ce: 0.1498 decode.acc_seg: 93.5882 aux.loss_ce: 0.0792 aux.acc_seg: 90.2254 2023/06/07 21:33:11 - mmengine - INFO - Iter(train) [ 65300/240000] lr: 7.5389e-03 eta: 1 day, 11:14:02 time: 0.7069 data_time: 0.3836 memory: 17392 loss: 0.2157 decode.loss_ce: 0.1400 decode.acc_seg: 94.9190 aux.loss_ce: 0.0758 aux.acc_seg: 93.7405 2023/06/07 21:33:47 - mmengine - INFO - Iter(train) [ 65350/240000] lr: 7.5370e-03 eta: 1 day, 11:13:24 time: 0.7121 data_time: 0.3889 memory: 17395 loss: 0.2235 decode.loss_ce: 0.1458 decode.acc_seg: 93.9158 aux.loss_ce: 0.0777 aux.acc_seg: 91.5939 2023/06/07 21:34:23 - mmengine - INFO - Iter(train) [ 65400/240000] lr: 7.5351e-03 eta: 1 day, 11:12:46 time: 0.7162 data_time: 0.3930 memory: 17395 loss: 0.2340 decode.loss_ce: 0.1521 decode.acc_seg: 92.1879 aux.loss_ce: 0.0819 aux.acc_seg: 88.9854 2023/06/07 21:34:58 - mmengine - INFO - Iter(train) [ 65450/240000] lr: 7.5332e-03 eta: 1 day, 11:12:08 time: 0.7214 data_time: 0.3983 memory: 17394 loss: 0.2101 decode.loss_ce: 0.1345 decode.acc_seg: 94.6460 aux.loss_ce: 0.0755 aux.acc_seg: 89.3018 2023/06/07 21:35:34 - mmengine - INFO - Iter(train) [ 65500/240000] lr: 7.5313e-03 eta: 1 day, 11:11:31 time: 0.6995 data_time: 0.3761 memory: 17395 loss: 0.2251 decode.loss_ce: 0.1476 decode.acc_seg: 93.1340 aux.loss_ce: 0.0775 aux.acc_seg: 91.3374 2023/06/07 21:36:10 - mmengine - INFO - Iter(train) [ 65550/240000] lr: 7.5293e-03 eta: 1 day, 11:10:52 time: 0.7124 data_time: 0.3891 memory: 17397 loss: 0.2294 decode.loss_ce: 0.1472 decode.acc_seg: 94.9018 aux.loss_ce: 0.0822 aux.acc_seg: 91.8768 2023/06/07 21:36:45 - mmengine - INFO - Iter(train) [ 65600/240000] lr: 7.5274e-03 eta: 1 day, 11:10:13 time: 0.7081 data_time: 0.3851 memory: 17391 loss: 0.2123 decode.loss_ce: 0.1366 decode.acc_seg: 94.7385 aux.loss_ce: 0.0757 aux.acc_seg: 93.0927 2023/06/07 21:37:21 - mmengine - INFO - Iter(train) [ 65650/240000] lr: 7.5255e-03 eta: 1 day, 11:09:35 time: 0.7140 data_time: 0.3903 memory: 17396 loss: 0.2263 decode.loss_ce: 0.1488 decode.acc_seg: 91.3952 aux.loss_ce: 0.0775 aux.acc_seg: 89.6955 2023/06/07 21:37:56 - mmengine - INFO - Iter(train) [ 65700/240000] lr: 7.5236e-03 eta: 1 day, 11:08:57 time: 0.7027 data_time: 0.3792 memory: 17395 loss: 0.2091 decode.loss_ce: 0.1352 decode.acc_seg: 93.3841 aux.loss_ce: 0.0739 aux.acc_seg: 91.2688 2023/06/07 21:38:32 - mmengine - INFO - Iter(train) [ 65750/240000] lr: 7.5217e-03 eta: 1 day, 11:08:18 time: 0.7191 data_time: 0.3957 memory: 17396 loss: 0.2239 decode.loss_ce: 0.1470 decode.acc_seg: 95.4824 aux.loss_ce: 0.0769 aux.acc_seg: 93.4432 2023/06/07 21:39:08 - mmengine - INFO - Iter(train) [ 65800/240000] lr: 7.5198e-03 eta: 1 day, 11:07:42 time: 0.7052 data_time: 0.3815 memory: 17392 loss: 0.2187 decode.loss_ce: 0.1399 decode.acc_seg: 94.1262 aux.loss_ce: 0.0788 aux.acc_seg: 91.2307 2023/06/07 21:39:43 - mmengine - INFO - Iter(train) [ 65850/240000] lr: 7.5178e-03 eta: 1 day, 11:07:03 time: 0.7017 data_time: 0.3780 memory: 17395 loss: 0.2401 decode.loss_ce: 0.1541 decode.acc_seg: 94.2838 aux.loss_ce: 0.0860 aux.acc_seg: 92.6177 2023/06/07 21:40:19 - mmengine - INFO - Iter(train) [ 65900/240000] lr: 7.5159e-03 eta: 1 day, 11:06:25 time: 0.7144 data_time: 0.3906 memory: 17391 loss: 0.2125 decode.loss_ce: 0.1365 decode.acc_seg: 91.5075 aux.loss_ce: 0.0760 aux.acc_seg: 89.9329 2023/06/07 21:40:55 - mmengine - INFO - Iter(train) [ 65950/240000] lr: 7.5140e-03 eta: 1 day, 11:05:47 time: 0.7134 data_time: 0.3669 memory: 17394 loss: 0.2391 decode.loss_ce: 0.1555 decode.acc_seg: 93.0355 aux.loss_ce: 0.0836 aux.acc_seg: 90.2407 2023/06/07 21:41:30 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 21:41:30 - mmengine - INFO - Iter(train) [ 66000/240000] lr: 7.5121e-03 eta: 1 day, 11:05:09 time: 0.7179 data_time: 0.0480 memory: 17396 loss: 0.2827 decode.loss_ce: 0.1847 decode.acc_seg: 91.9431 aux.loss_ce: 0.0980 aux.acc_seg: 87.5649 2023/06/07 21:42:06 - mmengine - INFO - Iter(train) [ 66050/240000] lr: 7.5102e-03 eta: 1 day, 11:04:32 time: 0.7103 data_time: 0.0118 memory: 17394 loss: 0.2254 decode.loss_ce: 0.1461 decode.acc_seg: 93.6910 aux.loss_ce: 0.0794 aux.acc_seg: 90.7999 2023/06/07 21:42:42 - mmengine - INFO - Iter(train) [ 66100/240000] lr: 7.5083e-03 eta: 1 day, 11:03:53 time: 0.7049 data_time: 0.0120 memory: 17393 loss: 0.2228 decode.loss_ce: 0.1445 decode.acc_seg: 95.7129 aux.loss_ce: 0.0782 aux.acc_seg: 92.5538 2023/06/07 21:43:18 - mmengine - INFO - Iter(train) [ 66150/240000] lr: 7.5063e-03 eta: 1 day, 11:03:17 time: 0.7166 data_time: 0.0120 memory: 17392 loss: 0.2248 decode.loss_ce: 0.1463 decode.acc_seg: 93.7132 aux.loss_ce: 0.0785 aux.acc_seg: 90.5625 2023/06/07 21:43:53 - mmengine - INFO - Iter(train) [ 66200/240000] lr: 7.5044e-03 eta: 1 day, 11:02:38 time: 0.7036 data_time: 0.0120 memory: 17393 loss: 0.2112 decode.loss_ce: 0.1382 decode.acc_seg: 93.8367 aux.loss_ce: 0.0730 aux.acc_seg: 91.5039 2023/06/07 21:44:29 - mmengine - INFO - Iter(train) [ 66250/240000] lr: 7.5025e-03 eta: 1 day, 11:02:02 time: 0.7102 data_time: 0.0150 memory: 17393 loss: 0.2628 decode.loss_ce: 0.1692 decode.acc_seg: 93.1947 aux.loss_ce: 0.0936 aux.acc_seg: 90.7088 2023/06/07 21:45:05 - mmengine - INFO - Iter(train) [ 66300/240000] lr: 7.5006e-03 eta: 1 day, 11:01:24 time: 0.7177 data_time: 0.1424 memory: 17393 loss: 0.2136 decode.loss_ce: 0.1387 decode.acc_seg: 93.4938 aux.loss_ce: 0.0749 aux.acc_seg: 91.3893 2023/06/07 21:45:41 - mmengine - INFO - Iter(train) [ 66350/240000] lr: 7.4987e-03 eta: 1 day, 11:00:45 time: 0.7157 data_time: 0.2210 memory: 17394 loss: 0.2215 decode.loss_ce: 0.1439 decode.acc_seg: 93.6078 aux.loss_ce: 0.0776 aux.acc_seg: 90.0948 2023/06/07 21:46:16 - mmengine - INFO - Iter(train) [ 66400/240000] lr: 7.4968e-03 eta: 1 day, 11:00:07 time: 0.7131 data_time: 0.3894 memory: 17394 loss: 0.2270 decode.loss_ce: 0.1468 decode.acc_seg: 90.7085 aux.loss_ce: 0.0801 aux.acc_seg: 88.5817 2023/06/07 21:46:52 - mmengine - INFO - Iter(train) [ 66450/240000] lr: 7.4948e-03 eta: 1 day, 10:59:29 time: 0.7155 data_time: 0.3917 memory: 17393 loss: 0.2260 decode.loss_ce: 0.1464 decode.acc_seg: 93.2237 aux.loss_ce: 0.0796 aux.acc_seg: 90.9158 2023/06/07 21:47:27 - mmengine - INFO - Iter(train) [ 66500/240000] lr: 7.4929e-03 eta: 1 day, 10:58:51 time: 0.7094 data_time: 0.3859 memory: 17395 loss: 0.2233 decode.loss_ce: 0.1427 decode.acc_seg: 95.4040 aux.loss_ce: 0.0806 aux.acc_seg: 92.3125 2023/06/07 21:48:03 - mmengine - INFO - Iter(train) [ 66550/240000] lr: 7.4910e-03 eta: 1 day, 10:58:13 time: 0.7051 data_time: 0.3821 memory: 17393 loss: 0.2159 decode.loss_ce: 0.1381 decode.acc_seg: 94.7035 aux.loss_ce: 0.0777 aux.acc_seg: 91.8060 2023/06/07 21:48:38 - mmengine - INFO - Iter(train) [ 66600/240000] lr: 7.4891e-03 eta: 1 day, 10:57:34 time: 0.7059 data_time: 0.3034 memory: 17395 loss: 0.2235 decode.loss_ce: 0.1448 decode.acc_seg: 92.9827 aux.loss_ce: 0.0788 aux.acc_seg: 90.4165 2023/06/07 21:49:14 - mmengine - INFO - Iter(train) [ 66650/240000] lr: 7.4872e-03 eta: 1 day, 10:56:56 time: 0.7078 data_time: 0.3842 memory: 17395 loss: 0.2109 decode.loss_ce: 0.1348 decode.acc_seg: 93.3406 aux.loss_ce: 0.0761 aux.acc_seg: 89.2634 2023/06/07 21:49:50 - mmengine - INFO - Iter(train) [ 66700/240000] lr: 7.4853e-03 eta: 1 day, 10:56:18 time: 0.7289 data_time: 0.4003 memory: 17397 loss: 0.2178 decode.loss_ce: 0.1413 decode.acc_seg: 95.7037 aux.loss_ce: 0.0765 aux.acc_seg: 93.6006 2023/06/07 21:50:25 - mmengine - INFO - Iter(train) [ 66750/240000] lr: 7.4833e-03 eta: 1 day, 10:55:40 time: 0.7139 data_time: 0.3902 memory: 17392 loss: 0.2153 decode.loss_ce: 0.1388 decode.acc_seg: 92.7971 aux.loss_ce: 0.0765 aux.acc_seg: 89.6521 2023/06/07 21:51:01 - mmengine - INFO - Iter(train) [ 66800/240000] lr: 7.4814e-03 eta: 1 day, 10:55:01 time: 0.7164 data_time: 0.3929 memory: 17395 loss: 0.2345 decode.loss_ce: 0.1512 decode.acc_seg: 91.9856 aux.loss_ce: 0.0833 aux.acc_seg: 87.9424 2023/06/07 21:51:36 - mmengine - INFO - Iter(train) [ 66850/240000] lr: 7.4795e-03 eta: 1 day, 10:54:23 time: 0.7106 data_time: 0.3870 memory: 17395 loss: 0.2532 decode.loss_ce: 0.1632 decode.acc_seg: 90.7891 aux.loss_ce: 0.0900 aux.acc_seg: 88.1165 2023/06/07 21:52:12 - mmengine - INFO - Iter(train) [ 66900/240000] lr: 7.4776e-03 eta: 1 day, 10:53:44 time: 0.7089 data_time: 0.3854 memory: 17394 loss: 0.2103 decode.loss_ce: 0.1354 decode.acc_seg: 92.7427 aux.loss_ce: 0.0748 aux.acc_seg: 90.8413 2023/06/07 21:52:47 - mmengine - INFO - Iter(train) [ 66950/240000] lr: 7.4757e-03 eta: 1 day, 10:53:06 time: 0.7187 data_time: 0.3953 memory: 17395 loss: 0.2246 decode.loss_ce: 0.1458 decode.acc_seg: 93.9286 aux.loss_ce: 0.0788 aux.acc_seg: 91.9326 2023/06/07 21:53:22 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 21:53:22 - mmengine - INFO - Iter(train) [ 67000/240000] lr: 7.4737e-03 eta: 1 day, 10:52:28 time: 0.6908 data_time: 0.3674 memory: 17395 loss: 0.2219 decode.loss_ce: 0.1454 decode.acc_seg: 94.1642 aux.loss_ce: 0.0765 aux.acc_seg: 92.0280 2023/06/07 21:53:58 - mmengine - INFO - Iter(train) [ 67050/240000] lr: 7.4718e-03 eta: 1 day, 10:51:49 time: 0.6866 data_time: 0.3342 memory: 17394 loss: 0.2293 decode.loss_ce: 0.1495 decode.acc_seg: 94.1674 aux.loss_ce: 0.0798 aux.acc_seg: 91.4006 2023/06/07 21:54:34 - mmengine - INFO - Iter(train) [ 67100/240000] lr: 7.4699e-03 eta: 1 day, 10:51:11 time: 0.7036 data_time: 0.3795 memory: 17395 loss: 0.2482 decode.loss_ce: 0.1650 decode.acc_seg: 90.4009 aux.loss_ce: 0.0832 aux.acc_seg: 87.3582 2023/06/07 21:55:09 - mmengine - INFO - Iter(train) [ 67150/240000] lr: 7.4680e-03 eta: 1 day, 10:50:33 time: 0.7163 data_time: 0.3928 memory: 17394 loss: 0.2167 decode.loss_ce: 0.1403 decode.acc_seg: 93.5906 aux.loss_ce: 0.0764 aux.acc_seg: 91.4698 2023/06/07 21:55:45 - mmengine - INFO - Iter(train) [ 67200/240000] lr: 7.4661e-03 eta: 1 day, 10:49:55 time: 0.7149 data_time: 0.3915 memory: 17396 loss: 0.2264 decode.loss_ce: 0.1484 decode.acc_seg: 90.6536 aux.loss_ce: 0.0780 aux.acc_seg: 88.9888 2023/06/07 21:56:20 - mmengine - INFO - Iter(train) [ 67250/240000] lr: 7.4642e-03 eta: 1 day, 10:49:17 time: 0.7102 data_time: 0.3862 memory: 17394 loss: 0.2477 decode.loss_ce: 0.1620 decode.acc_seg: 93.6002 aux.loss_ce: 0.0857 aux.acc_seg: 91.7071 2023/06/07 21:56:56 - mmengine - INFO - Iter(train) [ 67300/240000] lr: 7.4622e-03 eta: 1 day, 10:48:40 time: 0.7255 data_time: 0.4022 memory: 17394 loss: 0.2189 decode.loss_ce: 0.1411 decode.acc_seg: 93.8644 aux.loss_ce: 0.0778 aux.acc_seg: 91.8636 2023/06/07 21:57:32 - mmengine - INFO - Iter(train) [ 67350/240000] lr: 7.4603e-03 eta: 1 day, 10:48:02 time: 0.7179 data_time: 0.3946 memory: 17393 loss: 0.2002 decode.loss_ce: 0.1280 decode.acc_seg: 95.2146 aux.loss_ce: 0.0722 aux.acc_seg: 91.8625 2023/06/07 21:58:08 - mmengine - INFO - Iter(train) [ 67400/240000] lr: 7.4584e-03 eta: 1 day, 10:47:24 time: 0.7166 data_time: 0.3933 memory: 17395 loss: 0.2244 decode.loss_ce: 0.1469 decode.acc_seg: 91.6433 aux.loss_ce: 0.0776 aux.acc_seg: 86.0479 2023/06/07 21:58:43 - mmengine - INFO - Iter(train) [ 67450/240000] lr: 7.4565e-03 eta: 1 day, 10:46:46 time: 0.7229 data_time: 0.3949 memory: 17394 loss: 0.2441 decode.loss_ce: 0.1571 decode.acc_seg: 93.6136 aux.loss_ce: 0.0870 aux.acc_seg: 91.4682 2023/06/07 21:59:19 - mmengine - INFO - Iter(train) [ 67500/240000] lr: 7.4546e-03 eta: 1 day, 10:46:09 time: 0.7244 data_time: 0.4013 memory: 17395 loss: 0.2160 decode.loss_ce: 0.1414 decode.acc_seg: 93.5947 aux.loss_ce: 0.0746 aux.acc_seg: 91.5742 2023/06/07 21:59:55 - mmengine - INFO - Iter(train) [ 67550/240000] lr: 7.4526e-03 eta: 1 day, 10:45:31 time: 0.7125 data_time: 0.3894 memory: 17398 loss: 0.2246 decode.loss_ce: 0.1446 decode.acc_seg: 92.1699 aux.loss_ce: 0.0800 aux.acc_seg: 88.9117 2023/06/07 22:00:31 - mmengine - INFO - Iter(train) [ 67600/240000] lr: 7.4507e-03 eta: 1 day, 10:44:53 time: 0.7197 data_time: 0.3959 memory: 17392 loss: 0.2699 decode.loss_ce: 0.1781 decode.acc_seg: 90.0344 aux.loss_ce: 0.0918 aux.acc_seg: 85.9430 2023/06/07 22:01:06 - mmengine - INFO - Iter(train) [ 67650/240000] lr: 7.4488e-03 eta: 1 day, 10:44:15 time: 0.7098 data_time: 0.3865 memory: 17395 loss: 0.2822 decode.loss_ce: 0.1847 decode.acc_seg: 92.0750 aux.loss_ce: 0.0975 aux.acc_seg: 89.2083 2023/06/07 22:01:42 - mmengine - INFO - Iter(train) [ 67700/240000] lr: 7.4469e-03 eta: 1 day, 10:43:37 time: 0.7119 data_time: 0.3881 memory: 17395 loss: 0.2328 decode.loss_ce: 0.1507 decode.acc_seg: 93.7172 aux.loss_ce: 0.0821 aux.acc_seg: 90.0996 2023/06/07 22:02:18 - mmengine - INFO - Iter(train) [ 67750/240000] lr: 7.4450e-03 eta: 1 day, 10:43:00 time: 0.7250 data_time: 0.4018 memory: 17396 loss: 0.2135 decode.loss_ce: 0.1386 decode.acc_seg: 93.3911 aux.loss_ce: 0.0749 aux.acc_seg: 91.4055 2023/06/07 22:02:53 - mmengine - INFO - Iter(train) [ 67800/240000] lr: 7.4431e-03 eta: 1 day, 10:42:21 time: 0.7137 data_time: 0.3899 memory: 17392 loss: 0.2373 decode.loss_ce: 0.1559 decode.acc_seg: 93.2578 aux.loss_ce: 0.0814 aux.acc_seg: 91.7997 2023/06/07 22:03:29 - mmengine - INFO - Iter(train) [ 67850/240000] lr: 7.4411e-03 eta: 1 day, 10:41:44 time: 0.7217 data_time: 0.3981 memory: 17393 loss: 0.2265 decode.loss_ce: 0.1473 decode.acc_seg: 93.4489 aux.loss_ce: 0.0792 aux.acc_seg: 91.7123 2023/06/07 22:04:05 - mmengine - INFO - Iter(train) [ 67900/240000] lr: 7.4392e-03 eta: 1 day, 10:41:06 time: 0.7207 data_time: 0.3965 memory: 17395 loss: 0.2185 decode.loss_ce: 0.1413 decode.acc_seg: 94.4065 aux.loss_ce: 0.0771 aux.acc_seg: 92.1574 2023/06/07 22:04:40 - mmengine - INFO - Iter(train) [ 67950/240000] lr: 7.4373e-03 eta: 1 day, 10:40:29 time: 0.7070 data_time: 0.3840 memory: 17395 loss: 0.2227 decode.loss_ce: 0.1432 decode.acc_seg: 94.5353 aux.loss_ce: 0.0796 aux.acc_seg: 91.5737 2023/06/07 22:05:16 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 22:05:16 - mmengine - INFO - Iter(train) [ 68000/240000] lr: 7.4354e-03 eta: 1 day, 10:39:51 time: 0.7126 data_time: 0.3889 memory: 17392 loss: 0.1999 decode.loss_ce: 0.1287 decode.acc_seg: 94.7208 aux.loss_ce: 0.0712 aux.acc_seg: 92.5546 2023/06/07 22:05:52 - mmengine - INFO - Iter(train) [ 68050/240000] lr: 7.4335e-03 eta: 1 day, 10:39:13 time: 0.7117 data_time: 0.3879 memory: 17396 loss: 0.2401 decode.loss_ce: 0.1569 decode.acc_seg: 92.9187 aux.loss_ce: 0.0831 aux.acc_seg: 90.4360 2023/06/07 22:06:27 - mmengine - INFO - Iter(train) [ 68100/240000] lr: 7.4315e-03 eta: 1 day, 10:38:34 time: 0.7132 data_time: 0.3842 memory: 17394 loss: 0.2008 decode.loss_ce: 0.1284 decode.acc_seg: 94.4444 aux.loss_ce: 0.0724 aux.acc_seg: 91.7697 2023/06/07 22:07:02 - mmengine - INFO - Iter(train) [ 68150/240000] lr: 7.4296e-03 eta: 1 day, 10:37:56 time: 0.7099 data_time: 0.3192 memory: 17391 loss: 0.2411 decode.loss_ce: 0.1565 decode.acc_seg: 90.5150 aux.loss_ce: 0.0845 aux.acc_seg: 87.0160 2023/06/07 22:07:38 - mmengine - INFO - Iter(train) [ 68200/240000] lr: 7.4277e-03 eta: 1 day, 10:37:17 time: 0.7137 data_time: 0.3905 memory: 17393 loss: 0.2133 decode.loss_ce: 0.1387 decode.acc_seg: 93.0165 aux.loss_ce: 0.0746 aux.acc_seg: 90.9350 2023/06/07 22:08:13 - mmengine - INFO - Iter(train) [ 68250/240000] lr: 7.4258e-03 eta: 1 day, 10:36:38 time: 0.7074 data_time: 0.3664 memory: 17391 loss: 0.2331 decode.loss_ce: 0.1524 decode.acc_seg: 92.8034 aux.loss_ce: 0.0807 aux.acc_seg: 90.7640 2023/06/07 22:08:48 - mmengine - INFO - Iter(train) [ 68300/240000] lr: 7.4239e-03 eta: 1 day, 10:36:00 time: 0.7102 data_time: 0.2160 memory: 17395 loss: 0.2178 decode.loss_ce: 0.1434 decode.acc_seg: 93.8810 aux.loss_ce: 0.0744 aux.acc_seg: 91.9199 2023/06/07 22:09:24 - mmengine - INFO - Iter(train) [ 68350/240000] lr: 7.4219e-03 eta: 1 day, 10:35:23 time: 0.6995 data_time: 0.3761 memory: 17393 loss: 0.2497 decode.loss_ce: 0.1645 decode.acc_seg: 93.1655 aux.loss_ce: 0.0852 aux.acc_seg: 91.0552 2023/06/07 22:10:00 - mmengine - INFO - Iter(train) [ 68400/240000] lr: 7.4200e-03 eta: 1 day, 10:34:45 time: 0.7342 data_time: 0.4107 memory: 17394 loss: 0.2132 decode.loss_ce: 0.1375 decode.acc_seg: 93.2441 aux.loss_ce: 0.0757 aux.acc_seg: 89.7252 2023/06/07 22:10:36 - mmengine - INFO - Iter(train) [ 68450/240000] lr: 7.4181e-03 eta: 1 day, 10:34:07 time: 0.7166 data_time: 0.3937 memory: 17394 loss: 0.2084 decode.loss_ce: 0.1316 decode.acc_seg: 94.2944 aux.loss_ce: 0.0768 aux.acc_seg: 90.7372 2023/06/07 22:11:11 - mmengine - INFO - Iter(train) [ 68500/240000] lr: 7.4162e-03 eta: 1 day, 10:33:30 time: 0.7138 data_time: 0.3904 memory: 17393 loss: 0.2452 decode.loss_ce: 0.1621 decode.acc_seg: 94.3456 aux.loss_ce: 0.0831 aux.acc_seg: 91.0736 2023/06/07 22:11:47 - mmengine - INFO - Iter(train) [ 68550/240000] lr: 7.4143e-03 eta: 1 day, 10:32:52 time: 0.7094 data_time: 0.3859 memory: 17393 loss: 0.2284 decode.loss_ce: 0.1488 decode.acc_seg: 95.1252 aux.loss_ce: 0.0796 aux.acc_seg: 90.6705 2023/06/07 22:12:23 - mmengine - INFO - Iter(train) [ 68600/240000] lr: 7.4123e-03 eta: 1 day, 10:32:15 time: 0.7273 data_time: 0.4038 memory: 17393 loss: 0.2178 decode.loss_ce: 0.1406 decode.acc_seg: 93.4784 aux.loss_ce: 0.0771 aux.acc_seg: 91.4271 2023/06/07 22:12:59 - mmengine - INFO - Iter(train) [ 68650/240000] lr: 7.4104e-03 eta: 1 day, 10:31:37 time: 0.7177 data_time: 0.3944 memory: 17391 loss: 0.2190 decode.loss_ce: 0.1432 decode.acc_seg: 94.0604 aux.loss_ce: 0.0758 aux.acc_seg: 91.9044 2023/06/07 22:13:34 - mmengine - INFO - Iter(train) [ 68700/240000] lr: 7.4085e-03 eta: 1 day, 10:30:58 time: 0.7043 data_time: 0.3811 memory: 17395 loss: 0.2498 decode.loss_ce: 0.1639 decode.acc_seg: 92.0002 aux.loss_ce: 0.0859 aux.acc_seg: 88.7253 2023/06/07 22:14:09 - mmengine - INFO - Iter(train) [ 68750/240000] lr: 7.4066e-03 eta: 1 day, 10:30:20 time: 0.7088 data_time: 0.3851 memory: 17393 loss: 0.2114 decode.loss_ce: 0.1367 decode.acc_seg: 94.2520 aux.loss_ce: 0.0747 aux.acc_seg: 92.1134 2023/06/07 22:14:45 - mmengine - INFO - Iter(train) [ 68800/240000] lr: 7.4047e-03 eta: 1 day, 10:29:42 time: 0.7150 data_time: 0.3917 memory: 17394 loss: 0.2351 decode.loss_ce: 0.1528 decode.acc_seg: 93.5358 aux.loss_ce: 0.0824 aux.acc_seg: 90.9090 2023/06/07 22:15:21 - mmengine - INFO - Iter(train) [ 68850/240000] lr: 7.4027e-03 eta: 1 day, 10:29:05 time: 0.7168 data_time: 0.3938 memory: 17395 loss: 0.2184 decode.loss_ce: 0.1424 decode.acc_seg: 94.5343 aux.loss_ce: 0.0760 aux.acc_seg: 92.7274 2023/06/07 22:15:57 - mmengine - INFO - Iter(train) [ 68900/240000] lr: 7.4008e-03 eta: 1 day, 10:28:27 time: 0.7121 data_time: 0.3887 memory: 17394 loss: 0.2064 decode.loss_ce: 0.1346 decode.acc_seg: 95.0996 aux.loss_ce: 0.0719 aux.acc_seg: 93.5638 2023/06/07 22:16:32 - mmengine - INFO - Iter(train) [ 68950/240000] lr: 7.3989e-03 eta: 1 day, 10:27:49 time: 0.7095 data_time: 0.3860 memory: 17393 loss: 0.2172 decode.loss_ce: 0.1406 decode.acc_seg: 94.4450 aux.loss_ce: 0.0766 aux.acc_seg: 91.9624 2023/06/07 22:17:08 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 22:17:08 - mmengine - INFO - Iter(train) [ 69000/240000] lr: 7.3970e-03 eta: 1 day, 10:27:11 time: 0.7132 data_time: 0.3898 memory: 17394 loss: 0.3023 decode.loss_ce: 0.1997 decode.acc_seg: 89.2762 aux.loss_ce: 0.1025 aux.acc_seg: 85.2942 2023/06/07 22:17:43 - mmengine - INFO - Iter(train) [ 69050/240000] lr: 7.3951e-03 eta: 1 day, 10:26:32 time: 0.6947 data_time: 0.3712 memory: 17394 loss: 0.2457 decode.loss_ce: 0.1619 decode.acc_seg: 92.2834 aux.loss_ce: 0.0838 aux.acc_seg: 88.7083 2023/06/07 22:18:18 - mmengine - INFO - Iter(train) [ 69100/240000] lr: 7.3931e-03 eta: 1 day, 10:25:54 time: 0.7156 data_time: 0.3900 memory: 17394 loss: 0.2304 decode.loss_ce: 0.1484 decode.acc_seg: 91.8978 aux.loss_ce: 0.0820 aux.acc_seg: 89.2514 2023/06/07 22:18:54 - mmengine - INFO - Iter(train) [ 69150/240000] lr: 7.3912e-03 eta: 1 day, 10:25:16 time: 0.7127 data_time: 0.3893 memory: 17395 loss: 0.2089 decode.loss_ce: 0.1354 decode.acc_seg: 94.8387 aux.loss_ce: 0.0735 aux.acc_seg: 92.6573 2023/06/07 22:19:30 - mmengine - INFO - Iter(train) [ 69200/240000] lr: 7.3893e-03 eta: 1 day, 10:24:39 time: 0.7187 data_time: 0.3948 memory: 17391 loss: 0.2017 decode.loss_ce: 0.1291 decode.acc_seg: 93.5797 aux.loss_ce: 0.0726 aux.acc_seg: 90.6848 2023/06/07 22:20:05 - mmengine - INFO - Iter(train) [ 69250/240000] lr: 7.3874e-03 eta: 1 day, 10:24:01 time: 0.7217 data_time: 0.3973 memory: 17396 loss: 0.2273 decode.loss_ce: 0.1443 decode.acc_seg: 93.6400 aux.loss_ce: 0.0830 aux.acc_seg: 89.8885 2023/06/07 22:20:41 - mmengine - INFO - Iter(train) [ 69300/240000] lr: 7.3855e-03 eta: 1 day, 10:23:23 time: 0.7085 data_time: 0.3848 memory: 17393 loss: 0.2267 decode.loss_ce: 0.1474 decode.acc_seg: 93.5415 aux.loss_ce: 0.0793 aux.acc_seg: 91.1313 2023/06/07 22:21:17 - mmengine - INFO - Iter(train) [ 69350/240000] lr: 7.3835e-03 eta: 1 day, 10:22:44 time: 0.7048 data_time: 0.3817 memory: 17394 loss: 0.2205 decode.loss_ce: 0.1427 decode.acc_seg: 92.9454 aux.loss_ce: 0.0778 aux.acc_seg: 90.5368 2023/06/07 22:21:52 - mmengine - INFO - Iter(train) [ 69400/240000] lr: 7.3816e-03 eta: 1 day, 10:22:06 time: 0.7058 data_time: 0.3814 memory: 17392 loss: 0.2373 decode.loss_ce: 0.1552 decode.acc_seg: 92.7115 aux.loss_ce: 0.0821 aux.acc_seg: 89.5956 2023/06/07 22:22:27 - mmengine - INFO - Iter(train) [ 69450/240000] lr: 7.3797e-03 eta: 1 day, 10:21:27 time: 0.6963 data_time: 0.3666 memory: 17393 loss: 0.2173 decode.loss_ce: 0.1423 decode.acc_seg: 94.2605 aux.loss_ce: 0.0750 aux.acc_seg: 92.4884 2023/06/07 22:23:03 - mmengine - INFO - Iter(train) [ 69500/240000] lr: 7.3778e-03 eta: 1 day, 10:20:50 time: 0.7041 data_time: 0.1700 memory: 17395 loss: 0.2286 decode.loss_ce: 0.1480 decode.acc_seg: 92.8053 aux.loss_ce: 0.0806 aux.acc_seg: 91.8564 2023/06/07 22:23:38 - mmengine - INFO - Iter(train) [ 69550/240000] lr: 7.3759e-03 eta: 1 day, 10:20:12 time: 0.7131 data_time: 0.1677 memory: 17395 loss: 0.2292 decode.loss_ce: 0.1503 decode.acc_seg: 91.6722 aux.loss_ce: 0.0790 aux.acc_seg: 88.6360 2023/06/07 22:24:14 - mmengine - INFO - Iter(train) [ 69600/240000] lr: 7.3739e-03 eta: 1 day, 10:19:34 time: 0.6980 data_time: 0.0475 memory: 17394 loss: 0.2242 decode.loss_ce: 0.1458 decode.acc_seg: 94.0119 aux.loss_ce: 0.0784 aux.acc_seg: 91.3611 2023/06/07 22:24:50 - mmengine - INFO - Iter(train) [ 69650/240000] lr: 7.3720e-03 eta: 1 day, 10:18:56 time: 0.7116 data_time: 0.1646 memory: 17396 loss: 0.2223 decode.loss_ce: 0.1431 decode.acc_seg: 93.4185 aux.loss_ce: 0.0792 aux.acc_seg: 89.4830 2023/06/07 22:25:25 - mmengine - INFO - Iter(train) [ 69700/240000] lr: 7.3701e-03 eta: 1 day, 10:18:18 time: 0.6998 data_time: 0.0119 memory: 17393 loss: 0.2265 decode.loss_ce: 0.1470 decode.acc_seg: 91.8070 aux.loss_ce: 0.0795 aux.acc_seg: 89.5631 2023/06/07 22:26:01 - mmengine - INFO - Iter(train) [ 69750/240000] lr: 7.3682e-03 eta: 1 day, 10:17:40 time: 0.7020 data_time: 0.0192 memory: 17393 loss: 0.2301 decode.loss_ce: 0.1508 decode.acc_seg: 91.8528 aux.loss_ce: 0.0793 aux.acc_seg: 90.2142 2023/06/07 22:26:37 - mmengine - INFO - Iter(train) [ 69800/240000] lr: 7.3662e-03 eta: 1 day, 10:17:03 time: 0.7282 data_time: 0.0121 memory: 17396 loss: 0.2302 decode.loss_ce: 0.1490 decode.acc_seg: 94.2051 aux.loss_ce: 0.0812 aux.acc_seg: 92.6729 2023/06/07 22:27:12 - mmengine - INFO - Iter(train) [ 69850/240000] lr: 7.3643e-03 eta: 1 day, 10:16:25 time: 0.6933 data_time: 0.0121 memory: 17393 loss: 0.2253 decode.loss_ce: 0.1468 decode.acc_seg: 92.1423 aux.loss_ce: 0.0785 aux.acc_seg: 90.0793 2023/06/07 22:27:48 - mmengine - INFO - Iter(train) [ 69900/240000] lr: 7.3624e-03 eta: 1 day, 10:15:47 time: 0.7229 data_time: 0.0119 memory: 17394 loss: 0.2040 decode.loss_ce: 0.1323 decode.acc_seg: 92.4585 aux.loss_ce: 0.0717 aux.acc_seg: 91.2523 2023/06/07 22:28:24 - mmengine - INFO - Iter(train) [ 69950/240000] lr: 7.3605e-03 eta: 1 day, 10:15:10 time: 0.7206 data_time: 0.0122 memory: 17394 loss: 0.2178 decode.loss_ce: 0.1404 decode.acc_seg: 93.1455 aux.loss_ce: 0.0774 aux.acc_seg: 90.2743 2023/06/07 22:28:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 22:28:59 - mmengine - INFO - Iter(train) [ 70000/240000] lr: 7.3586e-03 eta: 1 day, 10:14:32 time: 0.7066 data_time: 0.0120 memory: 17394 loss: 0.2301 decode.loss_ce: 0.1478 decode.acc_seg: 91.7368 aux.loss_ce: 0.0823 aux.acc_seg: 89.5983 2023/06/07 22:29:35 - mmengine - INFO - Iter(train) [ 70050/240000] lr: 7.3566e-03 eta: 1 day, 10:13:55 time: 0.7125 data_time: 0.0123 memory: 17394 loss: 0.2326 decode.loss_ce: 0.1511 decode.acc_seg: 93.5399 aux.loss_ce: 0.0815 aux.acc_seg: 90.2892 2023/06/07 22:30:11 - mmengine - INFO - Iter(train) [ 70100/240000] lr: 7.3547e-03 eta: 1 day, 10:13:17 time: 0.7145 data_time: 0.0121 memory: 17392 loss: 0.2379 decode.loss_ce: 0.1534 decode.acc_seg: 92.6866 aux.loss_ce: 0.0845 aux.acc_seg: 88.5599 2023/06/07 22:30:47 - mmengine - INFO - Iter(train) [ 70150/240000] lr: 7.3528e-03 eta: 1 day, 10:12:40 time: 0.7070 data_time: 0.0122 memory: 17395 loss: 0.2457 decode.loss_ce: 0.1612 decode.acc_seg: 94.5200 aux.loss_ce: 0.0844 aux.acc_seg: 92.9881 2023/06/07 22:31:22 - mmengine - INFO - Iter(train) [ 70200/240000] lr: 7.3509e-03 eta: 1 day, 10:12:01 time: 0.7122 data_time: 0.2691 memory: 17396 loss: 0.2222 decode.loss_ce: 0.1439 decode.acc_seg: 93.3514 aux.loss_ce: 0.0782 aux.acc_seg: 89.8654 2023/06/07 22:31:58 - mmengine - INFO - Iter(train) [ 70250/240000] lr: 7.3490e-03 eta: 1 day, 10:11:24 time: 0.7131 data_time: 0.1424 memory: 17396 loss: 0.2419 decode.loss_ce: 0.1579 decode.acc_seg: 94.0831 aux.loss_ce: 0.0840 aux.acc_seg: 91.8631 2023/06/07 22:32:33 - mmengine - INFO - Iter(train) [ 70300/240000] lr: 7.3470e-03 eta: 1 day, 10:10:45 time: 0.7121 data_time: 0.3382 memory: 17392 loss: 0.1991 decode.loss_ce: 0.1290 decode.acc_seg: 94.4080 aux.loss_ce: 0.0701 aux.acc_seg: 92.1273 2023/06/07 22:33:08 - mmengine - INFO - Iter(train) [ 70350/240000] lr: 7.3451e-03 eta: 1 day, 10:10:07 time: 0.7064 data_time: 0.3796 memory: 17394 loss: 0.2131 decode.loss_ce: 0.1389 decode.acc_seg: 94.3015 aux.loss_ce: 0.0743 aux.acc_seg: 91.7872 2023/06/07 22:33:44 - mmengine - INFO - Iter(train) [ 70400/240000] lr: 7.3432e-03 eta: 1 day, 10:09:29 time: 0.7177 data_time: 0.2295 memory: 17396 loss: 0.2329 decode.loss_ce: 0.1479 decode.acc_seg: 93.3704 aux.loss_ce: 0.0849 aux.acc_seg: 90.7582 2023/06/07 22:34:19 - mmengine - INFO - Iter(train) [ 70450/240000] lr: 7.3413e-03 eta: 1 day, 10:08:50 time: 0.7036 data_time: 0.1200 memory: 17393 loss: 0.2167 decode.loss_ce: 0.1401 decode.acc_seg: 94.4720 aux.loss_ce: 0.0766 aux.acc_seg: 91.5923 2023/06/07 22:34:55 - mmengine - INFO - Iter(train) [ 70500/240000] lr: 7.3393e-03 eta: 1 day, 10:08:13 time: 0.7179 data_time: 0.0121 memory: 17395 loss: 0.2177 decode.loss_ce: 0.1394 decode.acc_seg: 94.5720 aux.loss_ce: 0.0783 aux.acc_seg: 93.3880 2023/06/07 22:35:31 - mmengine - INFO - Iter(train) [ 70550/240000] lr: 7.3374e-03 eta: 1 day, 10:07:35 time: 0.7058 data_time: 0.0119 memory: 17394 loss: 0.2516 decode.loss_ce: 0.1661 decode.acc_seg: 92.3426 aux.loss_ce: 0.0855 aux.acc_seg: 90.4186 2023/06/07 22:36:06 - mmengine - INFO - Iter(train) [ 70600/240000] lr: 7.3355e-03 eta: 1 day, 10:06:58 time: 0.7051 data_time: 0.0118 memory: 17392 loss: 0.2372 decode.loss_ce: 0.1542 decode.acc_seg: 93.8389 aux.loss_ce: 0.0829 aux.acc_seg: 91.6964 2023/06/07 22:36:42 - mmengine - INFO - Iter(train) [ 70650/240000] lr: 7.3336e-03 eta: 1 day, 10:06:20 time: 0.7270 data_time: 0.0121 memory: 17396 loss: 0.2242 decode.loss_ce: 0.1446 decode.acc_seg: 94.8197 aux.loss_ce: 0.0797 aux.acc_seg: 92.7499 2023/06/07 22:37:17 - mmengine - INFO - Iter(train) [ 70700/240000] lr: 7.3317e-03 eta: 1 day, 10:05:42 time: 0.7110 data_time: 0.1988 memory: 17394 loss: 0.2278 decode.loss_ce: 0.1496 decode.acc_seg: 94.7988 aux.loss_ce: 0.0782 aux.acc_seg: 93.3269 2023/06/07 22:37:53 - mmengine - INFO - Iter(train) [ 70750/240000] lr: 7.3297e-03 eta: 1 day, 10:05:04 time: 0.7080 data_time: 0.1337 memory: 17395 loss: 0.2163 decode.loss_ce: 0.1410 decode.acc_seg: 94.8123 aux.loss_ce: 0.0753 aux.acc_seg: 92.1922 2023/06/07 22:38:28 - mmengine - INFO - Iter(train) [ 70800/240000] lr: 7.3278e-03 eta: 1 day, 10:04:26 time: 0.7109 data_time: 0.3877 memory: 17395 loss: 0.2307 decode.loss_ce: 0.1508 decode.acc_seg: 91.6245 aux.loss_ce: 0.0799 aux.acc_seg: 88.4024 2023/06/07 22:39:04 - mmengine - INFO - Iter(train) [ 70850/240000] lr: 7.3259e-03 eta: 1 day, 10:03:48 time: 0.7162 data_time: 0.3932 memory: 17394 loss: 0.1975 decode.loss_ce: 0.1278 decode.acc_seg: 95.0712 aux.loss_ce: 0.0697 aux.acc_seg: 92.5304 2023/06/07 22:39:40 - mmengine - INFO - Iter(train) [ 70900/240000] lr: 7.3240e-03 eta: 1 day, 10:03:11 time: 0.7163 data_time: 0.3922 memory: 17397 loss: 0.2408 decode.loss_ce: 0.1572 decode.acc_seg: 91.8198 aux.loss_ce: 0.0835 aux.acc_seg: 88.8380 2023/06/07 22:40:16 - mmengine - INFO - Iter(train) [ 70950/240000] lr: 7.3220e-03 eta: 1 day, 10:02:33 time: 0.7122 data_time: 0.3888 memory: 17395 loss: 0.2029 decode.loss_ce: 0.1294 decode.acc_seg: 95.1210 aux.loss_ce: 0.0736 aux.acc_seg: 93.7389 2023/06/07 22:40:51 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 22:40:51 - mmengine - INFO - Iter(train) [ 71000/240000] lr: 7.3201e-03 eta: 1 day, 10:01:55 time: 0.7037 data_time: 0.3801 memory: 17395 loss: 0.2276 decode.loss_ce: 0.1459 decode.acc_seg: 94.9188 aux.loss_ce: 0.0817 aux.acc_seg: 92.8883 2023/06/07 22:41:27 - mmengine - INFO - Iter(train) [ 71050/240000] lr: 7.3182e-03 eta: 1 day, 10:01:17 time: 0.7073 data_time: 0.3812 memory: 17392 loss: 0.2205 decode.loss_ce: 0.1431 decode.acc_seg: 92.2317 aux.loss_ce: 0.0774 aux.acc_seg: 90.5315 2023/06/07 22:42:02 - mmengine - INFO - Iter(train) [ 71100/240000] lr: 7.3163e-03 eta: 1 day, 10:00:39 time: 0.7006 data_time: 0.3774 memory: 17395 loss: 0.2051 decode.loss_ce: 0.1328 decode.acc_seg: 93.3917 aux.loss_ce: 0.0723 aux.acc_seg: 91.1939 2023/06/07 22:42:37 - mmengine - INFO - Iter(train) [ 71150/240000] lr: 7.3144e-03 eta: 1 day, 10:00:01 time: 0.7062 data_time: 0.0979 memory: 17392 loss: 0.2221 decode.loss_ce: 0.1444 decode.acc_seg: 91.3959 aux.loss_ce: 0.0777 aux.acc_seg: 89.3249 2023/06/07 22:43:13 - mmengine - INFO - Iter(train) [ 71200/240000] lr: 7.3124e-03 eta: 1 day, 9:59:23 time: 0.7163 data_time: 0.1438 memory: 17395 loss: 0.2150 decode.loss_ce: 0.1385 decode.acc_seg: 94.9209 aux.loss_ce: 0.0765 aux.acc_seg: 92.9728 2023/06/07 22:43:49 - mmengine - INFO - Iter(train) [ 71250/240000] lr: 7.3105e-03 eta: 1 day, 9:58:45 time: 0.7112 data_time: 0.1144 memory: 17393 loss: 0.2165 decode.loss_ce: 0.1391 decode.acc_seg: 93.8208 aux.loss_ce: 0.0774 aux.acc_seg: 90.4378 2023/06/07 22:44:25 - mmengine - INFO - Iter(train) [ 71300/240000] lr: 7.3086e-03 eta: 1 day, 9:58:08 time: 0.7076 data_time: 0.0120 memory: 17392 loss: 0.2209 decode.loss_ce: 0.1430 decode.acc_seg: 91.1945 aux.loss_ce: 0.0779 aux.acc_seg: 88.7852 2023/06/07 22:45:01 - mmengine - INFO - Iter(train) [ 71350/240000] lr: 7.3067e-03 eta: 1 day, 9:57:31 time: 0.7121 data_time: 0.0122 memory: 17395 loss: 0.2080 decode.loss_ce: 0.1337 decode.acc_seg: 93.7203 aux.loss_ce: 0.0743 aux.acc_seg: 88.9376 2023/06/07 22:45:36 - mmengine - INFO - Iter(train) [ 71400/240000] lr: 7.3047e-03 eta: 1 day, 9:56:52 time: 0.6986 data_time: 0.1461 memory: 17395 loss: 0.2190 decode.loss_ce: 0.1419 decode.acc_seg: 93.6550 aux.loss_ce: 0.0771 aux.acc_seg: 90.8562 2023/06/07 22:46:11 - mmengine - INFO - Iter(train) [ 71450/240000] lr: 7.3028e-03 eta: 1 day, 9:56:14 time: 0.7139 data_time: 0.0119 memory: 17394 loss: 0.2367 decode.loss_ce: 0.1522 decode.acc_seg: 94.6577 aux.loss_ce: 0.0845 aux.acc_seg: 92.9933 2023/06/07 22:46:47 - mmengine - INFO - Iter(train) [ 71500/240000] lr: 7.3009e-03 eta: 1 day, 9:55:37 time: 0.7071 data_time: 0.0120 memory: 17394 loss: 0.2167 decode.loss_ce: 0.1396 decode.acc_seg: 92.6498 aux.loss_ce: 0.0771 aux.acc_seg: 89.2379 2023/06/07 22:47:22 - mmengine - INFO - Iter(train) [ 71550/240000] lr: 7.2990e-03 eta: 1 day, 9:54:58 time: 0.7077 data_time: 0.0117 memory: 17395 loss: 0.2084 decode.loss_ce: 0.1344 decode.acc_seg: 94.9204 aux.loss_ce: 0.0739 aux.acc_seg: 92.9127 2023/06/07 22:47:58 - mmengine - INFO - Iter(train) [ 71600/240000] lr: 7.2971e-03 eta: 1 day, 9:54:21 time: 0.7150 data_time: 0.0123 memory: 17394 loss: 0.2174 decode.loss_ce: 0.1408 decode.acc_seg: 94.5297 aux.loss_ce: 0.0766 aux.acc_seg: 92.5769 2023/06/07 22:48:34 - mmengine - INFO - Iter(train) [ 71650/240000] lr: 7.2951e-03 eta: 1 day, 9:53:43 time: 0.7267 data_time: 0.0120 memory: 17397 loss: 0.2097 decode.loss_ce: 0.1365 decode.acc_seg: 93.5031 aux.loss_ce: 0.0731 aux.acc_seg: 90.2472 2023/06/07 22:49:09 - mmengine - INFO - Iter(train) [ 71700/240000] lr: 7.2932e-03 eta: 1 day, 9:53:06 time: 0.7100 data_time: 0.0120 memory: 17396 loss: 0.2082 decode.loss_ce: 0.1322 decode.acc_seg: 95.3243 aux.loss_ce: 0.0760 aux.acc_seg: 92.7605 2023/06/07 22:49:45 - mmengine - INFO - Iter(train) [ 71750/240000] lr: 7.2913e-03 eta: 1 day, 9:52:28 time: 0.7253 data_time: 0.0123 memory: 17392 loss: 0.2223 decode.loss_ce: 0.1434 decode.acc_seg: 93.4857 aux.loss_ce: 0.0789 aux.acc_seg: 90.1137 2023/06/07 22:50:21 - mmengine - INFO - Iter(train) [ 71800/240000] lr: 7.2894e-03 eta: 1 day, 9:51:51 time: 0.7067 data_time: 0.0120 memory: 17393 loss: 0.2009 decode.loss_ce: 0.1298 decode.acc_seg: 95.4317 aux.loss_ce: 0.0711 aux.acc_seg: 94.0114 2023/06/07 22:50:56 - mmengine - INFO - Iter(train) [ 71850/240000] lr: 7.2874e-03 eta: 1 day, 9:51:13 time: 0.7169 data_time: 0.0122 memory: 17395 loss: 0.2263 decode.loss_ce: 0.1483 decode.acc_seg: 93.6166 aux.loss_ce: 0.0780 aux.acc_seg: 90.5340 2023/06/07 22:51:32 - mmengine - INFO - Iter(train) [ 71900/240000] lr: 7.2855e-03 eta: 1 day, 9:50:36 time: 0.7080 data_time: 0.0122 memory: 17394 loss: 0.2131 decode.loss_ce: 0.1371 decode.acc_seg: 93.8286 aux.loss_ce: 0.0761 aux.acc_seg: 91.6412 2023/06/07 22:52:08 - mmengine - INFO - Iter(train) [ 71950/240000] lr: 7.2836e-03 eta: 1 day, 9:49:59 time: 0.7209 data_time: 0.0120 memory: 17395 loss: 0.2271 decode.loss_ce: 0.1482 decode.acc_seg: 94.5806 aux.loss_ce: 0.0789 aux.acc_seg: 91.1297 2023/06/07 22:52:44 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 22:52:44 - mmengine - INFO - Iter(train) [ 72000/240000] lr: 7.2817e-03 eta: 1 day, 9:49:21 time: 0.6981 data_time: 0.0122 memory: 17393 loss: 0.2011 decode.loss_ce: 0.1292 decode.acc_seg: 93.3422 aux.loss_ce: 0.0719 aux.acc_seg: 91.2692 2023/06/07 22:52:44 - mmengine - INFO - Saving checkpoint at 72000 iterations 2023/06/07 22:52:45 - mmengine - INFO - Iter(val) [ 50/1297] eta: 0:00:35 time: 0.0286 data_time: 0.0207 memory: 203 2023/06/07 22:52:47 - mmengine - INFO - Iter(val) [ 100/1297] eta: 0:00:33 time: 0.0229 data_time: 0.0147 memory: 203 2023/06/07 22:52:48 - mmengine - INFO - Iter(val) [ 150/1297] eta: 0:00:31 time: 0.0289 data_time: 0.0207 memory: 203 2023/06/07 22:52:49 - mmengine - INFO - Iter(val) [ 200/1297] eta: 0:00:29 time: 0.0183 data_time: 0.0103 memory: 203 2023/06/07 22:52:50 - mmengine - INFO - Iter(val) [ 250/1297] eta: 0:00:27 time: 0.0253 data_time: 0.0172 memory: 203 2023/06/07 22:52:52 - mmengine - INFO - Iter(val) [ 300/1297] eta: 0:00:25 time: 0.0209 data_time: 0.0128 memory: 203 2023/06/07 22:52:53 - mmengine - INFO - Iter(val) [ 350/1297] eta: 0:00:24 time: 0.0265 data_time: 0.0184 memory: 203 2023/06/07 22:52:54 - mmengine - INFO - Iter(val) [ 400/1297] eta: 0:00:22 time: 0.0211 data_time: 0.0130 memory: 203 2023/06/07 22:52:55 - mmengine - INFO - Iter(val) [ 450/1297] eta: 0:00:21 time: 0.0242 data_time: 0.0160 memory: 203 2023/06/07 22:52:56 - mmengine - INFO - Iter(val) [ 500/1297] eta: 0:00:19 time: 0.0230 data_time: 0.0150 memory: 203 2023/06/07 22:52:58 - mmengine - INFO - Iter(val) [ 550/1297] eta: 0:00:18 time: 0.0283 data_time: 0.0202 memory: 203 2023/06/07 22:52:59 - mmengine - INFO - Iter(val) [ 600/1297] eta: 0:00:17 time: 0.0208 data_time: 0.0128 memory: 203 2023/06/07 22:53:00 - mmengine - INFO - Iter(val) [ 650/1297] eta: 0:00:16 time: 0.0277 data_time: 0.0196 memory: 203 2023/06/07 22:53:01 - mmengine - INFO - Iter(val) [ 700/1297] eta: 0:00:14 time: 0.0215 data_time: 0.0135 memory: 203 2023/06/07 22:53:02 - mmengine - INFO - Iter(val) [ 750/1297] eta: 0:00:13 time: 0.0278 data_time: 0.0199 memory: 203 2023/06/07 22:53:04 - mmengine - INFO - Iter(val) [ 800/1297] eta: 0:00:12 time: 0.0223 data_time: 0.0145 memory: 203 2023/06/07 22:53:05 - mmengine - INFO - Iter(val) [ 850/1297] eta: 0:00:10 time: 0.0284 data_time: 0.0203 memory: 203 2023/06/07 22:53:06 - mmengine - INFO - Iter(val) [ 900/1297] eta: 0:00:09 time: 0.0216 data_time: 0.0136 memory: 203 2023/06/07 22:53:07 - mmengine - INFO - Iter(val) [ 950/1297] eta: 0:00:08 time: 0.0244 data_time: 0.0163 memory: 203 2023/06/07 22:53:08 - mmengine - INFO - Iter(val) [1000/1297] eta: 0:00:07 time: 0.0205 data_time: 0.0124 memory: 203 2023/06/07 22:53:10 - mmengine - INFO - Iter(val) [1050/1297] eta: 0:00:06 time: 0.0270 data_time: 0.0187 memory: 203 2023/06/07 22:53:11 - mmengine - INFO - Iter(val) [1100/1297] eta: 0:00:04 time: 0.0280 data_time: 0.0198 memory: 203 2023/06/07 22:53:12 - mmengine - INFO - Iter(val) [1150/1297] eta: 0:00:03 time: 0.0235 data_time: 0.0154 memory: 203 2023/06/07 22:53:13 - mmengine - INFO - Iter(val) [1200/1297] eta: 0:00:02 time: 0.0261 data_time: 0.0178 memory: 203 2023/06/07 22:53:14 - mmengine - INFO - Iter(val) [1250/1297] eta: 0:00:01 time: 0.0227 data_time: 0.0145 memory: 203 2023/06/07 22:53:16 - mmengine - INFO - per class results: 2023/06/07 22:53:16 - mmengine - INFO - +------------+-------+-------+ | Class | IoU | Acc | +------------+-------+-------+ | background | 90.8 | 95.64 | | obstacle | 86.02 | 92.03 | | human | 54.72 | 65.48 | +------------+-------+-------+ 2023/06/07 22:53:16 - mmengine - INFO - Iter(val) [1297/1297] aAcc: 93.8500 mIoU: 77.1800 mAcc: 84.3800 data_time: 0.0159 time: 0.0241 2023/06/07 22:53:50 - mmengine - INFO - Iter(train) [ 72050/240000] lr: 7.2797e-03 eta: 1 day, 9:48:41 time: 0.7025 data_time: 0.1665 memory: 17396 loss: 0.2160 decode.loss_ce: 0.1411 decode.acc_seg: 93.0216 aux.loss_ce: 0.0749 aux.acc_seg: 91.1206 2023/06/07 22:54:26 - mmengine - INFO - Iter(train) [ 72100/240000] lr: 7.2778e-03 eta: 1 day, 9:48:04 time: 0.7215 data_time: 0.3981 memory: 17391 loss: 0.2055 decode.loss_ce: 0.1340 decode.acc_seg: 92.9305 aux.loss_ce: 0.0715 aux.acc_seg: 90.5391 2023/06/07 22:55:01 - mmengine - INFO - Iter(train) [ 72150/240000] lr: 7.2759e-03 eta: 1 day, 9:47:26 time: 0.7154 data_time: 0.3885 memory: 17396 loss: 0.2344 decode.loss_ce: 0.1539 decode.acc_seg: 92.1315 aux.loss_ce: 0.0805 aux.acc_seg: 89.8380 2023/06/07 22:55:37 - mmengine - INFO - Iter(train) [ 72200/240000] lr: 7.2740e-03 eta: 1 day, 9:46:47 time: 0.7016 data_time: 0.3156 memory: 17393 loss: 0.2091 decode.loss_ce: 0.1357 decode.acc_seg: 94.1373 aux.loss_ce: 0.0734 aux.acc_seg: 91.9014 2023/06/07 22:56:12 - mmengine - INFO - Iter(train) [ 72250/240000] lr: 7.2720e-03 eta: 1 day, 9:46:10 time: 0.7071 data_time: 0.3105 memory: 17395 loss: 0.2032 decode.loss_ce: 0.1311 decode.acc_seg: 93.9266 aux.loss_ce: 0.0721 aux.acc_seg: 91.0674 2023/06/07 22:56:48 - mmengine - INFO - Iter(train) [ 72300/240000] lr: 7.2701e-03 eta: 1 day, 9:45:32 time: 0.7175 data_time: 0.0643 memory: 17394 loss: 0.1951 decode.loss_ce: 0.1258 decode.acc_seg: 94.3163 aux.loss_ce: 0.0693 aux.acc_seg: 92.3770 2023/06/07 22:57:23 - mmengine - INFO - Iter(train) [ 72350/240000] lr: 7.2682e-03 eta: 1 day, 9:44:54 time: 0.7175 data_time: 0.0520 memory: 17395 loss: 0.2309 decode.loss_ce: 0.1507 decode.acc_seg: 92.7960 aux.loss_ce: 0.0802 aux.acc_seg: 88.8999 2023/06/07 22:57:59 - mmengine - INFO - Iter(train) [ 72400/240000] lr: 7.2663e-03 eta: 1 day, 9:44:15 time: 0.7046 data_time: 0.3646 memory: 17394 loss: 0.2490 decode.loss_ce: 0.1620 decode.acc_seg: 93.8803 aux.loss_ce: 0.0870 aux.acc_seg: 92.0862 2023/06/07 22:58:34 - mmengine - INFO - Iter(train) [ 72450/240000] lr: 7.2643e-03 eta: 1 day, 9:43:38 time: 0.7091 data_time: 0.1376 memory: 17394 loss: 0.2154 decode.loss_ce: 0.1391 decode.acc_seg: 93.3485 aux.loss_ce: 0.0762 aux.acc_seg: 89.5942 2023/06/07 22:59:10 - mmengine - INFO - Iter(train) [ 72500/240000] lr: 7.2624e-03 eta: 1 day, 9:43:00 time: 0.7075 data_time: 0.3144 memory: 17392 loss: 0.2051 decode.loss_ce: 0.1325 decode.acc_seg: 94.7154 aux.loss_ce: 0.0727 aux.acc_seg: 92.4825 2023/06/07 22:59:45 - mmengine - INFO - Iter(train) [ 72550/240000] lr: 7.2605e-03 eta: 1 day, 9:42:22 time: 0.7178 data_time: 0.3652 memory: 17395 loss: 0.2105 decode.loss_ce: 0.1358 decode.acc_seg: 92.2536 aux.loss_ce: 0.0747 aux.acc_seg: 89.9483 2023/06/07 23:00:21 - mmengine - INFO - Iter(train) [ 72600/240000] lr: 7.2586e-03 eta: 1 day, 9:41:44 time: 0.7083 data_time: 0.2938 memory: 17395 loss: 0.2152 decode.loss_ce: 0.1409 decode.acc_seg: 93.5123 aux.loss_ce: 0.0743 aux.acc_seg: 91.1998 2023/06/07 23:00:56 - mmengine - INFO - Iter(train) [ 72650/240000] lr: 7.2567e-03 eta: 1 day, 9:41:06 time: 0.7106 data_time: 0.2306 memory: 17395 loss: 0.2305 decode.loss_ce: 0.1499 decode.acc_seg: 94.6333 aux.loss_ce: 0.0806 aux.acc_seg: 93.3796 2023/06/07 23:01:32 - mmengine - INFO - Iter(train) [ 72700/240000] lr: 7.2547e-03 eta: 1 day, 9:40:28 time: 0.7098 data_time: 0.1280 memory: 17394 loss: 0.2231 decode.loss_ce: 0.1435 decode.acc_seg: 93.2761 aux.loss_ce: 0.0796 aux.acc_seg: 90.3453 2023/06/07 23:02:07 - mmengine - INFO - Iter(train) [ 72750/240000] lr: 7.2528e-03 eta: 1 day, 9:39:51 time: 0.7081 data_time: 0.0132 memory: 17395 loss: 0.1988 decode.loss_ce: 0.1271 decode.acc_seg: 94.4285 aux.loss_ce: 0.0718 aux.acc_seg: 91.8815 2023/06/07 23:02:43 - mmengine - INFO - Iter(train) [ 72800/240000] lr: 7.2509e-03 eta: 1 day, 9:39:12 time: 0.7039 data_time: 0.1560 memory: 17395 loss: 0.2571 decode.loss_ce: 0.1645 decode.acc_seg: 91.6496 aux.loss_ce: 0.0926 aux.acc_seg: 87.6036 2023/06/07 23:03:18 - mmengine - INFO - Iter(train) [ 72850/240000] lr: 7.2490e-03 eta: 1 day, 9:38:34 time: 0.7075 data_time: 0.1795 memory: 17392 loss: 0.2294 decode.loss_ce: 0.1515 decode.acc_seg: 92.4351 aux.loss_ce: 0.0779 aux.acc_seg: 90.0845 2023/06/07 23:03:54 - mmengine - INFO - Iter(train) [ 72900/240000] lr: 7.2470e-03 eta: 1 day, 9:37:56 time: 0.7055 data_time: 0.1009 memory: 17393 loss: 0.2234 decode.loss_ce: 0.1431 decode.acc_seg: 92.5649 aux.loss_ce: 0.0802 aux.acc_seg: 90.2972 2023/06/07 23:04:29 - mmengine - INFO - Iter(train) [ 72950/240000] lr: 7.2451e-03 eta: 1 day, 9:37:18 time: 0.7049 data_time: 0.1509 memory: 17392 loss: 0.2242 decode.loss_ce: 0.1472 decode.acc_seg: 94.3524 aux.loss_ce: 0.0770 aux.acc_seg: 92.1534 2023/06/07 23:05:04 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 23:05:04 - mmengine - INFO - Iter(train) [ 73000/240000] lr: 7.2432e-03 eta: 1 day, 9:36:40 time: 0.6990 data_time: 0.3356 memory: 17392 loss: 0.2072 decode.loss_ce: 0.1354 decode.acc_seg: 93.6000 aux.loss_ce: 0.0718 aux.acc_seg: 91.7686 2023/06/07 23:05:40 - mmengine - INFO - Iter(train) [ 73050/240000] lr: 7.2413e-03 eta: 1 day, 9:36:02 time: 0.7179 data_time: 0.3921 memory: 17393 loss: 0.2163 decode.loss_ce: 0.1409 decode.acc_seg: 94.8440 aux.loss_ce: 0.0755 aux.acc_seg: 92.7433 2023/06/07 23:06:15 - mmengine - INFO - Iter(train) [ 73100/240000] lr: 7.2393e-03 eta: 1 day, 9:35:24 time: 0.7108 data_time: 0.2588 memory: 17394 loss: 0.2117 decode.loss_ce: 0.1369 decode.acc_seg: 91.7979 aux.loss_ce: 0.0748 aux.acc_seg: 90.1239 2023/06/07 23:06:51 - mmengine - INFO - Iter(train) [ 73150/240000] lr: 7.2374e-03 eta: 1 day, 9:34:46 time: 0.7032 data_time: 0.0342 memory: 17395 loss: 0.2154 decode.loss_ce: 0.1400 decode.acc_seg: 92.7564 aux.loss_ce: 0.0754 aux.acc_seg: 90.9239 2023/06/07 23:07:26 - mmengine - INFO - Iter(train) [ 73200/240000] lr: 7.2355e-03 eta: 1 day, 9:34:08 time: 0.7207 data_time: 0.1546 memory: 17396 loss: 0.2112 decode.loss_ce: 0.1340 decode.acc_seg: 92.2424 aux.loss_ce: 0.0771 aux.acc_seg: 89.6151 2023/06/07 23:08:02 - mmengine - INFO - Iter(train) [ 73250/240000] lr: 7.2336e-03 eta: 1 day, 9:33:30 time: 0.7170 data_time: 0.3932 memory: 17394 loss: 0.2265 decode.loss_ce: 0.1472 decode.acc_seg: 92.3589 aux.loss_ce: 0.0793 aux.acc_seg: 89.6150 2023/06/07 23:08:38 - mmengine - INFO - Iter(train) [ 73300/240000] lr: 7.2316e-03 eta: 1 day, 9:32:53 time: 0.7246 data_time: 0.4008 memory: 17395 loss: 0.2354 decode.loss_ce: 0.1544 decode.acc_seg: 92.4285 aux.loss_ce: 0.0810 aux.acc_seg: 90.8208 2023/06/07 23:09:13 - mmengine - INFO - Iter(train) [ 73350/240000] lr: 7.2297e-03 eta: 1 day, 9:32:16 time: 0.7215 data_time: 0.3983 memory: 17392 loss: 0.1980 decode.loss_ce: 0.1251 decode.acc_seg: 95.7645 aux.loss_ce: 0.0729 aux.acc_seg: 93.4851 2023/06/07 23:09:49 - mmengine - INFO - Iter(train) [ 73400/240000] lr: 7.2278e-03 eta: 1 day, 9:31:38 time: 0.7020 data_time: 0.3783 memory: 17392 loss: 0.2254 decode.loss_ce: 0.1482 decode.acc_seg: 93.5461 aux.loss_ce: 0.0772 aux.acc_seg: 89.9820 2023/06/07 23:10:24 - mmengine - INFO - Iter(train) [ 73450/240000] lr: 7.2259e-03 eta: 1 day, 9:30:59 time: 0.7194 data_time: 0.1526 memory: 17395 loss: 0.2343 decode.loss_ce: 0.1549 decode.acc_seg: 89.3321 aux.loss_ce: 0.0794 aux.acc_seg: 86.4211 2023/06/07 23:11:00 - mmengine - INFO - Iter(train) [ 73500/240000] lr: 7.2239e-03 eta: 1 day, 9:30:22 time: 0.7226 data_time: 0.0121 memory: 17394 loss: 0.2159 decode.loss_ce: 0.1391 decode.acc_seg: 94.5190 aux.loss_ce: 0.0768 aux.acc_seg: 91.6748 2023/06/07 23:11:35 - mmengine - INFO - Iter(train) [ 73550/240000] lr: 7.2220e-03 eta: 1 day, 9:29:45 time: 0.7073 data_time: 0.0123 memory: 17392 loss: 0.2183 decode.loss_ce: 0.1414 decode.acc_seg: 93.4919 aux.loss_ce: 0.0768 aux.acc_seg: 91.3627 2023/06/07 23:12:11 - mmengine - INFO - Iter(train) [ 73600/240000] lr: 7.2201e-03 eta: 1 day, 9:29:07 time: 0.7150 data_time: 0.0124 memory: 17394 loss: 0.2438 decode.loss_ce: 0.1576 decode.acc_seg: 91.4928 aux.loss_ce: 0.0862 aux.acc_seg: 88.1195 2023/06/07 23:12:47 - mmengine - INFO - Iter(train) [ 73650/240000] lr: 7.2182e-03 eta: 1 day, 9:28:29 time: 0.7110 data_time: 0.0122 memory: 17396 loss: 0.2199 decode.loss_ce: 0.1422 decode.acc_seg: 93.4451 aux.loss_ce: 0.0777 aux.acc_seg: 91.7662 2023/06/07 23:13:22 - mmengine - INFO - Iter(train) [ 73700/240000] lr: 7.2162e-03 eta: 1 day, 9:27:51 time: 0.7129 data_time: 0.0122 memory: 17394 loss: 0.2310 decode.loss_ce: 0.1501 decode.acc_seg: 90.9432 aux.loss_ce: 0.0808 aux.acc_seg: 86.9452 2023/06/07 23:13:58 - mmengine - INFO - Iter(train) [ 73750/240000] lr: 7.2143e-03 eta: 1 day, 9:27:14 time: 0.7144 data_time: 0.0127 memory: 17394 loss: 0.2092 decode.loss_ce: 0.1355 decode.acc_seg: 95.0390 aux.loss_ce: 0.0737 aux.acc_seg: 93.2921 2023/06/07 23:14:34 - mmengine - INFO - Iter(train) [ 73800/240000] lr: 7.2124e-03 eta: 1 day, 9:26:37 time: 0.7269 data_time: 0.0124 memory: 17397 loss: 0.2115 decode.loss_ce: 0.1367 decode.acc_seg: 93.1729 aux.loss_ce: 0.0748 aux.acc_seg: 89.6768 2023/06/07 23:15:09 - mmengine - INFO - Iter(train) [ 73850/240000] lr: 7.2104e-03 eta: 1 day, 9:25:59 time: 0.7190 data_time: 0.0123 memory: 17392 loss: 0.2075 decode.loss_ce: 0.1349 decode.acc_seg: 93.7037 aux.loss_ce: 0.0726 aux.acc_seg: 90.6273 2023/06/07 23:15:45 - mmengine - INFO - Iter(train) [ 73900/240000] lr: 7.2085e-03 eta: 1 day, 9:25:21 time: 0.7062 data_time: 0.0125 memory: 17394 loss: 0.2317 decode.loss_ce: 0.1495 decode.acc_seg: 92.5442 aux.loss_ce: 0.0822 aux.acc_seg: 90.6905 2023/06/07 23:16:20 - mmengine - INFO - Iter(train) [ 73950/240000] lr: 7.2066e-03 eta: 1 day, 9:24:44 time: 0.7151 data_time: 0.0124 memory: 17392 loss: 0.2273 decode.loss_ce: 0.1484 decode.acc_seg: 92.9198 aux.loss_ce: 0.0789 aux.acc_seg: 90.0691 2023/06/07 23:16:56 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 23:16:56 - mmengine - INFO - Iter(train) [ 74000/240000] lr: 7.2047e-03 eta: 1 day, 9:24:06 time: 0.7198 data_time: 0.0124 memory: 17393 loss: 0.2198 decode.loss_ce: 0.1428 decode.acc_seg: 94.4611 aux.loss_ce: 0.0770 aux.acc_seg: 91.9184 2023/06/07 23:17:32 - mmengine - INFO - Iter(train) [ 74050/240000] lr: 7.2027e-03 eta: 1 day, 9:23:29 time: 0.7163 data_time: 0.0123 memory: 17396 loss: 0.2241 decode.loss_ce: 0.1446 decode.acc_seg: 93.3876 aux.loss_ce: 0.0795 aux.acc_seg: 90.7605 2023/06/07 23:18:08 - mmengine - INFO - Iter(train) [ 74100/240000] lr: 7.2008e-03 eta: 1 day, 9:22:52 time: 0.7246 data_time: 0.0124 memory: 17397 loss: 0.2155 decode.loss_ce: 0.1370 decode.acc_seg: 94.2121 aux.loss_ce: 0.0785 aux.acc_seg: 90.0688 2023/06/07 23:18:43 - mmengine - INFO - Iter(train) [ 74150/240000] lr: 7.1989e-03 eta: 1 day, 9:22:14 time: 0.7086 data_time: 0.0123 memory: 17393 loss: 0.2149 decode.loss_ce: 0.1391 decode.acc_seg: 94.1593 aux.loss_ce: 0.0758 aux.acc_seg: 92.3198 2023/06/07 23:19:19 - mmengine - INFO - Iter(train) [ 74200/240000] lr: 7.1970e-03 eta: 1 day, 9:21:36 time: 0.7161 data_time: 0.0122 memory: 17395 loss: 0.2103 decode.loss_ce: 0.1352 decode.acc_seg: 95.0239 aux.loss_ce: 0.0750 aux.acc_seg: 92.4796 2023/06/07 23:19:55 - mmengine - INFO - Iter(train) [ 74250/240000] lr: 7.1950e-03 eta: 1 day, 9:21:00 time: 0.7211 data_time: 0.0123 memory: 17394 loss: 0.2248 decode.loss_ce: 0.1466 decode.acc_seg: 94.7534 aux.loss_ce: 0.0782 aux.acc_seg: 93.4582 2023/06/07 23:20:31 - mmengine - INFO - Iter(train) [ 74300/240000] lr: 7.1931e-03 eta: 1 day, 9:20:23 time: 0.7212 data_time: 0.0122 memory: 17393 loss: 0.2261 decode.loss_ce: 0.1476 decode.acc_seg: 92.8831 aux.loss_ce: 0.0786 aux.acc_seg: 90.4613 2023/06/07 23:21:07 - mmengine - INFO - Iter(train) [ 74350/240000] lr: 7.1912e-03 eta: 1 day, 9:19:46 time: 0.7247 data_time: 0.0124 memory: 17393 loss: 0.1978 decode.loss_ce: 0.1286 decode.acc_seg: 90.3562 aux.loss_ce: 0.0692 aux.acc_seg: 88.3154 2023/06/07 23:21:42 - mmengine - INFO - Iter(train) [ 74400/240000] lr: 7.1893e-03 eta: 1 day, 9:19:09 time: 0.7246 data_time: 0.0124 memory: 17394 loss: 0.2122 decode.loss_ce: 0.1362 decode.acc_seg: 93.7415 aux.loss_ce: 0.0760 aux.acc_seg: 91.8654 2023/06/07 23:22:18 - mmengine - INFO - Iter(train) [ 74450/240000] lr: 7.1873e-03 eta: 1 day, 9:18:32 time: 0.7158 data_time: 0.0121 memory: 17394 loss: 0.2460 decode.loss_ce: 0.1600 decode.acc_seg: 93.5929 aux.loss_ce: 0.0861 aux.acc_seg: 91.2497 2023/06/07 23:22:54 - mmengine - INFO - Iter(train) [ 74500/240000] lr: 7.1854e-03 eta: 1 day, 9:17:55 time: 0.7158 data_time: 0.0122 memory: 17396 loss: 0.2106 decode.loss_ce: 0.1358 decode.acc_seg: 94.2001 aux.loss_ce: 0.0748 aux.acc_seg: 91.1420 2023/06/07 23:23:30 - mmengine - INFO - Iter(train) [ 74550/240000] lr: 7.1835e-03 eta: 1 day, 9:17:17 time: 0.7032 data_time: 0.0123 memory: 17394 loss: 0.2193 decode.loss_ce: 0.1390 decode.acc_seg: 94.9549 aux.loss_ce: 0.0803 aux.acc_seg: 93.3698 2023/06/07 23:24:05 - mmengine - INFO - Iter(train) [ 74600/240000] lr: 7.1816e-03 eta: 1 day, 9:16:39 time: 0.6980 data_time: 0.0122 memory: 17393 loss: 0.2137 decode.loss_ce: 0.1367 decode.acc_seg: 95.4386 aux.loss_ce: 0.0770 aux.acc_seg: 94.1544 2023/06/07 23:24:41 - mmengine - INFO - Iter(train) [ 74650/240000] lr: 7.1796e-03 eta: 1 day, 9:16:02 time: 0.7084 data_time: 0.0123 memory: 17391 loss: 0.2089 decode.loss_ce: 0.1368 decode.acc_seg: 95.0892 aux.loss_ce: 0.0721 aux.acc_seg: 93.7459 2023/06/07 23:25:17 - mmengine - INFO - Iter(train) [ 74700/240000] lr: 7.1777e-03 eta: 1 day, 9:15:25 time: 0.7051 data_time: 0.0122 memory: 17395 loss: 0.2097 decode.loss_ce: 0.1346 decode.acc_seg: 95.6068 aux.loss_ce: 0.0751 aux.acc_seg: 94.0407 2023/06/07 23:25:52 - mmengine - INFO - Iter(train) [ 74750/240000] lr: 7.1758e-03 eta: 1 day, 9:14:46 time: 0.7145 data_time: 0.0122 memory: 17393 loss: 0.2275 decode.loss_ce: 0.1457 decode.acc_seg: 94.3939 aux.loss_ce: 0.0818 aux.acc_seg: 92.0861 2023/06/07 23:26:28 - mmengine - INFO - Iter(train) [ 74800/240000] lr: 7.1738e-03 eta: 1 day, 9:14:09 time: 0.7080 data_time: 0.0119 memory: 17394 loss: 0.2072 decode.loss_ce: 0.1352 decode.acc_seg: 94.0314 aux.loss_ce: 0.0720 aux.acc_seg: 91.2610 2023/06/07 23:27:03 - mmengine - INFO - Iter(train) [ 74850/240000] lr: 7.1719e-03 eta: 1 day, 9:13:32 time: 0.7180 data_time: 0.0120 memory: 17393 loss: 0.2165 decode.loss_ce: 0.1399 decode.acc_seg: 94.1783 aux.loss_ce: 0.0767 aux.acc_seg: 90.9091 2023/06/07 23:27:39 - mmengine - INFO - Iter(train) [ 74900/240000] lr: 7.1700e-03 eta: 1 day, 9:12:54 time: 0.7132 data_time: 0.0127 memory: 17395 loss: 0.2165 decode.loss_ce: 0.1403 decode.acc_seg: 92.8730 aux.loss_ce: 0.0761 aux.acc_seg: 89.4844 2023/06/07 23:28:15 - mmengine - INFO - Iter(train) [ 74950/240000] lr: 7.1681e-03 eta: 1 day, 9:12:17 time: 0.7157 data_time: 0.0124 memory: 17394 loss: 0.2144 decode.loss_ce: 0.1363 decode.acc_seg: 94.0649 aux.loss_ce: 0.0781 aux.acc_seg: 91.2196 2023/06/07 23:28:50 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 23:28:50 - mmengine - INFO - Iter(train) [ 75000/240000] lr: 7.1661e-03 eta: 1 day, 9:11:39 time: 0.7112 data_time: 0.0122 memory: 17393 loss: 0.2231 decode.loss_ce: 0.1452 decode.acc_seg: 93.8042 aux.loss_ce: 0.0779 aux.acc_seg: 92.7165 2023/06/07 23:29:26 - mmengine - INFO - Iter(train) [ 75050/240000] lr: 7.1642e-03 eta: 1 day, 9:11:02 time: 0.7046 data_time: 0.0122 memory: 17394 loss: 0.2209 decode.loss_ce: 0.1415 decode.acc_seg: 94.6738 aux.loss_ce: 0.0793 aux.acc_seg: 92.3958 2023/06/07 23:30:01 - mmengine - INFO - Iter(train) [ 75100/240000] lr: 7.1623e-03 eta: 1 day, 9:10:23 time: 0.6976 data_time: 0.0122 memory: 17395 loss: 0.2077 decode.loss_ce: 0.1349 decode.acc_seg: 93.4632 aux.loss_ce: 0.0727 aux.acc_seg: 91.1827 2023/06/07 23:30:36 - mmengine - INFO - Iter(train) [ 75150/240000] lr: 7.1604e-03 eta: 1 day, 9:09:45 time: 0.7014 data_time: 0.0458 memory: 17395 loss: 0.2061 decode.loss_ce: 0.1318 decode.acc_seg: 95.0032 aux.loss_ce: 0.0743 aux.acc_seg: 93.2782 2023/06/07 23:31:12 - mmengine - INFO - Iter(train) [ 75200/240000] lr: 7.1584e-03 eta: 1 day, 9:09:07 time: 0.7170 data_time: 0.2624 memory: 17395 loss: 0.2038 decode.loss_ce: 0.1293 decode.acc_seg: 94.7202 aux.loss_ce: 0.0745 aux.acc_seg: 92.2661 2023/06/07 23:31:47 - mmengine - INFO - Iter(train) [ 75250/240000] lr: 7.1565e-03 eta: 1 day, 9:08:30 time: 0.7100 data_time: 0.3871 memory: 17395 loss: 0.2095 decode.loss_ce: 0.1356 decode.acc_seg: 94.7266 aux.loss_ce: 0.0739 aux.acc_seg: 92.7430 2023/06/07 23:32:23 - mmengine - INFO - Iter(train) [ 75300/240000] lr: 7.1546e-03 eta: 1 day, 9:07:52 time: 0.7000 data_time: 0.3763 memory: 17394 loss: 0.2176 decode.loss_ce: 0.1429 decode.acc_seg: 93.9110 aux.loss_ce: 0.0747 aux.acc_seg: 92.1188 2023/06/07 23:32:59 - mmengine - INFO - Iter(train) [ 75350/240000] lr: 7.1526e-03 eta: 1 day, 9:07:15 time: 0.7139 data_time: 0.3908 memory: 17396 loss: 0.2049 decode.loss_ce: 0.1310 decode.acc_seg: 93.5110 aux.loss_ce: 0.0739 aux.acc_seg: 89.3271 2023/06/07 23:33:34 - mmengine - INFO - Iter(train) [ 75400/240000] lr: 7.1507e-03 eta: 1 day, 9:06:37 time: 0.7195 data_time: 0.3961 memory: 17394 loss: 0.2110 decode.loss_ce: 0.1347 decode.acc_seg: 93.2975 aux.loss_ce: 0.0763 aux.acc_seg: 91.2129 2023/06/07 23:34:10 - mmengine - INFO - Iter(train) [ 75450/240000] lr: 7.1488e-03 eta: 1 day, 9:05:59 time: 0.7110 data_time: 0.3874 memory: 17394 loss: 0.2132 decode.loss_ce: 0.1381 decode.acc_seg: 94.2374 aux.loss_ce: 0.0751 aux.acc_seg: 90.9455 2023/06/07 23:34:45 - mmengine - INFO - Iter(train) [ 75500/240000] lr: 7.1469e-03 eta: 1 day, 9:05:21 time: 0.6950 data_time: 0.3717 memory: 17393 loss: 0.2188 decode.loss_ce: 0.1406 decode.acc_seg: 91.2517 aux.loss_ce: 0.0782 aux.acc_seg: 89.4893 2023/06/07 23:35:20 - mmengine - INFO - Iter(train) [ 75550/240000] lr: 7.1449e-03 eta: 1 day, 9:04:43 time: 0.7031 data_time: 0.3799 memory: 17393 loss: 0.2191 decode.loss_ce: 0.1422 decode.acc_seg: 92.4921 aux.loss_ce: 0.0769 aux.acc_seg: 90.2948 2023/06/07 23:35:56 - mmengine - INFO - Iter(train) [ 75600/240000] lr: 7.1430e-03 eta: 1 day, 9:04:06 time: 0.7053 data_time: 0.3817 memory: 17392 loss: 0.2139 decode.loss_ce: 0.1399 decode.acc_seg: 93.0540 aux.loss_ce: 0.0740 aux.acc_seg: 91.6377 2023/06/07 23:36:32 - mmengine - INFO - Iter(train) [ 75650/240000] lr: 7.1411e-03 eta: 1 day, 9:03:28 time: 0.7257 data_time: 0.4021 memory: 17393 loss: 0.2107 decode.loss_ce: 0.1351 decode.acc_seg: 94.4303 aux.loss_ce: 0.0757 aux.acc_seg: 92.7050 2023/06/07 23:37:07 - mmengine - INFO - Iter(train) [ 75700/240000] lr: 7.1392e-03 eta: 1 day, 9:02:50 time: 0.7178 data_time: 0.1121 memory: 17395 loss: 0.2300 decode.loss_ce: 0.1482 decode.acc_seg: 94.4668 aux.loss_ce: 0.0817 aux.acc_seg: 92.8195 2023/06/07 23:37:43 - mmengine - INFO - Iter(train) [ 75750/240000] lr: 7.1372e-03 eta: 1 day, 9:02:12 time: 0.7157 data_time: 0.1880 memory: 17393 loss: 0.2373 decode.loss_ce: 0.1556 decode.acc_seg: 95.2374 aux.loss_ce: 0.0816 aux.acc_seg: 93.0120 2023/06/07 23:38:18 - mmengine - INFO - Iter(train) [ 75800/240000] lr: 7.1353e-03 eta: 1 day, 9:01:34 time: 0.7003 data_time: 0.2973 memory: 17396 loss: 0.2056 decode.loss_ce: 0.1313 decode.acc_seg: 94.5875 aux.loss_ce: 0.0743 aux.acc_seg: 93.2132 2023/06/07 23:38:53 - mmengine - INFO - Iter(train) [ 75850/240000] lr: 7.1334e-03 eta: 1 day, 9:00:56 time: 0.7148 data_time: 0.3916 memory: 17397 loss: 0.1839 decode.loss_ce: 0.1183 decode.acc_seg: 93.8422 aux.loss_ce: 0.0656 aux.acc_seg: 92.2553 2023/06/07 23:39:29 - mmengine - INFO - Iter(train) [ 75900/240000] lr: 7.1314e-03 eta: 1 day, 9:00:18 time: 0.7110 data_time: 0.3565 memory: 17391 loss: 0.2314 decode.loss_ce: 0.1504 decode.acc_seg: 90.5721 aux.loss_ce: 0.0811 aux.acc_seg: 88.7999 2023/06/07 23:40:04 - mmengine - INFO - Iter(train) [ 75950/240000] lr: 7.1295e-03 eta: 1 day, 8:59:40 time: 0.7065 data_time: 0.3466 memory: 17394 loss: 0.2130 decode.loss_ce: 0.1377 decode.acc_seg: 92.6059 aux.loss_ce: 0.0753 aux.acc_seg: 90.6687 2023/06/07 23:40:40 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 23:40:40 - mmengine - INFO - Iter(train) [ 76000/240000] lr: 7.1276e-03 eta: 1 day, 8:59:03 time: 0.7223 data_time: 0.3988 memory: 17395 loss: 0.2189 decode.loss_ce: 0.1416 decode.acc_seg: 94.2984 aux.loss_ce: 0.0773 aux.acc_seg: 90.9406 2023/06/07 23:41:15 - mmengine - INFO - Iter(train) [ 76050/240000] lr: 7.1257e-03 eta: 1 day, 8:58:25 time: 0.7051 data_time: 0.3815 memory: 17397 loss: 0.2298 decode.loss_ce: 0.1483 decode.acc_seg: 94.6991 aux.loss_ce: 0.0815 aux.acc_seg: 91.6215 2023/06/07 23:41:50 - mmengine - INFO - Iter(train) [ 76100/240000] lr: 7.1237e-03 eta: 1 day, 8:57:47 time: 0.7131 data_time: 0.2905 memory: 17395 loss: 0.2394 decode.loss_ce: 0.1554 decode.acc_seg: 93.5146 aux.loss_ce: 0.0839 aux.acc_seg: 91.4886 2023/06/07 23:42:26 - mmengine - INFO - Iter(train) [ 76150/240000] lr: 7.1218e-03 eta: 1 day, 8:57:10 time: 0.7276 data_time: 0.0124 memory: 17391 loss: 0.2190 decode.loss_ce: 0.1412 decode.acc_seg: 93.8958 aux.loss_ce: 0.0779 aux.acc_seg: 92.1815 2023/06/07 23:43:02 - mmengine - INFO - Iter(train) [ 76200/240000] lr: 7.1199e-03 eta: 1 day, 8:56:33 time: 0.7122 data_time: 0.0123 memory: 17392 loss: 0.2154 decode.loss_ce: 0.1395 decode.acc_seg: 94.1208 aux.loss_ce: 0.0760 aux.acc_seg: 91.9203 2023/06/07 23:43:38 - mmengine - INFO - Iter(train) [ 76250/240000] lr: 7.1179e-03 eta: 1 day, 8:55:56 time: 0.7160 data_time: 0.0123 memory: 17394 loss: 0.2038 decode.loss_ce: 0.1313 decode.acc_seg: 94.1939 aux.loss_ce: 0.0725 aux.acc_seg: 91.6662 2023/06/07 23:44:13 - mmengine - INFO - Iter(train) [ 76300/240000] lr: 7.1160e-03 eta: 1 day, 8:55:18 time: 0.7029 data_time: 0.0125 memory: 17396 loss: 0.2274 decode.loss_ce: 0.1490 decode.acc_seg: 93.5888 aux.loss_ce: 0.0785 aux.acc_seg: 90.5702 2023/06/07 23:44:49 - mmengine - INFO - Iter(train) [ 76350/240000] lr: 7.1141e-03 eta: 1 day, 8:54:41 time: 0.7137 data_time: 0.0121 memory: 17393 loss: 0.2184 decode.loss_ce: 0.1411 decode.acc_seg: 92.3030 aux.loss_ce: 0.0773 aux.acc_seg: 90.1770 2023/06/07 23:45:25 - mmengine - INFO - Iter(train) [ 76400/240000] lr: 7.1122e-03 eta: 1 day, 8:54:03 time: 0.7038 data_time: 0.0121 memory: 17395 loss: 0.2046 decode.loss_ce: 0.1329 decode.acc_seg: 95.1972 aux.loss_ce: 0.0718 aux.acc_seg: 93.6599 2023/06/07 23:46:00 - mmengine - INFO - Iter(train) [ 76450/240000] lr: 7.1102e-03 eta: 1 day, 8:53:25 time: 0.7038 data_time: 0.0122 memory: 17394 loss: 0.2228 decode.loss_ce: 0.1450 decode.acc_seg: 94.5557 aux.loss_ce: 0.0778 aux.acc_seg: 92.9094 2023/06/07 23:46:36 - mmengine - INFO - Iter(train) [ 76500/240000] lr: 7.1083e-03 eta: 1 day, 8:52:48 time: 0.7039 data_time: 0.0122 memory: 17392 loss: 0.2382 decode.loss_ce: 0.1559 decode.acc_seg: 93.2178 aux.loss_ce: 0.0823 aux.acc_seg: 91.1091 2023/06/07 23:47:11 - mmengine - INFO - Iter(train) [ 76550/240000] lr: 7.1064e-03 eta: 1 day, 8:52:10 time: 0.7035 data_time: 0.0122 memory: 17398 loss: 0.2107 decode.loss_ce: 0.1354 decode.acc_seg: 93.4833 aux.loss_ce: 0.0753 aux.acc_seg: 90.6772 2023/06/07 23:47:47 - mmengine - INFO - Iter(train) [ 76600/240000] lr: 7.1044e-03 eta: 1 day, 8:51:33 time: 0.7065 data_time: 0.0123 memory: 17395 loss: 0.2127 decode.loss_ce: 0.1358 decode.acc_seg: 93.7872 aux.loss_ce: 0.0769 aux.acc_seg: 91.9260 2023/06/07 23:48:22 - mmengine - INFO - Iter(train) [ 76650/240000] lr: 7.1025e-03 eta: 1 day, 8:50:55 time: 0.7188 data_time: 0.0133 memory: 17394 loss: 0.2196 decode.loss_ce: 0.1405 decode.acc_seg: 94.4176 aux.loss_ce: 0.0790 aux.acc_seg: 92.4253 2023/06/07 23:48:58 - mmengine - INFO - Iter(train) [ 76700/240000] lr: 7.1006e-03 eta: 1 day, 8:50:18 time: 0.7092 data_time: 0.0121 memory: 17393 loss: 0.2268 decode.loss_ce: 0.1495 decode.acc_seg: 93.7539 aux.loss_ce: 0.0773 aux.acc_seg: 92.0110 2023/06/07 23:49:33 - mmengine - INFO - Iter(train) [ 76750/240000] lr: 7.0987e-03 eta: 1 day, 8:49:40 time: 0.7168 data_time: 0.0198 memory: 17394 loss: 0.2324 decode.loss_ce: 0.1492 decode.acc_seg: 93.5473 aux.loss_ce: 0.0832 aux.acc_seg: 88.1646 2023/06/07 23:50:09 - mmengine - INFO - Iter(train) [ 76800/240000] lr: 7.0967e-03 eta: 1 day, 8:49:02 time: 0.6968 data_time: 0.1555 memory: 17394 loss: 0.1972 decode.loss_ce: 0.1270 decode.acc_seg: 94.7787 aux.loss_ce: 0.0702 aux.acc_seg: 92.3856 2023/06/07 23:50:44 - mmengine - INFO - Iter(train) [ 76850/240000] lr: 7.0948e-03 eta: 1 day, 8:48:24 time: 0.7141 data_time: 0.2888 memory: 17393 loss: 0.2240 decode.loss_ce: 0.1455 decode.acc_seg: 94.6868 aux.loss_ce: 0.0785 aux.acc_seg: 93.3423 2023/06/07 23:51:20 - mmengine - INFO - Iter(train) [ 76900/240000] lr: 7.0929e-03 eta: 1 day, 8:47:46 time: 0.7114 data_time: 0.3638 memory: 17393 loss: 0.2141 decode.loss_ce: 0.1356 decode.acc_seg: 94.0012 aux.loss_ce: 0.0785 aux.acc_seg: 91.9863 2023/06/07 23:51:55 - mmengine - INFO - Iter(train) [ 76950/240000] lr: 7.0909e-03 eta: 1 day, 8:47:09 time: 0.7071 data_time: 0.0427 memory: 17394 loss: 0.2204 decode.loss_ce: 0.1410 decode.acc_seg: 92.8965 aux.loss_ce: 0.0794 aux.acc_seg: 91.5816 2023/06/07 23:52:31 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/07 23:52:31 - mmengine - INFO - Iter(train) [ 77000/240000] lr: 7.0890e-03 eta: 1 day, 8:46:31 time: 0.7092 data_time: 0.2209 memory: 17395 loss: 0.2059 decode.loss_ce: 0.1323 decode.acc_seg: 92.4345 aux.loss_ce: 0.0736 aux.acc_seg: 89.3745 2023/06/07 23:53:07 - mmengine - INFO - Iter(train) [ 77050/240000] lr: 7.0871e-03 eta: 1 day, 8:45:54 time: 0.7067 data_time: 0.0122 memory: 17394 loss: 0.2288 decode.loss_ce: 0.1495 decode.acc_seg: 93.1784 aux.loss_ce: 0.0793 aux.acc_seg: 90.9807 2023/06/07 23:53:42 - mmengine - INFO - Iter(train) [ 77100/240000] lr: 7.0851e-03 eta: 1 day, 8:45:17 time: 0.7166 data_time: 0.0123 memory: 17395 loss: 0.2252 decode.loss_ce: 0.1454 decode.acc_seg: 93.0808 aux.loss_ce: 0.0797 aux.acc_seg: 89.4722 2023/06/07 23:54:18 - mmengine - INFO - Iter(train) [ 77150/240000] lr: 7.0832e-03 eta: 1 day, 8:44:39 time: 0.7131 data_time: 0.0123 memory: 17392 loss: 0.1968 decode.loss_ce: 0.1275 decode.acc_seg: 94.3215 aux.loss_ce: 0.0693 aux.acc_seg: 92.6722 2023/06/07 23:54:53 - mmengine - INFO - Iter(train) [ 77200/240000] lr: 7.0813e-03 eta: 1 day, 8:44:02 time: 0.7156 data_time: 0.0122 memory: 17398 loss: 0.2235 decode.loss_ce: 0.1477 decode.acc_seg: 91.3549 aux.loss_ce: 0.0759 aux.acc_seg: 90.7284 2023/06/07 23:55:29 - mmengine - INFO - Iter(train) [ 77250/240000] lr: 7.0794e-03 eta: 1 day, 8:43:25 time: 0.7066 data_time: 0.0124 memory: 17393 loss: 0.2146 decode.loss_ce: 0.1374 decode.acc_seg: 95.2102 aux.loss_ce: 0.0771 aux.acc_seg: 93.0220 2023/06/07 23:56:05 - mmengine - INFO - Iter(train) [ 77300/240000] lr: 7.0774e-03 eta: 1 day, 8:42:47 time: 0.7322 data_time: 0.0124 memory: 17396 loss: 0.2027 decode.loss_ce: 0.1303 decode.acc_seg: 93.8514 aux.loss_ce: 0.0724 aux.acc_seg: 90.4081 2023/06/07 23:56:40 - mmengine - INFO - Iter(train) [ 77350/240000] lr: 7.0755e-03 eta: 1 day, 8:42:09 time: 0.7063 data_time: 0.0402 memory: 17392 loss: 0.2171 decode.loss_ce: 0.1399 decode.acc_seg: 92.7514 aux.loss_ce: 0.0772 aux.acc_seg: 92.3410 2023/06/07 23:57:15 - mmengine - INFO - Iter(train) [ 77400/240000] lr: 7.0736e-03 eta: 1 day, 8:41:31 time: 0.7013 data_time: 0.1590 memory: 17393 loss: 0.2078 decode.loss_ce: 0.1348 decode.acc_seg: 91.0307 aux.loss_ce: 0.0730 aux.acc_seg: 91.2576 2023/06/07 23:57:51 - mmengine - INFO - Iter(train) [ 77450/240000] lr: 7.0716e-03 eta: 1 day, 8:40:53 time: 0.7172 data_time: 0.3938 memory: 17395 loss: 0.2209 decode.loss_ce: 0.1433 decode.acc_seg: 95.3798 aux.loss_ce: 0.0777 aux.acc_seg: 93.6184 2023/06/07 23:58:26 - mmengine - INFO - Iter(train) [ 77500/240000] lr: 7.0697e-03 eta: 1 day, 8:40:16 time: 0.7102 data_time: 0.2635 memory: 17391 loss: 0.2445 decode.loss_ce: 0.1591 decode.acc_seg: 90.6970 aux.loss_ce: 0.0854 aux.acc_seg: 88.3310 2023/06/07 23:59:02 - mmengine - INFO - Iter(train) [ 77550/240000] lr: 7.0678e-03 eta: 1 day, 8:39:38 time: 0.7149 data_time: 0.2042 memory: 17394 loss: 0.2387 decode.loss_ce: 0.1552 decode.acc_seg: 92.8097 aux.loss_ce: 0.0835 aux.acc_seg: 90.4081 2023/06/07 23:59:37 - mmengine - INFO - Iter(train) [ 77600/240000] lr: 7.0659e-03 eta: 1 day, 8:39:00 time: 0.7131 data_time: 0.3867 memory: 17395 loss: 0.2043 decode.loss_ce: 0.1322 decode.acc_seg: 95.3483 aux.loss_ce: 0.0720 aux.acc_seg: 93.1625 2023/06/08 00:00:13 - mmengine - INFO - Iter(train) [ 77650/240000] lr: 7.0639e-03 eta: 1 day, 8:38:23 time: 0.7187 data_time: 0.3951 memory: 17393 loss: 0.2081 decode.loss_ce: 0.1345 decode.acc_seg: 94.2427 aux.loss_ce: 0.0737 aux.acc_seg: 92.4707 2023/06/08 00:00:48 - mmengine - INFO - Iter(train) [ 77700/240000] lr: 7.0620e-03 eta: 1 day, 8:37:45 time: 0.7140 data_time: 0.2923 memory: 17393 loss: 0.2022 decode.loss_ce: 0.1306 decode.acc_seg: 94.1561 aux.loss_ce: 0.0717 aux.acc_seg: 92.0628 2023/06/08 00:01:24 - mmengine - INFO - Iter(train) [ 77750/240000] lr: 7.0601e-03 eta: 1 day, 8:37:08 time: 0.7196 data_time: 0.0124 memory: 17398 loss: 0.2053 decode.loss_ce: 0.1297 decode.acc_seg: 95.3644 aux.loss_ce: 0.0757 aux.acc_seg: 91.8077 2023/06/08 00:02:00 - mmengine - INFO - Iter(train) [ 77800/240000] lr: 7.0581e-03 eta: 1 day, 8:36:31 time: 0.7374 data_time: 0.0123 memory: 17394 loss: 0.2241 decode.loss_ce: 0.1453 decode.acc_seg: 94.4299 aux.loss_ce: 0.0788 aux.acc_seg: 91.2455 2023/06/08 00:02:35 - mmengine - INFO - Iter(train) [ 77850/240000] lr: 7.0562e-03 eta: 1 day, 8:35:54 time: 0.7145 data_time: 0.0124 memory: 17392 loss: 0.2201 decode.loss_ce: 0.1439 decode.acc_seg: 94.1658 aux.loss_ce: 0.0762 aux.acc_seg: 92.6956 2023/06/08 00:03:11 - mmengine - INFO - Iter(train) [ 77900/240000] lr: 7.0543e-03 eta: 1 day, 8:35:16 time: 0.7168 data_time: 0.0123 memory: 17392 loss: 0.2227 decode.loss_ce: 0.1424 decode.acc_seg: 93.5533 aux.loss_ce: 0.0803 aux.acc_seg: 90.7227 2023/06/08 00:03:47 - mmengine - INFO - Iter(train) [ 77950/240000] lr: 7.0523e-03 eta: 1 day, 8:34:39 time: 0.7090 data_time: 0.0122 memory: 17394 loss: 0.2260 decode.loss_ce: 0.1475 decode.acc_seg: 93.1461 aux.loss_ce: 0.0785 aux.acc_seg: 90.0941 2023/06/08 00:04:22 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 00:04:22 - mmengine - INFO - Iter(train) [ 78000/240000] lr: 7.0504e-03 eta: 1 day, 8:34:02 time: 0.7101 data_time: 0.1045 memory: 17395 loss: 0.2086 decode.loss_ce: 0.1335 decode.acc_seg: 93.7934 aux.loss_ce: 0.0751 aux.acc_seg: 90.6939 2023/06/08 00:04:58 - mmengine - INFO - Iter(train) [ 78050/240000] lr: 7.0485e-03 eta: 1 day, 8:33:24 time: 0.7135 data_time: 0.0913 memory: 17395 loss: 0.2112 decode.loss_ce: 0.1370 decode.acc_seg: 94.9177 aux.loss_ce: 0.0742 aux.acc_seg: 93.4833 2023/06/08 00:05:34 - mmengine - INFO - Iter(train) [ 78100/240000] lr: 7.0465e-03 eta: 1 day, 8:32:47 time: 0.7056 data_time: 0.0248 memory: 17394 loss: 0.2204 decode.loss_ce: 0.1422 decode.acc_seg: 93.7983 aux.loss_ce: 0.0782 aux.acc_seg: 90.9735 2023/06/08 00:06:09 - mmengine - INFO - Iter(train) [ 78150/240000] lr: 7.0446e-03 eta: 1 day, 8:32:09 time: 0.6952 data_time: 0.0119 memory: 17391 loss: 0.2086 decode.loss_ce: 0.1363 decode.acc_seg: 93.8238 aux.loss_ce: 0.0722 aux.acc_seg: 91.4111 2023/06/08 00:06:45 - mmengine - INFO - Iter(train) [ 78200/240000] lr: 7.0427e-03 eta: 1 day, 8:31:32 time: 0.7034 data_time: 0.0122 memory: 17394 loss: 0.1938 decode.loss_ce: 0.1261 decode.acc_seg: 94.1544 aux.loss_ce: 0.0677 aux.acc_seg: 92.3106 2023/06/08 00:07:20 - mmengine - INFO - Iter(train) [ 78250/240000] lr: 7.0408e-03 eta: 1 day, 8:30:54 time: 0.7027 data_time: 0.0123 memory: 17392 loss: 0.2225 decode.loss_ce: 0.1433 decode.acc_seg: 93.1954 aux.loss_ce: 0.0793 aux.acc_seg: 90.9461 2023/06/08 00:07:56 - mmengine - INFO - Iter(train) [ 78300/240000] lr: 7.0388e-03 eta: 1 day, 8:30:17 time: 0.7054 data_time: 0.0122 memory: 17393 loss: 0.2315 decode.loss_ce: 0.1506 decode.acc_seg: 91.8905 aux.loss_ce: 0.0809 aux.acc_seg: 92.1364 2023/06/08 00:08:31 - mmengine - INFO - Iter(train) [ 78350/240000] lr: 7.0369e-03 eta: 1 day, 8:29:39 time: 0.7216 data_time: 0.0549 memory: 17396 loss: 0.2017 decode.loss_ce: 0.1294 decode.acc_seg: 94.2772 aux.loss_ce: 0.0723 aux.acc_seg: 92.5493 2023/06/08 00:09:07 - mmengine - INFO - Iter(train) [ 78400/240000] lr: 7.0350e-03 eta: 1 day, 8:29:02 time: 0.7067 data_time: 0.0247 memory: 17393 loss: 0.2292 decode.loss_ce: 0.1482 decode.acc_seg: 94.1886 aux.loss_ce: 0.0810 aux.acc_seg: 91.6444 2023/06/08 00:09:43 - mmengine - INFO - Iter(train) [ 78450/240000] lr: 7.0330e-03 eta: 1 day, 8:28:25 time: 0.7019 data_time: 0.0176 memory: 17395 loss: 0.2234 decode.loss_ce: 0.1458 decode.acc_seg: 94.7870 aux.loss_ce: 0.0776 aux.acc_seg: 92.2794 2023/06/08 00:10:18 - mmengine - INFO - Iter(train) [ 78500/240000] lr: 7.0311e-03 eta: 1 day, 8:27:47 time: 0.7162 data_time: 0.2082 memory: 17395 loss: 0.2240 decode.loss_ce: 0.1461 decode.acc_seg: 93.9867 aux.loss_ce: 0.0779 aux.acc_seg: 91.9055 2023/06/08 00:10:54 - mmengine - INFO - Iter(train) [ 78550/240000] lr: 7.0292e-03 eta: 1 day, 8:27:10 time: 0.7142 data_time: 0.0121 memory: 17393 loss: 0.2055 decode.loss_ce: 0.1320 decode.acc_seg: 94.8990 aux.loss_ce: 0.0735 aux.acc_seg: 93.7894 2023/06/08 00:11:30 - mmengine - INFO - Iter(train) [ 78600/240000] lr: 7.0272e-03 eta: 1 day, 8:26:33 time: 0.7158 data_time: 0.0122 memory: 17391 loss: 0.1957 decode.loss_ce: 0.1256 decode.acc_seg: 94.7515 aux.loss_ce: 0.0701 aux.acc_seg: 92.6719 2023/06/08 00:12:05 - mmengine - INFO - Iter(train) [ 78650/240000] lr: 7.0253e-03 eta: 1 day, 8:25:55 time: 0.7018 data_time: 0.0121 memory: 17394 loss: 0.2244 decode.loss_ce: 0.1480 decode.acc_seg: 94.0081 aux.loss_ce: 0.0764 aux.acc_seg: 91.0490 2023/06/08 00:12:41 - mmengine - INFO - Iter(train) [ 78700/240000] lr: 7.0234e-03 eta: 1 day, 8:25:18 time: 0.7188 data_time: 0.0122 memory: 17394 loss: 0.2195 decode.loss_ce: 0.1426 decode.acc_seg: 94.5938 aux.loss_ce: 0.0769 aux.acc_seg: 92.0056 2023/06/08 00:13:16 - mmengine - INFO - Iter(train) [ 78750/240000] lr: 7.0214e-03 eta: 1 day, 8:24:40 time: 0.7118 data_time: 0.0141 memory: 17395 loss: 0.2306 decode.loss_ce: 0.1476 decode.acc_seg: 94.0171 aux.loss_ce: 0.0830 aux.acc_seg: 90.1081 2023/06/08 00:13:51 - mmengine - INFO - Iter(train) [ 78800/240000] lr: 7.0195e-03 eta: 1 day, 8:24:02 time: 0.7318 data_time: 0.0178 memory: 17396 loss: 0.2018 decode.loss_ce: 0.1318 decode.acc_seg: 94.3165 aux.loss_ce: 0.0700 aux.acc_seg: 92.6281 2023/06/08 00:14:27 - mmengine - INFO - Iter(train) [ 78850/240000] lr: 7.0176e-03 eta: 1 day, 8:23:25 time: 0.7226 data_time: 0.0120 memory: 17392 loss: 0.2278 decode.loss_ce: 0.1478 decode.acc_seg: 90.3235 aux.loss_ce: 0.0800 aux.acc_seg: 88.7950 2023/06/08 00:15:02 - mmengine - INFO - Iter(train) [ 78900/240000] lr: 7.0156e-03 eta: 1 day, 8:22:47 time: 0.7118 data_time: 0.0122 memory: 17393 loss: 0.2188 decode.loss_ce: 0.1416 decode.acc_seg: 93.5055 aux.loss_ce: 0.0772 aux.acc_seg: 92.6532 2023/06/08 00:15:38 - mmengine - INFO - Iter(train) [ 78950/240000] lr: 7.0137e-03 eta: 1 day, 8:22:10 time: 0.7120 data_time: 0.0120 memory: 17393 loss: 0.2082 decode.loss_ce: 0.1352 decode.acc_seg: 94.5985 aux.loss_ce: 0.0730 aux.acc_seg: 92.5714 2023/06/08 00:16:14 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 00:16:14 - mmengine - INFO - Iter(train) [ 79000/240000] lr: 7.0118e-03 eta: 1 day, 8:21:33 time: 0.7185 data_time: 0.0125 memory: 17391 loss: 0.2066 decode.loss_ce: 0.1330 decode.acc_seg: 93.9702 aux.loss_ce: 0.0736 aux.acc_seg: 91.9373 2023/06/08 00:16:49 - mmengine - INFO - Iter(train) [ 79050/240000] lr: 7.0098e-03 eta: 1 day, 8:20:55 time: 0.7104 data_time: 0.0122 memory: 17392 loss: 0.2000 decode.loss_ce: 0.1277 decode.acc_seg: 93.6862 aux.loss_ce: 0.0724 aux.acc_seg: 91.7968 2023/06/08 00:17:25 - mmengine - INFO - Iter(train) [ 79100/240000] lr: 7.0079e-03 eta: 1 day, 8:20:18 time: 0.7083 data_time: 0.0122 memory: 17396 loss: 0.2066 decode.loss_ce: 0.1355 decode.acc_seg: 94.4092 aux.loss_ce: 0.0711 aux.acc_seg: 92.7564 2023/06/08 00:18:00 - mmengine - INFO - Iter(train) [ 79150/240000] lr: 7.0060e-03 eta: 1 day, 8:19:40 time: 0.6991 data_time: 0.0121 memory: 17393 loss: 0.1990 decode.loss_ce: 0.1291 decode.acc_seg: 92.9611 aux.loss_ce: 0.0699 aux.acc_seg: 90.8726 2023/06/08 00:18:36 - mmengine - INFO - Iter(train) [ 79200/240000] lr: 7.0041e-03 eta: 1 day, 8:19:03 time: 0.7163 data_time: 0.0123 memory: 17392 loss: 0.2121 decode.loss_ce: 0.1385 decode.acc_seg: 94.9906 aux.loss_ce: 0.0736 aux.acc_seg: 93.4796 2023/06/08 00:19:11 - mmengine - INFO - Iter(train) [ 79250/240000] lr: 7.0021e-03 eta: 1 day, 8:18:25 time: 0.7126 data_time: 0.0123 memory: 17395 loss: 0.2160 decode.loss_ce: 0.1394 decode.acc_seg: 93.8892 aux.loss_ce: 0.0766 aux.acc_seg: 91.8935 2023/06/08 00:19:47 - mmengine - INFO - Iter(train) [ 79300/240000] lr: 7.0002e-03 eta: 1 day, 8:17:47 time: 0.7044 data_time: 0.2123 memory: 17392 loss: 0.2203 decode.loss_ce: 0.1441 decode.acc_seg: 94.7932 aux.loss_ce: 0.0763 aux.acc_seg: 93.2261 2023/06/08 00:20:22 - mmengine - INFO - Iter(train) [ 79350/240000] lr: 6.9983e-03 eta: 1 day, 8:17:10 time: 0.7085 data_time: 0.1946 memory: 17393 loss: 0.2006 decode.loss_ce: 0.1304 decode.acc_seg: 95.6028 aux.loss_ce: 0.0702 aux.acc_seg: 93.9274 2023/06/08 00:20:58 - mmengine - INFO - Iter(train) [ 79400/240000] lr: 6.9963e-03 eta: 1 day, 8:16:32 time: 0.7028 data_time: 0.1421 memory: 17392 loss: 0.2167 decode.loss_ce: 0.1399 decode.acc_seg: 92.4897 aux.loss_ce: 0.0768 aux.acc_seg: 90.2247 2023/06/08 00:21:33 - mmengine - INFO - Iter(train) [ 79450/240000] lr: 6.9944e-03 eta: 1 day, 8:15:55 time: 0.7197 data_time: 0.1592 memory: 17395 loss: 0.2053 decode.loss_ce: 0.1342 decode.acc_seg: 93.9600 aux.loss_ce: 0.0711 aux.acc_seg: 91.1238 2023/06/08 00:22:09 - mmengine - INFO - Iter(train) [ 79500/240000] lr: 6.9925e-03 eta: 1 day, 8:15:17 time: 0.7237 data_time: 0.3978 memory: 17394 loss: 0.2100 decode.loss_ce: 0.1369 decode.acc_seg: 94.7448 aux.loss_ce: 0.0731 aux.acc_seg: 93.2131 2023/06/08 00:22:44 - mmengine - INFO - Iter(train) [ 79550/240000] lr: 6.9905e-03 eta: 1 day, 8:14:40 time: 0.6968 data_time: 0.3738 memory: 17393 loss: 0.2206 decode.loss_ce: 0.1427 decode.acc_seg: 94.2805 aux.loss_ce: 0.0778 aux.acc_seg: 90.2742 2023/06/08 00:23:20 - mmengine - INFO - Iter(train) [ 79600/240000] lr: 6.9886e-03 eta: 1 day, 8:14:02 time: 0.7096 data_time: 0.3866 memory: 17396 loss: 0.2318 decode.loss_ce: 0.1501 decode.acc_seg: 92.2897 aux.loss_ce: 0.0817 aux.acc_seg: 87.1288 2023/06/08 00:23:55 - mmengine - INFO - Iter(train) [ 79650/240000] lr: 6.9867e-03 eta: 1 day, 8:13:25 time: 0.7029 data_time: 0.3795 memory: 17393 loss: 0.2411 decode.loss_ce: 0.1560 decode.acc_seg: 94.8851 aux.loss_ce: 0.0851 aux.acc_seg: 92.6187 2023/06/08 00:24:31 - mmengine - INFO - Iter(train) [ 79700/240000] lr: 6.9847e-03 eta: 1 day, 8:12:47 time: 0.7078 data_time: 0.2551 memory: 17392 loss: 0.2269 decode.loss_ce: 0.1467 decode.acc_seg: 93.1937 aux.loss_ce: 0.0802 aux.acc_seg: 91.0770 2023/06/08 00:25:06 - mmengine - INFO - Iter(train) [ 79750/240000] lr: 6.9828e-03 eta: 1 day, 8:12:10 time: 0.7089 data_time: 0.0570 memory: 17393 loss: 0.2045 decode.loss_ce: 0.1315 decode.acc_seg: 93.8623 aux.loss_ce: 0.0731 aux.acc_seg: 91.2571 2023/06/08 00:25:42 - mmengine - INFO - Iter(train) [ 79800/240000] lr: 6.9809e-03 eta: 1 day, 8:11:33 time: 0.7160 data_time: 0.0120 memory: 17393 loss: 0.2194 decode.loss_ce: 0.1418 decode.acc_seg: 94.0434 aux.loss_ce: 0.0777 aux.acc_seg: 91.0349 2023/06/08 00:26:18 - mmengine - INFO - Iter(train) [ 79850/240000] lr: 6.9789e-03 eta: 1 day, 8:10:55 time: 0.6967 data_time: 0.0122 memory: 17395 loss: 0.2214 decode.loss_ce: 0.1425 decode.acc_seg: 95.4057 aux.loss_ce: 0.0789 aux.acc_seg: 92.0412 2023/06/08 00:26:53 - mmengine - INFO - Iter(train) [ 79900/240000] lr: 6.9770e-03 eta: 1 day, 8:10:17 time: 0.7113 data_time: 0.1616 memory: 17395 loss: 0.2279 decode.loss_ce: 0.1475 decode.acc_seg: 93.1805 aux.loss_ce: 0.0804 aux.acc_seg: 91.6604 2023/06/08 00:27:28 - mmengine - INFO - Iter(train) [ 79950/240000] lr: 6.9751e-03 eta: 1 day, 8:09:39 time: 0.7016 data_time: 0.1735 memory: 17395 loss: 0.2082 decode.loss_ce: 0.1354 decode.acc_seg: 95.2715 aux.loss_ce: 0.0728 aux.acc_seg: 92.7102 2023/06/08 00:28:04 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 00:28:04 - mmengine - INFO - Iter(train) [ 80000/240000] lr: 6.9731e-03 eta: 1 day, 8:09:02 time: 0.7069 data_time: 0.0120 memory: 17394 loss: 0.2032 decode.loss_ce: 0.1314 decode.acc_seg: 94.8272 aux.loss_ce: 0.0717 aux.acc_seg: 93.3027 2023/06/08 00:28:40 - mmengine - INFO - Iter(train) [ 80050/240000] lr: 6.9712e-03 eta: 1 day, 8:08:25 time: 0.7244 data_time: 0.0123 memory: 17393 loss: 0.2364 decode.loss_ce: 0.1529 decode.acc_seg: 93.3100 aux.loss_ce: 0.0835 aux.acc_seg: 90.6673 2023/06/08 00:29:15 - mmengine - INFO - Iter(train) [ 80100/240000] lr: 6.9693e-03 eta: 1 day, 8:07:48 time: 0.7163 data_time: 0.0124 memory: 17396 loss: 0.2443 decode.loss_ce: 0.1571 decode.acc_seg: 94.9373 aux.loss_ce: 0.0872 aux.acc_seg: 93.3721 2023/06/08 00:29:51 - mmengine - INFO - Iter(train) [ 80150/240000] lr: 6.9673e-03 eta: 1 day, 8:07:11 time: 0.7137 data_time: 0.0123 memory: 17395 loss: 0.2447 decode.loss_ce: 0.1583 decode.acc_seg: 92.4524 aux.loss_ce: 0.0864 aux.acc_seg: 87.4011 2023/06/08 00:30:27 - mmengine - INFO - Iter(train) [ 80200/240000] lr: 6.9654e-03 eta: 1 day, 8:06:33 time: 0.7147 data_time: 0.0123 memory: 17392 loss: 0.2328 decode.loss_ce: 0.1493 decode.acc_seg: 93.0101 aux.loss_ce: 0.0835 aux.acc_seg: 89.2997 2023/06/08 00:31:02 - mmengine - INFO - Iter(train) [ 80250/240000] lr: 6.9635e-03 eta: 1 day, 8:05:56 time: 0.7182 data_time: 0.0124 memory: 17393 loss: 0.2213 decode.loss_ce: 0.1434 decode.acc_seg: 92.9240 aux.loss_ce: 0.0779 aux.acc_seg: 89.7337 2023/06/08 00:31:38 - mmengine - INFO - Iter(train) [ 80300/240000] lr: 6.9615e-03 eta: 1 day, 8:05:19 time: 0.7146 data_time: 0.0120 memory: 17393 loss: 0.2220 decode.loss_ce: 0.1444 decode.acc_seg: 94.0745 aux.loss_ce: 0.0775 aux.acc_seg: 92.4870 2023/06/08 00:32:13 - mmengine - INFO - Iter(train) [ 80350/240000] lr: 6.9596e-03 eta: 1 day, 8:04:41 time: 0.7050 data_time: 0.0122 memory: 17394 loss: 0.2331 decode.loss_ce: 0.1488 decode.acc_seg: 94.1122 aux.loss_ce: 0.0844 aux.acc_seg: 90.6435 2023/06/08 00:32:49 - mmengine - INFO - Iter(train) [ 80400/240000] lr: 6.9577e-03 eta: 1 day, 8:04:04 time: 0.7117 data_time: 0.0123 memory: 17393 loss: 0.2251 decode.loss_ce: 0.1480 decode.acc_seg: 92.4316 aux.loss_ce: 0.0771 aux.acc_seg: 91.0699 2023/06/08 00:33:24 - mmengine - INFO - Iter(train) [ 80450/240000] lr: 6.9557e-03 eta: 1 day, 8:03:26 time: 0.7144 data_time: 0.0121 memory: 17395 loss: 0.2099 decode.loss_ce: 0.1359 decode.acc_seg: 95.3537 aux.loss_ce: 0.0740 aux.acc_seg: 93.5448 2023/06/08 00:34:00 - mmengine - INFO - Iter(train) [ 80500/240000] lr: 6.9538e-03 eta: 1 day, 8:02:49 time: 0.7047 data_time: 0.0122 memory: 17394 loss: 0.2254 decode.loss_ce: 0.1433 decode.acc_seg: 92.3089 aux.loss_ce: 0.0821 aux.acc_seg: 89.4369 2023/06/08 00:34:35 - mmengine - INFO - Iter(train) [ 80550/240000] lr: 6.9519e-03 eta: 1 day, 8:02:11 time: 0.7142 data_time: 0.0123 memory: 17395 loss: 0.2680 decode.loss_ce: 0.1714 decode.acc_seg: 91.5872 aux.loss_ce: 0.0966 aux.acc_seg: 90.0549 2023/06/08 00:35:11 - mmengine - INFO - Iter(train) [ 80600/240000] lr: 6.9499e-03 eta: 1 day, 8:01:34 time: 0.7107 data_time: 0.0121 memory: 17393 loss: 0.2403 decode.loss_ce: 0.1550 decode.acc_seg: 93.0318 aux.loss_ce: 0.0854 aux.acc_seg: 89.4912 2023/06/08 00:35:47 - mmengine - INFO - Iter(train) [ 80650/240000] lr: 6.9480e-03 eta: 1 day, 8:00:57 time: 0.7074 data_time: 0.0123 memory: 17394 loss: 0.2308 decode.loss_ce: 0.1513 decode.acc_seg: 92.9826 aux.loss_ce: 0.0796 aux.acc_seg: 91.2090 2023/06/08 00:36:22 - mmengine - INFO - Iter(train) [ 80700/240000] lr: 6.9461e-03 eta: 1 day, 8:00:19 time: 0.6985 data_time: 0.0122 memory: 17392 loss: 0.2198 decode.loss_ce: 0.1428 decode.acc_seg: 93.3138 aux.loss_ce: 0.0771 aux.acc_seg: 91.2839 2023/06/08 00:36:58 - mmengine - INFO - Iter(train) [ 80750/240000] lr: 6.9441e-03 eta: 1 day, 7:59:42 time: 0.7135 data_time: 0.0123 memory: 17394 loss: 0.2148 decode.loss_ce: 0.1378 decode.acc_seg: 93.5878 aux.loss_ce: 0.0770 aux.acc_seg: 90.7709 2023/06/08 00:37:33 - mmengine - INFO - Iter(train) [ 80800/240000] lr: 6.9422e-03 eta: 1 day, 7:59:04 time: 0.7103 data_time: 0.0405 memory: 17394 loss: 0.2192 decode.loss_ce: 0.1388 decode.acc_seg: 94.2439 aux.loss_ce: 0.0804 aux.acc_seg: 90.0112 2023/06/08 00:38:08 - mmengine - INFO - Iter(train) [ 80850/240000] lr: 6.9403e-03 eta: 1 day, 7:58:26 time: 0.7015 data_time: 0.3787 memory: 17396 loss: 0.2122 decode.loss_ce: 0.1384 decode.acc_seg: 93.7297 aux.loss_ce: 0.0737 aux.acc_seg: 91.6889 2023/06/08 00:38:44 - mmengine - INFO - Iter(train) [ 80900/240000] lr: 6.9383e-03 eta: 1 day, 7:57:49 time: 0.6995 data_time: 0.3765 memory: 17396 loss: 0.2132 decode.loss_ce: 0.1389 decode.acc_seg: 92.2498 aux.loss_ce: 0.0742 aux.acc_seg: 90.7188 2023/06/08 00:39:19 - mmengine - INFO - Iter(train) [ 80950/240000] lr: 6.9364e-03 eta: 1 day, 7:57:12 time: 0.7189 data_time: 0.3958 memory: 17394 loss: 0.2182 decode.loss_ce: 0.1409 decode.acc_seg: 94.1470 aux.loss_ce: 0.0772 aux.acc_seg: 92.3094 2023/06/08 00:39:55 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 00:39:55 - mmengine - INFO - Iter(train) [ 81000/240000] lr: 6.9345e-03 eta: 1 day, 7:56:34 time: 0.7103 data_time: 0.3871 memory: 17395 loss: 0.2177 decode.loss_ce: 0.1405 decode.acc_seg: 92.7717 aux.loss_ce: 0.0772 aux.acc_seg: 88.9598 2023/06/08 00:40:30 - mmengine - INFO - Iter(train) [ 81050/240000] lr: 6.9325e-03 eta: 1 day, 7:55:57 time: 0.7122 data_time: 0.3888 memory: 17393 loss: 0.2044 decode.loss_ce: 0.1297 decode.acc_seg: 94.4352 aux.loss_ce: 0.0747 aux.acc_seg: 89.9819 2023/06/08 00:41:06 - mmengine - INFO - Iter(train) [ 81100/240000] lr: 6.9306e-03 eta: 1 day, 7:55:20 time: 0.7149 data_time: 0.3915 memory: 17394 loss: 0.2376 decode.loss_ce: 0.1542 decode.acc_seg: 93.6453 aux.loss_ce: 0.0833 aux.acc_seg: 91.6881 2023/06/08 00:41:42 - mmengine - INFO - Iter(train) [ 81150/240000] lr: 6.9287e-03 eta: 1 day, 7:54:42 time: 0.7149 data_time: 0.3914 memory: 17391 loss: 0.2114 decode.loss_ce: 0.1361 decode.acc_seg: 93.8307 aux.loss_ce: 0.0753 aux.acc_seg: 90.9015 2023/06/08 00:42:17 - mmengine - INFO - Iter(train) [ 81200/240000] lr: 6.9267e-03 eta: 1 day, 7:54:04 time: 0.6996 data_time: 0.3765 memory: 17393 loss: 0.2019 decode.loss_ce: 0.1297 decode.acc_seg: 94.8153 aux.loss_ce: 0.0722 aux.acc_seg: 92.4318 2023/06/08 00:42:52 - mmengine - INFO - Iter(train) [ 81250/240000] lr: 6.9248e-03 eta: 1 day, 7:53:27 time: 0.7089 data_time: 0.3853 memory: 17397 loss: 0.2136 decode.loss_ce: 0.1384 decode.acc_seg: 93.7627 aux.loss_ce: 0.0752 aux.acc_seg: 92.1035 2023/06/08 00:43:28 - mmengine - INFO - Iter(train) [ 81300/240000] lr: 6.9229e-03 eta: 1 day, 7:52:50 time: 0.7130 data_time: 0.3900 memory: 17394 loss: 0.2362 decode.loss_ce: 0.1536 decode.acc_seg: 91.3951 aux.loss_ce: 0.0826 aux.acc_seg: 89.1914 2023/06/08 00:44:03 - mmengine - INFO - Iter(train) [ 81350/240000] lr: 6.9209e-03 eta: 1 day, 7:52:12 time: 0.7030 data_time: 0.3769 memory: 17393 loss: 0.2203 decode.loss_ce: 0.1430 decode.acc_seg: 93.8761 aux.loss_ce: 0.0773 aux.acc_seg: 90.5718 2023/06/08 00:44:39 - mmengine - INFO - Iter(train) [ 81400/240000] lr: 6.9190e-03 eta: 1 day, 7:51:35 time: 0.7215 data_time: 0.3981 memory: 17394 loss: 0.2281 decode.loss_ce: 0.1486 decode.acc_seg: 93.5170 aux.loss_ce: 0.0795 aux.acc_seg: 91.4963 2023/06/08 00:45:15 - mmengine - INFO - Iter(train) [ 81450/240000] lr: 6.9170e-03 eta: 1 day, 7:50:57 time: 0.6993 data_time: 0.3758 memory: 17396 loss: 0.2177 decode.loss_ce: 0.1402 decode.acc_seg: 94.8978 aux.loss_ce: 0.0775 aux.acc_seg: 92.4788 2023/06/08 00:45:50 - mmengine - INFO - Iter(train) [ 81500/240000] lr: 6.9151e-03 eta: 1 day, 7:50:20 time: 0.7144 data_time: 0.3661 memory: 17393 loss: 0.2172 decode.loss_ce: 0.1410 decode.acc_seg: 93.9816 aux.loss_ce: 0.0763 aux.acc_seg: 90.9820 2023/06/08 00:46:25 - mmengine - INFO - Iter(train) [ 81550/240000] lr: 6.9132e-03 eta: 1 day, 7:49:42 time: 0.7059 data_time: 0.3828 memory: 17395 loss: 0.2253 decode.loss_ce: 0.1449 decode.acc_seg: 92.7112 aux.loss_ce: 0.0804 aux.acc_seg: 88.4824 2023/06/08 00:47:01 - mmengine - INFO - Iter(train) [ 81600/240000] lr: 6.9112e-03 eta: 1 day, 7:49:05 time: 0.6997 data_time: 0.3765 memory: 17396 loss: 0.2311 decode.loss_ce: 0.1508 decode.acc_seg: 92.0421 aux.loss_ce: 0.0803 aux.acc_seg: 90.0684 2023/06/08 00:47:36 - mmengine - INFO - Iter(train) [ 81650/240000] lr: 6.9093e-03 eta: 1 day, 7:48:27 time: 0.7031 data_time: 0.3800 memory: 17393 loss: 0.2003 decode.loss_ce: 0.1291 decode.acc_seg: 92.7081 aux.loss_ce: 0.0712 aux.acc_seg: 90.5862 2023/06/08 00:48:12 - mmengine - INFO - Iter(train) [ 81700/240000] lr: 6.9074e-03 eta: 1 day, 7:47:50 time: 0.6984 data_time: 0.3752 memory: 17395 loss: 0.1977 decode.loss_ce: 0.1270 decode.acc_seg: 95.6871 aux.loss_ce: 0.0707 aux.acc_seg: 94.1586 2023/06/08 00:48:47 - mmengine - INFO - Iter(train) [ 81750/240000] lr: 6.9054e-03 eta: 1 day, 7:47:12 time: 0.7177 data_time: 0.3942 memory: 17393 loss: 0.2421 decode.loss_ce: 0.1617 decode.acc_seg: 92.5318 aux.loss_ce: 0.0804 aux.acc_seg: 92.4682 2023/06/08 00:49:23 - mmengine - INFO - Iter(train) [ 81800/240000] lr: 6.9035e-03 eta: 1 day, 7:46:35 time: 0.7185 data_time: 0.3907 memory: 17396 loss: 0.2143 decode.loss_ce: 0.1365 decode.acc_seg: 94.2870 aux.loss_ce: 0.0778 aux.acc_seg: 91.0141 2023/06/08 00:49:58 - mmengine - INFO - Iter(train) [ 81850/240000] lr: 6.9016e-03 eta: 1 day, 7:45:57 time: 0.6979 data_time: 0.1734 memory: 17395 loss: 0.2200 decode.loss_ce: 0.1406 decode.acc_seg: 94.0652 aux.loss_ce: 0.0794 aux.acc_seg: 91.2034 2023/06/08 00:50:34 - mmengine - INFO - Iter(train) [ 81900/240000] lr: 6.8996e-03 eta: 1 day, 7:45:19 time: 0.7052 data_time: 0.3817 memory: 17397 loss: 0.2310 decode.loss_ce: 0.1507 decode.acc_seg: 91.6627 aux.loss_ce: 0.0804 aux.acc_seg: 89.8602 2023/06/08 00:51:09 - mmengine - INFO - Iter(train) [ 81950/240000] lr: 6.8977e-03 eta: 1 day, 7:44:42 time: 0.7143 data_time: 0.1219 memory: 17395 loss: 0.2113 decode.loss_ce: 0.1348 decode.acc_seg: 92.8964 aux.loss_ce: 0.0765 aux.acc_seg: 90.3197 2023/06/08 00:51:45 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 00:51:45 - mmengine - INFO - Iter(train) [ 82000/240000] lr: 6.8958e-03 eta: 1 day, 7:44:05 time: 0.7106 data_time: 0.0121 memory: 17393 loss: 0.2050 decode.loss_ce: 0.1287 decode.acc_seg: 93.7665 aux.loss_ce: 0.0763 aux.acc_seg: 89.3697 2023/06/08 00:52:20 - mmengine - INFO - Iter(train) [ 82050/240000] lr: 6.8938e-03 eta: 1 day, 7:43:28 time: 0.7156 data_time: 0.0120 memory: 17393 loss: 0.2313 decode.loss_ce: 0.1486 decode.acc_seg: 95.3985 aux.loss_ce: 0.0827 aux.acc_seg: 90.9865 2023/06/08 00:52:56 - mmengine - INFO - Iter(train) [ 82100/240000] lr: 6.8919e-03 eta: 1 day, 7:42:50 time: 0.7239 data_time: 0.0121 memory: 17395 loss: 0.2299 decode.loss_ce: 0.1494 decode.acc_seg: 95.0657 aux.loss_ce: 0.0804 aux.acc_seg: 92.0359 2023/06/08 00:53:31 - mmengine - INFO - Iter(train) [ 82150/240000] lr: 6.8900e-03 eta: 1 day, 7:42:13 time: 0.7175 data_time: 0.0208 memory: 17394 loss: 0.2235 decode.loss_ce: 0.1451 decode.acc_seg: 94.3113 aux.loss_ce: 0.0784 aux.acc_seg: 92.7673 2023/06/08 00:54:07 - mmengine - INFO - Iter(train) [ 82200/240000] lr: 6.8880e-03 eta: 1 day, 7:41:36 time: 0.7157 data_time: 0.0121 memory: 17392 loss: 0.2151 decode.loss_ce: 0.1413 decode.acc_seg: 94.3406 aux.loss_ce: 0.0738 aux.acc_seg: 92.6939 2023/06/08 00:54:43 - mmengine - INFO - Iter(train) [ 82250/240000] lr: 6.8861e-03 eta: 1 day, 7:40:59 time: 0.7076 data_time: 0.0121 memory: 17393 loss: 0.2241 decode.loss_ce: 0.1459 decode.acc_seg: 93.4876 aux.loss_ce: 0.0782 aux.acc_seg: 90.5734 2023/06/08 00:55:18 - mmengine - INFO - Iter(train) [ 82300/240000] lr: 6.8841e-03 eta: 1 day, 7:40:21 time: 0.7092 data_time: 0.0122 memory: 17394 loss: 0.2329 decode.loss_ce: 0.1515 decode.acc_seg: 91.9698 aux.loss_ce: 0.0814 aux.acc_seg: 90.4070 2023/06/08 00:55:53 - mmengine - INFO - Iter(train) [ 82350/240000] lr: 6.8822e-03 eta: 1 day, 7:39:44 time: 0.7063 data_time: 0.0124 memory: 17397 loss: 0.2031 decode.loss_ce: 0.1318 decode.acc_seg: 92.7487 aux.loss_ce: 0.0714 aux.acc_seg: 91.0732 2023/06/08 00:56:29 - mmengine - INFO - Iter(train) [ 82400/240000] lr: 6.8803e-03 eta: 1 day, 7:39:07 time: 0.7079 data_time: 0.0121 memory: 17394 loss: 0.2169 decode.loss_ce: 0.1397 decode.acc_seg: 95.0668 aux.loss_ce: 0.0773 aux.acc_seg: 92.5085 2023/06/08 00:57:05 - mmengine - INFO - Iter(train) [ 82450/240000] lr: 6.8783e-03 eta: 1 day, 7:38:30 time: 0.7098 data_time: 0.0122 memory: 17395 loss: 0.1933 decode.loss_ce: 0.1239 decode.acc_seg: 95.4572 aux.loss_ce: 0.0694 aux.acc_seg: 92.7767 2023/06/08 00:57:41 - mmengine - INFO - Iter(train) [ 82500/240000] lr: 6.8764e-03 eta: 1 day, 7:37:53 time: 0.7177 data_time: 0.0121 memory: 17393 loss: 0.2336 decode.loss_ce: 0.1526 decode.acc_seg: 92.4791 aux.loss_ce: 0.0810 aux.acc_seg: 90.9940 2023/06/08 00:58:16 - mmengine - INFO - Iter(train) [ 82550/240000] lr: 6.8745e-03 eta: 1 day, 7:37:16 time: 0.7051 data_time: 0.0122 memory: 17396 loss: 0.2188 decode.loss_ce: 0.1408 decode.acc_seg: 93.4347 aux.loss_ce: 0.0781 aux.acc_seg: 89.9731 2023/06/08 00:58:52 - mmengine - INFO - Iter(train) [ 82600/240000] lr: 6.8725e-03 eta: 1 day, 7:36:38 time: 0.7052 data_time: 0.0122 memory: 17394 loss: 0.1941 decode.loss_ce: 0.1242 decode.acc_seg: 94.9788 aux.loss_ce: 0.0699 aux.acc_seg: 93.2908 2023/06/08 00:59:27 - mmengine - INFO - Iter(train) [ 82650/240000] lr: 6.8706e-03 eta: 1 day, 7:36:00 time: 0.7221 data_time: 0.0123 memory: 17393 loss: 0.2171 decode.loss_ce: 0.1384 decode.acc_seg: 92.4826 aux.loss_ce: 0.0786 aux.acc_seg: 89.6671 2023/06/08 01:00:02 - mmengine - INFO - Iter(train) [ 82700/240000] lr: 6.8687e-03 eta: 1 day, 7:35:23 time: 0.6934 data_time: 0.0123 memory: 17396 loss: 0.2237 decode.loss_ce: 0.1448 decode.acc_seg: 94.6664 aux.loss_ce: 0.0789 aux.acc_seg: 92.6508 2023/06/08 01:00:38 - mmengine - INFO - Iter(train) [ 82750/240000] lr: 6.8667e-03 eta: 1 day, 7:34:45 time: 0.6995 data_time: 0.0123 memory: 17396 loss: 0.2034 decode.loss_ce: 0.1308 decode.acc_seg: 91.8402 aux.loss_ce: 0.0726 aux.acc_seg: 88.9776 2023/06/08 01:01:14 - mmengine - INFO - Iter(train) [ 82800/240000] lr: 6.8648e-03 eta: 1 day, 7:34:08 time: 0.7193 data_time: 0.0124 memory: 17393 loss: 0.2256 decode.loss_ce: 0.1452 decode.acc_seg: 94.0989 aux.loss_ce: 0.0804 aux.acc_seg: 91.8510 2023/06/08 01:01:49 - mmengine - INFO - Iter(train) [ 82850/240000] lr: 6.8628e-03 eta: 1 day, 7:33:31 time: 0.7044 data_time: 0.0931 memory: 17392 loss: 0.2202 decode.loss_ce: 0.1427 decode.acc_seg: 94.9043 aux.loss_ce: 0.0775 aux.acc_seg: 93.4518 2023/06/08 01:02:24 - mmengine - INFO - Iter(train) [ 82900/240000] lr: 6.8609e-03 eta: 1 day, 7:32:53 time: 0.7079 data_time: 0.1110 memory: 17395 loss: 0.2132 decode.loss_ce: 0.1393 decode.acc_seg: 93.8267 aux.loss_ce: 0.0739 aux.acc_seg: 90.7339 2023/06/08 01:03:00 - mmengine - INFO - Iter(train) [ 82950/240000] lr: 6.8590e-03 eta: 1 day, 7:32:16 time: 0.7095 data_time: 0.0856 memory: 17396 loss: 0.2073 decode.loss_ce: 0.1335 decode.acc_seg: 92.0024 aux.loss_ce: 0.0738 aux.acc_seg: 89.6426 2023/06/08 01:03:35 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 01:03:35 - mmengine - INFO - Iter(train) [ 83000/240000] lr: 6.8570e-03 eta: 1 day, 7:31:38 time: 0.7050 data_time: 0.2776 memory: 17396 loss: 0.2170 decode.loss_ce: 0.1395 decode.acc_seg: 94.1757 aux.loss_ce: 0.0775 aux.acc_seg: 91.2460 2023/06/08 01:04:11 - mmengine - INFO - Iter(train) [ 83050/240000] lr: 6.8551e-03 eta: 1 day, 7:31:01 time: 0.7186 data_time: 0.1167 memory: 17397 loss: 0.2161 decode.loss_ce: 0.1432 decode.acc_seg: 93.9783 aux.loss_ce: 0.0729 aux.acc_seg: 91.9892 2023/06/08 01:04:47 - mmengine - INFO - Iter(train) [ 83100/240000] lr: 6.8532e-03 eta: 1 day, 7:30:24 time: 0.7097 data_time: 0.0122 memory: 17394 loss: 0.2179 decode.loss_ce: 0.1406 decode.acc_seg: 94.3513 aux.loss_ce: 0.0772 aux.acc_seg: 92.2244 2023/06/08 01:05:22 - mmengine - INFO - Iter(train) [ 83150/240000] lr: 6.8512e-03 eta: 1 day, 7:29:47 time: 0.7067 data_time: 0.0122 memory: 17393 loss: 0.2108 decode.loss_ce: 0.1366 decode.acc_seg: 94.3063 aux.loss_ce: 0.0742 aux.acc_seg: 91.8288 2023/06/08 01:05:58 - mmengine - INFO - Iter(train) [ 83200/240000] lr: 6.8493e-03 eta: 1 day, 7:29:10 time: 0.7084 data_time: 0.0121 memory: 17395 loss: 0.2011 decode.loss_ce: 0.1283 decode.acc_seg: 93.6295 aux.loss_ce: 0.0728 aux.acc_seg: 91.8855 2023/06/08 01:06:34 - mmengine - INFO - Iter(train) [ 83250/240000] lr: 6.8474e-03 eta: 1 day, 7:28:33 time: 0.7202 data_time: 0.0124 memory: 17395 loss: 0.2064 decode.loss_ce: 0.1321 decode.acc_seg: 93.0123 aux.loss_ce: 0.0743 aux.acc_seg: 88.7883 2023/06/08 01:07:09 - mmengine - INFO - Iter(train) [ 83300/240000] lr: 6.8454e-03 eta: 1 day, 7:27:56 time: 0.7187 data_time: 0.0124 memory: 17395 loss: 0.2065 decode.loss_ce: 0.1312 decode.acc_seg: 96.2263 aux.loss_ce: 0.0753 aux.acc_seg: 91.5133 2023/06/08 01:07:45 - mmengine - INFO - Iter(train) [ 83350/240000] lr: 6.8435e-03 eta: 1 day, 7:27:18 time: 0.7068 data_time: 0.0122 memory: 17396 loss: 0.2132 decode.loss_ce: 0.1400 decode.acc_seg: 94.1653 aux.loss_ce: 0.0733 aux.acc_seg: 92.0909 2023/06/08 01:08:20 - mmengine - INFO - Iter(train) [ 83400/240000] lr: 6.8415e-03 eta: 1 day, 7:26:41 time: 0.7037 data_time: 0.0124 memory: 17395 loss: 0.2295 decode.loss_ce: 0.1464 decode.acc_seg: 93.5253 aux.loss_ce: 0.0832 aux.acc_seg: 90.6170 2023/06/08 01:08:56 - mmengine - INFO - Iter(train) [ 83450/240000] lr: 6.8396e-03 eta: 1 day, 7:26:04 time: 0.7051 data_time: 0.0123 memory: 17394 loss: 0.2085 decode.loss_ce: 0.1321 decode.acc_seg: 91.6672 aux.loss_ce: 0.0764 aux.acc_seg: 88.8362 2023/06/08 01:09:32 - mmengine - INFO - Iter(train) [ 83500/240000] lr: 6.8377e-03 eta: 1 day, 7:25:27 time: 0.7266 data_time: 0.0121 memory: 17395 loss: 0.2107 decode.loss_ce: 0.1354 decode.acc_seg: 94.3207 aux.loss_ce: 0.0753 aux.acc_seg: 91.6742 2023/06/08 01:10:07 - mmengine - INFO - Iter(train) [ 83550/240000] lr: 6.8357e-03 eta: 1 day, 7:24:49 time: 0.6977 data_time: 0.0121 memory: 17395 loss: 0.2045 decode.loss_ce: 0.1309 decode.acc_seg: 92.4741 aux.loss_ce: 0.0736 aux.acc_seg: 90.7370 2023/06/08 01:10:43 - mmengine - INFO - Iter(train) [ 83600/240000] lr: 6.8338e-03 eta: 1 day, 7:24:12 time: 0.7073 data_time: 0.0959 memory: 17392 loss: 0.2096 decode.loss_ce: 0.1335 decode.acc_seg: 94.4051 aux.loss_ce: 0.0761 aux.acc_seg: 92.5430 2023/06/08 01:11:18 - mmengine - INFO - Iter(train) [ 83650/240000] lr: 6.8319e-03 eta: 1 day, 7:23:34 time: 0.7051 data_time: 0.3690 memory: 17394 loss: 0.2117 decode.loss_ce: 0.1361 decode.acc_seg: 93.3763 aux.loss_ce: 0.0756 aux.acc_seg: 90.6073 2023/06/08 01:11:53 - mmengine - INFO - Iter(train) [ 83700/240000] lr: 6.8299e-03 eta: 1 day, 7:22:57 time: 0.7161 data_time: 0.3412 memory: 17396 loss: 0.2065 decode.loss_ce: 0.1324 decode.acc_seg: 94.5296 aux.loss_ce: 0.0741 aux.acc_seg: 93.0769 2023/06/08 01:12:29 - mmengine - INFO - Iter(train) [ 83750/240000] lr: 6.8280e-03 eta: 1 day, 7:22:20 time: 0.7115 data_time: 0.3884 memory: 17394 loss: 0.2137 decode.loss_ce: 0.1360 decode.acc_seg: 94.0357 aux.loss_ce: 0.0778 aux.acc_seg: 92.1270 2023/06/08 01:13:04 - mmengine - INFO - Iter(train) [ 83800/240000] lr: 6.8260e-03 eta: 1 day, 7:21:42 time: 0.7175 data_time: 0.3945 memory: 17393 loss: 0.2087 decode.loss_ce: 0.1348 decode.acc_seg: 95.3761 aux.loss_ce: 0.0739 aux.acc_seg: 92.8845 2023/06/08 01:13:40 - mmengine - INFO - Iter(train) [ 83850/240000] lr: 6.8241e-03 eta: 1 day, 7:21:05 time: 0.6971 data_time: 0.3738 memory: 17393 loss: 0.2453 decode.loss_ce: 0.1595 decode.acc_seg: 93.4046 aux.loss_ce: 0.0858 aux.acc_seg: 90.4439 2023/06/08 01:14:15 - mmengine - INFO - Iter(train) [ 83900/240000] lr: 6.8222e-03 eta: 1 day, 7:20:27 time: 0.6963 data_time: 0.3728 memory: 17395 loss: 0.2584 decode.loss_ce: 0.1675 decode.acc_seg: 92.5672 aux.loss_ce: 0.0910 aux.acc_seg: 89.7320 2023/06/08 01:14:51 - mmengine - INFO - Iter(train) [ 83950/240000] lr: 6.8202e-03 eta: 1 day, 7:19:50 time: 0.7189 data_time: 0.3952 memory: 17392 loss: 0.2390 decode.loss_ce: 0.1579 decode.acc_seg: 91.8443 aux.loss_ce: 0.0811 aux.acc_seg: 90.4257 2023/06/08 01:15:26 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 01:15:26 - mmengine - INFO - Iter(train) [ 84000/240000] lr: 6.8183e-03 eta: 1 day, 7:19:12 time: 0.7129 data_time: 0.3548 memory: 17394 loss: 0.2332 decode.loss_ce: 0.1527 decode.acc_seg: 94.0970 aux.loss_ce: 0.0805 aux.acc_seg: 91.4090 2023/06/08 01:16:02 - mmengine - INFO - Iter(train) [ 84050/240000] lr: 6.8164e-03 eta: 1 day, 7:18:35 time: 0.7143 data_time: 0.3862 memory: 17396 loss: 0.2003 decode.loss_ce: 0.1270 decode.acc_seg: 94.9708 aux.loss_ce: 0.0732 aux.acc_seg: 92.7654 2023/06/08 01:16:37 - mmengine - INFO - Iter(train) [ 84100/240000] lr: 6.8144e-03 eta: 1 day, 7:17:58 time: 0.7109 data_time: 0.3877 memory: 17396 loss: 0.2074 decode.loss_ce: 0.1335 decode.acc_seg: 94.0732 aux.loss_ce: 0.0739 aux.acc_seg: 91.6986 2023/06/08 01:17:13 - mmengine - INFO - Iter(train) [ 84150/240000] lr: 6.8125e-03 eta: 1 day, 7:17:21 time: 0.7153 data_time: 0.2943 memory: 17395 loss: 0.2201 decode.loss_ce: 0.1419 decode.acc_seg: 93.0437 aux.loss_ce: 0.0782 aux.acc_seg: 88.6146 2023/06/08 01:17:48 - mmengine - INFO - Iter(train) [ 84200/240000] lr: 6.8105e-03 eta: 1 day, 7:16:43 time: 0.7049 data_time: 0.3708 memory: 17394 loss: 0.2034 decode.loss_ce: 0.1307 decode.acc_seg: 94.3354 aux.loss_ce: 0.0726 aux.acc_seg: 92.3083 2023/06/08 01:18:24 - mmengine - INFO - Iter(train) [ 84250/240000] lr: 6.8086e-03 eta: 1 day, 7:16:06 time: 0.7178 data_time: 0.3951 memory: 17393 loss: 0.1904 decode.loss_ce: 0.1217 decode.acc_seg: 94.4557 aux.loss_ce: 0.0687 aux.acc_seg: 92.5615 2023/06/08 01:18:59 - mmengine - INFO - Iter(train) [ 84300/240000] lr: 6.8067e-03 eta: 1 day, 7:15:29 time: 0.7072 data_time: 0.3836 memory: 17397 loss: 0.2230 decode.loss_ce: 0.1446 decode.acc_seg: 94.3732 aux.loss_ce: 0.0784 aux.acc_seg: 90.6071 2023/06/08 01:19:35 - mmengine - INFO - Iter(train) [ 84350/240000] lr: 6.8047e-03 eta: 1 day, 7:14:52 time: 0.7037 data_time: 0.3802 memory: 17393 loss: 0.2183 decode.loss_ce: 0.1403 decode.acc_seg: 95.1494 aux.loss_ce: 0.0779 aux.acc_seg: 93.7278 2023/06/08 01:20:11 - mmengine - INFO - Iter(train) [ 84400/240000] lr: 6.8028e-03 eta: 1 day, 7:14:15 time: 0.7152 data_time: 0.3919 memory: 17395 loss: 0.2242 decode.loss_ce: 0.1440 decode.acc_seg: 92.4766 aux.loss_ce: 0.0802 aux.acc_seg: 88.2030 2023/06/08 01:20:46 - mmengine - INFO - Iter(train) [ 84450/240000] lr: 6.8008e-03 eta: 1 day, 7:13:38 time: 0.7002 data_time: 0.3765 memory: 17392 loss: 0.2329 decode.loss_ce: 0.1494 decode.acc_seg: 92.0674 aux.loss_ce: 0.0834 aux.acc_seg: 89.4791 2023/06/08 01:21:21 - mmengine - INFO - Iter(train) [ 84500/240000] lr: 6.7989e-03 eta: 1 day, 7:13:00 time: 0.7077 data_time: 0.3846 memory: 17394 loss: 0.2113 decode.loss_ce: 0.1360 decode.acc_seg: 94.5097 aux.loss_ce: 0.0754 aux.acc_seg: 91.9995 2023/06/08 01:21:57 - mmengine - INFO - Iter(train) [ 84550/240000] lr: 6.7970e-03 eta: 1 day, 7:12:22 time: 0.7002 data_time: 0.3771 memory: 17393 loss: 0.2287 decode.loss_ce: 0.1464 decode.acc_seg: 95.3497 aux.loss_ce: 0.0824 aux.acc_seg: 93.4215 2023/06/08 01:22:32 - mmengine - INFO - Iter(train) [ 84600/240000] lr: 6.7950e-03 eta: 1 day, 7:11:45 time: 0.7079 data_time: 0.3532 memory: 17393 loss: 0.2009 decode.loss_ce: 0.1301 decode.acc_seg: 93.5482 aux.loss_ce: 0.0709 aux.acc_seg: 92.7649 2023/06/08 01:23:08 - mmengine - INFO - Iter(train) [ 84650/240000] lr: 6.7931e-03 eta: 1 day, 7:11:07 time: 0.7039 data_time: 0.3811 memory: 17394 loss: 0.2093 decode.loss_ce: 0.1354 decode.acc_seg: 93.1706 aux.loss_ce: 0.0739 aux.acc_seg: 90.0387 2023/06/08 01:23:43 - mmengine - INFO - Iter(train) [ 84700/240000] lr: 6.7912e-03 eta: 1 day, 7:10:30 time: 0.7103 data_time: 0.3869 memory: 17394 loss: 0.2192 decode.loss_ce: 0.1413 decode.acc_seg: 94.2185 aux.loss_ce: 0.0779 aux.acc_seg: 92.6103 2023/06/08 01:24:19 - mmengine - INFO - Iter(train) [ 84750/240000] lr: 6.7892e-03 eta: 1 day, 7:09:53 time: 0.7098 data_time: 0.3869 memory: 17394 loss: 0.2086 decode.loss_ce: 0.1335 decode.acc_seg: 94.3559 aux.loss_ce: 0.0751 aux.acc_seg: 92.4571 2023/06/08 01:24:54 - mmengine - INFO - Iter(train) [ 84800/240000] lr: 6.7873e-03 eta: 1 day, 7:09:16 time: 0.7034 data_time: 0.3797 memory: 17394 loss: 0.2236 decode.loss_ce: 0.1449 decode.acc_seg: 93.2580 aux.loss_ce: 0.0788 aux.acc_seg: 89.9949 2023/06/08 01:25:30 - mmengine - INFO - Iter(train) [ 84850/240000] lr: 6.7853e-03 eta: 1 day, 7:08:39 time: 0.7174 data_time: 0.3936 memory: 17395 loss: 0.2087 decode.loss_ce: 0.1356 decode.acc_seg: 92.5858 aux.loss_ce: 0.0731 aux.acc_seg: 89.5643 2023/06/08 01:26:06 - mmengine - INFO - Iter(train) [ 84900/240000] lr: 6.7834e-03 eta: 1 day, 7:08:02 time: 0.7125 data_time: 0.3890 memory: 17394 loss: 0.2050 decode.loss_ce: 0.1333 decode.acc_seg: 95.4474 aux.loss_ce: 0.0717 aux.acc_seg: 93.8181 2023/06/08 01:26:41 - mmengine - INFO - Iter(train) [ 84950/240000] lr: 6.7815e-03 eta: 1 day, 7:07:24 time: 0.7154 data_time: 0.3926 memory: 17393 loss: 0.2087 decode.loss_ce: 0.1348 decode.acc_seg: 93.1412 aux.loss_ce: 0.0740 aux.acc_seg: 92.3746 2023/06/08 01:27:17 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 01:27:17 - mmengine - INFO - Iter(train) [ 85000/240000] lr: 6.7795e-03 eta: 1 day, 7:06:47 time: 0.7078 data_time: 0.3847 memory: 17392 loss: 0.2046 decode.loss_ce: 0.1311 decode.acc_seg: 93.6879 aux.loss_ce: 0.0735 aux.acc_seg: 91.3833 2023/06/08 01:27:53 - mmengine - INFO - Iter(train) [ 85050/240000] lr: 6.7776e-03 eta: 1 day, 7:06:11 time: 0.7092 data_time: 0.3865 memory: 17396 loss: 0.2104 decode.loss_ce: 0.1349 decode.acc_seg: 93.2072 aux.loss_ce: 0.0755 aux.acc_seg: 91.1626 2023/06/08 01:28:28 - mmengine - INFO - Iter(train) [ 85100/240000] lr: 6.7756e-03 eta: 1 day, 7:05:33 time: 0.7063 data_time: 0.3837 memory: 17397 loss: 0.2289 decode.loss_ce: 0.1500 decode.acc_seg: 91.3392 aux.loss_ce: 0.0789 aux.acc_seg: 89.2276 2023/06/08 01:29:03 - mmengine - INFO - Iter(train) [ 85150/240000] lr: 6.7737e-03 eta: 1 day, 7:04:56 time: 0.7115 data_time: 0.3882 memory: 17394 loss: 0.2181 decode.loss_ce: 0.1414 decode.acc_seg: 93.2490 aux.loss_ce: 0.0767 aux.acc_seg: 90.6698 2023/06/08 01:29:39 - mmengine - INFO - Iter(train) [ 85200/240000] lr: 6.7718e-03 eta: 1 day, 7:04:19 time: 0.7115 data_time: 0.3889 memory: 17396 loss: 0.2163 decode.loss_ce: 0.1421 decode.acc_seg: 90.9730 aux.loss_ce: 0.0742 aux.acc_seg: 90.1134 2023/06/08 01:30:15 - mmengine - INFO - Iter(train) [ 85250/240000] lr: 6.7698e-03 eta: 1 day, 7:03:42 time: 0.7134 data_time: 0.3903 memory: 17394 loss: 0.2209 decode.loss_ce: 0.1409 decode.acc_seg: 95.1274 aux.loss_ce: 0.0799 aux.acc_seg: 92.7259 2023/06/08 01:30:50 - mmengine - INFO - Iter(train) [ 85300/240000] lr: 6.7679e-03 eta: 1 day, 7:03:04 time: 0.7265 data_time: 0.4032 memory: 17391 loss: 0.1980 decode.loss_ce: 0.1273 decode.acc_seg: 95.2962 aux.loss_ce: 0.0707 aux.acc_seg: 93.0789 2023/06/08 01:31:25 - mmengine - INFO - Iter(train) [ 85350/240000] lr: 6.7659e-03 eta: 1 day, 7:02:27 time: 0.7014 data_time: 0.3782 memory: 17392 loss: 0.2292 decode.loss_ce: 0.1465 decode.acc_seg: 92.0304 aux.loss_ce: 0.0827 aux.acc_seg: 87.3946 2023/06/08 01:32:01 - mmengine - INFO - Iter(train) [ 85400/240000] lr: 6.7640e-03 eta: 1 day, 7:01:49 time: 0.7051 data_time: 0.3819 memory: 17393 loss: 0.2060 decode.loss_ce: 0.1302 decode.acc_seg: 93.5458 aux.loss_ce: 0.0758 aux.acc_seg: 91.2166 2023/06/08 01:32:36 - mmengine - INFO - Iter(train) [ 85450/240000] lr: 6.7621e-03 eta: 1 day, 7:01:12 time: 0.7005 data_time: 0.3777 memory: 17395 loss: 0.2138 decode.loss_ce: 0.1375 decode.acc_seg: 94.1903 aux.loss_ce: 0.0763 aux.acc_seg: 91.4307 2023/06/08 01:33:12 - mmengine - INFO - Iter(train) [ 85500/240000] lr: 6.7601e-03 eta: 1 day, 7:00:35 time: 0.7042 data_time: 0.3746 memory: 17393 loss: 0.2060 decode.loss_ce: 0.1340 decode.acc_seg: 93.7625 aux.loss_ce: 0.0719 aux.acc_seg: 91.6034 2023/06/08 01:33:47 - mmengine - INFO - Iter(train) [ 85550/240000] lr: 6.7582e-03 eta: 1 day, 6:59:57 time: 0.7078 data_time: 0.2872 memory: 17393 loss: 0.2200 decode.loss_ce: 0.1419 decode.acc_seg: 94.0020 aux.loss_ce: 0.0780 aux.acc_seg: 90.6395 2023/06/08 01:34:23 - mmengine - INFO - Iter(train) [ 85600/240000] lr: 6.7562e-03 eta: 1 day, 6:59:20 time: 0.7096 data_time: 0.2985 memory: 17392 loss: 0.1894 decode.loss_ce: 0.1221 decode.acc_seg: 93.3312 aux.loss_ce: 0.0673 aux.acc_seg: 91.3376 2023/06/08 01:34:58 - mmengine - INFO - Iter(train) [ 85650/240000] lr: 6.7543e-03 eta: 1 day, 6:58:42 time: 0.7115 data_time: 0.3815 memory: 17393 loss: 0.2070 decode.loss_ce: 0.1309 decode.acc_seg: 94.3190 aux.loss_ce: 0.0761 aux.acc_seg: 90.5077 2023/06/08 01:35:34 - mmengine - INFO - Iter(train) [ 85700/240000] lr: 6.7524e-03 eta: 1 day, 6:58:05 time: 0.7126 data_time: 0.3898 memory: 17396 loss: 0.1937 decode.loss_ce: 0.1242 decode.acc_seg: 94.8031 aux.loss_ce: 0.0695 aux.acc_seg: 92.3191 2023/06/08 01:36:09 - mmengine - INFO - Iter(train) [ 85750/240000] lr: 6.7504e-03 eta: 1 day, 6:57:28 time: 0.7049 data_time: 0.3815 memory: 17395 loss: 0.2351 decode.loss_ce: 0.1523 decode.acc_seg: 92.6547 aux.loss_ce: 0.0827 aux.acc_seg: 89.3013 2023/06/08 01:36:44 - mmengine - INFO - Iter(train) [ 85800/240000] lr: 6.7485e-03 eta: 1 day, 6:56:50 time: 0.7044 data_time: 0.3815 memory: 17392 loss: 0.2003 decode.loss_ce: 0.1286 decode.acc_seg: 95.0573 aux.loss_ce: 0.0716 aux.acc_seg: 92.1260 2023/06/08 01:37:20 - mmengine - INFO - Iter(train) [ 85850/240000] lr: 6.7465e-03 eta: 1 day, 6:56:13 time: 0.7047 data_time: 0.3385 memory: 17393 loss: 0.1982 decode.loss_ce: 0.1288 decode.acc_seg: 95.7439 aux.loss_ce: 0.0694 aux.acc_seg: 94.2168 2023/06/08 01:37:55 - mmengine - INFO - Iter(train) [ 85900/240000] lr: 6.7446e-03 eta: 1 day, 6:55:36 time: 0.7044 data_time: 0.3784 memory: 17394 loss: 0.1873 decode.loss_ce: 0.1187 decode.acc_seg: 93.7749 aux.loss_ce: 0.0686 aux.acc_seg: 91.8523 2023/06/08 01:38:31 - mmengine - INFO - Iter(train) [ 85950/240000] lr: 6.7427e-03 eta: 1 day, 6:54:59 time: 0.7047 data_time: 0.3820 memory: 17393 loss: 0.2132 decode.loss_ce: 0.1367 decode.acc_seg: 94.6301 aux.loss_ce: 0.0766 aux.acc_seg: 92.1032 2023/06/08 01:39:07 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 01:39:07 - mmengine - INFO - Iter(train) [ 86000/240000] lr: 6.7407e-03 eta: 1 day, 6:54:22 time: 0.7142 data_time: 0.3910 memory: 17395 loss: 0.2038 decode.loss_ce: 0.1303 decode.acc_seg: 95.3373 aux.loss_ce: 0.0735 aux.acc_seg: 91.8671 2023/06/08 01:39:42 - mmengine - INFO - Iter(train) [ 86050/240000] lr: 6.7388e-03 eta: 1 day, 6:53:45 time: 0.7134 data_time: 0.3907 memory: 17393 loss: 0.1900 decode.loss_ce: 0.1206 decode.acc_seg: 94.6592 aux.loss_ce: 0.0694 aux.acc_seg: 92.6523 2023/06/08 01:40:18 - mmengine - INFO - Iter(train) [ 86100/240000] lr: 6.7368e-03 eta: 1 day, 6:53:08 time: 0.7045 data_time: 0.3817 memory: 17393 loss: 0.2175 decode.loss_ce: 0.1411 decode.acc_seg: 93.6305 aux.loss_ce: 0.0764 aux.acc_seg: 91.6319 2023/06/08 01:40:53 - mmengine - INFO - Iter(train) [ 86150/240000] lr: 6.7349e-03 eta: 1 day, 6:52:30 time: 0.7036 data_time: 0.3806 memory: 17395 loss: 0.2091 decode.loss_ce: 0.1341 decode.acc_seg: 95.4204 aux.loss_ce: 0.0749 aux.acc_seg: 93.3792 2023/06/08 01:41:29 - mmengine - INFO - Iter(train) [ 86200/240000] lr: 6.7330e-03 eta: 1 day, 6:51:53 time: 0.7158 data_time: 0.3925 memory: 17394 loss: 0.2048 decode.loss_ce: 0.1321 decode.acc_seg: 91.8022 aux.loss_ce: 0.0726 aux.acc_seg: 88.2092 2023/06/08 01:42:04 - mmengine - INFO - Iter(train) [ 86250/240000] lr: 6.7310e-03 eta: 1 day, 6:51:16 time: 0.7070 data_time: 0.3842 memory: 17395 loss: 0.2001 decode.loss_ce: 0.1288 decode.acc_seg: 95.0005 aux.loss_ce: 0.0713 aux.acc_seg: 92.4797 2023/06/08 01:42:40 - mmengine - INFO - Iter(train) [ 86300/240000] lr: 6.7291e-03 eta: 1 day, 6:50:39 time: 0.7029 data_time: 0.3798 memory: 17393 loss: 0.2167 decode.loss_ce: 0.1388 decode.acc_seg: 94.9572 aux.loss_ce: 0.0779 aux.acc_seg: 91.7866 2023/06/08 01:43:16 - mmengine - INFO - Iter(train) [ 86350/240000] lr: 6.7271e-03 eta: 1 day, 6:50:02 time: 0.7121 data_time: 0.3890 memory: 17393 loss: 0.2047 decode.loss_ce: 0.1315 decode.acc_seg: 95.7243 aux.loss_ce: 0.0732 aux.acc_seg: 94.2119 2023/06/08 01:43:51 - mmengine - INFO - Iter(train) [ 86400/240000] lr: 6.7252e-03 eta: 1 day, 6:49:24 time: 0.7139 data_time: 0.3913 memory: 17394 loss: 0.1976 decode.loss_ce: 0.1288 decode.acc_seg: 95.4006 aux.loss_ce: 0.0688 aux.acc_seg: 94.1725 2023/06/08 01:44:26 - mmengine - INFO - Iter(train) [ 86450/240000] lr: 6.7233e-03 eta: 1 day, 6:48:47 time: 0.7011 data_time: 0.3779 memory: 17398 loss: 0.2268 decode.loss_ce: 0.1454 decode.acc_seg: 94.1209 aux.loss_ce: 0.0813 aux.acc_seg: 91.4190 2023/06/08 01:45:02 - mmengine - INFO - Iter(train) [ 86500/240000] lr: 6.7213e-03 eta: 1 day, 6:48:10 time: 0.7183 data_time: 0.3946 memory: 17394 loss: 0.2257 decode.loss_ce: 0.1451 decode.acc_seg: 94.0572 aux.loss_ce: 0.0805 aux.acc_seg: 92.3441 2023/06/08 01:45:38 - mmengine - INFO - Iter(train) [ 86550/240000] lr: 6.7194e-03 eta: 1 day, 6:47:33 time: 0.7100 data_time: 0.3871 memory: 17393 loss: 0.2076 decode.loss_ce: 0.1342 decode.acc_seg: 93.4743 aux.loss_ce: 0.0734 aux.acc_seg: 91.3064 2023/06/08 01:46:13 - mmengine - INFO - Iter(train) [ 86600/240000] lr: 6.7174e-03 eta: 1 day, 6:46:56 time: 0.6922 data_time: 0.3697 memory: 17394 loss: 0.2083 decode.loss_ce: 0.1343 decode.acc_seg: 93.7566 aux.loss_ce: 0.0740 aux.acc_seg: 90.8622 2023/06/08 01:46:49 - mmengine - INFO - Iter(train) [ 86650/240000] lr: 6.7155e-03 eta: 1 day, 6:46:19 time: 0.7005 data_time: 0.3769 memory: 17393 loss: 0.2378 decode.loss_ce: 0.1533 decode.acc_seg: 91.6505 aux.loss_ce: 0.0845 aux.acc_seg: 88.5182 2023/06/08 01:47:24 - mmengine - INFO - Iter(train) [ 86700/240000] lr: 6.7136e-03 eta: 1 day, 6:45:41 time: 0.7059 data_time: 0.3827 memory: 17394 loss: 0.2301 decode.loss_ce: 0.1471 decode.acc_seg: 93.2070 aux.loss_ce: 0.0830 aux.acc_seg: 88.6792 2023/06/08 01:48:00 - mmengine - INFO - Iter(train) [ 86750/240000] lr: 6.7116e-03 eta: 1 day, 6:45:04 time: 0.7145 data_time: 0.3902 memory: 17394 loss: 0.2017 decode.loss_ce: 0.1281 decode.acc_seg: 92.9636 aux.loss_ce: 0.0736 aux.acc_seg: 90.7493 2023/06/08 01:48:35 - mmengine - INFO - Iter(train) [ 86800/240000] lr: 6.7097e-03 eta: 1 day, 6:44:27 time: 0.7065 data_time: 0.3838 memory: 17393 loss: 0.2057 decode.loss_ce: 0.1317 decode.acc_seg: 93.9402 aux.loss_ce: 0.0741 aux.acc_seg: 90.5484 2023/06/08 01:49:10 - mmengine - INFO - Iter(train) [ 86850/240000] lr: 6.7077e-03 eta: 1 day, 6:43:49 time: 0.7104 data_time: 0.3873 memory: 17394 loss: 0.2689 decode.loss_ce: 0.1811 decode.acc_seg: 93.1870 aux.loss_ce: 0.0878 aux.acc_seg: 91.3624 2023/06/08 01:49:46 - mmengine - INFO - Iter(train) [ 86900/240000] lr: 6.7058e-03 eta: 1 day, 6:43:12 time: 0.7082 data_time: 0.3848 memory: 17396 loss: 0.2244 decode.loss_ce: 0.1461 decode.acc_seg: 93.3484 aux.loss_ce: 0.0784 aux.acc_seg: 89.7193 2023/06/08 01:50:21 - mmengine - INFO - Iter(train) [ 86950/240000] lr: 6.7038e-03 eta: 1 day, 6:42:35 time: 0.7163 data_time: 0.3937 memory: 17394 loss: 0.1977 decode.loss_ce: 0.1264 decode.acc_seg: 94.6594 aux.loss_ce: 0.0713 aux.acc_seg: 91.6476 2023/06/08 01:50:57 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 01:50:57 - mmengine - INFO - Iter(train) [ 87000/240000] lr: 6.7019e-03 eta: 1 day, 6:41:58 time: 0.7107 data_time: 0.3873 memory: 17394 loss: 0.2125 decode.loss_ce: 0.1356 decode.acc_seg: 94.7224 aux.loss_ce: 0.0769 aux.acc_seg: 92.7236 2023/06/08 01:51:33 - mmengine - INFO - Iter(train) [ 87050/240000] lr: 6.7000e-03 eta: 1 day, 6:41:21 time: 0.7032 data_time: 0.3803 memory: 17392 loss: 0.2034 decode.loss_ce: 0.1308 decode.acc_seg: 94.2094 aux.loss_ce: 0.0726 aux.acc_seg: 92.8590 2023/06/08 01:52:08 - mmengine - INFO - Iter(train) [ 87100/240000] lr: 6.6980e-03 eta: 1 day, 6:40:43 time: 0.7042 data_time: 0.3810 memory: 17394 loss: 0.2102 decode.loss_ce: 0.1345 decode.acc_seg: 94.9854 aux.loss_ce: 0.0757 aux.acc_seg: 92.4997 2023/06/08 01:52:44 - mmengine - INFO - Iter(train) [ 87150/240000] lr: 6.6961e-03 eta: 1 day, 6:40:06 time: 0.7191 data_time: 0.3957 memory: 17395 loss: 0.2075 decode.loss_ce: 0.1321 decode.acc_seg: 93.8447 aux.loss_ce: 0.0754 aux.acc_seg: 91.5442 2023/06/08 01:53:19 - mmengine - INFO - Iter(train) [ 87200/240000] lr: 6.6941e-03 eta: 1 day, 6:39:29 time: 0.7173 data_time: 0.3942 memory: 17393 loss: 0.2193 decode.loss_ce: 0.1400 decode.acc_seg: 93.6298 aux.loss_ce: 0.0793 aux.acc_seg: 90.3596 2023/06/08 01:53:55 - mmengine - INFO - Iter(train) [ 87250/240000] lr: 6.6922e-03 eta: 1 day, 6:38:52 time: 0.7047 data_time: 0.3818 memory: 17393 loss: 0.2034 decode.loss_ce: 0.1310 decode.acc_seg: 94.1863 aux.loss_ce: 0.0723 aux.acc_seg: 91.1566 2023/06/08 01:54:30 - mmengine - INFO - Iter(train) [ 87300/240000] lr: 6.6902e-03 eta: 1 day, 6:38:14 time: 0.7081 data_time: 0.3514 memory: 17394 loss: 0.1937 decode.loss_ce: 0.1247 decode.acc_seg: 94.6271 aux.loss_ce: 0.0690 aux.acc_seg: 91.9349 2023/06/08 01:55:06 - mmengine - INFO - Iter(train) [ 87350/240000] lr: 6.6883e-03 eta: 1 day, 6:37:37 time: 0.7135 data_time: 0.0631 memory: 17392 loss: 0.2045 decode.loss_ce: 0.1295 decode.acc_seg: 94.8043 aux.loss_ce: 0.0750 aux.acc_seg: 92.0479 2023/06/08 01:55:41 - mmengine - INFO - Iter(train) [ 87400/240000] lr: 6.6864e-03 eta: 1 day, 6:37:00 time: 0.7256 data_time: 0.4026 memory: 17396 loss: 0.2173 decode.loss_ce: 0.1399 decode.acc_seg: 93.0419 aux.loss_ce: 0.0774 aux.acc_seg: 91.5762 2023/06/08 01:56:17 - mmengine - INFO - Iter(train) [ 87450/240000] lr: 6.6844e-03 eta: 1 day, 6:36:23 time: 0.7094 data_time: 0.3863 memory: 17392 loss: 0.2243 decode.loss_ce: 0.1449 decode.acc_seg: 94.7569 aux.loss_ce: 0.0794 aux.acc_seg: 92.1795 2023/06/08 01:56:52 - mmengine - INFO - Iter(train) [ 87500/240000] lr: 6.6825e-03 eta: 1 day, 6:35:46 time: 0.7004 data_time: 0.3777 memory: 17393 loss: 0.2154 decode.loss_ce: 0.1378 decode.acc_seg: 93.9987 aux.loss_ce: 0.0775 aux.acc_seg: 91.7719 2023/06/08 01:57:28 - mmengine - INFO - Iter(train) [ 87550/240000] lr: 6.6805e-03 eta: 1 day, 6:35:09 time: 0.7043 data_time: 0.3814 memory: 17393 loss: 0.1955 decode.loss_ce: 0.1254 decode.acc_seg: 94.7745 aux.loss_ce: 0.0702 aux.acc_seg: 92.3337 2023/06/08 01:58:03 - mmengine - INFO - Iter(train) [ 87600/240000] lr: 6.6786e-03 eta: 1 day, 6:34:32 time: 0.7152 data_time: 0.3913 memory: 17396 loss: 0.2254 decode.loss_ce: 0.1451 decode.acc_seg: 93.7401 aux.loss_ce: 0.0803 aux.acc_seg: 91.3827 2023/06/08 01:58:39 - mmengine - INFO - Iter(train) [ 87650/240000] lr: 6.6767e-03 eta: 1 day, 6:33:55 time: 0.7084 data_time: 0.3857 memory: 17395 loss: 0.2141 decode.loss_ce: 0.1384 decode.acc_seg: 93.9401 aux.loss_ce: 0.0758 aux.acc_seg: 91.3946 2023/06/08 01:59:15 - mmengine - INFO - Iter(train) [ 87700/240000] lr: 6.6747e-03 eta: 1 day, 6:33:18 time: 0.7260 data_time: 0.4029 memory: 17393 loss: 0.2042 decode.loss_ce: 0.1310 decode.acc_seg: 92.9417 aux.loss_ce: 0.0732 aux.acc_seg: 91.4473 2023/06/08 01:59:50 - mmengine - INFO - Iter(train) [ 87750/240000] lr: 6.6728e-03 eta: 1 day, 6:32:41 time: 0.7235 data_time: 0.4005 memory: 17395 loss: 0.2037 decode.loss_ce: 0.1302 decode.acc_seg: 93.4901 aux.loss_ce: 0.0734 aux.acc_seg: 90.8571 2023/06/08 02:00:26 - mmengine - INFO - Iter(train) [ 87800/240000] lr: 6.6708e-03 eta: 1 day, 6:32:04 time: 0.7072 data_time: 0.3838 memory: 17391 loss: 0.2012 decode.loss_ce: 0.1279 decode.acc_seg: 93.6441 aux.loss_ce: 0.0733 aux.acc_seg: 90.2070 2023/06/08 02:01:01 - mmengine - INFO - Iter(train) [ 87850/240000] lr: 6.6689e-03 eta: 1 day, 6:31:26 time: 0.6965 data_time: 0.3727 memory: 17395 loss: 0.2188 decode.loss_ce: 0.1399 decode.acc_seg: 94.0408 aux.loss_ce: 0.0790 aux.acc_seg: 92.4091 2023/06/08 02:01:37 - mmengine - INFO - Iter(train) [ 87900/240000] lr: 6.6669e-03 eta: 1 day, 6:30:49 time: 0.7129 data_time: 0.3897 memory: 17393 loss: 0.2197 decode.loss_ce: 0.1393 decode.acc_seg: 93.6561 aux.loss_ce: 0.0803 aux.acc_seg: 89.5860 2023/06/08 02:02:12 - mmengine - INFO - Iter(train) [ 87950/240000] lr: 6.6650e-03 eta: 1 day, 6:30:12 time: 0.7068 data_time: 0.3832 memory: 17392 loss: 0.2163 decode.loss_ce: 0.1403 decode.acc_seg: 95.3039 aux.loss_ce: 0.0760 aux.acc_seg: 92.8862 2023/06/08 02:02:48 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 02:02:48 - mmengine - INFO - Iter(train) [ 88000/240000] lr: 6.6631e-03 eta: 1 day, 6:29:35 time: 0.6940 data_time: 0.3706 memory: 17398 loss: 0.2644 decode.loss_ce: 0.1724 decode.acc_seg: 92.9106 aux.loss_ce: 0.0920 aux.acc_seg: 88.8711 2023/06/08 02:03:23 - mmengine - INFO - Iter(train) [ 88050/240000] lr: 6.6611e-03 eta: 1 day, 6:28:58 time: 0.7044 data_time: 0.3819 memory: 17396 loss: 0.2238 decode.loss_ce: 0.1447 decode.acc_seg: 93.1120 aux.loss_ce: 0.0791 aux.acc_seg: 91.6616 2023/06/08 02:03:59 - mmengine - INFO - Iter(train) [ 88100/240000] lr: 6.6592e-03 eta: 1 day, 6:28:21 time: 0.7222 data_time: 0.3991 memory: 17394 loss: 0.2169 decode.loss_ce: 0.1416 decode.acc_seg: 89.8206 aux.loss_ce: 0.0753 aux.acc_seg: 87.9237 2023/06/08 02:04:34 - mmengine - INFO - Iter(train) [ 88150/240000] lr: 6.6572e-03 eta: 1 day, 6:27:43 time: 0.6980 data_time: 0.3744 memory: 17392 loss: 0.2553 decode.loss_ce: 0.1653 decode.acc_seg: 93.0759 aux.loss_ce: 0.0900 aux.acc_seg: 91.2181 2023/06/08 02:05:09 - mmengine - INFO - Iter(train) [ 88200/240000] lr: 6.6553e-03 eta: 1 day, 6:27:06 time: 0.7079 data_time: 0.3849 memory: 17394 loss: 0.2401 decode.loss_ce: 0.1574 decode.acc_seg: 92.0849 aux.loss_ce: 0.0827 aux.acc_seg: 90.2669 2023/06/08 02:05:45 - mmengine - INFO - Iter(train) [ 88250/240000] lr: 6.6533e-03 eta: 1 day, 6:26:28 time: 0.7126 data_time: 0.2994 memory: 17396 loss: 0.2140 decode.loss_ce: 0.1387 decode.acc_seg: 92.2011 aux.loss_ce: 0.0754 aux.acc_seg: 91.4760 2023/06/08 02:06:20 - mmengine - INFO - Iter(train) [ 88300/240000] lr: 6.6514e-03 eta: 1 day, 6:25:51 time: 0.7180 data_time: 0.3949 memory: 17395 loss: 0.1977 decode.loss_ce: 0.1264 decode.acc_seg: 95.1956 aux.loss_ce: 0.0713 aux.acc_seg: 92.8005 2023/06/08 02:06:56 - mmengine - INFO - Iter(train) [ 88350/240000] lr: 6.6495e-03 eta: 1 day, 6:25:15 time: 0.7177 data_time: 0.3945 memory: 17397 loss: 0.2195 decode.loss_ce: 0.1413 decode.acc_seg: 91.8242 aux.loss_ce: 0.0782 aux.acc_seg: 90.8261 2023/06/08 02:07:31 - mmengine - INFO - Iter(train) [ 88400/240000] lr: 6.6475e-03 eta: 1 day, 6:24:37 time: 0.7151 data_time: 0.3921 memory: 17396 loss: 0.2019 decode.loss_ce: 0.1297 decode.acc_seg: 94.2730 aux.loss_ce: 0.0722 aux.acc_seg: 92.2998 2023/06/08 02:08:07 - mmengine - INFO - Iter(train) [ 88450/240000] lr: 6.6456e-03 eta: 1 day, 6:24:00 time: 0.7100 data_time: 0.3872 memory: 17396 loss: 0.2146 decode.loss_ce: 0.1348 decode.acc_seg: 95.2660 aux.loss_ce: 0.0798 aux.acc_seg: 90.8246 2023/06/08 02:08:43 - mmengine - INFO - Iter(train) [ 88500/240000] lr: 6.6436e-03 eta: 1 day, 6:23:23 time: 0.7144 data_time: 0.3918 memory: 17393 loss: 0.2258 decode.loss_ce: 0.1435 decode.acc_seg: 89.4352 aux.loss_ce: 0.0822 aux.acc_seg: 85.9214 2023/06/08 02:09:18 - mmengine - INFO - Iter(train) [ 88550/240000] lr: 6.6417e-03 eta: 1 day, 6:22:46 time: 0.7055 data_time: 0.3827 memory: 17393 loss: 0.2027 decode.loss_ce: 0.1304 decode.acc_seg: 93.8530 aux.loss_ce: 0.0723 aux.acc_seg: 90.8386 2023/06/08 02:09:54 - mmengine - INFO - Iter(train) [ 88600/240000] lr: 6.6397e-03 eta: 1 day, 6:22:09 time: 0.7235 data_time: 0.3017 memory: 17392 loss: 0.1968 decode.loss_ce: 0.1262 decode.acc_seg: 93.8663 aux.loss_ce: 0.0706 aux.acc_seg: 92.6350 2023/06/08 02:10:29 - mmengine - INFO - Iter(train) [ 88650/240000] lr: 6.6378e-03 eta: 1 day, 6:21:32 time: 0.7064 data_time: 0.3619 memory: 17395 loss: 0.2084 decode.loss_ce: 0.1331 decode.acc_seg: 94.8633 aux.loss_ce: 0.0753 aux.acc_seg: 93.3407 2023/06/08 02:11:05 - mmengine - INFO - Iter(train) [ 88700/240000] lr: 6.6358e-03 eta: 1 day, 6:20:55 time: 0.7152 data_time: 0.3914 memory: 17394 loss: 0.2307 decode.loss_ce: 0.1490 decode.acc_seg: 93.6244 aux.loss_ce: 0.0817 aux.acc_seg: 85.4523 2023/06/08 02:11:40 - mmengine - INFO - Iter(train) [ 88750/240000] lr: 6.6339e-03 eta: 1 day, 6:20:18 time: 0.7184 data_time: 0.3953 memory: 17395 loss: 0.2242 decode.loss_ce: 0.1439 decode.acc_seg: 94.9730 aux.loss_ce: 0.0803 aux.acc_seg: 92.8504 2023/06/08 02:12:15 - mmengine - INFO - Iter(train) [ 88800/240000] lr: 6.6320e-03 eta: 1 day, 6:19:40 time: 0.7021 data_time: 0.3786 memory: 17393 loss: 0.2378 decode.loss_ce: 0.1514 decode.acc_seg: 92.6580 aux.loss_ce: 0.0864 aux.acc_seg: 90.0355 2023/06/08 02:12:51 - mmengine - INFO - Iter(train) [ 88850/240000] lr: 6.6300e-03 eta: 1 day, 6:19:03 time: 0.7111 data_time: 0.3879 memory: 17396 loss: 0.2047 decode.loss_ce: 0.1321 decode.acc_seg: 93.5177 aux.loss_ce: 0.0726 aux.acc_seg: 91.1393 2023/06/08 02:13:26 - mmengine - INFO - Iter(train) [ 88900/240000] lr: 6.6281e-03 eta: 1 day, 6:18:26 time: 0.7007 data_time: 0.1850 memory: 17397 loss: 0.2096 decode.loss_ce: 0.1343 decode.acc_seg: 91.3410 aux.loss_ce: 0.0752 aux.acc_seg: 89.0324 2023/06/08 02:14:02 - mmengine - INFO - Iter(train) [ 88950/240000] lr: 6.6261e-03 eta: 1 day, 6:17:49 time: 0.7094 data_time: 0.0123 memory: 17392 loss: 0.2159 decode.loss_ce: 0.1395 decode.acc_seg: 94.2618 aux.loss_ce: 0.0764 aux.acc_seg: 92.7924 2023/06/08 02:14:38 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 02:14:38 - mmengine - INFO - Iter(train) [ 89000/240000] lr: 6.6242e-03 eta: 1 day, 6:17:12 time: 0.7021 data_time: 0.0124 memory: 17393 loss: 0.2512 decode.loss_ce: 0.1647 decode.acc_seg: 93.5079 aux.loss_ce: 0.0865 aux.acc_seg: 90.8418 2023/06/08 02:15:13 - mmengine - INFO - Iter(train) [ 89050/240000] lr: 6.6222e-03 eta: 1 day, 6:16:35 time: 0.7163 data_time: 0.0124 memory: 17396 loss: 0.2247 decode.loss_ce: 0.1453 decode.acc_seg: 94.0207 aux.loss_ce: 0.0794 aux.acc_seg: 91.3078 2023/06/08 02:15:49 - mmengine - INFO - Iter(train) [ 89100/240000] lr: 6.6203e-03 eta: 1 day, 6:15:58 time: 0.7051 data_time: 0.0123 memory: 17393 loss: 0.1996 decode.loss_ce: 0.1287 decode.acc_seg: 94.6804 aux.loss_ce: 0.0709 aux.acc_seg: 93.0149 2023/06/08 02:16:24 - mmengine - INFO - Iter(train) [ 89150/240000] lr: 6.6183e-03 eta: 1 day, 6:15:21 time: 0.7015 data_time: 0.0121 memory: 17395 loss: 0.2159 decode.loss_ce: 0.1383 decode.acc_seg: 93.1219 aux.loss_ce: 0.0775 aux.acc_seg: 90.9622 2023/06/08 02:17:00 - mmengine - INFO - Iter(train) [ 89200/240000] lr: 6.6164e-03 eta: 1 day, 6:14:44 time: 0.7157 data_time: 0.0120 memory: 17392 loss: 0.1971 decode.loss_ce: 0.1261 decode.acc_seg: 93.9809 aux.loss_ce: 0.0710 aux.acc_seg: 92.3239 2023/06/08 02:17:35 - mmengine - INFO - Iter(train) [ 89250/240000] lr: 6.6145e-03 eta: 1 day, 6:14:07 time: 0.6966 data_time: 0.0123 memory: 17394 loss: 0.2268 decode.loss_ce: 0.1459 decode.acc_seg: 94.5410 aux.loss_ce: 0.0808 aux.acc_seg: 90.7889 2023/06/08 02:18:11 - mmengine - INFO - Iter(train) [ 89300/240000] lr: 6.6125e-03 eta: 1 day, 6:13:30 time: 0.7117 data_time: 0.0121 memory: 17396 loss: 0.2042 decode.loss_ce: 0.1310 decode.acc_seg: 93.1774 aux.loss_ce: 0.0733 aux.acc_seg: 90.1339 2023/06/08 02:18:47 - mmengine - INFO - Iter(train) [ 89350/240000] lr: 6.6106e-03 eta: 1 day, 6:12:53 time: 0.7164 data_time: 0.0123 memory: 17395 loss: 0.2366 decode.loss_ce: 0.1532 decode.acc_seg: 93.1374 aux.loss_ce: 0.0834 aux.acc_seg: 89.6828 2023/06/08 02:19:22 - mmengine - INFO - Iter(train) [ 89400/240000] lr: 6.6086e-03 eta: 1 day, 6:12:16 time: 0.7151 data_time: 0.0126 memory: 17393 loss: 0.2329 decode.loss_ce: 0.1486 decode.acc_seg: 93.0388 aux.loss_ce: 0.0844 aux.acc_seg: 88.5833 2023/06/08 02:19:58 - mmengine - INFO - Iter(train) [ 89450/240000] lr: 6.6067e-03 eta: 1 day, 6:11:39 time: 0.7179 data_time: 0.0125 memory: 17395 loss: 0.2104 decode.loss_ce: 0.1356 decode.acc_seg: 94.2637 aux.loss_ce: 0.0747 aux.acc_seg: 92.1396 2023/06/08 02:20:33 - mmengine - INFO - Iter(train) [ 89500/240000] lr: 6.6047e-03 eta: 1 day, 6:11:02 time: 0.7171 data_time: 0.0122 memory: 17393 loss: 0.2272 decode.loss_ce: 0.1482 decode.acc_seg: 94.5019 aux.loss_ce: 0.0790 aux.acc_seg: 92.4771 2023/06/08 02:21:09 - mmengine - INFO - Iter(train) [ 89550/240000] lr: 6.6028e-03 eta: 1 day, 6:10:25 time: 0.7099 data_time: 0.0122 memory: 17393 loss: 0.2084 decode.loss_ce: 0.1335 decode.acc_seg: 93.0102 aux.loss_ce: 0.0749 aux.acc_seg: 91.3882 2023/06/08 02:21:44 - mmengine - INFO - Iter(train) [ 89600/240000] lr: 6.6008e-03 eta: 1 day, 6:09:47 time: 0.7084 data_time: 0.0123 memory: 17396 loss: 0.1962 decode.loss_ce: 0.1248 decode.acc_seg: 94.6183 aux.loss_ce: 0.0714 aux.acc_seg: 92.0150 2023/06/08 02:22:20 - mmengine - INFO - Iter(train) [ 89650/240000] lr: 6.5989e-03 eta: 1 day, 6:09:10 time: 0.7099 data_time: 0.0121 memory: 17394 loss: 0.2183 decode.loss_ce: 0.1403 decode.acc_seg: 92.4057 aux.loss_ce: 0.0780 aux.acc_seg: 90.8206 2023/06/08 02:22:55 - mmengine - INFO - Iter(train) [ 89700/240000] lr: 6.5970e-03 eta: 1 day, 6:08:33 time: 0.7153 data_time: 0.0266 memory: 17397 loss: 0.2186 decode.loss_ce: 0.1397 decode.acc_seg: 95.0333 aux.loss_ce: 0.0789 aux.acc_seg: 90.6307 2023/06/08 02:23:31 - mmengine - INFO - Iter(train) [ 89750/240000] lr: 6.5950e-03 eta: 1 day, 6:07:56 time: 0.7090 data_time: 0.0247 memory: 17393 loss: 0.1991 decode.loss_ce: 0.1285 decode.acc_seg: 94.0650 aux.loss_ce: 0.0707 aux.acc_seg: 91.9354 2023/06/08 02:24:06 - mmengine - INFO - Iter(train) [ 89800/240000] lr: 6.5931e-03 eta: 1 day, 6:07:19 time: 0.7217 data_time: 0.2203 memory: 17393 loss: 0.2389 decode.loss_ce: 0.1595 decode.acc_seg: 94.0402 aux.loss_ce: 0.0793 aux.acc_seg: 90.3829 2023/06/08 02:24:42 - mmengine - INFO - Iter(train) [ 89850/240000] lr: 6.5911e-03 eta: 1 day, 6:06:42 time: 0.7135 data_time: 0.1008 memory: 17393 loss: 0.2072 decode.loss_ce: 0.1319 decode.acc_seg: 95.9884 aux.loss_ce: 0.0753 aux.acc_seg: 94.1308 2023/06/08 02:25:17 - mmengine - INFO - Iter(train) [ 89900/240000] lr: 6.5892e-03 eta: 1 day, 6:06:05 time: 0.7158 data_time: 0.2419 memory: 17391 loss: 0.1942 decode.loss_ce: 0.1234 decode.acc_seg: 95.5628 aux.loss_ce: 0.0708 aux.acc_seg: 93.8669 2023/06/08 02:25:53 - mmengine - INFO - Iter(train) [ 89950/240000] lr: 6.5872e-03 eta: 1 day, 6:05:28 time: 0.7140 data_time: 0.0121 memory: 17391 loss: 0.1970 decode.loss_ce: 0.1274 decode.acc_seg: 95.7741 aux.loss_ce: 0.0696 aux.acc_seg: 93.9636 2023/06/08 02:26:28 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 02:26:28 - mmengine - INFO - Iter(train) [ 90000/240000] lr: 6.5853e-03 eta: 1 day, 6:04:51 time: 0.7047 data_time: 0.0122 memory: 17395 loss: 0.2054 decode.loss_ce: 0.1317 decode.acc_seg: 95.1807 aux.loss_ce: 0.0737 aux.acc_seg: 94.1505 2023/06/08 02:27:04 - mmengine - INFO - Iter(train) [ 90050/240000] lr: 6.5833e-03 eta: 1 day, 6:04:14 time: 0.6988 data_time: 0.0462 memory: 17393 loss: 0.2186 decode.loss_ce: 0.1399 decode.acc_seg: 94.2981 aux.loss_ce: 0.0787 aux.acc_seg: 91.0573 2023/06/08 02:27:39 - mmengine - INFO - Iter(train) [ 90100/240000] lr: 6.5814e-03 eta: 1 day, 6:03:37 time: 0.7000 data_time: 0.0418 memory: 17395 loss: 0.2392 decode.loss_ce: 0.1513 decode.acc_seg: 92.5328 aux.loss_ce: 0.0879 aux.acc_seg: 89.2707 2023/06/08 02:28:15 - mmengine - INFO - Iter(train) [ 90150/240000] lr: 6.5794e-03 eta: 1 day, 6:02:59 time: 0.7211 data_time: 0.3544 memory: 17396 loss: 0.2099 decode.loss_ce: 0.1349 decode.acc_seg: 93.4475 aux.loss_ce: 0.0750 aux.acc_seg: 91.1124 2023/06/08 02:28:50 - mmengine - INFO - Iter(train) [ 90200/240000] lr: 6.5775e-03 eta: 1 day, 6:02:22 time: 0.6963 data_time: 0.2327 memory: 17394 loss: 0.2015 decode.loss_ce: 0.1269 decode.acc_seg: 94.2914 aux.loss_ce: 0.0746 aux.acc_seg: 91.3402 2023/06/08 02:29:26 - mmengine - INFO - Iter(train) [ 90250/240000] lr: 6.5756e-03 eta: 1 day, 6:01:45 time: 0.7005 data_time: 0.1405 memory: 17393 loss: 0.2167 decode.loss_ce: 0.1403 decode.acc_seg: 92.8005 aux.loss_ce: 0.0764 aux.acc_seg: 90.4133 2023/06/08 02:30:01 - mmengine - INFO - Iter(train) [ 90300/240000] lr: 6.5736e-03 eta: 1 day, 6:01:08 time: 0.7132 data_time: 0.3880 memory: 17392 loss: 0.2034 decode.loss_ce: 0.1300 decode.acc_seg: 94.2566 aux.loss_ce: 0.0734 aux.acc_seg: 91.9963 2023/06/08 02:30:36 - mmengine - INFO - Iter(train) [ 90350/240000] lr: 6.5717e-03 eta: 1 day, 6:00:30 time: 0.6960 data_time: 0.3681 memory: 17396 loss: 0.2240 decode.loss_ce: 0.1429 decode.acc_seg: 94.1495 aux.loss_ce: 0.0811 aux.acc_seg: 89.5468 2023/06/08 02:31:12 - mmengine - INFO - Iter(train) [ 90400/240000] lr: 6.5697e-03 eta: 1 day, 5:59:54 time: 0.7119 data_time: 0.2083 memory: 17392 loss: 0.2064 decode.loss_ce: 0.1305 decode.acc_seg: 94.5755 aux.loss_ce: 0.0760 aux.acc_seg: 93.0696 2023/06/08 02:31:48 - mmengine - INFO - Iter(train) [ 90450/240000] lr: 6.5678e-03 eta: 1 day, 5:59:17 time: 0.7144 data_time: 0.0122 memory: 17394 loss: 0.2089 decode.loss_ce: 0.1353 decode.acc_seg: 91.9057 aux.loss_ce: 0.0736 aux.acc_seg: 89.6983 2023/06/08 02:32:23 - mmengine - INFO - Iter(train) [ 90500/240000] lr: 6.5658e-03 eta: 1 day, 5:58:40 time: 0.6989 data_time: 0.0121 memory: 17395 loss: 0.2142 decode.loss_ce: 0.1359 decode.acc_seg: 95.3780 aux.loss_ce: 0.0783 aux.acc_seg: 92.0740 2023/06/08 02:32:59 - mmengine - INFO - Iter(train) [ 90550/240000] lr: 6.5639e-03 eta: 1 day, 5:58:03 time: 0.7013 data_time: 0.0121 memory: 17396 loss: 0.2068 decode.loss_ce: 0.1312 decode.acc_seg: 94.6759 aux.loss_ce: 0.0756 aux.acc_seg: 92.6545 2023/06/08 02:33:34 - mmengine - INFO - Iter(train) [ 90600/240000] lr: 6.5619e-03 eta: 1 day, 5:57:26 time: 0.7025 data_time: 0.0186 memory: 17395 loss: 0.2102 decode.loss_ce: 0.1366 decode.acc_seg: 95.2609 aux.loss_ce: 0.0736 aux.acc_seg: 93.6848 2023/06/08 02:34:10 - mmengine - INFO - Iter(train) [ 90650/240000] lr: 6.5600e-03 eta: 1 day, 5:56:48 time: 0.7084 data_time: 0.1887 memory: 17392 loss: 0.2243 decode.loss_ce: 0.1432 decode.acc_seg: 92.3258 aux.loss_ce: 0.0811 aux.acc_seg: 90.3561 2023/06/08 02:34:45 - mmengine - INFO - Iter(train) [ 90700/240000] lr: 6.5580e-03 eta: 1 day, 5:56:12 time: 0.7153 data_time: 0.3925 memory: 17393 loss: 0.2151 decode.loss_ce: 0.1377 decode.acc_seg: 94.3065 aux.loss_ce: 0.0774 aux.acc_seg: 91.3727 2023/06/08 02:35:21 - mmengine - INFO - Iter(train) [ 90750/240000] lr: 6.5561e-03 eta: 1 day, 5:55:35 time: 0.7086 data_time: 0.3859 memory: 17394 loss: 0.2018 decode.loss_ce: 0.1295 decode.acc_seg: 94.9753 aux.loss_ce: 0.0723 aux.acc_seg: 93.4436 2023/06/08 02:35:56 - mmengine - INFO - Iter(train) [ 90800/240000] lr: 6.5541e-03 eta: 1 day, 5:54:57 time: 0.7106 data_time: 0.3878 memory: 17394 loss: 0.2044 decode.loss_ce: 0.1284 decode.acc_seg: 95.5383 aux.loss_ce: 0.0760 aux.acc_seg: 91.9602 2023/06/08 02:36:32 - mmengine - INFO - Iter(train) [ 90850/240000] lr: 6.5522e-03 eta: 1 day, 5:54:20 time: 0.7108 data_time: 0.3877 memory: 17395 loss: 0.2094 decode.loss_ce: 0.1335 decode.acc_seg: 95.1001 aux.loss_ce: 0.0759 aux.acc_seg: 92.8706 2023/06/08 02:37:07 - mmengine - INFO - Iter(train) [ 90900/240000] lr: 6.5503e-03 eta: 1 day, 5:53:43 time: 0.7152 data_time: 0.3928 memory: 17392 loss: 0.2260 decode.loss_ce: 0.1441 decode.acc_seg: 93.1076 aux.loss_ce: 0.0819 aux.acc_seg: 91.1354 2023/06/08 02:37:43 - mmengine - INFO - Iter(train) [ 90950/240000] lr: 6.5483e-03 eta: 1 day, 5:53:06 time: 0.7080 data_time: 0.3852 memory: 17392 loss: 0.1973 decode.loss_ce: 0.1261 decode.acc_seg: 94.9277 aux.loss_ce: 0.0712 aux.acc_seg: 93.0206 2023/06/08 02:38:18 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 02:38:18 - mmengine - INFO - Iter(train) [ 91000/240000] lr: 6.5464e-03 eta: 1 day, 5:52:29 time: 0.7002 data_time: 0.3772 memory: 17394 loss: 0.2130 decode.loss_ce: 0.1348 decode.acc_seg: 94.5853 aux.loss_ce: 0.0782 aux.acc_seg: 92.2539 2023/06/08 02:38:54 - mmengine - INFO - Iter(train) [ 91050/240000] lr: 6.5444e-03 eta: 1 day, 5:51:52 time: 0.7054 data_time: 0.3823 memory: 17393 loss: 0.2389 decode.loss_ce: 0.1551 decode.acc_seg: 92.0946 aux.loss_ce: 0.0837 aux.acc_seg: 89.7664 2023/06/08 02:39:29 - mmengine - INFO - Iter(train) [ 91100/240000] lr: 6.5425e-03 eta: 1 day, 5:51:15 time: 0.7068 data_time: 0.3838 memory: 17393 loss: 0.1948 decode.loss_ce: 0.1245 decode.acc_seg: 95.1618 aux.loss_ce: 0.0702 aux.acc_seg: 92.8538 2023/06/08 02:40:05 - mmengine - INFO - Iter(train) [ 91150/240000] lr: 6.5405e-03 eta: 1 day, 5:50:38 time: 0.7085 data_time: 0.3854 memory: 17396 loss: 0.2323 decode.loss_ce: 0.1496 decode.acc_seg: 92.3074 aux.loss_ce: 0.0828 aux.acc_seg: 90.3643 2023/06/08 02:40:41 - mmengine - INFO - Iter(train) [ 91200/240000] lr: 6.5386e-03 eta: 1 day, 5:50:01 time: 0.7197 data_time: 0.3964 memory: 17393 loss: 0.2056 decode.loss_ce: 0.1307 decode.acc_seg: 91.1954 aux.loss_ce: 0.0748 aux.acc_seg: 89.3084 2023/06/08 02:41:16 - mmengine - INFO - Iter(train) [ 91250/240000] lr: 6.5366e-03 eta: 1 day, 5:49:25 time: 0.7049 data_time: 0.3821 memory: 17391 loss: 0.1814 decode.loss_ce: 0.1176 decode.acc_seg: 95.4714 aux.loss_ce: 0.0638 aux.acc_seg: 93.6095 2023/06/08 02:41:52 - mmengine - INFO - Iter(train) [ 91300/240000] lr: 6.5347e-03 eta: 1 day, 5:48:47 time: 0.7089 data_time: 0.3861 memory: 17393 loss: 0.2152 decode.loss_ce: 0.1386 decode.acc_seg: 95.0339 aux.loss_ce: 0.0766 aux.acc_seg: 91.6526 2023/06/08 02:42:28 - mmengine - INFO - Iter(train) [ 91350/240000] lr: 6.5327e-03 eta: 1 day, 5:48:11 time: 0.7087 data_time: 0.3857 memory: 17394 loss: 0.2000 decode.loss_ce: 0.1262 decode.acc_seg: 94.7218 aux.loss_ce: 0.0738 aux.acc_seg: 91.2688 2023/06/08 02:43:03 - mmengine - INFO - Iter(train) [ 91400/240000] lr: 6.5308e-03 eta: 1 day, 5:47:34 time: 0.7192 data_time: 0.3958 memory: 17395 loss: 0.1744 decode.loss_ce: 0.1108 decode.acc_seg: 96.1575 aux.loss_ce: 0.0636 aux.acc_seg: 94.6478 2023/06/08 02:43:39 - mmengine - INFO - Iter(train) [ 91450/240000] lr: 6.5288e-03 eta: 1 day, 5:46:57 time: 0.7162 data_time: 0.3938 memory: 17392 loss: 0.1900 decode.loss_ce: 0.1199 decode.acc_seg: 93.8643 aux.loss_ce: 0.0701 aux.acc_seg: 91.2195 2023/06/08 02:44:14 - mmengine - INFO - Iter(train) [ 91500/240000] lr: 6.5269e-03 eta: 1 day, 5:46:20 time: 0.7094 data_time: 0.3864 memory: 17393 loss: 0.2234 decode.loss_ce: 0.1456 decode.acc_seg: 93.8382 aux.loss_ce: 0.0778 aux.acc_seg: 92.0161 2023/06/08 02:44:50 - mmengine - INFO - Iter(train) [ 91550/240000] lr: 6.5249e-03 eta: 1 day, 5:45:43 time: 0.7085 data_time: 0.3859 memory: 17393 loss: 0.2074 decode.loss_ce: 0.1308 decode.acc_seg: 93.5776 aux.loss_ce: 0.0766 aux.acc_seg: 89.1375 2023/06/08 02:45:25 - mmengine - INFO - Iter(train) [ 91600/240000] lr: 6.5230e-03 eta: 1 day, 5:45:05 time: 0.7176 data_time: 0.3946 memory: 17393 loss: 0.2001 decode.loss_ce: 0.1269 decode.acc_seg: 95.2917 aux.loss_ce: 0.0731 aux.acc_seg: 91.8240 2023/06/08 02:46:00 - mmengine - INFO - Iter(train) [ 91650/240000] lr: 6.5210e-03 eta: 1 day, 5:44:28 time: 0.6989 data_time: 0.3753 memory: 17396 loss: 0.2080 decode.loss_ce: 0.1331 decode.acc_seg: 92.8164 aux.loss_ce: 0.0749 aux.acc_seg: 90.1809 2023/06/08 02:46:36 - mmengine - INFO - Iter(train) [ 91700/240000] lr: 6.5191e-03 eta: 1 day, 5:43:51 time: 0.6989 data_time: 0.3756 memory: 17396 loss: 0.2209 decode.loss_ce: 0.1413 decode.acc_seg: 93.7819 aux.loss_ce: 0.0796 aux.acc_seg: 90.1435 2023/06/08 02:47:11 - mmengine - INFO - Iter(train) [ 91750/240000] lr: 6.5171e-03 eta: 1 day, 5:43:14 time: 0.7016 data_time: 0.3321 memory: 17393 loss: 0.2033 decode.loss_ce: 0.1314 decode.acc_seg: 95.0806 aux.loss_ce: 0.0719 aux.acc_seg: 93.2205 2023/06/08 02:47:47 - mmengine - INFO - Iter(train) [ 91800/240000] lr: 6.5152e-03 eta: 1 day, 5:42:37 time: 0.7171 data_time: 0.3945 memory: 17389 loss: 0.1923 decode.loss_ce: 0.1227 decode.acc_seg: 93.9019 aux.loss_ce: 0.0696 aux.acc_seg: 91.4954 2023/06/08 02:48:23 - mmengine - INFO - Iter(train) [ 91850/240000] lr: 6.5133e-03 eta: 1 day, 5:42:00 time: 0.7110 data_time: 0.3882 memory: 17397 loss: 0.2287 decode.loss_ce: 0.1461 decode.acc_seg: 93.6917 aux.loss_ce: 0.0826 aux.acc_seg: 91.2925 2023/06/08 02:48:58 - mmengine - INFO - Iter(train) [ 91900/240000] lr: 6.5113e-03 eta: 1 day, 5:41:24 time: 0.7100 data_time: 0.3870 memory: 17394 loss: 0.1901 decode.loss_ce: 0.1225 decode.acc_seg: 95.1412 aux.loss_ce: 0.0676 aux.acc_seg: 93.5304 2023/06/08 02:49:34 - mmengine - INFO - Iter(train) [ 91950/240000] lr: 6.5094e-03 eta: 1 day, 5:40:47 time: 0.7073 data_time: 0.3844 memory: 17395 loss: 0.1892 decode.loss_ce: 0.1213 decode.acc_seg: 93.2597 aux.loss_ce: 0.0679 aux.acc_seg: 91.3070 2023/06/08 02:50:09 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 02:50:09 - mmengine - INFO - Iter(train) [ 92000/240000] lr: 6.5074e-03 eta: 1 day, 5:40:10 time: 0.7028 data_time: 0.3793 memory: 17394 loss: 0.2818 decode.loss_ce: 0.1851 decode.acc_seg: 88.5260 aux.loss_ce: 0.0967 aux.acc_seg: 86.0402 2023/06/08 02:50:45 - mmengine - INFO - Iter(train) [ 92050/240000] lr: 6.5055e-03 eta: 1 day, 5:39:33 time: 0.7121 data_time: 0.3887 memory: 17397 loss: 0.2417 decode.loss_ce: 0.1563 decode.acc_seg: 95.2274 aux.loss_ce: 0.0854 aux.acc_seg: 93.5548 2023/06/08 02:51:20 - mmengine - INFO - Iter(train) [ 92100/240000] lr: 6.5035e-03 eta: 1 day, 5:38:55 time: 0.6920 data_time: 0.3689 memory: 17396 loss: 0.2169 decode.loss_ce: 0.1398 decode.acc_seg: 91.4804 aux.loss_ce: 0.0771 aux.acc_seg: 90.0425 2023/06/08 02:51:56 - mmengine - INFO - Iter(train) [ 92150/240000] lr: 6.5016e-03 eta: 1 day, 5:38:18 time: 0.6990 data_time: 0.3759 memory: 17394 loss: 0.2102 decode.loss_ce: 0.1338 decode.acc_seg: 93.9283 aux.loss_ce: 0.0765 aux.acc_seg: 90.7016 2023/06/08 02:52:31 - mmengine - INFO - Iter(train) [ 92200/240000] lr: 6.4996e-03 eta: 1 day, 5:37:41 time: 0.7067 data_time: 0.3703 memory: 17395 loss: 0.2280 decode.loss_ce: 0.1469 decode.acc_seg: 93.9279 aux.loss_ce: 0.0811 aux.acc_seg: 91.2047 2023/06/08 02:53:07 - mmengine - INFO - Iter(train) [ 92250/240000] lr: 6.4977e-03 eta: 1 day, 5:37:04 time: 0.7092 data_time: 0.1668 memory: 17395 loss: 0.2075 decode.loss_ce: 0.1308 decode.acc_seg: 94.9227 aux.loss_ce: 0.0767 aux.acc_seg: 92.7065 2023/06/08 02:53:42 - mmengine - INFO - Iter(train) [ 92300/240000] lr: 6.4957e-03 eta: 1 day, 5:36:27 time: 0.7172 data_time: 0.2476 memory: 17396 loss: 0.2235 decode.loss_ce: 0.1450 decode.acc_seg: 94.3225 aux.loss_ce: 0.0786 aux.acc_seg: 93.0595 2023/06/08 02:54:18 - mmengine - INFO - Iter(train) [ 92350/240000] lr: 6.4938e-03 eta: 1 day, 5:35:51 time: 0.7195 data_time: 0.0267 memory: 17394 loss: 0.1951 decode.loss_ce: 0.1250 decode.acc_seg: 94.0446 aux.loss_ce: 0.0702 aux.acc_seg: 92.5409 2023/06/08 02:54:53 - mmengine - INFO - Iter(train) [ 92400/240000] lr: 6.4918e-03 eta: 1 day, 5:35:13 time: 0.7119 data_time: 0.0122 memory: 17391 loss: 0.2057 decode.loss_ce: 0.1304 decode.acc_seg: 92.9872 aux.loss_ce: 0.0754 aux.acc_seg: 90.5352 2023/06/08 02:55:29 - mmengine - INFO - Iter(train) [ 92450/240000] lr: 6.4899e-03 eta: 1 day, 5:34:36 time: 0.7081 data_time: 0.0145 memory: 17395 loss: 0.2153 decode.loss_ce: 0.1378 decode.acc_seg: 93.5680 aux.loss_ce: 0.0775 aux.acc_seg: 90.8962 2023/06/08 02:56:04 - mmengine - INFO - Iter(train) [ 92500/240000] lr: 6.4879e-03 eta: 1 day, 5:33:59 time: 0.7032 data_time: 0.0542 memory: 17395 loss: 0.2016 decode.loss_ce: 0.1284 decode.acc_seg: 94.7093 aux.loss_ce: 0.0732 aux.acc_seg: 91.1005 2023/06/08 02:56:40 - mmengine - INFO - Iter(train) [ 92550/240000] lr: 6.4860e-03 eta: 1 day, 5:33:23 time: 0.7156 data_time: 0.0121 memory: 17394 loss: 0.2278 decode.loss_ce: 0.1467 decode.acc_seg: 93.5276 aux.loss_ce: 0.0811 aux.acc_seg: 89.6415 2023/06/08 02:57:15 - mmengine - INFO - Iter(train) [ 92600/240000] lr: 6.4840e-03 eta: 1 day, 5:32:46 time: 0.7075 data_time: 0.1761 memory: 17394 loss: 0.2246 decode.loss_ce: 0.1458 decode.acc_seg: 92.2607 aux.loss_ce: 0.0789 aux.acc_seg: 90.3280 2023/06/08 02:57:51 - mmengine - INFO - Iter(train) [ 92650/240000] lr: 6.4821e-03 eta: 1 day, 5:32:09 time: 0.7167 data_time: 0.2198 memory: 17397 loss: 0.2058 decode.loss_ce: 0.1319 decode.acc_seg: 93.4546 aux.loss_ce: 0.0739 aux.acc_seg: 91.4128 2023/06/08 02:58:26 - mmengine - INFO - Iter(train) [ 92700/240000] lr: 6.4801e-03 eta: 1 day, 5:31:31 time: 0.7108 data_time: 0.2558 memory: 17395 loss: 0.2166 decode.loss_ce: 0.1400 decode.acc_seg: 94.8611 aux.loss_ce: 0.0766 aux.acc_seg: 92.7894 2023/06/08 02:59:02 - mmengine - INFO - Iter(train) [ 92750/240000] lr: 6.4782e-03 eta: 1 day, 5:30:54 time: 0.7148 data_time: 0.3913 memory: 17394 loss: 0.2180 decode.loss_ce: 0.1385 decode.acc_seg: 91.9705 aux.loss_ce: 0.0795 aux.acc_seg: 88.4384 2023/06/08 02:59:37 - mmengine - INFO - Iter(train) [ 92800/240000] lr: 6.4762e-03 eta: 1 day, 5:30:17 time: 0.7146 data_time: 0.2994 memory: 17391 loss: 0.2082 decode.loss_ce: 0.1336 decode.acc_seg: 95.1758 aux.loss_ce: 0.0747 aux.acc_seg: 93.1299 2023/06/08 03:00:13 - mmengine - INFO - Iter(train) [ 92850/240000] lr: 6.4743e-03 eta: 1 day, 5:29:40 time: 0.7116 data_time: 0.2094 memory: 17391 loss: 0.2114 decode.loss_ce: 0.1353 decode.acc_seg: 94.7280 aux.loss_ce: 0.0761 aux.acc_seg: 93.1692 2023/06/08 03:00:48 - mmengine - INFO - Iter(train) [ 92900/240000] lr: 6.4723e-03 eta: 1 day, 5:29:03 time: 0.7175 data_time: 0.0121 memory: 17394 loss: 0.2173 decode.loss_ce: 0.1392 decode.acc_seg: 92.5170 aux.loss_ce: 0.0782 aux.acc_seg: 90.3797 2023/06/08 03:01:24 - mmengine - INFO - Iter(train) [ 92950/240000] lr: 6.4704e-03 eta: 1 day, 5:28:27 time: 0.7143 data_time: 0.0123 memory: 17394 loss: 0.2094 decode.loss_ce: 0.1352 decode.acc_seg: 92.5682 aux.loss_ce: 0.0742 aux.acc_seg: 88.3614 2023/06/08 03:01:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 03:01:59 - mmengine - INFO - Iter(train) [ 93000/240000] lr: 6.4684e-03 eta: 1 day, 5:27:49 time: 0.7024 data_time: 0.0123 memory: 17392 loss: 0.2036 decode.loss_ce: 0.1309 decode.acc_seg: 94.5081 aux.loss_ce: 0.0727 aux.acc_seg: 91.3368 2023/06/08 03:02:35 - mmengine - INFO - Iter(train) [ 93050/240000] lr: 6.4665e-03 eta: 1 day, 5:27:13 time: 0.7105 data_time: 0.0121 memory: 17395 loss: 0.2492 decode.loss_ce: 0.1614 decode.acc_seg: 92.4047 aux.loss_ce: 0.0879 aux.acc_seg: 89.3143 2023/06/08 03:03:11 - mmengine - INFO - Iter(train) [ 93100/240000] lr: 6.4645e-03 eta: 1 day, 5:26:36 time: 0.7148 data_time: 0.0121 memory: 17396 loss: 0.2218 decode.loss_ce: 0.1399 decode.acc_seg: 93.8999 aux.loss_ce: 0.0820 aux.acc_seg: 90.4135 2023/06/08 03:03:46 - mmengine - INFO - Iter(train) [ 93150/240000] lr: 6.4626e-03 eta: 1 day, 5:25:59 time: 0.7070 data_time: 0.0121 memory: 17393 loss: 0.2074 decode.loss_ce: 0.1331 decode.acc_seg: 95.1370 aux.loss_ce: 0.0743 aux.acc_seg: 93.2669 2023/06/08 03:04:22 - mmengine - INFO - Iter(train) [ 93200/240000] lr: 6.4606e-03 eta: 1 day, 5:25:22 time: 0.7111 data_time: 0.0123 memory: 17396 loss: 0.2485 decode.loss_ce: 0.1575 decode.acc_seg: 92.7073 aux.loss_ce: 0.0910 aux.acc_seg: 89.2088 2023/06/08 03:04:57 - mmengine - INFO - Iter(train) [ 93250/240000] lr: 6.4587e-03 eta: 1 day, 5:24:45 time: 0.7164 data_time: 0.0122 memory: 17395 loss: 0.2409 decode.loss_ce: 0.1567 decode.acc_seg: 94.2866 aux.loss_ce: 0.0842 aux.acc_seg: 92.5865 2023/06/08 03:05:33 - mmengine - INFO - Iter(train) [ 93300/240000] lr: 6.4567e-03 eta: 1 day, 5:24:08 time: 0.7147 data_time: 0.0121 memory: 17392 loss: 0.2251 decode.loss_ce: 0.1452 decode.acc_seg: 94.2431 aux.loss_ce: 0.0798 aux.acc_seg: 90.5764 2023/06/08 03:06:08 - mmengine - INFO - Iter(train) [ 93350/240000] lr: 6.4548e-03 eta: 1 day, 5:23:31 time: 0.7236 data_time: 0.0123 memory: 17395 loss: 0.2251 decode.loss_ce: 0.1450 decode.acc_seg: 94.0460 aux.loss_ce: 0.0801 aux.acc_seg: 91.8610 2023/06/08 03:06:44 - mmengine - INFO - Iter(train) [ 93400/240000] lr: 6.4528e-03 eta: 1 day, 5:22:54 time: 0.7064 data_time: 0.0119 memory: 17397 loss: 0.2066 decode.loss_ce: 0.1352 decode.acc_seg: 93.9115 aux.loss_ce: 0.0715 aux.acc_seg: 91.9756 2023/06/08 03:07:19 - mmengine - INFO - Iter(train) [ 93450/240000] lr: 6.4509e-03 eta: 1 day, 5:22:17 time: 0.7042 data_time: 0.0124 memory: 17394 loss: 0.2074 decode.loss_ce: 0.1320 decode.acc_seg: 94.3181 aux.loss_ce: 0.0754 aux.acc_seg: 92.5079 2023/06/08 03:07:55 - mmengine - INFO - Iter(train) [ 93500/240000] lr: 6.4489e-03 eta: 1 day, 5:21:41 time: 0.7250 data_time: 0.0123 memory: 17396 loss: 0.2093 decode.loss_ce: 0.1339 decode.acc_seg: 94.5562 aux.loss_ce: 0.0754 aux.acc_seg: 92.0919 2023/06/08 03:08:30 - mmengine - INFO - Iter(train) [ 93550/240000] lr: 6.4470e-03 eta: 1 day, 5:21:03 time: 0.7144 data_time: 0.0125 memory: 17392 loss: 0.2058 decode.loss_ce: 0.1290 decode.acc_seg: 93.9703 aux.loss_ce: 0.0768 aux.acc_seg: 90.1068 2023/06/08 03:09:06 - mmengine - INFO - Iter(train) [ 93600/240000] lr: 6.4450e-03 eta: 1 day, 5:20:26 time: 0.7079 data_time: 0.0124 memory: 17397 loss: 0.2035 decode.loss_ce: 0.1283 decode.acc_seg: 93.2284 aux.loss_ce: 0.0752 aux.acc_seg: 90.8945 2023/06/08 03:09:41 - mmengine - INFO - Iter(train) [ 93650/240000] lr: 6.4431e-03 eta: 1 day, 5:19:49 time: 0.7054 data_time: 0.0123 memory: 17394 loss: 0.2085 decode.loss_ce: 0.1341 decode.acc_seg: 95.0854 aux.loss_ce: 0.0744 aux.acc_seg: 91.8440 2023/06/08 03:10:17 - mmengine - INFO - Iter(train) [ 93700/240000] lr: 6.4411e-03 eta: 1 day, 5:19:12 time: 0.7142 data_time: 0.1433 memory: 17393 loss: 0.2004 decode.loss_ce: 0.1281 decode.acc_seg: 94.2882 aux.loss_ce: 0.0723 aux.acc_seg: 92.0283 2023/06/08 03:10:52 - mmengine - INFO - Iter(train) [ 93750/240000] lr: 6.4392e-03 eta: 1 day, 5:18:35 time: 0.7112 data_time: 0.2861 memory: 17395 loss: 0.1763 decode.loss_ce: 0.1119 decode.acc_seg: 95.3291 aux.loss_ce: 0.0644 aux.acc_seg: 92.2194 2023/06/08 03:11:28 - mmengine - INFO - Iter(train) [ 93800/240000] lr: 6.4372e-03 eta: 1 day, 5:17:58 time: 0.7136 data_time: 0.3820 memory: 17393 loss: 0.2088 decode.loss_ce: 0.1353 decode.acc_seg: 92.3502 aux.loss_ce: 0.0735 aux.acc_seg: 89.7938 2023/06/08 03:12:03 - mmengine - INFO - Iter(train) [ 93850/240000] lr: 6.4353e-03 eta: 1 day, 5:17:21 time: 0.7061 data_time: 0.3831 memory: 17393 loss: 0.2047 decode.loss_ce: 0.1291 decode.acc_seg: 93.6111 aux.loss_ce: 0.0756 aux.acc_seg: 90.2337 2023/06/08 03:12:38 - mmengine - INFO - Iter(train) [ 93900/240000] lr: 6.4333e-03 eta: 1 day, 5:16:44 time: 0.7163 data_time: 0.3934 memory: 17395 loss: 0.1931 decode.loss_ce: 0.1223 decode.acc_seg: 94.7869 aux.loss_ce: 0.0708 aux.acc_seg: 93.3147 2023/06/08 03:13:14 - mmengine - INFO - Iter(train) [ 93950/240000] lr: 6.4314e-03 eta: 1 day, 5:16:06 time: 0.6980 data_time: 0.3496 memory: 17393 loss: 0.2260 decode.loss_ce: 0.1460 decode.acc_seg: 92.1423 aux.loss_ce: 0.0800 aux.acc_seg: 89.7749 2023/06/08 03:13:49 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 03:13:49 - mmengine - INFO - Iter(train) [ 94000/240000] lr: 6.4294e-03 eta: 1 day, 5:15:29 time: 0.7039 data_time: 0.2773 memory: 17394 loss: 0.2036 decode.loss_ce: 0.1303 decode.acc_seg: 93.5753 aux.loss_ce: 0.0733 aux.acc_seg: 90.6027 2023/06/08 03:14:25 - mmengine - INFO - Iter(train) [ 94050/240000] lr: 6.4275e-03 eta: 1 day, 5:14:53 time: 0.7069 data_time: 0.3731 memory: 17395 loss: 0.1961 decode.loss_ce: 0.1246 decode.acc_seg: 94.9233 aux.loss_ce: 0.0715 aux.acc_seg: 90.4083 2023/06/08 03:15:01 - mmengine - INFO - Iter(train) [ 94100/240000] lr: 6.4255e-03 eta: 1 day, 5:14:16 time: 0.7107 data_time: 0.0221 memory: 17398 loss: 0.1962 decode.loss_ce: 0.1265 decode.acc_seg: 94.7119 aux.loss_ce: 0.0697 aux.acc_seg: 93.2474 2023/06/08 03:15:36 - mmengine - INFO - Iter(train) [ 94150/240000] lr: 6.4236e-03 eta: 1 day, 5:13:39 time: 0.7088 data_time: 0.0795 memory: 17395 loss: 0.2071 decode.loss_ce: 0.1327 decode.acc_seg: 93.9751 aux.loss_ce: 0.0744 aux.acc_seg: 92.5292 2023/06/08 03:16:12 - mmengine - INFO - Iter(train) [ 94200/240000] lr: 6.4216e-03 eta: 1 day, 5:13:02 time: 0.7049 data_time: 0.2639 memory: 17395 loss: 0.1969 decode.loss_ce: 0.1249 decode.acc_seg: 96.2600 aux.loss_ce: 0.0720 aux.acc_seg: 92.8819 2023/06/08 03:16:47 - mmengine - INFO - Iter(train) [ 94250/240000] lr: 6.4197e-03 eta: 1 day, 5:12:25 time: 0.7024 data_time: 0.2436 memory: 17394 loss: 0.2005 decode.loss_ce: 0.1273 decode.acc_seg: 95.1107 aux.loss_ce: 0.0733 aux.acc_seg: 93.1042 2023/06/08 03:17:23 - mmengine - INFO - Iter(train) [ 94300/240000] lr: 6.4177e-03 eta: 1 day, 5:11:48 time: 0.7359 data_time: 0.0744 memory: 17397 loss: 0.1958 decode.loss_ce: 0.1238 decode.acc_seg: 94.3987 aux.loss_ce: 0.0720 aux.acc_seg: 92.1386 2023/06/08 03:17:58 - mmengine - INFO - Iter(train) [ 94350/240000] lr: 6.4158e-03 eta: 1 day, 5:11:11 time: 0.7154 data_time: 0.0987 memory: 17393 loss: 0.2154 decode.loss_ce: 0.1396 decode.acc_seg: 93.7014 aux.loss_ce: 0.0757 aux.acc_seg: 90.7180 2023/06/08 03:18:34 - mmengine - INFO - Iter(train) [ 94400/240000] lr: 6.4138e-03 eta: 1 day, 5:10:35 time: 0.7136 data_time: 0.0120 memory: 17394 loss: 0.2066 decode.loss_ce: 0.1335 decode.acc_seg: 91.7288 aux.loss_ce: 0.0731 aux.acc_seg: 89.5024 2023/06/08 03:19:09 - mmengine - INFO - Iter(train) [ 94450/240000] lr: 6.4119e-03 eta: 1 day, 5:09:58 time: 0.7181 data_time: 0.0120 memory: 17395 loss: 0.2126 decode.loss_ce: 0.1371 decode.acc_seg: 94.6641 aux.loss_ce: 0.0755 aux.acc_seg: 91.9310 2023/06/08 03:19:45 - mmengine - INFO - Iter(train) [ 94500/240000] lr: 6.4099e-03 eta: 1 day, 5:09:21 time: 0.7204 data_time: 0.0123 memory: 17395 loss: 0.2091 decode.loss_ce: 0.1321 decode.acc_seg: 93.6151 aux.loss_ce: 0.0771 aux.acc_seg: 90.2780 2023/06/08 03:20:20 - mmengine - INFO - Iter(train) [ 94550/240000] lr: 6.4080e-03 eta: 1 day, 5:08:44 time: 0.7047 data_time: 0.0123 memory: 17397 loss: 0.1975 decode.loss_ce: 0.1260 decode.acc_seg: 95.4258 aux.loss_ce: 0.0716 aux.acc_seg: 93.7607 2023/06/08 03:20:56 - mmengine - INFO - Iter(train) [ 94600/240000] lr: 6.4060e-03 eta: 1 day, 5:08:06 time: 0.6986 data_time: 0.0124 memory: 17393 loss: 0.2256 decode.loss_ce: 0.1451 decode.acc_seg: 92.1957 aux.loss_ce: 0.0805 aux.acc_seg: 89.9591 2023/06/08 03:21:31 - mmengine - INFO - Iter(train) [ 94650/240000] lr: 6.4041e-03 eta: 1 day, 5:07:29 time: 0.7115 data_time: 0.1721 memory: 17393 loss: 0.2135 decode.loss_ce: 0.1378 decode.acc_seg: 94.0407 aux.loss_ce: 0.0756 aux.acc_seg: 92.2904 2023/06/08 03:22:07 - mmengine - INFO - Iter(train) [ 94700/240000] lr: 6.4021e-03 eta: 1 day, 5:06:53 time: 0.7028 data_time: 0.0366 memory: 17394 loss: 0.2147 decode.loss_ce: 0.1373 decode.acc_seg: 95.2671 aux.loss_ce: 0.0774 aux.acc_seg: 93.8581 2023/06/08 03:22:42 - mmengine - INFO - Iter(train) [ 94750/240000] lr: 6.4002e-03 eta: 1 day, 5:06:15 time: 0.7119 data_time: 0.1717 memory: 17396 loss: 0.1954 decode.loss_ce: 0.1247 decode.acc_seg: 94.6815 aux.loss_ce: 0.0707 aux.acc_seg: 92.8968 2023/06/08 03:23:18 - mmengine - INFO - Iter(train) [ 94800/240000] lr: 6.3982e-03 eta: 1 day, 5:05:39 time: 0.6978 data_time: 0.0676 memory: 17398 loss: 0.2057 decode.loss_ce: 0.1320 decode.acc_seg: 94.8232 aux.loss_ce: 0.0738 aux.acc_seg: 93.0085 2023/06/08 03:23:53 - mmengine - INFO - Iter(train) [ 94850/240000] lr: 6.3963e-03 eta: 1 day, 5:05:01 time: 0.7113 data_time: 0.3874 memory: 17395 loss: 0.2009 decode.loss_ce: 0.1265 decode.acc_seg: 94.8658 aux.loss_ce: 0.0744 aux.acc_seg: 90.6894 2023/06/08 03:24:28 - mmengine - INFO - Iter(train) [ 94900/240000] lr: 6.3943e-03 eta: 1 day, 5:04:24 time: 0.7120 data_time: 0.3434 memory: 17393 loss: 0.2076 decode.loss_ce: 0.1341 decode.acc_seg: 93.7098 aux.loss_ce: 0.0736 aux.acc_seg: 91.4861 2023/06/08 03:25:04 - mmengine - INFO - Iter(train) [ 94950/240000] lr: 6.3923e-03 eta: 1 day, 5:03:47 time: 0.7131 data_time: 0.3903 memory: 17393 loss: 0.1885 decode.loss_ce: 0.1218 decode.acc_seg: 93.9543 aux.loss_ce: 0.0667 aux.acc_seg: 91.7412 2023/06/08 03:25:39 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 03:25:39 - mmengine - INFO - Iter(train) [ 95000/240000] lr: 6.3904e-03 eta: 1 day, 5:03:10 time: 0.6911 data_time: 0.3681 memory: 17396 loss: 0.2021 decode.loss_ce: 0.1303 decode.acc_seg: 94.2077 aux.loss_ce: 0.0718 aux.acc_seg: 91.1604 2023/06/08 03:26:15 - mmengine - INFO - Iter(train) [ 95050/240000] lr: 6.3884e-03 eta: 1 day, 5:02:33 time: 0.7191 data_time: 0.3964 memory: 17394 loss: 0.1988 decode.loss_ce: 0.1269 decode.acc_seg: 95.1385 aux.loss_ce: 0.0719 aux.acc_seg: 92.8440 2023/06/08 03:26:50 - mmengine - INFO - Iter(train) [ 95100/240000] lr: 6.3865e-03 eta: 1 day, 5:01:57 time: 0.7101 data_time: 0.3872 memory: 17394 loss: 0.1928 decode.loss_ce: 0.1237 decode.acc_seg: 95.1535 aux.loss_ce: 0.0691 aux.acc_seg: 92.3642 2023/06/08 03:27:26 - mmengine - INFO - Iter(train) [ 95150/240000] lr: 6.3845e-03 eta: 1 day, 5:01:20 time: 0.7212 data_time: 0.2672 memory: 17396 loss: 0.1958 decode.loss_ce: 0.1271 decode.acc_seg: 95.6968 aux.loss_ce: 0.0687 aux.acc_seg: 94.6028 2023/06/08 03:28:01 - mmengine - INFO - Iter(train) [ 95200/240000] lr: 6.3826e-03 eta: 1 day, 5:00:43 time: 0.7200 data_time: 0.0637 memory: 17393 loss: 0.2146 decode.loss_ce: 0.1393 decode.acc_seg: 95.1152 aux.loss_ce: 0.0753 aux.acc_seg: 92.5771 2023/06/08 03:28:37 - mmengine - INFO - Iter(train) [ 95250/240000] lr: 6.3806e-03 eta: 1 day, 5:00:06 time: 0.7066 data_time: 0.0325 memory: 17393 loss: 0.2298 decode.loss_ce: 0.1493 decode.acc_seg: 93.6841 aux.loss_ce: 0.0804 aux.acc_seg: 91.6929 2023/06/08 03:29:12 - mmengine - INFO - Iter(train) [ 95300/240000] lr: 6.3787e-03 eta: 1 day, 4:59:29 time: 0.7112 data_time: 0.0421 memory: 17393 loss: 0.2408 decode.loss_ce: 0.1545 decode.acc_seg: 93.4647 aux.loss_ce: 0.0863 aux.acc_seg: 90.5394 2023/06/08 03:29:48 - mmengine - INFO - Iter(train) [ 95350/240000] lr: 6.3767e-03 eta: 1 day, 4:58:52 time: 0.6989 data_time: 0.0121 memory: 17395 loss: 0.2070 decode.loss_ce: 0.1319 decode.acc_seg: 93.5328 aux.loss_ce: 0.0751 aux.acc_seg: 91.9116 2023/06/08 03:30:24 - mmengine - INFO - Iter(train) [ 95400/240000] lr: 6.3748e-03 eta: 1 day, 4:58:16 time: 0.7114 data_time: 0.0122 memory: 17394 loss: 0.2147 decode.loss_ce: 0.1370 decode.acc_seg: 94.6726 aux.loss_ce: 0.0777 aux.acc_seg: 91.3905 2023/06/08 03:30:59 - mmengine - INFO - Iter(train) [ 95450/240000] lr: 6.3728e-03 eta: 1 day, 4:57:39 time: 0.7207 data_time: 0.0122 memory: 17395 loss: 0.2077 decode.loss_ce: 0.1335 decode.acc_seg: 94.4476 aux.loss_ce: 0.0742 aux.acc_seg: 91.6435 2023/06/08 03:31:35 - mmengine - INFO - Iter(train) [ 95500/240000] lr: 6.3709e-03 eta: 1 day, 4:57:02 time: 0.6991 data_time: 0.0122 memory: 17396 loss: 0.2195 decode.loss_ce: 0.1373 decode.acc_seg: 94.9578 aux.loss_ce: 0.0822 aux.acc_seg: 92.4394 2023/06/08 03:32:10 - mmengine - INFO - Iter(train) [ 95550/240000] lr: 6.3689e-03 eta: 1 day, 4:56:25 time: 0.7107 data_time: 0.0122 memory: 17392 loss: 0.2175 decode.loss_ce: 0.1400 decode.acc_seg: 94.5237 aux.loss_ce: 0.0775 aux.acc_seg: 91.3324 2023/06/08 03:32:46 - mmengine - INFO - Iter(train) [ 95600/240000] lr: 6.3670e-03 eta: 1 day, 4:55:48 time: 0.7028 data_time: 0.0124 memory: 17392 loss: 0.2163 decode.loss_ce: 0.1377 decode.acc_seg: 93.5662 aux.loss_ce: 0.0786 aux.acc_seg: 89.5003 2023/06/08 03:33:22 - mmengine - INFO - Iter(train) [ 95650/240000] lr: 6.3650e-03 eta: 1 day, 4:55:12 time: 0.7107 data_time: 0.0122 memory: 17392 loss: 0.1965 decode.loss_ce: 0.1262 decode.acc_seg: 95.0902 aux.loss_ce: 0.0703 aux.acc_seg: 93.0914 2023/06/08 03:33:57 - mmengine - INFO - Iter(train) [ 95700/240000] lr: 6.3631e-03 eta: 1 day, 4:54:34 time: 0.7148 data_time: 0.0123 memory: 17394 loss: 0.1982 decode.loss_ce: 0.1262 decode.acc_seg: 95.0236 aux.loss_ce: 0.0720 aux.acc_seg: 91.9263 2023/06/08 03:34:32 - mmengine - INFO - Iter(train) [ 95750/240000] lr: 6.3611e-03 eta: 1 day, 4:53:57 time: 0.7105 data_time: 0.0124 memory: 17394 loss: 0.2017 decode.loss_ce: 0.1289 decode.acc_seg: 95.0490 aux.loss_ce: 0.0728 aux.acc_seg: 92.2059 2023/06/08 03:35:08 - mmengine - INFO - Iter(train) [ 95800/240000] lr: 6.3592e-03 eta: 1 day, 4:53:21 time: 0.7084 data_time: 0.0125 memory: 17391 loss: 0.1993 decode.loss_ce: 0.1282 decode.acc_seg: 94.2205 aux.loss_ce: 0.0711 aux.acc_seg: 92.1652 2023/06/08 03:35:44 - mmengine - INFO - Iter(train) [ 95850/240000] lr: 6.3572e-03 eta: 1 day, 4:52:44 time: 0.7021 data_time: 0.0125 memory: 17397 loss: 0.1875 decode.loss_ce: 0.1197 decode.acc_seg: 94.6248 aux.loss_ce: 0.0678 aux.acc_seg: 92.8159 2023/06/08 03:36:19 - mmengine - INFO - Iter(train) [ 95900/240000] lr: 6.3552e-03 eta: 1 day, 4:52:07 time: 0.7138 data_time: 0.0122 memory: 17393 loss: 0.2219 decode.loss_ce: 0.1425 decode.acc_seg: 93.4711 aux.loss_ce: 0.0794 aux.acc_seg: 91.3772 2023/06/08 03:36:55 - mmengine - INFO - Iter(train) [ 95950/240000] lr: 6.3533e-03 eta: 1 day, 4:51:30 time: 0.7205 data_time: 0.0124 memory: 17393 loss: 0.2158 decode.loss_ce: 0.1389 decode.acc_seg: 93.6604 aux.loss_ce: 0.0769 aux.acc_seg: 91.5994 2023/06/08 03:37:30 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 03:37:30 - mmengine - INFO - Iter(train) [ 96000/240000] lr: 6.3513e-03 eta: 1 day, 4:50:54 time: 0.7211 data_time: 0.0122 memory: 17394 loss: 0.2052 decode.loss_ce: 0.1308 decode.acc_seg: 93.9164 aux.loss_ce: 0.0744 aux.acc_seg: 92.3199 2023/06/08 03:37:30 - mmengine - INFO - Saving checkpoint at 96000 iterations 2023/06/08 03:37:32 - mmengine - INFO - Iter(val) [ 50/1297] eta: 0:00:34 time: 0.0301 data_time: 0.0222 memory: 203 2023/06/08 03:37:34 - mmengine - INFO - Iter(val) [ 100/1297] eta: 0:00:32 time: 0.0197 data_time: 0.0115 memory: 203 2023/06/08 03:37:35 - mmengine - INFO - Iter(val) [ 150/1297] eta: 0:00:31 time: 0.0300 data_time: 0.0219 memory: 203 2023/06/08 03:37:36 - mmengine - INFO - Iter(val) [ 200/1297] eta: 0:00:28 time: 0.0198 data_time: 0.0117 memory: 203 2023/06/08 03:37:37 - mmengine - INFO - Iter(val) [ 250/1297] eta: 0:00:27 time: 0.0281 data_time: 0.0199 memory: 203 2023/06/08 03:37:39 - mmengine - INFO - Iter(val) [ 300/1297] eta: 0:00:25 time: 0.0179 data_time: 0.0098 memory: 203 2023/06/08 03:37:40 - mmengine - INFO - Iter(val) [ 350/1297] eta: 0:00:24 time: 0.0269 data_time: 0.0188 memory: 203 2023/06/08 03:37:41 - mmengine - INFO - Iter(val) [ 400/1297] eta: 0:00:22 time: 0.0225 data_time: 0.0144 memory: 203 2023/06/08 03:37:42 - mmengine - INFO - Iter(val) [ 450/1297] eta: 0:00:21 time: 0.0251 data_time: 0.0169 memory: 203 2023/06/08 03:37:43 - mmengine - INFO - Iter(val) [ 500/1297] eta: 0:00:19 time: 0.0237 data_time: 0.0157 memory: 203 2023/06/08 03:37:45 - mmengine - INFO - Iter(val) [ 550/1297] eta: 0:00:18 time: 0.0286 data_time: 0.0204 memory: 203 2023/06/08 03:37:46 - mmengine - INFO - Iter(val) [ 600/1297] eta: 0:00:17 time: 0.0212 data_time: 0.0131 memory: 203 2023/06/08 03:37:47 - mmengine - INFO - Iter(val) [ 650/1297] eta: 0:00:16 time: 0.0264 data_time: 0.0182 memory: 203 2023/06/08 03:37:48 - mmengine - INFO - Iter(val) [ 700/1297] eta: 0:00:14 time: 0.0234 data_time: 0.0152 memory: 203 2023/06/08 03:37:49 - mmengine - INFO - Iter(val) [ 750/1297] eta: 0:00:13 time: 0.0293 data_time: 0.0211 memory: 203 2023/06/08 03:37:51 - mmengine - INFO - Iter(val) [ 800/1297] eta: 0:00:12 time: 0.0204 data_time: 0.0122 memory: 203 2023/06/08 03:37:52 - mmengine - INFO - Iter(val) [ 850/1297] eta: 0:00:10 time: 0.0266 data_time: 0.0184 memory: 203 2023/06/08 03:37:53 - mmengine - INFO - Iter(val) [ 900/1297] eta: 0:00:09 time: 0.0196 data_time: 0.0115 memory: 203 2023/06/08 03:37:54 - mmengine - INFO - Iter(val) [ 950/1297] eta: 0:00:08 time: 0.0263 data_time: 0.0182 memory: 203 2023/06/08 03:37:55 - mmengine - INFO - Iter(val) [1000/1297] eta: 0:00:07 time: 0.0176 data_time: 0.0094 memory: 203 2023/06/08 03:37:57 - mmengine - INFO - Iter(val) [1050/1297] eta: 0:00:06 time: 0.0278 data_time: 0.0200 memory: 203 2023/06/08 03:37:58 - mmengine - INFO - Iter(val) [1100/1297] eta: 0:00:04 time: 0.0230 data_time: 0.0149 memory: 203 2023/06/08 03:37:59 - mmengine - INFO - Iter(val) [1150/1297] eta: 0:00:03 time: 0.0295 data_time: 0.0214 memory: 203 2023/06/08 03:38:00 - mmengine - INFO - Iter(val) [1200/1297] eta: 0:00:02 time: 0.0210 data_time: 0.0131 memory: 203 2023/06/08 03:38:01 - mmengine - INFO - Iter(val) [1250/1297] eta: 0:00:01 time: 0.0277 data_time: 0.0199 memory: 203 2023/06/08 03:38:02 - mmengine - INFO - per class results: 2023/06/08 03:38:02 - mmengine - INFO - +------------+-------+-------+ | Class | IoU | Acc | +------------+-------+-------+ | background | 91.06 | 96.52 | | obstacle | 86.26 | 91.17 | | human | 55.44 | 64.51 | +------------+-------+-------+ 2023/06/08 03:38:02 - mmengine - INFO - Iter(val) [1297/1297] aAcc: 94.0100 mIoU: 77.5900 mAcc: 84.0700 data_time: 0.0158 time: 0.0238 2023/06/08 03:38:37 - mmengine - INFO - Iter(train) [ 96050/240000] lr: 6.3494e-03 eta: 1 day, 4:50:16 time: 0.7081 data_time: 0.1302 memory: 17395 loss: 0.2194 decode.loss_ce: 0.1417 decode.acc_seg: 92.2648 aux.loss_ce: 0.0777 aux.acc_seg: 89.4592 2023/06/08 03:39:13 - mmengine - INFO - Iter(train) [ 96100/240000] lr: 6.3474e-03 eta: 1 day, 4:49:40 time: 0.7133 data_time: 0.1248 memory: 17398 loss: 0.2218 decode.loss_ce: 0.1431 decode.acc_seg: 93.4497 aux.loss_ce: 0.0787 aux.acc_seg: 89.6838 2023/06/08 03:39:48 - mmengine - INFO - Iter(train) [ 96150/240000] lr: 6.3455e-03 eta: 1 day, 4:49:03 time: 0.7153 data_time: 0.0122 memory: 17395 loss: 0.2131 decode.loss_ce: 0.1363 decode.acc_seg: 94.4685 aux.loss_ce: 0.0767 aux.acc_seg: 91.3441 2023/06/08 03:40:24 - mmengine - INFO - Iter(train) [ 96200/240000] lr: 6.3435e-03 eta: 1 day, 4:48:26 time: 0.7231 data_time: 0.0123 memory: 17392 loss: 0.2224 decode.loss_ce: 0.1434 decode.acc_seg: 91.3718 aux.loss_ce: 0.0791 aux.acc_seg: 88.3340 2023/06/08 03:40:59 - mmengine - INFO - Iter(train) [ 96250/240000] lr: 6.3416e-03 eta: 1 day, 4:47:49 time: 0.7191 data_time: 0.0125 memory: 17394 loss: 0.2199 decode.loss_ce: 0.1412 decode.acc_seg: 90.8211 aux.loss_ce: 0.0787 aux.acc_seg: 88.7817 2023/06/08 03:41:35 - mmengine - INFO - Iter(train) [ 96300/240000] lr: 6.3396e-03 eta: 1 day, 4:47:13 time: 0.7205 data_time: 0.0120 memory: 17395 loss: 0.2215 decode.loss_ce: 0.1422 decode.acc_seg: 94.1614 aux.loss_ce: 0.0793 aux.acc_seg: 92.1300 2023/06/08 03:42:10 - mmengine - INFO - Iter(train) [ 96350/240000] lr: 6.3377e-03 eta: 1 day, 4:46:36 time: 0.6936 data_time: 0.0124 memory: 17391 loss: 0.2078 decode.loss_ce: 0.1315 decode.acc_seg: 93.9777 aux.loss_ce: 0.0763 aux.acc_seg: 91.6171 2023/06/08 03:42:46 - mmengine - INFO - Iter(train) [ 96400/240000] lr: 6.3357e-03 eta: 1 day, 4:45:59 time: 0.7197 data_time: 0.0124 memory: 17393 loss: 0.1944 decode.loss_ce: 0.1249 decode.acc_seg: 94.1442 aux.loss_ce: 0.0696 aux.acc_seg: 91.5693 2023/06/08 03:43:22 - mmengine - INFO - Iter(train) [ 96450/240000] lr: 6.3338e-03 eta: 1 day, 4:45:22 time: 0.7148 data_time: 0.0123 memory: 17394 loss: 0.1922 decode.loss_ce: 0.1235 decode.acc_seg: 95.4140 aux.loss_ce: 0.0687 aux.acc_seg: 93.1252 2023/06/08 03:43:57 - mmengine - INFO - Iter(train) [ 96500/240000] lr: 6.3318e-03 eta: 1 day, 4:44:45 time: 0.7129 data_time: 0.0124 memory: 17393 loss: 0.2123 decode.loss_ce: 0.1370 decode.acc_seg: 92.6244 aux.loss_ce: 0.0753 aux.acc_seg: 88.8608 2023/06/08 03:44:33 - mmengine - INFO - Iter(train) [ 96550/240000] lr: 6.3298e-03 eta: 1 day, 4:44:08 time: 0.7239 data_time: 0.0124 memory: 17395 loss: 0.2114 decode.loss_ce: 0.1354 decode.acc_seg: 92.8991 aux.loss_ce: 0.0760 aux.acc_seg: 90.6107 2023/06/08 03:45:09 - mmengine - INFO - Iter(train) [ 96600/240000] lr: 6.3279e-03 eta: 1 day, 4:43:32 time: 0.7194 data_time: 0.0122 memory: 17395 loss: 0.2047 decode.loss_ce: 0.1303 decode.acc_seg: 94.7724 aux.loss_ce: 0.0744 aux.acc_seg: 90.9136 2023/06/08 03:45:44 - mmengine - INFO - Iter(train) [ 96650/240000] lr: 6.3259e-03 eta: 1 day, 4:42:55 time: 0.7070 data_time: 0.0126 memory: 17393 loss: 0.2421 decode.loss_ce: 0.1548 decode.acc_seg: 93.2372 aux.loss_ce: 0.0874 aux.acc_seg: 89.5486 2023/06/08 03:46:20 - mmengine - INFO - Iter(train) [ 96700/240000] lr: 6.3240e-03 eta: 1 day, 4:42:18 time: 0.7014 data_time: 0.0123 memory: 17394 loss: 0.2075 decode.loss_ce: 0.1335 decode.acc_seg: 93.2818 aux.loss_ce: 0.0740 aux.acc_seg: 90.9273 2023/06/08 03:46:55 - mmengine - INFO - Iter(train) [ 96750/240000] lr: 6.3220e-03 eta: 1 day, 4:41:41 time: 0.7050 data_time: 0.0124 memory: 17395 loss: 0.2164 decode.loss_ce: 0.1405 decode.acc_seg: 93.6161 aux.loss_ce: 0.0759 aux.acc_seg: 91.4889 2023/06/08 03:47:31 - mmengine - INFO - Iter(train) [ 96800/240000] lr: 6.3201e-03 eta: 1 day, 4:41:05 time: 0.7166 data_time: 0.0124 memory: 17394 loss: 0.2039 decode.loss_ce: 0.1293 decode.acc_seg: 96.1929 aux.loss_ce: 0.0746 aux.acc_seg: 93.9332 2023/06/08 03:48:06 - mmengine - INFO - Iter(train) [ 96850/240000] lr: 6.3181e-03 eta: 1 day, 4:40:28 time: 0.7046 data_time: 0.0123 memory: 17393 loss: 0.1964 decode.loss_ce: 0.1268 decode.acc_seg: 93.5515 aux.loss_ce: 0.0696 aux.acc_seg: 91.0100 2023/06/08 03:48:42 - mmengine - INFO - Iter(train) [ 96900/240000] lr: 6.3162e-03 eta: 1 day, 4:39:51 time: 0.7031 data_time: 0.0123 memory: 17395 loss: 0.2137 decode.loss_ce: 0.1363 decode.acc_seg: 93.9763 aux.loss_ce: 0.0774 aux.acc_seg: 90.7214 2023/06/08 03:49:17 - mmengine - INFO - Iter(train) [ 96950/240000] lr: 6.3142e-03 eta: 1 day, 4:39:14 time: 0.7093 data_time: 0.0129 memory: 17393 loss: 0.2111 decode.loss_ce: 0.1359 decode.acc_seg: 94.8848 aux.loss_ce: 0.0752 aux.acc_seg: 91.5578 2023/06/08 03:49:53 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 03:49:53 - mmengine - INFO - Iter(train) [ 97000/240000] lr: 6.3123e-03 eta: 1 day, 4:38:38 time: 0.7131 data_time: 0.0119 memory: 17393 loss: 0.2191 decode.loss_ce: 0.1382 decode.acc_seg: 93.1014 aux.loss_ce: 0.0809 aux.acc_seg: 90.0854 2023/06/08 03:50:29 - mmengine - INFO - Iter(train) [ 97050/240000] lr: 6.3103e-03 eta: 1 day, 4:38:02 time: 0.7123 data_time: 0.0124 memory: 17394 loss: 0.1951 decode.loss_ce: 0.1239 decode.acc_seg: 94.9811 aux.loss_ce: 0.0712 aux.acc_seg: 93.5387 2023/06/08 03:51:05 - mmengine - INFO - Iter(train) [ 97100/240000] lr: 6.3083e-03 eta: 1 day, 4:37:25 time: 0.7140 data_time: 0.0121 memory: 17394 loss: 0.2112 decode.loss_ce: 0.1361 decode.acc_seg: 92.1485 aux.loss_ce: 0.0750 aux.acc_seg: 89.0610 2023/06/08 03:51:40 - mmengine - INFO - Iter(train) [ 97150/240000] lr: 6.3064e-03 eta: 1 day, 4:36:48 time: 0.7001 data_time: 0.0121 memory: 17394 loss: 0.1972 decode.loss_ce: 0.1269 decode.acc_seg: 94.0524 aux.loss_ce: 0.0702 aux.acc_seg: 90.9391 2023/06/08 03:52:16 - mmengine - INFO - Iter(train) [ 97200/240000] lr: 6.3044e-03 eta: 1 day, 4:36:11 time: 0.7091 data_time: 0.0121 memory: 17394 loss: 0.1989 decode.loss_ce: 0.1280 decode.acc_seg: 93.6203 aux.loss_ce: 0.0709 aux.acc_seg: 92.4975 2023/06/08 03:52:52 - mmengine - INFO - Iter(train) [ 97250/240000] lr: 6.3025e-03 eta: 1 day, 4:35:35 time: 0.7166 data_time: 0.0123 memory: 17394 loss: 0.1930 decode.loss_ce: 0.1215 decode.acc_seg: 94.5907 aux.loss_ce: 0.0715 aux.acc_seg: 92.7932 2023/06/08 03:53:27 - mmengine - INFO - Iter(train) [ 97300/240000] lr: 6.3005e-03 eta: 1 day, 4:34:57 time: 0.7008 data_time: 0.0121 memory: 17395 loss: 0.1945 decode.loss_ce: 0.1258 decode.acc_seg: 95.6129 aux.loss_ce: 0.0686 aux.acc_seg: 93.8006 2023/06/08 03:54:02 - mmengine - INFO - Iter(train) [ 97350/240000] lr: 6.2986e-03 eta: 1 day, 4:34:20 time: 0.7161 data_time: 0.0122 memory: 17394 loss: 0.2023 decode.loss_ce: 0.1302 decode.acc_seg: 94.3236 aux.loss_ce: 0.0720 aux.acc_seg: 91.4567 2023/06/08 03:54:38 - mmengine - INFO - Iter(train) [ 97400/240000] lr: 6.2966e-03 eta: 1 day, 4:33:44 time: 0.7115 data_time: 0.0122 memory: 17392 loss: 0.1967 decode.loss_ce: 0.1257 decode.acc_seg: 93.8662 aux.loss_ce: 0.0710 aux.acc_seg: 91.7726 2023/06/08 03:55:14 - mmengine - INFO - Iter(train) [ 97450/240000] lr: 6.2947e-03 eta: 1 day, 4:33:07 time: 0.7232 data_time: 0.0121 memory: 17391 loss: 0.1879 decode.loss_ce: 0.1198 decode.acc_seg: 94.7077 aux.loss_ce: 0.0681 aux.acc_seg: 91.3651 2023/06/08 03:55:49 - mmengine - INFO - Iter(train) [ 97500/240000] lr: 6.2927e-03 eta: 1 day, 4:32:31 time: 0.7037 data_time: 0.0128 memory: 17394 loss: 0.1860 decode.loss_ce: 0.1181 decode.acc_seg: 95.1850 aux.loss_ce: 0.0678 aux.acc_seg: 93.0667 2023/06/08 03:56:25 - mmengine - INFO - Iter(train) [ 97550/240000] lr: 6.2907e-03 eta: 1 day, 4:31:54 time: 0.6985 data_time: 0.0356 memory: 17395 loss: 0.2042 decode.loss_ce: 0.1326 decode.acc_seg: 94.8861 aux.loss_ce: 0.0716 aux.acc_seg: 93.1072 2023/06/08 03:57:00 - mmengine - INFO - Iter(train) [ 97600/240000] lr: 6.2888e-03 eta: 1 day, 4:31:17 time: 0.7147 data_time: 0.1697 memory: 17398 loss: 0.2042 decode.loss_ce: 0.1302 decode.acc_seg: 94.6849 aux.loss_ce: 0.0740 aux.acc_seg: 91.9033 2023/06/08 03:57:36 - mmengine - INFO - Iter(train) [ 97650/240000] lr: 6.2868e-03 eta: 1 day, 4:30:40 time: 0.7155 data_time: 0.3929 memory: 17395 loss: 0.2149 decode.loss_ce: 0.1385 decode.acc_seg: 94.5480 aux.loss_ce: 0.0764 aux.acc_seg: 91.9700 2023/06/08 03:58:11 - mmengine - INFO - Iter(train) [ 97700/240000] lr: 6.2849e-03 eta: 1 day, 4:30:03 time: 0.7032 data_time: 0.3804 memory: 17394 loss: 0.2027 decode.loss_ce: 0.1291 decode.acc_seg: 94.2182 aux.loss_ce: 0.0736 aux.acc_seg: 89.9812 2023/06/08 03:58:47 - mmengine - INFO - Iter(train) [ 97750/240000] lr: 6.2829e-03 eta: 1 day, 4:29:26 time: 0.6973 data_time: 0.3746 memory: 17394 loss: 0.2091 decode.loss_ce: 0.1344 decode.acc_seg: 92.3331 aux.loss_ce: 0.0747 aux.acc_seg: 89.1840 2023/06/08 03:59:22 - mmengine - INFO - Iter(train) [ 97800/240000] lr: 6.2810e-03 eta: 1 day, 4:28:49 time: 0.7070 data_time: 0.3850 memory: 17392 loss: 0.1934 decode.loss_ce: 0.1233 decode.acc_seg: 92.8713 aux.loss_ce: 0.0701 aux.acc_seg: 90.7476 2023/06/08 03:59:58 - mmengine - INFO - Iter(train) [ 97850/240000] lr: 6.2790e-03 eta: 1 day, 4:28:13 time: 0.7009 data_time: 0.3777 memory: 17396 loss: 0.2160 decode.loss_ce: 0.1390 decode.acc_seg: 94.3010 aux.loss_ce: 0.0770 aux.acc_seg: 92.4907 2023/06/08 04:00:34 - mmengine - INFO - Iter(train) [ 97900/240000] lr: 6.2771e-03 eta: 1 day, 4:27:36 time: 0.7121 data_time: 0.3892 memory: 17393 loss: 0.2547 decode.loss_ce: 0.1628 decode.acc_seg: 92.4966 aux.loss_ce: 0.0919 aux.acc_seg: 89.0601 2023/06/08 04:01:09 - mmengine - INFO - Iter(train) [ 97950/240000] lr: 6.2751e-03 eta: 1 day, 4:26:59 time: 0.7075 data_time: 0.3844 memory: 17394 loss: 0.2240 decode.loss_ce: 0.1416 decode.acc_seg: 92.7069 aux.loss_ce: 0.0824 aux.acc_seg: 91.0622 2023/06/08 04:01:44 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 04:01:44 - mmengine - INFO - Iter(train) [ 98000/240000] lr: 6.2731e-03 eta: 1 day, 4:26:22 time: 0.7065 data_time: 0.3834 memory: 17393 loss: 0.2083 decode.loss_ce: 0.1343 decode.acc_seg: 95.1249 aux.loss_ce: 0.0740 aux.acc_seg: 91.7002 2023/06/08 04:02:20 - mmengine - INFO - Iter(train) [ 98050/240000] lr: 6.2712e-03 eta: 1 day, 4:25:45 time: 0.7113 data_time: 0.3886 memory: 17394 loss: 0.1987 decode.loss_ce: 0.1275 decode.acc_seg: 94.9664 aux.loss_ce: 0.0713 aux.acc_seg: 93.1782 2023/06/08 04:02:55 - mmengine - INFO - Iter(train) [ 98100/240000] lr: 6.2692e-03 eta: 1 day, 4:25:08 time: 0.7020 data_time: 0.3787 memory: 17391 loss: 0.1840 decode.loss_ce: 0.1176 decode.acc_seg: 95.0879 aux.loss_ce: 0.0664 aux.acc_seg: 92.9193 2023/06/08 04:03:31 - mmengine - INFO - Iter(train) [ 98150/240000] lr: 6.2673e-03 eta: 1 day, 4:24:32 time: 0.7071 data_time: 0.3839 memory: 17396 loss: 0.2009 decode.loss_ce: 0.1283 decode.acc_seg: 94.0797 aux.loss_ce: 0.0726 aux.acc_seg: 91.6266 2023/06/08 04:04:06 - mmengine - INFO - Iter(train) [ 98200/240000] lr: 6.2653e-03 eta: 1 day, 4:23:54 time: 0.7065 data_time: 0.3832 memory: 17393 loss: 0.2157 decode.loss_ce: 0.1377 decode.acc_seg: 93.7859 aux.loss_ce: 0.0779 aux.acc_seg: 91.8230 2023/06/08 04:04:42 - mmengine - INFO - Iter(train) [ 98250/240000] lr: 6.2634e-03 eta: 1 day, 4:23:18 time: 0.7181 data_time: 0.3955 memory: 17394 loss: 0.2159 decode.loss_ce: 0.1395 decode.acc_seg: 95.2066 aux.loss_ce: 0.0764 aux.acc_seg: 92.8724 2023/06/08 04:05:17 - mmengine - INFO - Iter(train) [ 98300/240000] lr: 6.2614e-03 eta: 1 day, 4:22:41 time: 0.7012 data_time: 0.3783 memory: 17396 loss: 0.1921 decode.loss_ce: 0.1197 decode.acc_seg: 95.5908 aux.loss_ce: 0.0723 aux.acc_seg: 93.9602 2023/06/08 04:05:53 - mmengine - INFO - Iter(train) [ 98350/240000] lr: 6.2594e-03 eta: 1 day, 4:22:04 time: 0.7116 data_time: 0.3884 memory: 17396 loss: 0.2054 decode.loss_ce: 0.1321 decode.acc_seg: 94.6688 aux.loss_ce: 0.0733 aux.acc_seg: 92.4691 2023/06/08 04:06:28 - mmengine - INFO - Iter(train) [ 98400/240000] lr: 6.2575e-03 eta: 1 day, 4:21:27 time: 0.7060 data_time: 0.3620 memory: 17393 loss: 0.2005 decode.loss_ce: 0.1270 decode.acc_seg: 94.2787 aux.loss_ce: 0.0735 aux.acc_seg: 90.1496 2023/06/08 04:07:04 - mmengine - INFO - Iter(train) [ 98450/240000] lr: 6.2555e-03 eta: 1 day, 4:20:50 time: 0.7064 data_time: 0.3510 memory: 17394 loss: 0.2050 decode.loss_ce: 0.1317 decode.acc_seg: 93.7460 aux.loss_ce: 0.0733 aux.acc_seg: 90.5049 2023/06/08 04:07:39 - mmengine - INFO - Iter(train) [ 98500/240000] lr: 6.2536e-03 eta: 1 day, 4:20:13 time: 0.7075 data_time: 0.3846 memory: 17393 loss: 0.2260 decode.loss_ce: 0.1443 decode.acc_seg: 93.3738 aux.loss_ce: 0.0817 aux.acc_seg: 89.6030 2023/06/08 04:08:15 - mmengine - INFO - Iter(train) [ 98550/240000] lr: 6.2516e-03 eta: 1 day, 4:19:36 time: 0.7225 data_time: 0.3992 memory: 17393 loss: 0.2107 decode.loss_ce: 0.1351 decode.acc_seg: 94.1316 aux.loss_ce: 0.0755 aux.acc_seg: 92.0411 2023/06/08 04:08:50 - mmengine - INFO - Iter(train) [ 98600/240000] lr: 6.2497e-03 eta: 1 day, 4:18:59 time: 0.7078 data_time: 0.3850 memory: 17397 loss: 0.2153 decode.loss_ce: 0.1402 decode.acc_seg: 93.1552 aux.loss_ce: 0.0751 aux.acc_seg: 91.7477 2023/06/08 04:09:26 - mmengine - INFO - Iter(train) [ 98650/240000] lr: 6.2477e-03 eta: 1 day, 4:18:23 time: 0.7246 data_time: 0.4019 memory: 17395 loss: 0.2109 decode.loss_ce: 0.1348 decode.acc_seg: 94.4226 aux.loss_ce: 0.0761 aux.acc_seg: 91.7705 2023/06/08 04:10:01 - mmengine - INFO - Iter(train) [ 98700/240000] lr: 6.2457e-03 eta: 1 day, 4:17:46 time: 0.7053 data_time: 0.2963 memory: 17394 loss: 0.1974 decode.loss_ce: 0.1281 decode.acc_seg: 94.0362 aux.loss_ce: 0.0694 aux.acc_seg: 92.0511 2023/06/08 04:10:37 - mmengine - INFO - Iter(train) [ 98750/240000] lr: 6.2438e-03 eta: 1 day, 4:17:09 time: 0.7231 data_time: 0.0136 memory: 17394 loss: 0.2073 decode.loss_ce: 0.1321 decode.acc_seg: 93.9927 aux.loss_ce: 0.0752 aux.acc_seg: 91.8236 2023/06/08 04:11:12 - mmengine - INFO - Iter(train) [ 98800/240000] lr: 6.2418e-03 eta: 1 day, 4:16:33 time: 0.7116 data_time: 0.0120 memory: 17395 loss: 0.2326 decode.loss_ce: 0.1506 decode.acc_seg: 93.5156 aux.loss_ce: 0.0819 aux.acc_seg: 91.3975 2023/06/08 04:11:48 - mmengine - INFO - Iter(train) [ 98850/240000] lr: 6.2399e-03 eta: 1 day, 4:15:56 time: 0.7098 data_time: 0.0123 memory: 17392 loss: 0.1931 decode.loss_ce: 0.1211 decode.acc_seg: 93.8856 aux.loss_ce: 0.0720 aux.acc_seg: 91.1159 2023/06/08 04:12:24 - mmengine - INFO - Iter(train) [ 98900/240000] lr: 6.2379e-03 eta: 1 day, 4:15:20 time: 0.7154 data_time: 0.0123 memory: 17395 loss: 0.2090 decode.loss_ce: 0.1326 decode.acc_seg: 94.4000 aux.loss_ce: 0.0765 aux.acc_seg: 91.9119 2023/06/08 04:12:59 - mmengine - INFO - Iter(train) [ 98950/240000] lr: 6.2360e-03 eta: 1 day, 4:14:43 time: 0.7146 data_time: 0.0122 memory: 17393 loss: 0.1998 decode.loss_ce: 0.1290 decode.acc_seg: 93.1835 aux.loss_ce: 0.0708 aux.acc_seg: 88.3621 2023/06/08 04:13:35 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 04:13:35 - mmengine - INFO - Iter(train) [ 99000/240000] lr: 6.2340e-03 eta: 1 day, 4:14:06 time: 0.7146 data_time: 0.0125 memory: 17395 loss: 0.1948 decode.loss_ce: 0.1244 decode.acc_seg: 95.1983 aux.loss_ce: 0.0704 aux.acc_seg: 92.7740 2023/06/08 04:14:11 - mmengine - INFO - Iter(train) [ 99050/240000] lr: 6.2320e-03 eta: 1 day, 4:13:30 time: 0.7154 data_time: 0.0122 memory: 17393 loss: 0.2157 decode.loss_ce: 0.1371 decode.acc_seg: 93.4701 aux.loss_ce: 0.0786 aux.acc_seg: 90.7449 2023/06/08 04:14:46 - mmengine - INFO - Iter(train) [ 99100/240000] lr: 6.2301e-03 eta: 1 day, 4:12:53 time: 0.7133 data_time: 0.0125 memory: 17395 loss: 0.2051 decode.loss_ce: 0.1308 decode.acc_seg: 94.4838 aux.loss_ce: 0.0743 aux.acc_seg: 91.8464 2023/06/08 04:15:22 - mmengine - INFO - Iter(train) [ 99150/240000] lr: 6.2281e-03 eta: 1 day, 4:12:16 time: 0.7122 data_time: 0.0124 memory: 17393 loss: 0.1988 decode.loss_ce: 0.1271 decode.acc_seg: 94.7057 aux.loss_ce: 0.0717 aux.acc_seg: 92.7257 2023/06/08 04:15:57 - mmengine - INFO - Iter(train) [ 99200/240000] lr: 6.2262e-03 eta: 1 day, 4:11:39 time: 0.7031 data_time: 0.0123 memory: 17396 loss: 0.2121 decode.loss_ce: 0.1355 decode.acc_seg: 93.8437 aux.loss_ce: 0.0766 aux.acc_seg: 90.9763 2023/06/08 04:16:33 - mmengine - INFO - Iter(train) [ 99250/240000] lr: 6.2242e-03 eta: 1 day, 4:11:03 time: 0.7146 data_time: 0.0123 memory: 17397 loss: 0.2131 decode.loss_ce: 0.1372 decode.acc_seg: 92.6301 aux.loss_ce: 0.0760 aux.acc_seg: 90.1810 2023/06/08 04:17:08 - mmengine - INFO - Iter(train) [ 99300/240000] lr: 6.2223e-03 eta: 1 day, 4:10:26 time: 0.6991 data_time: 0.0122 memory: 17394 loss: 0.1939 decode.loss_ce: 0.1247 decode.acc_seg: 92.6639 aux.loss_ce: 0.0692 aux.acc_seg: 90.3607 2023/06/08 04:17:44 - mmengine - INFO - Iter(train) [ 99350/240000] lr: 6.2203e-03 eta: 1 day, 4:09:49 time: 0.7103 data_time: 0.0123 memory: 17395 loss: 0.1865 decode.loss_ce: 0.1187 decode.acc_seg: 94.7761 aux.loss_ce: 0.0678 aux.acc_seg: 92.3948 2023/06/08 04:18:20 - mmengine - INFO - Iter(train) [ 99400/240000] lr: 6.2183e-03 eta: 1 day, 4:09:13 time: 0.7086 data_time: 0.0131 memory: 17396 loss: 0.2017 decode.loss_ce: 0.1286 decode.acc_seg: 92.8113 aux.loss_ce: 0.0732 aux.acc_seg: 89.5906 2023/06/08 04:18:55 - mmengine - INFO - Iter(train) [ 99450/240000] lr: 6.2164e-03 eta: 1 day, 4:08:35 time: 0.7145 data_time: 0.0124 memory: 17398 loss: 0.2189 decode.loss_ce: 0.1394 decode.acc_seg: 91.2343 aux.loss_ce: 0.0795 aux.acc_seg: 87.9612 2023/06/08 04:19:31 - mmengine - INFO - Iter(train) [ 99500/240000] lr: 6.2144e-03 eta: 1 day, 4:07:59 time: 0.7091 data_time: 0.0125 memory: 17394 loss: 0.1988 decode.loss_ce: 0.1263 decode.acc_seg: 93.9224 aux.loss_ce: 0.0726 aux.acc_seg: 89.6645 2023/06/08 04:20:06 - mmengine - INFO - Iter(train) [ 99550/240000] lr: 6.2125e-03 eta: 1 day, 4:07:22 time: 0.7089 data_time: 0.0127 memory: 17394 loss: 0.1909 decode.loss_ce: 0.1210 decode.acc_seg: 93.8672 aux.loss_ce: 0.0699 aux.acc_seg: 91.2779 2023/06/08 04:20:41 - mmengine - INFO - Iter(train) [ 99600/240000] lr: 6.2105e-03 eta: 1 day, 4:06:45 time: 0.7125 data_time: 0.0122 memory: 17395 loss: 0.2052 decode.loss_ce: 0.1311 decode.acc_seg: 94.6974 aux.loss_ce: 0.0741 aux.acc_seg: 92.1692 2023/06/08 04:21:17 - mmengine - INFO - Iter(train) [ 99650/240000] lr: 6.2085e-03 eta: 1 day, 4:06:08 time: 0.7026 data_time: 0.0125 memory: 17393 loss: 0.2099 decode.loss_ce: 0.1337 decode.acc_seg: 93.2835 aux.loss_ce: 0.0762 aux.acc_seg: 91.7174 2023/06/08 04:21:53 - mmengine - INFO - Iter(train) [ 99700/240000] lr: 6.2066e-03 eta: 1 day, 4:05:32 time: 0.7036 data_time: 0.0123 memory: 17393 loss: 0.2123 decode.loss_ce: 0.1364 decode.acc_seg: 91.5581 aux.loss_ce: 0.0758 aux.acc_seg: 91.6119 2023/06/08 04:22:28 - mmengine - INFO - Iter(train) [ 99750/240000] lr: 6.2046e-03 eta: 1 day, 4:04:55 time: 0.7227 data_time: 0.0124 memory: 17393 loss: 0.2114 decode.loss_ce: 0.1343 decode.acc_seg: 94.1427 aux.loss_ce: 0.0771 aux.acc_seg: 91.2753 2023/06/08 04:23:04 - mmengine - INFO - Iter(train) [ 99800/240000] lr: 6.2027e-03 eta: 1 day, 4:04:18 time: 0.7171 data_time: 0.0122 memory: 17393 loss: 0.2069 decode.loss_ce: 0.1322 decode.acc_seg: 94.5743 aux.loss_ce: 0.0747 aux.acc_seg: 92.0562 2023/06/08 04:23:39 - mmengine - INFO - Iter(train) [ 99850/240000] lr: 6.2007e-03 eta: 1 day, 4:03:42 time: 0.7070 data_time: 0.0123 memory: 17391 loss: 0.1864 decode.loss_ce: 0.1177 decode.acc_seg: 93.8673 aux.loss_ce: 0.0687 aux.acc_seg: 90.9896 2023/06/08 04:24:15 - mmengine - INFO - Iter(train) [ 99900/240000] lr: 6.1988e-03 eta: 1 day, 4:03:05 time: 0.7086 data_time: 0.0120 memory: 17393 loss: 0.2122 decode.loss_ce: 0.1360 decode.acc_seg: 93.5242 aux.loss_ce: 0.0761 aux.acc_seg: 91.7098 2023/06/08 04:24:50 - mmengine - INFO - Iter(train) [ 99950/240000] lr: 6.1968e-03 eta: 1 day, 4:02:28 time: 0.7120 data_time: 0.0121 memory: 17393 loss: 0.1868 decode.loss_ce: 0.1200 decode.acc_seg: 94.1250 aux.loss_ce: 0.0668 aux.acc_seg: 92.2629 2023/06/08 04:25:26 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 04:25:26 - mmengine - INFO - Iter(train) [100000/240000] lr: 6.1948e-03 eta: 1 day, 4:01:51 time: 0.7029 data_time: 0.0121 memory: 17394 loss: 0.2069 decode.loss_ce: 0.1326 decode.acc_seg: 93.6046 aux.loss_ce: 0.0742 aux.acc_seg: 89.9186 2023/06/08 04:26:02 - mmengine - INFO - Iter(train) [100050/240000] lr: 6.1929e-03 eta: 1 day, 4:01:14 time: 0.7225 data_time: 0.0123 memory: 17394 loss: 0.2048 decode.loss_ce: 0.1314 decode.acc_seg: 93.8898 aux.loss_ce: 0.0734 aux.acc_seg: 88.2432 2023/06/08 04:26:37 - mmengine - INFO - Iter(train) [100100/240000] lr: 6.1909e-03 eta: 1 day, 4:00:38 time: 0.7111 data_time: 0.0123 memory: 17394 loss: 0.2007 decode.loss_ce: 0.1287 decode.acc_seg: 94.4116 aux.loss_ce: 0.0720 aux.acc_seg: 91.3031 2023/06/08 04:27:13 - mmengine - INFO - Iter(train) [100150/240000] lr: 6.1890e-03 eta: 1 day, 4:00:01 time: 0.7038 data_time: 0.0123 memory: 17395 loss: 0.1990 decode.loss_ce: 0.1280 decode.acc_seg: 94.7084 aux.loss_ce: 0.0710 aux.acc_seg: 91.5134 2023/06/08 04:27:48 - mmengine - INFO - Iter(train) [100200/240000] lr: 6.1870e-03 eta: 1 day, 3:59:24 time: 0.7210 data_time: 0.0124 memory: 17391 loss: 0.2418 decode.loss_ce: 0.1504 decode.acc_seg: 94.3894 aux.loss_ce: 0.0914 aux.acc_seg: 90.4171 2023/06/08 04:28:24 - mmengine - INFO - Iter(train) [100250/240000] lr: 6.1850e-03 eta: 1 day, 3:58:48 time: 0.7217 data_time: 0.0122 memory: 17393 loss: 0.2264 decode.loss_ce: 0.1435 decode.acc_seg: 93.8576 aux.loss_ce: 0.0829 aux.acc_seg: 90.7303 2023/06/08 04:28:59 - mmengine - INFO - Iter(train) [100300/240000] lr: 6.1831e-03 eta: 1 day, 3:58:11 time: 0.7144 data_time: 0.0124 memory: 17395 loss: 0.2104 decode.loss_ce: 0.1345 decode.acc_seg: 94.9795 aux.loss_ce: 0.0759 aux.acc_seg: 92.3185 2023/06/08 04:29:35 - mmengine - INFO - Iter(train) [100350/240000] lr: 6.1811e-03 eta: 1 day, 3:57:35 time: 0.7061 data_time: 0.0121 memory: 17396 loss: 0.2254 decode.loss_ce: 0.1428 decode.acc_seg: 92.9592 aux.loss_ce: 0.0826 aux.acc_seg: 90.7713 2023/06/08 04:30:11 - mmengine - INFO - Iter(train) [100400/240000] lr: 6.1792e-03 eta: 1 day, 3:56:58 time: 0.7069 data_time: 0.0125 memory: 17397 loss: 0.2230 decode.loss_ce: 0.1449 decode.acc_seg: 94.0148 aux.loss_ce: 0.0780 aux.acc_seg: 93.2200 2023/06/08 04:30:46 - mmengine - INFO - Iter(train) [100450/240000] lr: 6.1772e-03 eta: 1 day, 3:56:21 time: 0.7041 data_time: 0.0229 memory: 17393 loss: 0.1984 decode.loss_ce: 0.1267 decode.acc_seg: 95.0193 aux.loss_ce: 0.0717 aux.acc_seg: 92.4775 2023/06/08 04:31:21 - mmengine - INFO - Iter(train) [100500/240000] lr: 6.1752e-03 eta: 1 day, 3:55:44 time: 0.7001 data_time: 0.0121 memory: 17395 loss: 0.2166 decode.loss_ce: 0.1352 decode.acc_seg: 93.2270 aux.loss_ce: 0.0814 aux.acc_seg: 87.8982 2023/06/08 04:31:57 - mmengine - INFO - Iter(train) [100550/240000] lr: 6.1733e-03 eta: 1 day, 3:55:07 time: 0.6900 data_time: 0.2206 memory: 17396 loss: 0.2201 decode.loss_ce: 0.1413 decode.acc_seg: 93.3972 aux.loss_ce: 0.0788 aux.acc_seg: 91.1687 2023/06/08 04:32:32 - mmengine - INFO - Iter(train) [100600/240000] lr: 6.1713e-03 eta: 1 day, 3:54:30 time: 0.7058 data_time: 0.3128 memory: 17395 loss: 0.2126 decode.loss_ce: 0.1359 decode.acc_seg: 93.2434 aux.loss_ce: 0.0767 aux.acc_seg: 91.4668 2023/06/08 04:33:08 - mmengine - INFO - Iter(train) [100650/240000] lr: 6.1694e-03 eta: 1 day, 3:53:53 time: 0.7086 data_time: 0.3860 memory: 17394 loss: 0.2138 decode.loss_ce: 0.1391 decode.acc_seg: 93.0799 aux.loss_ce: 0.0748 aux.acc_seg: 89.8373 2023/06/08 04:33:43 - mmengine - INFO - Iter(train) [100700/240000] lr: 6.1674e-03 eta: 1 day, 3:53:17 time: 0.7137 data_time: 0.3907 memory: 17393 loss: 0.2033 decode.loss_ce: 0.1299 decode.acc_seg: 93.7736 aux.loss_ce: 0.0734 aux.acc_seg: 91.6643 2023/06/08 04:34:19 - mmengine - INFO - Iter(train) [100750/240000] lr: 6.1654e-03 eta: 1 day, 3:52:40 time: 0.7030 data_time: 0.3804 memory: 17391 loss: 0.2139 decode.loss_ce: 0.1361 decode.acc_seg: 94.4955 aux.loss_ce: 0.0778 aux.acc_seg: 91.6904 2023/06/08 04:34:55 - mmengine - INFO - Iter(train) [100800/240000] lr: 6.1635e-03 eta: 1 day, 3:52:03 time: 0.7333 data_time: 0.4107 memory: 17392 loss: 0.2148 decode.loss_ce: 0.1396 decode.acc_seg: 94.9991 aux.loss_ce: 0.0753 aux.acc_seg: 93.2420 2023/06/08 04:35:30 - mmengine - INFO - Iter(train) [100850/240000] lr: 6.1615e-03 eta: 1 day, 3:51:27 time: 0.7163 data_time: 0.3935 memory: 17395 loss: 0.2224 decode.loss_ce: 0.1418 decode.acc_seg: 91.0834 aux.loss_ce: 0.0806 aux.acc_seg: 88.5545 2023/06/08 04:36:05 - mmengine - INFO - Iter(train) [100900/240000] lr: 6.1596e-03 eta: 1 day, 3:50:49 time: 0.6995 data_time: 0.3759 memory: 17392 loss: 0.1979 decode.loss_ce: 0.1262 decode.acc_seg: 94.5977 aux.loss_ce: 0.0717 aux.acc_seg: 91.7465 2023/06/08 04:36:41 - mmengine - INFO - Iter(train) [100950/240000] lr: 6.1576e-03 eta: 1 day, 3:50:13 time: 0.7116 data_time: 0.3887 memory: 17394 loss: 0.1976 decode.loss_ce: 0.1267 decode.acc_seg: 95.1638 aux.loss_ce: 0.0709 aux.acc_seg: 92.6360 2023/06/08 04:37:16 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 04:37:16 - mmengine - INFO - Iter(train) [101000/240000] lr: 6.1556e-03 eta: 1 day, 3:49:36 time: 0.7050 data_time: 0.3821 memory: 17394 loss: 0.2075 decode.loss_ce: 0.1322 decode.acc_seg: 94.8917 aux.loss_ce: 0.0754 aux.acc_seg: 92.7778 2023/06/08 04:37:52 - mmengine - INFO - Iter(train) [101050/240000] lr: 6.1537e-03 eta: 1 day, 3:48:59 time: 0.7119 data_time: 0.3886 memory: 17395 loss: 0.1839 decode.loss_ce: 0.1171 decode.acc_seg: 95.3617 aux.loss_ce: 0.0668 aux.acc_seg: 93.4390 2023/06/08 04:38:27 - mmengine - INFO - Iter(train) [101100/240000] lr: 6.1517e-03 eta: 1 day, 3:48:22 time: 0.7053 data_time: 0.3822 memory: 17395 loss: 0.1987 decode.loss_ce: 0.1249 decode.acc_seg: 94.7625 aux.loss_ce: 0.0739 aux.acc_seg: 90.8001 2023/06/08 04:39:03 - mmengine - INFO - Iter(train) [101150/240000] lr: 6.1498e-03 eta: 1 day, 3:47:45 time: 0.7075 data_time: 0.3842 memory: 17395 loss: 0.1925 decode.loss_ce: 0.1241 decode.acc_seg: 94.0868 aux.loss_ce: 0.0684 aux.acc_seg: 91.7885 2023/06/08 04:39:38 - mmengine - INFO - Iter(train) [101200/240000] lr: 6.1478e-03 eta: 1 day, 3:47:08 time: 0.7105 data_time: 0.3879 memory: 17395 loss: 0.1939 decode.loss_ce: 0.1238 decode.acc_seg: 95.0916 aux.loss_ce: 0.0701 aux.acc_seg: 93.2868 2023/06/08 04:40:14 - mmengine - INFO - Iter(train) [101250/240000] lr: 6.1458e-03 eta: 1 day, 3:46:32 time: 0.7087 data_time: 0.3828 memory: 17398 loss: 0.2045 decode.loss_ce: 0.1300 decode.acc_seg: 92.7675 aux.loss_ce: 0.0745 aux.acc_seg: 88.8170 2023/06/08 04:40:49 - mmengine - INFO - Iter(train) [101300/240000] lr: 6.1439e-03 eta: 1 day, 3:45:55 time: 0.7014 data_time: 0.1210 memory: 17393 loss: 0.2082 decode.loss_ce: 0.1320 decode.acc_seg: 94.6902 aux.loss_ce: 0.0762 aux.acc_seg: 92.2658 2023/06/08 04:41:24 - mmengine - INFO - Iter(train) [101350/240000] lr: 6.1419e-03 eta: 1 day, 3:45:18 time: 0.6990 data_time: 0.2585 memory: 17393 loss: 0.2117 decode.loss_ce: 0.1346 decode.acc_seg: 92.5431 aux.loss_ce: 0.0771 aux.acc_seg: 88.1756 2023/06/08 04:42:00 - mmengine - INFO - Iter(train) [101400/240000] lr: 6.1400e-03 eta: 1 day, 3:44:41 time: 0.7048 data_time: 0.3081 memory: 17392 loss: 0.2203 decode.loss_ce: 0.1412 decode.acc_seg: 93.3904 aux.loss_ce: 0.0791 aux.acc_seg: 91.3116 2023/06/08 04:42:35 - mmengine - INFO - Iter(train) [101450/240000] lr: 6.1380e-03 eta: 1 day, 3:44:04 time: 0.7087 data_time: 0.2718 memory: 17395 loss: 0.1972 decode.loss_ce: 0.1270 decode.acc_seg: 94.1883 aux.loss_ce: 0.0702 aux.acc_seg: 92.8772 2023/06/08 04:43:11 - mmengine - INFO - Iter(train) [101500/240000] lr: 6.1360e-03 eta: 1 day, 3:43:27 time: 0.7065 data_time: 0.1307 memory: 17395 loss: 0.2078 decode.loss_ce: 0.1343 decode.acc_seg: 94.5401 aux.loss_ce: 0.0735 aux.acc_seg: 92.4076 2023/06/08 04:43:46 - mmengine - INFO - Iter(train) [101550/240000] lr: 6.1341e-03 eta: 1 day, 3:42:50 time: 0.7184 data_time: 0.2997 memory: 17393 loss: 0.2006 decode.loss_ce: 0.1274 decode.acc_seg: 95.0764 aux.loss_ce: 0.0732 aux.acc_seg: 92.0790 2023/06/08 04:44:22 - mmengine - INFO - Iter(train) [101600/240000] lr: 6.1321e-03 eta: 1 day, 3:42:14 time: 0.7119 data_time: 0.3870 memory: 17394 loss: 0.1956 decode.loss_ce: 0.1254 decode.acc_seg: 95.0225 aux.loss_ce: 0.0702 aux.acc_seg: 92.0177 2023/06/08 04:44:57 - mmengine - INFO - Iter(train) [101650/240000] lr: 6.1301e-03 eta: 1 day, 3:41:37 time: 0.7209 data_time: 0.3983 memory: 17396 loss: 0.2063 decode.loss_ce: 0.1343 decode.acc_seg: 95.9824 aux.loss_ce: 0.0720 aux.acc_seg: 94.3040 2023/06/08 04:45:33 - mmengine - INFO - Iter(train) [101700/240000] lr: 6.1282e-03 eta: 1 day, 3:41:00 time: 0.7063 data_time: 0.3829 memory: 17394 loss: 0.2145 decode.loss_ce: 0.1379 decode.acc_seg: 93.7731 aux.loss_ce: 0.0766 aux.acc_seg: 90.9190 2023/06/08 04:46:08 - mmengine - INFO - Iter(train) [101750/240000] lr: 6.1262e-03 eta: 1 day, 3:40:23 time: 0.6995 data_time: 0.3767 memory: 17392 loss: 0.2084 decode.loss_ce: 0.1340 decode.acc_seg: 92.2414 aux.loss_ce: 0.0744 aux.acc_seg: 89.5841 2023/06/08 04:46:44 - mmengine - INFO - Iter(train) [101800/240000] lr: 6.1243e-03 eta: 1 day, 3:39:47 time: 0.7217 data_time: 0.3988 memory: 17395 loss: 0.2022 decode.loss_ce: 0.1281 decode.acc_seg: 94.5945 aux.loss_ce: 0.0741 aux.acc_seg: 92.0612 2023/06/08 04:47:20 - mmengine - INFO - Iter(train) [101850/240000] lr: 6.1223e-03 eta: 1 day, 3:39:10 time: 0.7130 data_time: 0.3900 memory: 17394 loss: 0.2030 decode.loss_ce: 0.1312 decode.acc_seg: 93.6750 aux.loss_ce: 0.0717 aux.acc_seg: 91.0932 2023/06/08 04:47:55 - mmengine - INFO - Iter(train) [101900/240000] lr: 6.1203e-03 eta: 1 day, 3:38:33 time: 0.7119 data_time: 0.3891 memory: 17395 loss: 0.2007 decode.loss_ce: 0.1276 decode.acc_seg: 93.9947 aux.loss_ce: 0.0731 aux.acc_seg: 91.7360 2023/06/08 04:48:30 - mmengine - INFO - Iter(train) [101950/240000] lr: 6.1184e-03 eta: 1 day, 3:37:57 time: 0.7123 data_time: 0.3892 memory: 17394 loss: 0.2078 decode.loss_ce: 0.1362 decode.acc_seg: 90.7289 aux.loss_ce: 0.0716 aux.acc_seg: 89.4393 2023/06/08 04:49:06 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 04:49:06 - mmengine - INFO - Iter(train) [102000/240000] lr: 6.1164e-03 eta: 1 day, 3:37:20 time: 0.7093 data_time: 0.3856 memory: 17394 loss: 0.2064 decode.loss_ce: 0.1317 decode.acc_seg: 94.5396 aux.loss_ce: 0.0747 aux.acc_seg: 92.7094 2023/06/08 04:49:42 - mmengine - INFO - Iter(train) [102050/240000] lr: 6.1145e-03 eta: 1 day, 3:36:44 time: 0.7113 data_time: 0.3885 memory: 17393 loss: 0.2118 decode.loss_ce: 0.1362 decode.acc_seg: 93.8785 aux.loss_ce: 0.0756 aux.acc_seg: 91.5119 2023/06/08 04:50:17 - mmengine - INFO - Iter(train) [102100/240000] lr: 6.1125e-03 eta: 1 day, 3:36:07 time: 0.7025 data_time: 0.3796 memory: 17394 loss: 0.2031 decode.loss_ce: 0.1303 decode.acc_seg: 93.9796 aux.loss_ce: 0.0728 aux.acc_seg: 91.2728 2023/06/08 04:50:53 - mmengine - INFO - Iter(train) [102150/240000] lr: 6.1105e-03 eta: 1 day, 3:35:30 time: 0.7158 data_time: 0.3933 memory: 17393 loss: 0.1857 decode.loss_ce: 0.1184 decode.acc_seg: 95.5208 aux.loss_ce: 0.0673 aux.acc_seg: 94.1462 2023/06/08 04:51:28 - mmengine - INFO - Iter(train) [102200/240000] lr: 6.1086e-03 eta: 1 day, 3:34:54 time: 0.7134 data_time: 0.3864 memory: 17395 loss: 0.1835 decode.loss_ce: 0.1144 decode.acc_seg: 95.3993 aux.loss_ce: 0.0691 aux.acc_seg: 93.4132 2023/06/08 04:52:04 - mmengine - INFO - Iter(train) [102250/240000] lr: 6.1066e-03 eta: 1 day, 3:34:17 time: 0.7128 data_time: 0.3896 memory: 17394 loss: 0.2116 decode.loss_ce: 0.1352 decode.acc_seg: 90.3502 aux.loss_ce: 0.0764 aux.acc_seg: 87.5180 2023/06/08 04:52:40 - mmengine - INFO - Iter(train) [102300/240000] lr: 6.1046e-03 eta: 1 day, 3:33:40 time: 0.7070 data_time: 0.3843 memory: 17392 loss: 0.2039 decode.loss_ce: 0.1288 decode.acc_seg: 94.1590 aux.loss_ce: 0.0751 aux.acc_seg: 91.9424 2023/06/08 04:53:15 - mmengine - INFO - Iter(train) [102350/240000] lr: 6.1027e-03 eta: 1 day, 3:33:04 time: 0.7067 data_time: 0.3843 memory: 17396 loss: 0.2167 decode.loss_ce: 0.1397 decode.acc_seg: 92.8026 aux.loss_ce: 0.0769 aux.acc_seg: 91.0389 2023/06/08 04:53:51 - mmengine - INFO - Iter(train) [102400/240000] lr: 6.1007e-03 eta: 1 day, 3:32:27 time: 0.7134 data_time: 0.3907 memory: 17394 loss: 0.2328 decode.loss_ce: 0.1500 decode.acc_seg: 92.9259 aux.loss_ce: 0.0828 aux.acc_seg: 90.3635 2023/06/08 04:54:26 - mmengine - INFO - Iter(train) [102450/240000] lr: 6.0988e-03 eta: 1 day, 3:31:50 time: 0.7183 data_time: 0.3952 memory: 17392 loss: 0.2182 decode.loss_ce: 0.1416 decode.acc_seg: 94.6777 aux.loss_ce: 0.0766 aux.acc_seg: 92.0358 2023/06/08 04:55:02 - mmengine - INFO - Iter(train) [102500/240000] lr: 6.0968e-03 eta: 1 day, 3:31:14 time: 0.7092 data_time: 0.3859 memory: 17398 loss: 0.1873 decode.loss_ce: 0.1195 decode.acc_seg: 95.1076 aux.loss_ce: 0.0678 aux.acc_seg: 92.6343 2023/06/08 04:55:37 - mmengine - INFO - Iter(train) [102550/240000] lr: 6.0948e-03 eta: 1 day, 3:30:37 time: 0.6933 data_time: 0.3705 memory: 17395 loss: 0.2048 decode.loss_ce: 0.1292 decode.acc_seg: 95.2716 aux.loss_ce: 0.0756 aux.acc_seg: 92.6725 2023/06/08 04:56:13 - mmengine - INFO - Iter(train) [102600/240000] lr: 6.0929e-03 eta: 1 day, 3:30:00 time: 0.7111 data_time: 0.3748 memory: 17394 loss: 0.1904 decode.loss_ce: 0.1205 decode.acc_seg: 94.4904 aux.loss_ce: 0.0698 aux.acc_seg: 91.7901 2023/06/08 04:56:48 - mmengine - INFO - Iter(train) [102650/240000] lr: 6.0909e-03 eta: 1 day, 3:29:23 time: 0.7070 data_time: 0.3842 memory: 17394 loss: 0.1946 decode.loss_ce: 0.1234 decode.acc_seg: 94.0702 aux.loss_ce: 0.0712 aux.acc_seg: 90.7856 2023/06/08 04:57:24 - mmengine - INFO - Iter(train) [102700/240000] lr: 6.0889e-03 eta: 1 day, 3:28:47 time: 0.7311 data_time: 0.4035 memory: 17392 loss: 0.1982 decode.loss_ce: 0.1268 decode.acc_seg: 95.4575 aux.loss_ce: 0.0714 aux.acc_seg: 93.2277 2023/06/08 04:57:59 - mmengine - INFO - Iter(train) [102750/240000] lr: 6.0870e-03 eta: 1 day, 3:28:10 time: 0.7097 data_time: 0.3876 memory: 17391 loss: 0.2016 decode.loss_ce: 0.1290 decode.acc_seg: 93.7317 aux.loss_ce: 0.0726 aux.acc_seg: 90.8217 2023/06/08 04:58:35 - mmengine - INFO - Iter(train) [102800/240000] lr: 6.0850e-03 eta: 1 day, 3:27:33 time: 0.7094 data_time: 0.3856 memory: 17392 loss: 0.2064 decode.loss_ce: 0.1315 decode.acc_seg: 94.7882 aux.loss_ce: 0.0749 aux.acc_seg: 93.3445 2023/06/08 04:59:10 - mmengine - INFO - Iter(train) [102850/240000] lr: 6.0831e-03 eta: 1 day, 3:26:56 time: 0.7152 data_time: 0.3919 memory: 17394 loss: 0.2041 decode.loss_ce: 0.1314 decode.acc_seg: 95.6212 aux.loss_ce: 0.0728 aux.acc_seg: 93.6251 2023/06/08 04:59:46 - mmengine - INFO - Iter(train) [102900/240000] lr: 6.0811e-03 eta: 1 day, 3:26:19 time: 0.7071 data_time: 0.3838 memory: 17395 loss: 0.2172 decode.loss_ce: 0.1381 decode.acc_seg: 95.2044 aux.loss_ce: 0.0791 aux.acc_seg: 93.6320 2023/06/08 05:00:21 - mmengine - INFO - Iter(train) [102950/240000] lr: 6.0791e-03 eta: 1 day, 3:25:43 time: 0.7119 data_time: 0.3817 memory: 17398 loss: 0.2051 decode.loss_ce: 0.1313 decode.acc_seg: 95.4823 aux.loss_ce: 0.0738 aux.acc_seg: 93.5639 2023/06/08 05:00:57 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 05:00:57 - mmengine - INFO - Iter(train) [103000/240000] lr: 6.0772e-03 eta: 1 day, 3:25:06 time: 0.6962 data_time: 0.1415 memory: 17392 loss: 0.2209 decode.loss_ce: 0.1412 decode.acc_seg: 91.1538 aux.loss_ce: 0.0797 aux.acc_seg: 87.0440 2023/06/08 05:01:32 - mmengine - INFO - Iter(train) [103050/240000] lr: 6.0752e-03 eta: 1 day, 3:24:29 time: 0.7080 data_time: 0.2625 memory: 17393 loss: 0.2032 decode.loss_ce: 0.1302 decode.acc_seg: 90.4711 aux.loss_ce: 0.0729 aux.acc_seg: 88.2909 2023/06/08 05:02:07 - mmengine - INFO - Iter(train) [103100/240000] lr: 6.0732e-03 eta: 1 day, 3:23:52 time: 0.7080 data_time: 0.3853 memory: 17393 loss: 0.2110 decode.loss_ce: 0.1348 decode.acc_seg: 94.9638 aux.loss_ce: 0.0762 aux.acc_seg: 93.5201 2023/06/08 05:02:43 - mmengine - INFO - Iter(train) [103150/240000] lr: 6.0713e-03 eta: 1 day, 3:23:15 time: 0.7127 data_time: 0.3686 memory: 17392 loss: 0.2215 decode.loss_ce: 0.1435 decode.acc_seg: 93.3003 aux.loss_ce: 0.0780 aux.acc_seg: 91.0108 2023/06/08 05:03:18 - mmengine - INFO - Iter(train) [103200/240000] lr: 6.0693e-03 eta: 1 day, 3:22:38 time: 0.7064 data_time: 0.3343 memory: 17393 loss: 0.2188 decode.loss_ce: 0.1394 decode.acc_seg: 92.3030 aux.loss_ce: 0.0794 aux.acc_seg: 89.2953 2023/06/08 05:03:53 - mmengine - INFO - Iter(train) [103250/240000] lr: 6.0673e-03 eta: 1 day, 3:22:02 time: 0.7082 data_time: 0.3353 memory: 17394 loss: 0.2401 decode.loss_ce: 0.1493 decode.acc_seg: 92.3874 aux.loss_ce: 0.0908 aux.acc_seg: 89.3957 2023/06/08 05:04:29 - mmengine - INFO - Iter(train) [103300/240000] lr: 6.0654e-03 eta: 1 day, 3:21:25 time: 0.7206 data_time: 0.1805 memory: 17396 loss: 0.2190 decode.loss_ce: 0.1422 decode.acc_seg: 94.7667 aux.loss_ce: 0.0769 aux.acc_seg: 93.0742 2023/06/08 05:05:04 - mmengine - INFO - Iter(train) [103350/240000] lr: 6.0634e-03 eta: 1 day, 3:20:48 time: 0.7088 data_time: 0.2934 memory: 17395 loss: 0.2016 decode.loss_ce: 0.1263 decode.acc_seg: 94.2768 aux.loss_ce: 0.0753 aux.acc_seg: 91.7525 2023/06/08 05:05:40 - mmengine - INFO - Iter(train) [103400/240000] lr: 6.0615e-03 eta: 1 day, 3:20:11 time: 0.7160 data_time: 0.1935 memory: 17395 loss: 0.1986 decode.loss_ce: 0.1268 decode.acc_seg: 94.4319 aux.loss_ce: 0.0718 aux.acc_seg: 93.0056 2023/06/08 05:06:15 - mmengine - INFO - Iter(train) [103450/240000] lr: 6.0595e-03 eta: 1 day, 3:19:34 time: 0.7136 data_time: 0.2195 memory: 17393 loss: 0.2047 decode.loss_ce: 0.1308 decode.acc_seg: 94.4616 aux.loss_ce: 0.0739 aux.acc_seg: 92.9426 2023/06/08 05:06:51 - mmengine - INFO - Iter(train) [103500/240000] lr: 6.0575e-03 eta: 1 day, 3:18:58 time: 0.7066 data_time: 0.0121 memory: 17397 loss: 0.1996 decode.loss_ce: 0.1269 decode.acc_seg: 95.2371 aux.loss_ce: 0.0727 aux.acc_seg: 92.5912 2023/06/08 05:07:26 - mmengine - INFO - Iter(train) [103550/240000] lr: 6.0556e-03 eta: 1 day, 3:18:21 time: 0.7180 data_time: 0.0689 memory: 17397 loss: 0.1981 decode.loss_ce: 0.1267 decode.acc_seg: 93.9866 aux.loss_ce: 0.0714 aux.acc_seg: 91.1487 2023/06/08 05:08:01 - mmengine - INFO - Iter(train) [103600/240000] lr: 6.0536e-03 eta: 1 day, 3:17:44 time: 0.7029 data_time: 0.0135 memory: 17393 loss: 0.2359 decode.loss_ce: 0.1534 decode.acc_seg: 92.5547 aux.loss_ce: 0.0825 aux.acc_seg: 88.6494 2023/06/08 05:08:37 - mmengine - INFO - Iter(train) [103650/240000] lr: 6.0516e-03 eta: 1 day, 3:17:07 time: 0.7064 data_time: 0.0121 memory: 17394 loss: 0.1952 decode.loss_ce: 0.1239 decode.acc_seg: 94.2213 aux.loss_ce: 0.0713 aux.acc_seg: 91.5686 2023/06/08 05:09:12 - mmengine - INFO - Iter(train) [103700/240000] lr: 6.0497e-03 eta: 1 day, 3:16:30 time: 0.7070 data_time: 0.0375 memory: 17395 loss: 0.1958 decode.loss_ce: 0.1257 decode.acc_seg: 92.1465 aux.loss_ce: 0.0701 aux.acc_seg: 90.2258 2023/06/08 05:09:48 - mmengine - INFO - Iter(train) [103750/240000] lr: 6.0477e-03 eta: 1 day, 3:15:54 time: 0.7045 data_time: 0.0123 memory: 17391 loss: 0.2062 decode.loss_ce: 0.1322 decode.acc_seg: 94.1893 aux.loss_ce: 0.0740 aux.acc_seg: 91.5302 2023/06/08 05:10:24 - mmengine - INFO - Iter(train) [103800/240000] lr: 6.0457e-03 eta: 1 day, 3:15:17 time: 0.7217 data_time: 0.0261 memory: 17393 loss: 0.1978 decode.loss_ce: 0.1267 decode.acc_seg: 96.1185 aux.loss_ce: 0.0710 aux.acc_seg: 94.6417 2023/06/08 05:10:59 - mmengine - INFO - Iter(train) [103850/240000] lr: 6.0438e-03 eta: 1 day, 3:14:41 time: 0.7167 data_time: 0.0121 memory: 17398 loss: 0.2232 decode.loss_ce: 0.1418 decode.acc_seg: 93.9793 aux.loss_ce: 0.0814 aux.acc_seg: 91.5650 2023/06/08 05:11:35 - mmengine - INFO - Iter(train) [103900/240000] lr: 6.0418e-03 eta: 1 day, 3:14:04 time: 0.7072 data_time: 0.0124 memory: 17394 loss: 0.2073 decode.loss_ce: 0.1320 decode.acc_seg: 93.7591 aux.loss_ce: 0.0754 aux.acc_seg: 90.7018 2023/06/08 05:12:11 - mmengine - INFO - Iter(train) [103950/240000] lr: 6.0398e-03 eta: 1 day, 3:13:28 time: 0.6960 data_time: 0.0123 memory: 17396 loss: 0.2080 decode.loss_ce: 0.1306 decode.acc_seg: 94.5504 aux.loss_ce: 0.0774 aux.acc_seg: 91.8854 2023/06/08 05:12:46 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 05:12:46 - mmengine - INFO - Iter(train) [104000/240000] lr: 6.0379e-03 eta: 1 day, 3:12:51 time: 0.7111 data_time: 0.0123 memory: 17393 loss: 0.1955 decode.loss_ce: 0.1232 decode.acc_seg: 94.0689 aux.loss_ce: 0.0723 aux.acc_seg: 89.5946 2023/06/08 05:13:21 - mmengine - INFO - Iter(train) [104050/240000] lr: 6.0359e-03 eta: 1 day, 3:12:14 time: 0.7155 data_time: 0.0124 memory: 17394 loss: 0.2109 decode.loss_ce: 0.1378 decode.acc_seg: 94.2583 aux.loss_ce: 0.0731 aux.acc_seg: 91.1011 2023/06/08 05:13:57 - mmengine - INFO - Iter(train) [104100/240000] lr: 6.0340e-03 eta: 1 day, 3:11:37 time: 0.7008 data_time: 0.0124 memory: 17394 loss: 0.2034 decode.loss_ce: 0.1286 decode.acc_seg: 92.6432 aux.loss_ce: 0.0749 aux.acc_seg: 89.9053 2023/06/08 05:14:32 - mmengine - INFO - Iter(train) [104150/240000] lr: 6.0320e-03 eta: 1 day, 3:11:01 time: 0.7133 data_time: 0.0122 memory: 17394 loss: 0.2005 decode.loss_ce: 0.1286 decode.acc_seg: 94.0760 aux.loss_ce: 0.0719 aux.acc_seg: 91.7904 2023/06/08 05:15:08 - mmengine - INFO - Iter(train) [104200/240000] lr: 6.0300e-03 eta: 1 day, 3:10:24 time: 0.7197 data_time: 0.0123 memory: 17395 loss: 0.2269 decode.loss_ce: 0.1433 decode.acc_seg: 94.6535 aux.loss_ce: 0.0836 aux.acc_seg: 90.0299 2023/06/08 05:15:44 - mmengine - INFO - Iter(train) [104250/240000] lr: 6.0281e-03 eta: 1 day, 3:09:47 time: 0.7102 data_time: 0.0122 memory: 17393 loss: 0.2008 decode.loss_ce: 0.1270 decode.acc_seg: 93.9758 aux.loss_ce: 0.0737 aux.acc_seg: 91.4091 2023/06/08 05:16:19 - mmengine - INFO - Iter(train) [104300/240000] lr: 6.0261e-03 eta: 1 day, 3:09:11 time: 0.7031 data_time: 0.0122 memory: 17394 loss: 0.2200 decode.loss_ce: 0.1416 decode.acc_seg: 95.0021 aux.loss_ce: 0.0784 aux.acc_seg: 93.1749 2023/06/08 05:16:55 - mmengine - INFO - Iter(train) [104350/240000] lr: 6.0241e-03 eta: 1 day, 3:08:34 time: 0.7003 data_time: 0.0124 memory: 17393 loss: 0.2084 decode.loss_ce: 0.1334 decode.acc_seg: 93.9036 aux.loss_ce: 0.0750 aux.acc_seg: 92.1697 2023/06/08 05:17:30 - mmengine - INFO - Iter(train) [104400/240000] lr: 6.0222e-03 eta: 1 day, 3:07:58 time: 0.7175 data_time: 0.0123 memory: 17394 loss: 0.2109 decode.loss_ce: 0.1355 decode.acc_seg: 93.1357 aux.loss_ce: 0.0754 aux.acc_seg: 91.0201 2023/06/08 05:18:06 - mmengine - INFO - Iter(train) [104450/240000] lr: 6.0202e-03 eta: 1 day, 3:07:21 time: 0.7180 data_time: 0.0124 memory: 17391 loss: 0.1904 decode.loss_ce: 0.1209 decode.acc_seg: 95.6047 aux.loss_ce: 0.0695 aux.acc_seg: 93.6714 2023/06/08 05:18:42 - mmengine - INFO - Iter(train) [104500/240000] lr: 6.0182e-03 eta: 1 day, 3:06:45 time: 0.7052 data_time: 0.0125 memory: 17393 loss: 0.2076 decode.loss_ce: 0.1329 decode.acc_seg: 94.8983 aux.loss_ce: 0.0748 aux.acc_seg: 92.8196 2023/06/08 05:19:17 - mmengine - INFO - Iter(train) [104550/240000] lr: 6.0163e-03 eta: 1 day, 3:06:08 time: 0.7000 data_time: 0.0124 memory: 17394 loss: 0.2402 decode.loss_ce: 0.1546 decode.acc_seg: 94.8253 aux.loss_ce: 0.0856 aux.acc_seg: 91.0726 2023/06/08 05:19:53 - mmengine - INFO - Iter(train) [104600/240000] lr: 6.0143e-03 eta: 1 day, 3:05:31 time: 0.7144 data_time: 0.0122 memory: 17394 loss: 0.2087 decode.loss_ce: 0.1339 decode.acc_seg: 93.2327 aux.loss_ce: 0.0748 aux.acc_seg: 90.2590 2023/06/08 05:20:28 - mmengine - INFO - Iter(train) [104650/240000] lr: 6.0123e-03 eta: 1 day, 3:04:55 time: 0.7178 data_time: 0.0125 memory: 17395 loss: 0.2517 decode.loss_ce: 0.1698 decode.acc_seg: 91.9654 aux.loss_ce: 0.0819 aux.acc_seg: 90.5655 2023/06/08 05:21:04 - mmengine - INFO - Iter(train) [104700/240000] lr: 6.0104e-03 eta: 1 day, 3:04:18 time: 0.7075 data_time: 0.0122 memory: 17394 loss: 0.2281 decode.loss_ce: 0.1488 decode.acc_seg: 94.1911 aux.loss_ce: 0.0794 aux.acc_seg: 91.9699 2023/06/08 05:21:39 - mmengine - INFO - Iter(train) [104750/240000] lr: 6.0084e-03 eta: 1 day, 3:03:42 time: 0.7056 data_time: 0.0124 memory: 17394 loss: 0.2387 decode.loss_ce: 0.1543 decode.acc_seg: 94.9631 aux.loss_ce: 0.0843 aux.acc_seg: 93.1053 2023/06/08 05:22:15 - mmengine - INFO - Iter(train) [104800/240000] lr: 6.0064e-03 eta: 1 day, 3:03:05 time: 0.7141 data_time: 0.0124 memory: 17393 loss: 0.2060 decode.loss_ce: 0.1320 decode.acc_seg: 95.0864 aux.loss_ce: 0.0740 aux.acc_seg: 92.3112 2023/06/08 05:22:50 - mmengine - INFO - Iter(train) [104850/240000] lr: 6.0045e-03 eta: 1 day, 3:02:28 time: 0.6981 data_time: 0.0123 memory: 17394 loss: 0.2102 decode.loss_ce: 0.1342 decode.acc_seg: 94.3307 aux.loss_ce: 0.0760 aux.acc_seg: 92.3042 2023/06/08 05:23:26 - mmengine - INFO - Iter(train) [104900/240000] lr: 6.0025e-03 eta: 1 day, 3:01:51 time: 0.7076 data_time: 0.0122 memory: 17394 loss: 0.2157 decode.loss_ce: 0.1387 decode.acc_seg: 94.3915 aux.loss_ce: 0.0770 aux.acc_seg: 92.0763 2023/06/08 05:24:01 - mmengine - INFO - Iter(train) [104950/240000] lr: 6.0005e-03 eta: 1 day, 3:01:15 time: 0.7092 data_time: 0.0122 memory: 17395 loss: 0.2171 decode.loss_ce: 0.1388 decode.acc_seg: 94.0002 aux.loss_ce: 0.0783 aux.acc_seg: 90.2901 2023/06/08 05:24:37 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 05:24:37 - mmengine - INFO - Iter(train) [105000/240000] lr: 5.9986e-03 eta: 1 day, 3:00:38 time: 0.7224 data_time: 0.0123 memory: 17395 loss: 0.2184 decode.loss_ce: 0.1382 decode.acc_seg: 91.1916 aux.loss_ce: 0.0802 aux.acc_seg: 88.9631 2023/06/08 05:25:13 - mmengine - INFO - Iter(train) [105050/240000] lr: 5.9966e-03 eta: 1 day, 3:00:02 time: 0.7155 data_time: 0.0122 memory: 17396 loss: 0.1913 decode.loss_ce: 0.1225 decode.acc_seg: 95.2382 aux.loss_ce: 0.0687 aux.acc_seg: 93.0986 2023/06/08 05:25:48 - mmengine - INFO - Iter(train) [105100/240000] lr: 5.9946e-03 eta: 1 day, 2:59:25 time: 0.7118 data_time: 0.0175 memory: 17395 loss: 0.1984 decode.loss_ce: 0.1267 decode.acc_seg: 95.2690 aux.loss_ce: 0.0716 aux.acc_seg: 92.6947 2023/06/08 05:26:24 - mmengine - INFO - Iter(train) [105150/240000] lr: 5.9927e-03 eta: 1 day, 2:58:49 time: 0.7277 data_time: 0.0123 memory: 17392 loss: 0.1740 decode.loss_ce: 0.1109 decode.acc_seg: 95.1156 aux.loss_ce: 0.0630 aux.acc_seg: 92.7529 2023/06/08 05:26:59 - mmengine - INFO - Iter(train) [105200/240000] lr: 5.9907e-03 eta: 1 day, 2:58:12 time: 0.7175 data_time: 0.0127 memory: 17395 loss: 0.2080 decode.loss_ce: 0.1324 decode.acc_seg: 93.0159 aux.loss_ce: 0.0756 aux.acc_seg: 88.9398 2023/06/08 05:27:35 - mmengine - INFO - Iter(train) [105250/240000] lr: 5.9887e-03 eta: 1 day, 2:57:36 time: 0.7081 data_time: 0.0124 memory: 17393 loss: 0.2051 decode.loss_ce: 0.1350 decode.acc_seg: 94.7629 aux.loss_ce: 0.0701 aux.acc_seg: 93.2622 2023/06/08 05:28:11 - mmengine - INFO - Iter(train) [105300/240000] lr: 5.9868e-03 eta: 1 day, 2:56:59 time: 0.7172 data_time: 0.0126 memory: 17395 loss: 0.2023 decode.loss_ce: 0.1286 decode.acc_seg: 94.6069 aux.loss_ce: 0.0737 aux.acc_seg: 91.7490 2023/06/08 05:28:46 - mmengine - INFO - Iter(train) [105350/240000] lr: 5.9848e-03 eta: 1 day, 2:56:22 time: 0.7097 data_time: 0.0122 memory: 17396 loss: 0.2022 decode.loss_ce: 0.1253 decode.acc_seg: 94.7060 aux.loss_ce: 0.0769 aux.acc_seg: 92.0997 2023/06/08 05:29:22 - mmengine - INFO - Iter(train) [105400/240000] lr: 5.9828e-03 eta: 1 day, 2:55:46 time: 0.7053 data_time: 0.0121 memory: 17393 loss: 0.2055 decode.loss_ce: 0.1304 decode.acc_seg: 95.0302 aux.loss_ce: 0.0751 aux.acc_seg: 91.9413 2023/06/08 05:29:57 - mmengine - INFO - Iter(train) [105450/240000] lr: 5.9809e-03 eta: 1 day, 2:55:09 time: 0.7145 data_time: 0.0153 memory: 17395 loss: 0.2054 decode.loss_ce: 0.1313 decode.acc_seg: 93.5778 aux.loss_ce: 0.0741 aux.acc_seg: 90.3702 2023/06/08 05:30:32 - mmengine - INFO - Iter(train) [105500/240000] lr: 5.9789e-03 eta: 1 day, 2:54:32 time: 0.7038 data_time: 0.0122 memory: 17394 loss: 0.2017 decode.loss_ce: 0.1289 decode.acc_seg: 94.0558 aux.loss_ce: 0.0728 aux.acc_seg: 91.8329 2023/06/08 05:31:08 - mmengine - INFO - Iter(train) [105550/240000] lr: 5.9769e-03 eta: 1 day, 2:53:56 time: 0.7006 data_time: 0.0122 memory: 17394 loss: 0.1972 decode.loss_ce: 0.1241 decode.acc_seg: 94.4063 aux.loss_ce: 0.0731 aux.acc_seg: 91.8297 2023/06/08 05:31:44 - mmengine - INFO - Iter(train) [105600/240000] lr: 5.9750e-03 eta: 1 day, 2:53:19 time: 0.7095 data_time: 0.0124 memory: 17392 loss: 0.2099 decode.loss_ce: 0.1358 decode.acc_seg: 93.5949 aux.loss_ce: 0.0741 aux.acc_seg: 91.7257 2023/06/08 05:32:19 - mmengine - INFO - Iter(train) [105650/240000] lr: 5.9730e-03 eta: 1 day, 2:52:42 time: 0.7053 data_time: 0.0122 memory: 17394 loss: 0.1955 decode.loss_ce: 0.1246 decode.acc_seg: 93.8631 aux.loss_ce: 0.0709 aux.acc_seg: 91.3211 2023/06/08 05:32:54 - mmengine - INFO - Iter(train) [105700/240000] lr: 5.9710e-03 eta: 1 day, 2:52:05 time: 0.7051 data_time: 0.0121 memory: 17392 loss: 0.2331 decode.loss_ce: 0.1490 decode.acc_seg: 91.9334 aux.loss_ce: 0.0841 aux.acc_seg: 89.6265 2023/06/08 05:33:30 - mmengine - INFO - Iter(train) [105750/240000] lr: 5.9691e-03 eta: 1 day, 2:51:29 time: 0.7153 data_time: 0.0126 memory: 17394 loss: 0.2189 decode.loss_ce: 0.1384 decode.acc_seg: 94.6322 aux.loss_ce: 0.0805 aux.acc_seg: 93.0390 2023/06/08 05:34:06 - mmengine - INFO - Iter(train) [105800/240000] lr: 5.9671e-03 eta: 1 day, 2:50:52 time: 0.7026 data_time: 0.0122 memory: 17395 loss: 0.1962 decode.loss_ce: 0.1256 decode.acc_seg: 94.4191 aux.loss_ce: 0.0706 aux.acc_seg: 92.0652 2023/06/08 05:34:41 - mmengine - INFO - Iter(train) [105850/240000] lr: 5.9651e-03 eta: 1 day, 2:50:16 time: 0.7129 data_time: 0.0600 memory: 17395 loss: 0.2174 decode.loss_ce: 0.1404 decode.acc_seg: 93.8694 aux.loss_ce: 0.0770 aux.acc_seg: 92.3424 2023/06/08 05:35:17 - mmengine - INFO - Iter(train) [105900/240000] lr: 5.9632e-03 eta: 1 day, 2:49:39 time: 0.7263 data_time: 0.0275 memory: 17395 loss: 0.2081 decode.loss_ce: 0.1335 decode.acc_seg: 93.4142 aux.loss_ce: 0.0746 aux.acc_seg: 89.5112 2023/06/08 05:35:52 - mmengine - INFO - Iter(train) [105950/240000] lr: 5.9612e-03 eta: 1 day, 2:49:03 time: 0.7214 data_time: 0.0124 memory: 17394 loss: 0.2213 decode.loss_ce: 0.1423 decode.acc_seg: 94.8606 aux.loss_ce: 0.0789 aux.acc_seg: 90.7419 2023/06/08 05:36:28 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 05:36:28 - mmengine - INFO - Iter(train) [106000/240000] lr: 5.9592e-03 eta: 1 day, 2:48:26 time: 0.7129 data_time: 0.2052 memory: 17397 loss: 0.1834 decode.loss_ce: 0.1164 decode.acc_seg: 94.6756 aux.loss_ce: 0.0670 aux.acc_seg: 92.2276 2023/06/08 05:37:04 - mmengine - INFO - Iter(train) [106050/240000] lr: 5.9573e-03 eta: 1 day, 2:47:50 time: 0.7110 data_time: 0.0122 memory: 17395 loss: 0.2040 decode.loss_ce: 0.1304 decode.acc_seg: 94.9412 aux.loss_ce: 0.0736 aux.acc_seg: 93.1361 2023/06/08 05:37:39 - mmengine - INFO - Iter(train) [106100/240000] lr: 5.9553e-03 eta: 1 day, 2:47:13 time: 0.7129 data_time: 0.0123 memory: 17393 loss: 0.1854 decode.loss_ce: 0.1154 decode.acc_seg: 92.8625 aux.loss_ce: 0.0700 aux.acc_seg: 91.0677 2023/06/08 05:38:15 - mmengine - INFO - Iter(train) [106150/240000] lr: 5.9533e-03 eta: 1 day, 2:46:36 time: 0.7092 data_time: 0.0124 memory: 17394 loss: 0.1964 decode.loss_ce: 0.1262 decode.acc_seg: 92.8582 aux.loss_ce: 0.0702 aux.acc_seg: 89.9096 2023/06/08 05:38:50 - mmengine - INFO - Iter(train) [106200/240000] lr: 5.9514e-03 eta: 1 day, 2:46:00 time: 0.7123 data_time: 0.0122 memory: 17397 loss: 0.2124 decode.loss_ce: 0.1353 decode.acc_seg: 94.7862 aux.loss_ce: 0.0772 aux.acc_seg: 92.6311 2023/06/08 05:39:26 - mmengine - INFO - Iter(train) [106250/240000] lr: 5.9494e-03 eta: 1 day, 2:45:24 time: 0.7145 data_time: 0.0124 memory: 17392 loss: 0.2002 decode.loss_ce: 0.1263 decode.acc_seg: 93.5836 aux.loss_ce: 0.0739 aux.acc_seg: 90.3419 2023/06/08 05:40:02 - mmengine - INFO - Iter(train) [106300/240000] lr: 5.9474e-03 eta: 1 day, 2:44:47 time: 0.7159 data_time: 0.0122 memory: 17393 loss: 0.2022 decode.loss_ce: 0.1281 decode.acc_seg: 93.8876 aux.loss_ce: 0.0741 aux.acc_seg: 89.3987 2023/06/08 05:40:37 - mmengine - INFO - Iter(train) [106350/240000] lr: 5.9455e-03 eta: 1 day, 2:44:10 time: 0.7148 data_time: 0.0123 memory: 17398 loss: 0.2221 decode.loss_ce: 0.1415 decode.acc_seg: 94.6018 aux.loss_ce: 0.0806 aux.acc_seg: 91.4034 2023/06/08 05:41:13 - mmengine - INFO - Iter(train) [106400/240000] lr: 5.9435e-03 eta: 1 day, 2:43:34 time: 0.7042 data_time: 0.0128 memory: 17395 loss: 0.1848 decode.loss_ce: 0.1178 decode.acc_seg: 95.2947 aux.loss_ce: 0.0671 aux.acc_seg: 92.5310 2023/06/08 05:41:48 - mmengine - INFO - Iter(train) [106450/240000] lr: 5.9415e-03 eta: 1 day, 2:42:57 time: 0.7108 data_time: 0.0123 memory: 17394 loss: 0.2026 decode.loss_ce: 0.1281 decode.acc_seg: 93.5358 aux.loss_ce: 0.0746 aux.acc_seg: 91.4177 2023/06/08 05:42:23 - mmengine - INFO - Iter(train) [106500/240000] lr: 5.9396e-03 eta: 1 day, 2:42:20 time: 0.7089 data_time: 0.1534 memory: 17396 loss: 0.1987 decode.loss_ce: 0.1258 decode.acc_seg: 93.9866 aux.loss_ce: 0.0729 aux.acc_seg: 91.1971 2023/06/08 05:42:59 - mmengine - INFO - Iter(train) [106550/240000] lr: 5.9376e-03 eta: 1 day, 2:41:43 time: 0.7091 data_time: 0.3433 memory: 17395 loss: 0.1925 decode.loss_ce: 0.1229 decode.acc_seg: 91.7005 aux.loss_ce: 0.0696 aux.acc_seg: 88.8392 2023/06/08 05:43:34 - mmengine - INFO - Iter(train) [106600/240000] lr: 5.9356e-03 eta: 1 day, 2:41:07 time: 0.7097 data_time: 0.3866 memory: 17396 loss: 0.2040 decode.loss_ce: 0.1300 decode.acc_seg: 95.0482 aux.loss_ce: 0.0740 aux.acc_seg: 92.9383 2023/06/08 05:44:10 - mmengine - INFO - Iter(train) [106650/240000] lr: 5.9337e-03 eta: 1 day, 2:40:30 time: 0.7104 data_time: 0.3877 memory: 17397 loss: 0.1915 decode.loss_ce: 0.1221 decode.acc_seg: 95.1155 aux.loss_ce: 0.0694 aux.acc_seg: 93.1227 2023/06/08 05:44:45 - mmengine - INFO - Iter(train) [106700/240000] lr: 5.9317e-03 eta: 1 day, 2:39:53 time: 0.7086 data_time: 0.3855 memory: 17393 loss: 0.2035 decode.loss_ce: 0.1287 decode.acc_seg: 95.0555 aux.loss_ce: 0.0748 aux.acc_seg: 91.9569 2023/06/08 05:45:20 - mmengine - INFO - Iter(train) [106750/240000] lr: 5.9297e-03 eta: 1 day, 2:39:16 time: 0.6983 data_time: 0.2035 memory: 17394 loss: 0.1953 decode.loss_ce: 0.1244 decode.acc_seg: 94.4608 aux.loss_ce: 0.0710 aux.acc_seg: 92.8627 2023/06/08 05:45:56 - mmengine - INFO - Iter(train) [106800/240000] lr: 5.9277e-03 eta: 1 day, 2:38:40 time: 0.7285 data_time: 0.4059 memory: 17394 loss: 0.2025 decode.loss_ce: 0.1302 decode.acc_seg: 94.6764 aux.loss_ce: 0.0724 aux.acc_seg: 92.8389 2023/06/08 05:46:32 - mmengine - INFO - Iter(train) [106850/240000] lr: 5.9258e-03 eta: 1 day, 2:38:04 time: 0.7164 data_time: 0.3940 memory: 17394 loss: 0.2283 decode.loss_ce: 0.1482 decode.acc_seg: 91.2288 aux.loss_ce: 0.0801 aux.acc_seg: 88.8675 2023/06/08 05:47:07 - mmengine - INFO - Iter(train) [106900/240000] lr: 5.9238e-03 eta: 1 day, 2:37:27 time: 0.6915 data_time: 0.3223 memory: 17394 loss: 0.2299 decode.loss_ce: 0.1501 decode.acc_seg: 94.2519 aux.loss_ce: 0.0798 aux.acc_seg: 91.2279 2023/06/08 05:47:43 - mmengine - INFO - Iter(train) [106950/240000] lr: 5.9218e-03 eta: 1 day, 2:36:50 time: 0.7053 data_time: 0.3823 memory: 17395 loss: 0.2075 decode.loss_ce: 0.1321 decode.acc_seg: 93.4868 aux.loss_ce: 0.0755 aux.acc_seg: 90.3323 2023/06/08 05:48:18 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 05:48:18 - mmengine - INFO - Iter(train) [107000/240000] lr: 5.9199e-03 eta: 1 day, 2:36:13 time: 0.7040 data_time: 0.2909 memory: 17392 loss: 0.1848 decode.loss_ce: 0.1179 decode.acc_seg: 93.6479 aux.loss_ce: 0.0670 aux.acc_seg: 91.6653 2023/06/08 05:48:54 - mmengine - INFO - Iter(train) [107050/240000] lr: 5.9179e-03 eta: 1 day, 2:35:37 time: 0.7159 data_time: 0.2308 memory: 17395 loss: 0.2248 decode.loss_ce: 0.1402 decode.acc_seg: 94.6912 aux.loss_ce: 0.0846 aux.acc_seg: 92.6280 2023/06/08 05:49:30 - mmengine - INFO - Iter(train) [107100/240000] lr: 5.9159e-03 eta: 1 day, 2:35:01 time: 0.7239 data_time: 0.0121 memory: 17395 loss: 0.2237 decode.loss_ce: 0.1426 decode.acc_seg: 94.0587 aux.loss_ce: 0.0811 aux.acc_seg: 91.4375 2023/06/08 05:50:05 - mmengine - INFO - Iter(train) [107150/240000] lr: 5.9140e-03 eta: 1 day, 2:34:24 time: 0.7040 data_time: 0.0122 memory: 17394 loss: 0.2337 decode.loss_ce: 0.1491 decode.acc_seg: 90.4910 aux.loss_ce: 0.0847 aux.acc_seg: 88.5452 2023/06/08 05:50:41 - mmengine - INFO - Iter(train) [107200/240000] lr: 5.9120e-03 eta: 1 day, 2:33:48 time: 0.7194 data_time: 0.0127 memory: 17392 loss: 0.2029 decode.loss_ce: 0.1291 decode.acc_seg: 93.9736 aux.loss_ce: 0.0738 aux.acc_seg: 91.6703 2023/06/08 05:51:16 - mmengine - INFO - Iter(train) [107250/240000] lr: 5.9100e-03 eta: 1 day, 2:33:11 time: 0.7092 data_time: 0.0122 memory: 17393 loss: 0.2058 decode.loss_ce: 0.1337 decode.acc_seg: 94.9406 aux.loss_ce: 0.0721 aux.acc_seg: 92.2427 2023/06/08 05:51:52 - mmengine - INFO - Iter(train) [107300/240000] lr: 5.9081e-03 eta: 1 day, 2:32:35 time: 0.7080 data_time: 0.0124 memory: 17392 loss: 0.2223 decode.loss_ce: 0.1435 decode.acc_seg: 92.0764 aux.loss_ce: 0.0788 aux.acc_seg: 89.9243 2023/06/08 05:52:28 - mmengine - INFO - Iter(train) [107350/240000] lr: 5.9061e-03 eta: 1 day, 2:31:58 time: 0.7038 data_time: 0.0122 memory: 17393 loss: 0.1983 decode.loss_ce: 0.1271 decode.acc_seg: 95.9307 aux.loss_ce: 0.0711 aux.acc_seg: 92.3511 2023/06/08 05:53:03 - mmengine - INFO - Iter(train) [107400/240000] lr: 5.9041e-03 eta: 1 day, 2:31:21 time: 0.7091 data_time: 0.1909 memory: 17398 loss: 0.2056 decode.loss_ce: 0.1312 decode.acc_seg: 93.5438 aux.loss_ce: 0.0745 aux.acc_seg: 89.3837 2023/06/08 05:53:38 - mmengine - INFO - Iter(train) [107450/240000] lr: 5.9021e-03 eta: 1 day, 2:30:44 time: 0.7045 data_time: 0.1072 memory: 17395 loss: 0.1983 decode.loss_ce: 0.1262 decode.acc_seg: 93.0267 aux.loss_ce: 0.0721 aux.acc_seg: 90.9656 2023/06/08 05:54:14 - mmengine - INFO - Iter(train) [107500/240000] lr: 5.9002e-03 eta: 1 day, 2:30:07 time: 0.7140 data_time: 0.3823 memory: 17393 loss: 0.1827 decode.loss_ce: 0.1148 decode.acc_seg: 96.1939 aux.loss_ce: 0.0679 aux.acc_seg: 94.8309 2023/06/08 05:54:49 - mmengine - INFO - Iter(train) [107550/240000] lr: 5.8982e-03 eta: 1 day, 2:29:31 time: 0.7099 data_time: 0.3870 memory: 17395 loss: 0.1922 decode.loss_ce: 0.1202 decode.acc_seg: 94.9747 aux.loss_ce: 0.0720 aux.acc_seg: 92.5007 2023/06/08 05:55:25 - mmengine - INFO - Iter(train) [107600/240000] lr: 5.8962e-03 eta: 1 day, 2:28:54 time: 0.7189 data_time: 0.3724 memory: 17394 loss: 0.1857 decode.loss_ce: 0.1179 decode.acc_seg: 95.7292 aux.loss_ce: 0.0678 aux.acc_seg: 93.7629 2023/06/08 05:56:00 - mmengine - INFO - Iter(train) [107650/240000] lr: 5.8943e-03 eta: 1 day, 2:28:17 time: 0.7141 data_time: 0.3828 memory: 17393 loss: 0.1891 decode.loss_ce: 0.1188 decode.acc_seg: 95.7667 aux.loss_ce: 0.0702 aux.acc_seg: 93.2567 2023/06/08 05:56:35 - mmengine - INFO - Iter(train) [107700/240000] lr: 5.8923e-03 eta: 1 day, 2:27:41 time: 0.7103 data_time: 0.2991 memory: 17394 loss: 0.2103 decode.loss_ce: 0.1349 decode.acc_seg: 93.8098 aux.loss_ce: 0.0754 aux.acc_seg: 92.0092 2023/06/08 05:57:11 - mmengine - INFO - Iter(train) [107750/240000] lr: 5.8903e-03 eta: 1 day, 2:27:04 time: 0.7004 data_time: 0.3161 memory: 17393 loss: 0.2049 decode.loss_ce: 0.1302 decode.acc_seg: 94.5641 aux.loss_ce: 0.0747 aux.acc_seg: 91.6444 2023/06/08 05:57:46 - mmengine - INFO - Iter(train) [107800/240000] lr: 5.8884e-03 eta: 1 day, 2:26:27 time: 0.7132 data_time: 0.1651 memory: 17393 loss: 0.2331 decode.loss_ce: 0.1519 decode.acc_seg: 91.9281 aux.loss_ce: 0.0812 aux.acc_seg: 88.7077 2023/06/08 05:58:22 - mmengine - INFO - Iter(train) [107850/240000] lr: 5.8864e-03 eta: 1 day, 2:25:51 time: 0.7154 data_time: 0.3924 memory: 17394 loss: 0.2211 decode.loss_ce: 0.1430 decode.acc_seg: 91.5270 aux.loss_ce: 0.0780 aux.acc_seg: 89.2595 2023/06/08 05:58:58 - mmengine - INFO - Iter(train) [107900/240000] lr: 5.8844e-03 eta: 1 day, 2:25:14 time: 0.7110 data_time: 0.3881 memory: 17392 loss: 0.2120 decode.loss_ce: 0.1351 decode.acc_seg: 95.5582 aux.loss_ce: 0.0770 aux.acc_seg: 93.3225 2023/06/08 05:59:33 - mmengine - INFO - Iter(train) [107950/240000] lr: 5.8824e-03 eta: 1 day, 2:24:38 time: 0.7048 data_time: 0.3817 memory: 17397 loss: 0.2155 decode.loss_ce: 0.1370 decode.acc_seg: 93.7967 aux.loss_ce: 0.0785 aux.acc_seg: 91.0155 2023/06/08 06:00:08 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 06:00:08 - mmengine - INFO - Iter(train) [108000/240000] lr: 5.8805e-03 eta: 1 day, 2:24:01 time: 0.6977 data_time: 0.3750 memory: 17397 loss: 0.2101 decode.loss_ce: 0.1349 decode.acc_seg: 94.1138 aux.loss_ce: 0.0752 aux.acc_seg: 91.1246 2023/06/08 06:00:44 - mmengine - INFO - Iter(train) [108050/240000] lr: 5.8785e-03 eta: 1 day, 2:23:24 time: 0.7173 data_time: 0.3944 memory: 17392 loss: 0.1894 decode.loss_ce: 0.1210 decode.acc_seg: 94.3245 aux.loss_ce: 0.0684 aux.acc_seg: 91.5389 2023/06/08 06:01:19 - mmengine - INFO - Iter(train) [108100/240000] lr: 5.8765e-03 eta: 1 day, 2:22:48 time: 0.7141 data_time: 0.3916 memory: 17394 loss: 0.2035 decode.loss_ce: 0.1303 decode.acc_seg: 93.7405 aux.loss_ce: 0.0732 aux.acc_seg: 91.5658 2023/06/08 06:01:55 - mmengine - INFO - Iter(train) [108150/240000] lr: 5.8746e-03 eta: 1 day, 2:22:11 time: 0.7123 data_time: 0.3898 memory: 17396 loss: 0.1984 decode.loss_ce: 0.1254 decode.acc_seg: 95.6372 aux.loss_ce: 0.0731 aux.acc_seg: 93.2392 2023/06/08 06:02:31 - mmengine - INFO - Iter(train) [108200/240000] lr: 5.8726e-03 eta: 1 day, 2:21:35 time: 0.7022 data_time: 0.3796 memory: 17393 loss: 0.1961 decode.loss_ce: 0.1253 decode.acc_seg: 94.3382 aux.loss_ce: 0.0708 aux.acc_seg: 92.2533 2023/06/08 06:03:06 - mmengine - INFO - Iter(train) [108250/240000] lr: 5.8706e-03 eta: 1 day, 2:20:58 time: 0.7026 data_time: 0.3801 memory: 17398 loss: 0.2009 decode.loss_ce: 0.1284 decode.acc_seg: 92.7666 aux.loss_ce: 0.0726 aux.acc_seg: 90.8601 2023/06/08 06:03:41 - mmengine - INFO - Iter(train) [108300/240000] lr: 5.8686e-03 eta: 1 day, 2:20:21 time: 0.6950 data_time: 0.3721 memory: 17393 loss: 0.2246 decode.loss_ce: 0.1437 decode.acc_seg: 94.8178 aux.loss_ce: 0.0808 aux.acc_seg: 91.5637 2023/06/08 06:04:17 - mmengine - INFO - Iter(train) [108350/240000] lr: 5.8667e-03 eta: 1 day, 2:19:45 time: 0.7158 data_time: 0.3930 memory: 17394 loss: 0.2142 decode.loss_ce: 0.1388 decode.acc_seg: 94.3572 aux.loss_ce: 0.0753 aux.acc_seg: 90.8592 2023/06/08 06:04:53 - mmengine - INFO - Iter(train) [108400/240000] lr: 5.8647e-03 eta: 1 day, 2:19:08 time: 0.7267 data_time: 0.4038 memory: 17393 loss: 0.1985 decode.loss_ce: 0.1232 decode.acc_seg: 95.2691 aux.loss_ce: 0.0753 aux.acc_seg: 93.0413 2023/06/08 06:05:28 - mmengine - INFO - Iter(train) [108450/240000] lr: 5.8627e-03 eta: 1 day, 2:18:32 time: 0.7246 data_time: 0.4015 memory: 17395 loss: 0.1937 decode.loss_ce: 0.1230 decode.acc_seg: 94.5439 aux.loss_ce: 0.0707 aux.acc_seg: 92.1911 2023/06/08 06:06:03 - mmengine - INFO - Iter(train) [108500/240000] lr: 5.8608e-03 eta: 1 day, 2:17:55 time: 0.7115 data_time: 0.3887 memory: 17394 loss: 0.1917 decode.loss_ce: 0.1212 decode.acc_seg: 94.2658 aux.loss_ce: 0.0705 aux.acc_seg: 91.7900 2023/06/08 06:06:39 - mmengine - INFO - Iter(train) [108550/240000] lr: 5.8588e-03 eta: 1 day, 2:17:18 time: 0.6929 data_time: 0.3701 memory: 17395 loss: 0.1867 decode.loss_ce: 0.1197 decode.acc_seg: 95.6824 aux.loss_ce: 0.0670 aux.acc_seg: 93.7815 2023/06/08 06:07:14 - mmengine - INFO - Iter(train) [108600/240000] lr: 5.8568e-03 eta: 1 day, 2:16:42 time: 0.7165 data_time: 0.3938 memory: 17397 loss: 0.1836 decode.loss_ce: 0.1146 decode.acc_seg: 93.8461 aux.loss_ce: 0.0690 aux.acc_seg: 90.5583 2023/06/08 06:07:50 - mmengine - INFO - Iter(train) [108650/240000] lr: 5.8548e-03 eta: 1 day, 2:16:05 time: 0.6970 data_time: 0.3739 memory: 17393 loss: 0.2058 decode.loss_ce: 0.1317 decode.acc_seg: 93.2969 aux.loss_ce: 0.0741 aux.acc_seg: 90.7179 2023/06/08 06:08:25 - mmengine - INFO - Iter(train) [108700/240000] lr: 5.8529e-03 eta: 1 day, 2:15:28 time: 0.7168 data_time: 0.2271 memory: 17397 loss: 0.2068 decode.loss_ce: 0.1322 decode.acc_seg: 94.3415 aux.loss_ce: 0.0746 aux.acc_seg: 91.8240 2023/06/08 06:09:01 - mmengine - INFO - Iter(train) [108750/240000] lr: 5.8509e-03 eta: 1 day, 2:14:52 time: 0.7170 data_time: 0.0485 memory: 17396 loss: 0.2105 decode.loss_ce: 0.1339 decode.acc_seg: 95.7801 aux.loss_ce: 0.0766 aux.acc_seg: 94.0276 2023/06/08 06:09:37 - mmengine - INFO - Iter(train) [108800/240000] lr: 5.8489e-03 eta: 1 day, 2:14:16 time: 0.7119 data_time: 0.0290 memory: 17391 loss: 0.2089 decode.loss_ce: 0.1330 decode.acc_seg: 93.4705 aux.loss_ce: 0.0760 aux.acc_seg: 91.0727 2023/06/08 06:10:12 - mmengine - INFO - Iter(train) [108850/240000] lr: 5.8470e-03 eta: 1 day, 2:13:39 time: 0.7241 data_time: 0.0283 memory: 17395 loss: 0.2043 decode.loss_ce: 0.1304 decode.acc_seg: 93.2304 aux.loss_ce: 0.0739 aux.acc_seg: 88.4330 2023/06/08 06:10:48 - mmengine - INFO - Iter(train) [108900/240000] lr: 5.8450e-03 eta: 1 day, 2:13:02 time: 0.7133 data_time: 0.0313 memory: 17394 loss: 0.2037 decode.loss_ce: 0.1326 decode.acc_seg: 96.1985 aux.loss_ce: 0.0711 aux.acc_seg: 94.7940 2023/06/08 06:11:24 - mmengine - INFO - Iter(train) [108950/240000] lr: 5.8430e-03 eta: 1 day, 2:12:26 time: 0.7197 data_time: 0.0204 memory: 17394 loss: 0.1995 decode.loss_ce: 0.1278 decode.acc_seg: 93.0608 aux.loss_ce: 0.0717 aux.acc_seg: 89.4701 2023/06/08 06:11:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 06:11:59 - mmengine - INFO - Iter(train) [109000/240000] lr: 5.8410e-03 eta: 1 day, 2:11:50 time: 0.7187 data_time: 0.0123 memory: 17392 loss: 0.2177 decode.loss_ce: 0.1373 decode.acc_seg: 92.0403 aux.loss_ce: 0.0804 aux.acc_seg: 89.6163 2023/06/08 06:12:35 - mmengine - INFO - Iter(train) [109050/240000] lr: 5.8391e-03 eta: 1 day, 2:11:13 time: 0.7155 data_time: 0.0122 memory: 17397 loss: 0.2028 decode.loss_ce: 0.1284 decode.acc_seg: 95.4486 aux.loss_ce: 0.0745 aux.acc_seg: 93.2130 2023/06/08 06:13:10 - mmengine - INFO - Iter(train) [109100/240000] lr: 5.8371e-03 eta: 1 day, 2:10:37 time: 0.7087 data_time: 0.0577 memory: 17394 loss: 0.1956 decode.loss_ce: 0.1259 decode.acc_seg: 92.8245 aux.loss_ce: 0.0697 aux.acc_seg: 89.1493 2023/06/08 06:13:46 - mmengine - INFO - Iter(train) [109150/240000] lr: 5.8351e-03 eta: 1 day, 2:10:00 time: 0.7114 data_time: 0.1005 memory: 17396 loss: 0.1859 decode.loss_ce: 0.1177 decode.acc_seg: 93.3443 aux.loss_ce: 0.0682 aux.acc_seg: 90.4911 2023/06/08 06:14:21 - mmengine - INFO - Iter(train) [109200/240000] lr: 5.8332e-03 eta: 1 day, 2:09:23 time: 0.7169 data_time: 0.3881 memory: 17396 loss: 0.2139 decode.loss_ce: 0.1377 decode.acc_seg: 95.3644 aux.loss_ce: 0.0763 aux.acc_seg: 93.1338 2023/06/08 06:14:56 - mmengine - INFO - Iter(train) [109250/240000] lr: 5.8312e-03 eta: 1 day, 2:08:46 time: 0.7018 data_time: 0.3627 memory: 17394 loss: 0.1825 decode.loss_ce: 0.1162 decode.acc_seg: 95.3281 aux.loss_ce: 0.0663 aux.acc_seg: 92.5856 2023/06/08 06:15:32 - mmengine - INFO - Iter(train) [109300/240000] lr: 5.8292e-03 eta: 1 day, 2:08:10 time: 0.6979 data_time: 0.1493 memory: 17391 loss: 0.1818 decode.loss_ce: 0.1153 decode.acc_seg: 94.5633 aux.loss_ce: 0.0665 aux.acc_seg: 93.0020 2023/06/08 06:16:07 - mmengine - INFO - Iter(train) [109350/240000] lr: 5.8272e-03 eta: 1 day, 2:07:33 time: 0.7223 data_time: 0.0162 memory: 17396 loss: 0.1941 decode.loss_ce: 0.1224 decode.acc_seg: 94.9233 aux.loss_ce: 0.0717 aux.acc_seg: 89.4811 2023/06/08 06:16:43 - mmengine - INFO - Iter(train) [109400/240000] lr: 5.8253e-03 eta: 1 day, 2:06:56 time: 0.6990 data_time: 0.0124 memory: 17395 loss: 0.1988 decode.loss_ce: 0.1260 decode.acc_seg: 94.7859 aux.loss_ce: 0.0728 aux.acc_seg: 92.6139 2023/06/08 06:17:18 - mmengine - INFO - Iter(train) [109450/240000] lr: 5.8233e-03 eta: 1 day, 2:06:20 time: 0.7222 data_time: 0.0163 memory: 17394 loss: 0.2012 decode.loss_ce: 0.1258 decode.acc_seg: 95.2544 aux.loss_ce: 0.0754 aux.acc_seg: 92.9044 2023/06/08 06:17:54 - mmengine - INFO - Iter(train) [109500/240000] lr: 5.8213e-03 eta: 1 day, 2:05:43 time: 0.7202 data_time: 0.1569 memory: 17395 loss: 0.1940 decode.loss_ce: 0.1251 decode.acc_seg: 93.9162 aux.loss_ce: 0.0689 aux.acc_seg: 91.6375 2023/06/08 06:18:29 - mmengine - INFO - Iter(train) [109550/240000] lr: 5.8193e-03 eta: 1 day, 2:05:07 time: 0.7083 data_time: 0.0396 memory: 17394 loss: 0.2072 decode.loss_ce: 0.1317 decode.acc_seg: 94.5191 aux.loss_ce: 0.0755 aux.acc_seg: 92.0088 2023/06/08 06:19:05 - mmengine - INFO - Iter(train) [109600/240000] lr: 5.8174e-03 eta: 1 day, 2:04:30 time: 0.7096 data_time: 0.0119 memory: 17392 loss: 0.1923 decode.loss_ce: 0.1224 decode.acc_seg: 93.2531 aux.loss_ce: 0.0699 aux.acc_seg: 91.3493 2023/06/08 06:19:40 - mmengine - INFO - Iter(train) [109650/240000] lr: 5.8154e-03 eta: 1 day, 2:03:53 time: 0.7031 data_time: 0.0869 memory: 17396 loss: 0.1989 decode.loss_ce: 0.1273 decode.acc_seg: 94.5268 aux.loss_ce: 0.0716 aux.acc_seg: 92.4097 2023/06/08 06:20:16 - mmengine - INFO - Iter(train) [109700/240000] lr: 5.8134e-03 eta: 1 day, 2:03:17 time: 0.7158 data_time: 0.3927 memory: 17396 loss: 0.2695 decode.loss_ce: 0.1730 decode.acc_seg: 93.2843 aux.loss_ce: 0.0964 aux.acc_seg: 91.2394 2023/06/08 06:20:51 - mmengine - INFO - Iter(train) [109750/240000] lr: 5.8115e-03 eta: 1 day, 2:02:40 time: 0.6970 data_time: 0.3738 memory: 17396 loss: 0.2009 decode.loss_ce: 0.1265 decode.acc_seg: 94.9436 aux.loss_ce: 0.0744 aux.acc_seg: 92.8220 2023/06/08 06:21:27 - mmengine - INFO - Iter(train) [109800/240000] lr: 5.8095e-03 eta: 1 day, 2:02:04 time: 0.7006 data_time: 0.3774 memory: 17394 loss: 0.2182 decode.loss_ce: 0.1366 decode.acc_seg: 93.7256 aux.loss_ce: 0.0816 aux.acc_seg: 91.5168 2023/06/08 06:22:02 - mmengine - INFO - Iter(train) [109850/240000] lr: 5.8075e-03 eta: 1 day, 2:01:27 time: 0.7001 data_time: 0.3771 memory: 17395 loss: 0.2172 decode.loss_ce: 0.1382 decode.acc_seg: 93.3275 aux.loss_ce: 0.0790 aux.acc_seg: 89.6085 2023/06/08 06:22:37 - mmengine - INFO - Iter(train) [109900/240000] lr: 5.8055e-03 eta: 1 day, 2:00:50 time: 0.7069 data_time: 0.3840 memory: 17396 loss: 0.2170 decode.loss_ce: 0.1378 decode.acc_seg: 94.4188 aux.loss_ce: 0.0792 aux.acc_seg: 90.9498 2023/06/08 06:23:13 - mmengine - INFO - Iter(train) [109950/240000] lr: 5.8036e-03 eta: 1 day, 2:00:14 time: 0.7229 data_time: 0.3999 memory: 17393 loss: 0.2311 decode.loss_ce: 0.1478 decode.acc_seg: 92.7675 aux.loss_ce: 0.0832 aux.acc_seg: 89.9498 2023/06/08 06:23:49 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 06:23:49 - mmengine - INFO - Iter(train) [110000/240000] lr: 5.8016e-03 eta: 1 day, 1:59:37 time: 0.7211 data_time: 0.3974 memory: 17393 loss: 0.2270 decode.loss_ce: 0.1412 decode.acc_seg: 94.5747 aux.loss_ce: 0.0858 aux.acc_seg: 91.1923 2023/06/08 06:24:24 - mmengine - INFO - Iter(train) [110050/240000] lr: 5.7996e-03 eta: 1 day, 1:59:01 time: 0.7014 data_time: 0.3784 memory: 17393 loss: 0.2214 decode.loss_ce: 0.1403 decode.acc_seg: 95.1795 aux.loss_ce: 0.0811 aux.acc_seg: 91.2245 2023/06/08 06:25:00 - mmengine - INFO - Iter(train) [110100/240000] lr: 5.7976e-03 eta: 1 day, 1:58:24 time: 0.7027 data_time: 0.3790 memory: 17395 loss: 0.1971 decode.loss_ce: 0.1258 decode.acc_seg: 95.3033 aux.loss_ce: 0.0712 aux.acc_seg: 92.9104 2023/06/08 06:25:35 - mmengine - INFO - Iter(train) [110150/240000] lr: 5.7957e-03 eta: 1 day, 1:57:47 time: 0.7079 data_time: 0.3849 memory: 17397 loss: 0.1970 decode.loss_ce: 0.1252 decode.acc_seg: 94.6044 aux.loss_ce: 0.0718 aux.acc_seg: 92.2482 2023/06/08 06:26:10 - mmengine - INFO - Iter(train) [110200/240000] lr: 5.7937e-03 eta: 1 day, 1:57:11 time: 0.7092 data_time: 0.3861 memory: 17393 loss: 0.2242 decode.loss_ce: 0.1410 decode.acc_seg: 93.5810 aux.loss_ce: 0.0831 aux.acc_seg: 90.3592 2023/06/08 06:26:46 - mmengine - INFO - Iter(train) [110250/240000] lr: 5.7917e-03 eta: 1 day, 1:56:34 time: 0.7170 data_time: 0.3939 memory: 17393 loss: 0.2033 decode.loss_ce: 0.1299 decode.acc_seg: 92.8840 aux.loss_ce: 0.0733 aux.acc_seg: 90.7279 2023/06/08 06:27:21 - mmengine - INFO - Iter(train) [110300/240000] lr: 5.7897e-03 eta: 1 day, 1:55:57 time: 0.7102 data_time: 0.3871 memory: 17394 loss: 0.2170 decode.loss_ce: 0.1366 decode.acc_seg: 95.4926 aux.loss_ce: 0.0804 aux.acc_seg: 91.1274 2023/06/08 06:27:57 - mmengine - INFO - Iter(train) [110350/240000] lr: 5.7878e-03 eta: 1 day, 1:55:21 time: 0.7201 data_time: 0.3967 memory: 17393 loss: 0.2185 decode.loss_ce: 0.1408 decode.acc_seg: 92.6167 aux.loss_ce: 0.0777 aux.acc_seg: 89.5990 2023/06/08 06:28:32 - mmengine - INFO - Iter(train) [110400/240000] lr: 5.7858e-03 eta: 1 day, 1:54:44 time: 0.7041 data_time: 0.3809 memory: 17394 loss: 0.2126 decode.loss_ce: 0.1365 decode.acc_seg: 93.5867 aux.loss_ce: 0.0761 aux.acc_seg: 90.2179 2023/06/08 06:29:07 - mmengine - INFO - Iter(train) [110450/240000] lr: 5.7838e-03 eta: 1 day, 1:54:07 time: 0.7090 data_time: 0.3858 memory: 17395 loss: 0.1956 decode.loss_ce: 0.1251 decode.acc_seg: 91.9339 aux.loss_ce: 0.0704 aux.acc_seg: 90.2976 2023/06/08 06:29:43 - mmengine - INFO - Iter(train) [110500/240000] lr: 5.7818e-03 eta: 1 day, 1:53:31 time: 0.7079 data_time: 0.3850 memory: 17394 loss: 0.2054 decode.loss_ce: 0.1297 decode.acc_seg: 94.1863 aux.loss_ce: 0.0757 aux.acc_seg: 88.7860 2023/06/08 06:30:18 - mmengine - INFO - Iter(train) [110550/240000] lr: 5.7799e-03 eta: 1 day, 1:52:54 time: 0.7175 data_time: 0.3947 memory: 17394 loss: 0.2066 decode.loss_ce: 0.1308 decode.acc_seg: 95.1957 aux.loss_ce: 0.0758 aux.acc_seg: 92.5840 2023/06/08 06:30:54 - mmengine - INFO - Iter(train) [110600/240000] lr: 5.7779e-03 eta: 1 day, 1:52:17 time: 0.7151 data_time: 0.3315 memory: 17393 loss: 0.1956 decode.loss_ce: 0.1244 decode.acc_seg: 93.2673 aux.loss_ce: 0.0712 aux.acc_seg: 90.3962 2023/06/08 06:31:29 - mmengine - INFO - Iter(train) [110650/240000] lr: 5.7759e-03 eta: 1 day, 1:51:41 time: 0.7138 data_time: 0.3769 memory: 17397 loss: 0.1856 decode.loss_ce: 0.1182 decode.acc_seg: 93.9733 aux.loss_ce: 0.0674 aux.acc_seg: 91.9928 2023/06/08 06:32:05 - mmengine - INFO - Iter(train) [110700/240000] lr: 5.7739e-03 eta: 1 day, 1:51:04 time: 0.7039 data_time: 0.3728 memory: 17398 loss: 0.2458 decode.loss_ce: 0.1568 decode.acc_seg: 94.6520 aux.loss_ce: 0.0891 aux.acc_seg: 90.5611 2023/06/08 06:32:40 - mmengine - INFO - Iter(train) [110750/240000] lr: 5.7720e-03 eta: 1 day, 1:50:27 time: 0.7020 data_time: 0.3696 memory: 17394 loss: 0.2339 decode.loss_ce: 0.1525 decode.acc_seg: 93.8331 aux.loss_ce: 0.0814 aux.acc_seg: 90.5514 2023/06/08 06:33:16 - mmengine - INFO - Iter(train) [110800/240000] lr: 5.7700e-03 eta: 1 day, 1:49:51 time: 0.7139 data_time: 0.3899 memory: 17394 loss: 0.2047 decode.loss_ce: 0.1309 decode.acc_seg: 93.6588 aux.loss_ce: 0.0738 aux.acc_seg: 91.5617 2023/06/08 06:33:51 - mmengine - INFO - Iter(train) [110850/240000] lr: 5.7680e-03 eta: 1 day, 1:49:15 time: 0.7101 data_time: 0.3871 memory: 17396 loss: 0.2137 decode.loss_ce: 0.1383 decode.acc_seg: 94.1785 aux.loss_ce: 0.0754 aux.acc_seg: 92.2684 2023/06/08 06:34:27 - mmengine - INFO - Iter(train) [110900/240000] lr: 5.7660e-03 eta: 1 day, 1:48:38 time: 0.7107 data_time: 0.3876 memory: 17393 loss: 0.2171 decode.loss_ce: 0.1396 decode.acc_seg: 92.4724 aux.loss_ce: 0.0776 aux.acc_seg: 90.3365 2023/06/08 06:35:02 - mmengine - INFO - Iter(train) [110950/240000] lr: 5.7641e-03 eta: 1 day, 1:48:01 time: 0.7121 data_time: 0.3890 memory: 17394 loss: 0.2191 decode.loss_ce: 0.1415 decode.acc_seg: 92.9419 aux.loss_ce: 0.0776 aux.acc_seg: 92.0033 2023/06/08 06:35:38 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 06:35:38 - mmengine - INFO - Iter(train) [111000/240000] lr: 5.7621e-03 eta: 1 day, 1:47:25 time: 0.7166 data_time: 0.3942 memory: 17395 loss: 0.1856 decode.loss_ce: 0.1165 decode.acc_seg: 94.9937 aux.loss_ce: 0.0692 aux.acc_seg: 92.3442 2023/06/08 06:36:13 - mmengine - INFO - Iter(train) [111050/240000] lr: 5.7601e-03 eta: 1 day, 1:46:48 time: 0.7070 data_time: 0.3837 memory: 17391 loss: 0.2224 decode.loss_ce: 0.1432 decode.acc_seg: 92.0237 aux.loss_ce: 0.0792 aux.acc_seg: 89.0557 2023/06/08 06:36:48 - mmengine - INFO - Iter(train) [111100/240000] lr: 5.7581e-03 eta: 1 day, 1:46:11 time: 0.7006 data_time: 0.3773 memory: 17395 loss: 0.2240 decode.loss_ce: 0.1434 decode.acc_seg: 95.7406 aux.loss_ce: 0.0806 aux.acc_seg: 93.5846 2023/06/08 06:37:24 - mmengine - INFO - Iter(train) [111150/240000] lr: 5.7562e-03 eta: 1 day, 1:45:35 time: 0.7129 data_time: 0.3899 memory: 17393 loss: 0.1894 decode.loss_ce: 0.1187 decode.acc_seg: 94.9396 aux.loss_ce: 0.0707 aux.acc_seg: 92.8708 2023/06/08 06:38:00 - mmengine - INFO - Iter(train) [111200/240000] lr: 5.7542e-03 eta: 1 day, 1:44:59 time: 0.7175 data_time: 0.3953 memory: 17392 loss: 0.1893 decode.loss_ce: 0.1189 decode.acc_seg: 94.5665 aux.loss_ce: 0.0704 aux.acc_seg: 90.5640 2023/06/08 06:38:36 - mmengine - INFO - Iter(train) [111250/240000] lr: 5.7522e-03 eta: 1 day, 1:44:23 time: 0.7189 data_time: 0.3959 memory: 17392 loss: 0.2036 decode.loss_ce: 0.1267 decode.acc_seg: 94.0188 aux.loss_ce: 0.0769 aux.acc_seg: 89.8326 2023/06/08 06:39:11 - mmengine - INFO - Iter(train) [111300/240000] lr: 5.7502e-03 eta: 1 day, 1:43:46 time: 0.7132 data_time: 0.3901 memory: 17394 loss: 0.1861 decode.loss_ce: 0.1190 decode.acc_seg: 94.3854 aux.loss_ce: 0.0671 aux.acc_seg: 92.2353 2023/06/08 06:39:47 - mmengine - INFO - Iter(train) [111350/240000] lr: 5.7483e-03 eta: 1 day, 1:43:10 time: 0.6970 data_time: 0.3743 memory: 17393 loss: 0.2063 decode.loss_ce: 0.1283 decode.acc_seg: 94.9449 aux.loss_ce: 0.0780 aux.acc_seg: 92.8613 2023/06/08 06:40:22 - mmengine - INFO - Iter(train) [111400/240000] lr: 5.7463e-03 eta: 1 day, 1:42:33 time: 0.7055 data_time: 0.3827 memory: 17395 loss: 0.2095 decode.loss_ce: 0.1315 decode.acc_seg: 95.7620 aux.loss_ce: 0.0780 aux.acc_seg: 93.6417 2023/06/08 06:40:58 - mmengine - INFO - Iter(train) [111450/240000] lr: 5.7443e-03 eta: 1 day, 1:41:57 time: 0.7174 data_time: 0.3947 memory: 17392 loss: 0.2155 decode.loss_ce: 0.1394 decode.acc_seg: 94.5402 aux.loss_ce: 0.0761 aux.acc_seg: 92.8820 2023/06/08 06:41:34 - mmengine - INFO - Iter(train) [111500/240000] lr: 5.7423e-03 eta: 1 day, 1:41:20 time: 0.7071 data_time: 0.3843 memory: 17392 loss: 0.2080 decode.loss_ce: 0.1345 decode.acc_seg: 94.1847 aux.loss_ce: 0.0735 aux.acc_seg: 92.1857 2023/06/08 06:42:09 - mmengine - INFO - Iter(train) [111550/240000] lr: 5.7404e-03 eta: 1 day, 1:40:43 time: 0.7061 data_time: 0.3828 memory: 17391 loss: 0.2109 decode.loss_ce: 0.1328 decode.acc_seg: 93.7573 aux.loss_ce: 0.0781 aux.acc_seg: 91.8924 2023/06/08 06:42:44 - mmengine - INFO - Iter(train) [111600/240000] lr: 5.7384e-03 eta: 1 day, 1:40:07 time: 0.7094 data_time: 0.3863 memory: 17392 loss: 0.2059 decode.loss_ce: 0.1333 decode.acc_seg: 89.3776 aux.loss_ce: 0.0726 aux.acc_seg: 88.5048 2023/06/08 06:43:20 - mmengine - INFO - Iter(train) [111650/240000] lr: 5.7364e-03 eta: 1 day, 1:39:30 time: 0.7128 data_time: 0.3900 memory: 17396 loss: 0.2238 decode.loss_ce: 0.1423 decode.acc_seg: 92.6201 aux.loss_ce: 0.0815 aux.acc_seg: 89.0551 2023/06/08 06:43:55 - mmengine - INFO - Iter(train) [111700/240000] lr: 5.7344e-03 eta: 1 day, 1:38:54 time: 0.7060 data_time: 0.3832 memory: 17397 loss: 0.1875 decode.loss_ce: 0.1187 decode.acc_seg: 94.3925 aux.loss_ce: 0.0688 aux.acc_seg: 92.1473 2023/06/08 06:44:31 - mmengine - INFO - Iter(train) [111750/240000] lr: 5.7325e-03 eta: 1 day, 1:38:17 time: 0.7047 data_time: 0.3816 memory: 17392 loss: 0.2113 decode.loss_ce: 0.1318 decode.acc_seg: 93.8956 aux.loss_ce: 0.0795 aux.acc_seg: 91.4646 2023/06/08 06:45:07 - mmengine - INFO - Iter(train) [111800/240000] lr: 5.7305e-03 eta: 1 day, 1:37:41 time: 0.7063 data_time: 0.3836 memory: 17396 loss: 0.2040 decode.loss_ce: 0.1294 decode.acc_seg: 95.3058 aux.loss_ce: 0.0746 aux.acc_seg: 93.3795 2023/06/08 06:45:42 - mmengine - INFO - Iter(train) [111850/240000] lr: 5.7285e-03 eta: 1 day, 1:37:05 time: 0.7096 data_time: 0.3866 memory: 17395 loss: 0.1985 decode.loss_ce: 0.1270 decode.acc_seg: 90.7203 aux.loss_ce: 0.0715 aux.acc_seg: 87.8720 2023/06/08 06:46:18 - mmengine - INFO - Iter(train) [111900/240000] lr: 5.7265e-03 eta: 1 day, 1:36:28 time: 0.7062 data_time: 0.3831 memory: 17396 loss: 0.1999 decode.loss_ce: 0.1278 decode.acc_seg: 93.3465 aux.loss_ce: 0.0721 aux.acc_seg: 91.0654 2023/06/08 06:46:53 - mmengine - INFO - Iter(train) [111950/240000] lr: 5.7246e-03 eta: 1 day, 1:35:52 time: 0.7067 data_time: 0.3840 memory: 17394 loss: 0.1887 decode.loss_ce: 0.1200 decode.acc_seg: 95.2841 aux.loss_ce: 0.0687 aux.acc_seg: 92.1520 2023/06/08 06:47:29 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 06:47:29 - mmengine - INFO - Iter(train) [112000/240000] lr: 5.7226e-03 eta: 1 day, 1:35:15 time: 0.7135 data_time: 0.3906 memory: 17394 loss: 0.2208 decode.loss_ce: 0.1457 decode.acc_seg: 93.0773 aux.loss_ce: 0.0750 aux.acc_seg: 90.9935 2023/06/08 06:48:04 - mmengine - INFO - Iter(train) [112050/240000] lr: 5.7206e-03 eta: 1 day, 1:34:38 time: 0.7053 data_time: 0.3827 memory: 17393 loss: 0.2075 decode.loss_ce: 0.1332 decode.acc_seg: 94.0491 aux.loss_ce: 0.0743 aux.acc_seg: 92.7227 2023/06/08 06:48:39 - mmengine - INFO - Iter(train) [112100/240000] lr: 5.7186e-03 eta: 1 day, 1:34:01 time: 0.7005 data_time: 0.3768 memory: 17393 loss: 0.1957 decode.loss_ce: 0.1244 decode.acc_seg: 94.8401 aux.loss_ce: 0.0713 aux.acc_seg: 91.6840 2023/06/08 06:49:15 - mmengine - INFO - Iter(train) [112150/240000] lr: 5.7167e-03 eta: 1 day, 1:33:25 time: 0.7104 data_time: 0.3873 memory: 17394 loss: 0.1878 decode.loss_ce: 0.1189 decode.acc_seg: 94.8342 aux.loss_ce: 0.0689 aux.acc_seg: 92.9117 2023/06/08 06:49:50 - mmengine - INFO - Iter(train) [112200/240000] lr: 5.7147e-03 eta: 1 day, 1:32:48 time: 0.6983 data_time: 0.2018 memory: 17395 loss: 0.1775 decode.loss_ce: 0.1106 decode.acc_seg: 94.8141 aux.loss_ce: 0.0669 aux.acc_seg: 93.0578 2023/06/08 06:50:26 - mmengine - INFO - Iter(train) [112250/240000] lr: 5.7127e-03 eta: 1 day, 1:32:12 time: 0.7127 data_time: 0.1645 memory: 17395 loss: 0.1906 decode.loss_ce: 0.1198 decode.acc_seg: 95.3786 aux.loss_ce: 0.0708 aux.acc_seg: 93.6789 2023/06/08 06:51:02 - mmengine - INFO - Iter(train) [112300/240000] lr: 5.7107e-03 eta: 1 day, 1:31:36 time: 0.7092 data_time: 0.3151 memory: 17392 loss: 0.2200 decode.loss_ce: 0.1407 decode.acc_seg: 94.2422 aux.loss_ce: 0.0793 aux.acc_seg: 91.5367 2023/06/08 06:51:37 - mmengine - INFO - Iter(train) [112350/240000] lr: 5.7087e-03 eta: 1 day, 1:30:59 time: 0.7090 data_time: 0.3861 memory: 17395 loss: 0.2013 decode.loss_ce: 0.1286 decode.acc_seg: 94.2497 aux.loss_ce: 0.0726 aux.acc_seg: 91.6866 2023/06/08 06:52:13 - mmengine - INFO - Iter(train) [112400/240000] lr: 5.7068e-03 eta: 1 day, 1:30:23 time: 0.7149 data_time: 0.1815 memory: 17392 loss: 0.1960 decode.loss_ce: 0.1236 decode.acc_seg: 93.7202 aux.loss_ce: 0.0724 aux.acc_seg: 90.5101 2023/06/08 06:52:48 - mmengine - INFO - Iter(train) [112450/240000] lr: 5.7048e-03 eta: 1 day, 1:29:46 time: 0.7106 data_time: 0.3039 memory: 17394 loss: 0.2043 decode.loss_ce: 0.1317 decode.acc_seg: 94.5302 aux.loss_ce: 0.0727 aux.acc_seg: 92.4796 2023/06/08 06:53:24 - mmengine - INFO - Iter(train) [112500/240000] lr: 5.7028e-03 eta: 1 day, 1:29:10 time: 0.7123 data_time: 0.0321 memory: 17393 loss: 0.1868 decode.loss_ce: 0.1174 decode.acc_seg: 95.5583 aux.loss_ce: 0.0694 aux.acc_seg: 93.2776 2023/06/08 06:54:00 - mmengine - INFO - Iter(train) [112550/240000] lr: 5.7008e-03 eta: 1 day, 1:28:33 time: 0.7095 data_time: 0.0119 memory: 17394 loss: 0.2078 decode.loss_ce: 0.1327 decode.acc_seg: 95.7959 aux.loss_ce: 0.0751 aux.acc_seg: 93.7793 2023/06/08 06:54:35 - mmengine - INFO - Iter(train) [112600/240000] lr: 5.6989e-03 eta: 1 day, 1:27:57 time: 0.7164 data_time: 0.0121 memory: 17394 loss: 0.2298 decode.loss_ce: 0.1483 decode.acc_seg: 90.3265 aux.loss_ce: 0.0815 aux.acc_seg: 88.2394 2023/06/08 06:55:11 - mmengine - INFO - Iter(train) [112650/240000] lr: 5.6969e-03 eta: 1 day, 1:27:21 time: 0.7110 data_time: 0.0119 memory: 17395 loss: 0.2176 decode.loss_ce: 0.1393 decode.acc_seg: 94.7020 aux.loss_ce: 0.0783 aux.acc_seg: 92.3725 2023/06/08 06:55:47 - mmengine - INFO - Iter(train) [112700/240000] lr: 5.6949e-03 eta: 1 day, 1:26:44 time: 0.7210 data_time: 0.0124 memory: 17395 loss: 0.1963 decode.loss_ce: 0.1252 decode.acc_seg: 94.0912 aux.loss_ce: 0.0711 aux.acc_seg: 91.2599 2023/06/08 06:56:22 - mmengine - INFO - Iter(train) [112750/240000] lr: 5.6929e-03 eta: 1 day, 1:26:08 time: 0.7054 data_time: 0.0123 memory: 17393 loss: 0.2013 decode.loss_ce: 0.1282 decode.acc_seg: 94.4167 aux.loss_ce: 0.0731 aux.acc_seg: 90.5679 2023/06/08 06:56:57 - mmengine - INFO - Iter(train) [112800/240000] lr: 5.6909e-03 eta: 1 day, 1:25:31 time: 0.7168 data_time: 0.0122 memory: 17397 loss: 0.2214 decode.loss_ce: 0.1424 decode.acc_seg: 93.3143 aux.loss_ce: 0.0790 aux.acc_seg: 90.3926 2023/06/08 06:57:33 - mmengine - INFO - Iter(train) [112850/240000] lr: 5.6890e-03 eta: 1 day, 1:24:55 time: 0.7197 data_time: 0.0124 memory: 17395 loss: 0.2110 decode.loss_ce: 0.1333 decode.acc_seg: 92.7088 aux.loss_ce: 0.0777 aux.acc_seg: 90.5862 2023/06/08 06:58:09 - mmengine - INFO - Iter(train) [112900/240000] lr: 5.6870e-03 eta: 1 day, 1:24:18 time: 0.7068 data_time: 0.0125 memory: 17393 loss: 0.1956 decode.loss_ce: 0.1244 decode.acc_seg: 93.9908 aux.loss_ce: 0.0713 aux.acc_seg: 91.4579 2023/06/08 06:58:44 - mmengine - INFO - Iter(train) [112950/240000] lr: 5.6850e-03 eta: 1 day, 1:23:42 time: 0.7045 data_time: 0.0123 memory: 17394 loss: 0.1980 decode.loss_ce: 0.1259 decode.acc_seg: 95.1494 aux.loss_ce: 0.0721 aux.acc_seg: 91.6453 2023/06/08 06:59:20 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 06:59:20 - mmengine - INFO - Iter(train) [113000/240000] lr: 5.6830e-03 eta: 1 day, 1:23:05 time: 0.6904 data_time: 0.0123 memory: 17396 loss: 0.2223 decode.loss_ce: 0.1431 decode.acc_seg: 94.9981 aux.loss_ce: 0.0792 aux.acc_seg: 93.3893 2023/06/08 06:59:55 - mmengine - INFO - Iter(train) [113050/240000] lr: 5.6811e-03 eta: 1 day, 1:22:29 time: 0.7048 data_time: 0.0124 memory: 17394 loss: 0.2015 decode.loss_ce: 0.1303 decode.acc_seg: 95.4867 aux.loss_ce: 0.0712 aux.acc_seg: 93.3456 2023/06/08 07:00:31 - mmengine - INFO - Iter(train) [113100/240000] lr: 5.6791e-03 eta: 1 day, 1:21:52 time: 0.7042 data_time: 0.0123 memory: 17395 loss: 0.2142 decode.loss_ce: 0.1354 decode.acc_seg: 93.4763 aux.loss_ce: 0.0787 aux.acc_seg: 90.3025 2023/06/08 07:01:06 - mmengine - INFO - Iter(train) [113150/240000] lr: 5.6771e-03 eta: 1 day, 1:21:15 time: 0.6965 data_time: 0.0485 memory: 17394 loss: 0.1889 decode.loss_ce: 0.1210 decode.acc_seg: 95.1169 aux.loss_ce: 0.0679 aux.acc_seg: 93.0073 2023/06/08 07:01:41 - mmengine - INFO - Iter(train) [113200/240000] lr: 5.6751e-03 eta: 1 day, 1:20:39 time: 0.7074 data_time: 0.0224 memory: 17395 loss: 0.1872 decode.loss_ce: 0.1176 decode.acc_seg: 93.7117 aux.loss_ce: 0.0696 aux.acc_seg: 91.1429 2023/06/08 07:02:17 - mmengine - INFO - Iter(train) [113250/240000] lr: 5.6731e-03 eta: 1 day, 1:20:02 time: 0.7188 data_time: 0.1544 memory: 17392 loss: 0.2057 decode.loss_ce: 0.1295 decode.acc_seg: 93.9758 aux.loss_ce: 0.0762 aux.acc_seg: 91.2033 2023/06/08 07:02:52 - mmengine - INFO - Iter(train) [113300/240000] lr: 5.6712e-03 eta: 1 day, 1:19:26 time: 0.7131 data_time: 0.0877 memory: 17392 loss: 0.1896 decode.loss_ce: 0.1205 decode.acc_seg: 92.6582 aux.loss_ce: 0.0691 aux.acc_seg: 90.5101 2023/06/08 07:03:27 - mmengine - INFO - Iter(train) [113350/240000] lr: 5.6692e-03 eta: 1 day, 1:18:49 time: 0.7202 data_time: 0.1213 memory: 17395 loss: 0.1792 decode.loss_ce: 0.1156 decode.acc_seg: 96.3072 aux.loss_ce: 0.0635 aux.acc_seg: 94.6029 2023/06/08 07:04:03 - mmengine - INFO - Iter(train) [113400/240000] lr: 5.6672e-03 eta: 1 day, 1:18:13 time: 0.7139 data_time: 0.0872 memory: 17393 loss: 0.1905 decode.loss_ce: 0.1208 decode.acc_seg: 93.3333 aux.loss_ce: 0.0697 aux.acc_seg: 89.7424 2023/06/08 07:04:38 - mmengine - INFO - Iter(train) [113450/240000] lr: 5.6652e-03 eta: 1 day, 1:17:36 time: 0.7097 data_time: 0.0593 memory: 17392 loss: 0.1932 decode.loss_ce: 0.1198 decode.acc_seg: 94.5719 aux.loss_ce: 0.0734 aux.acc_seg: 91.1120 2023/06/08 07:05:14 - mmengine - INFO - Iter(train) [113500/240000] lr: 5.6632e-03 eta: 1 day, 1:17:00 time: 0.7118 data_time: 0.1565 memory: 17392 loss: 0.1801 decode.loss_ce: 0.1145 decode.acc_seg: 95.8221 aux.loss_ce: 0.0655 aux.acc_seg: 93.2263 2023/06/08 07:05:49 - mmengine - INFO - Iter(train) [113550/240000] lr: 5.6613e-03 eta: 1 day, 1:16:23 time: 0.7020 data_time: 0.3698 memory: 17394 loss: 0.2063 decode.loss_ce: 0.1329 decode.acc_seg: 93.7068 aux.loss_ce: 0.0734 aux.acc_seg: 90.7578 2023/06/08 07:06:25 - mmengine - INFO - Iter(train) [113600/240000] lr: 5.6593e-03 eta: 1 day, 1:15:46 time: 0.7094 data_time: 0.3766 memory: 17395 loss: 0.1931 decode.loss_ce: 0.1216 decode.acc_seg: 92.9728 aux.loss_ce: 0.0716 aux.acc_seg: 89.4403 2023/06/08 07:07:00 - mmengine - INFO - Iter(train) [113650/240000] lr: 5.6573e-03 eta: 1 day, 1:15:10 time: 0.7016 data_time: 0.3787 memory: 17396 loss: 0.1971 decode.loss_ce: 0.1260 decode.acc_seg: 95.3385 aux.loss_ce: 0.0711 aux.acc_seg: 93.3121 2023/06/08 07:07:36 - mmengine - INFO - Iter(train) [113700/240000] lr: 5.6553e-03 eta: 1 day, 1:14:33 time: 0.7008 data_time: 0.3782 memory: 17396 loss: 0.2100 decode.loss_ce: 0.1349 decode.acc_seg: 93.9465 aux.loss_ce: 0.0751 aux.acc_seg: 91.6472 2023/06/08 07:08:11 - mmengine - INFO - Iter(train) [113750/240000] lr: 5.6533e-03 eta: 1 day, 1:13:57 time: 0.7088 data_time: 0.3860 memory: 17398 loss: 0.2027 decode.loss_ce: 0.1294 decode.acc_seg: 91.8261 aux.loss_ce: 0.0733 aux.acc_seg: 88.7438 2023/06/08 07:08:47 - mmengine - INFO - Iter(train) [113800/240000] lr: 5.6514e-03 eta: 1 day, 1:13:20 time: 0.7271 data_time: 0.4043 memory: 17395 loss: 0.1902 decode.loss_ce: 0.1217 decode.acc_seg: 95.2485 aux.loss_ce: 0.0685 aux.acc_seg: 92.9644 2023/06/08 07:09:23 - mmengine - INFO - Iter(train) [113850/240000] lr: 5.6494e-03 eta: 1 day, 1:12:44 time: 0.7201 data_time: 0.3976 memory: 17393 loss: 0.1752 decode.loss_ce: 0.1110 decode.acc_seg: 94.7133 aux.loss_ce: 0.0642 aux.acc_seg: 93.1118 2023/06/08 07:09:58 - mmengine - INFO - Iter(train) [113900/240000] lr: 5.6474e-03 eta: 1 day, 1:12:07 time: 0.7071 data_time: 0.3841 memory: 17394 loss: 0.2165 decode.loss_ce: 0.1351 decode.acc_seg: 95.4885 aux.loss_ce: 0.0814 aux.acc_seg: 92.3659 2023/06/08 07:10:33 - mmengine - INFO - Iter(train) [113950/240000] lr: 5.6454e-03 eta: 1 day, 1:11:31 time: 0.7081 data_time: 0.3856 memory: 17391 loss: 0.2016 decode.loss_ce: 0.1290 decode.acc_seg: 93.3973 aux.loss_ce: 0.0726 aux.acc_seg: 91.4840 2023/06/08 07:11:09 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 07:11:09 - mmengine - INFO - Iter(train) [114000/240000] lr: 5.6435e-03 eta: 1 day, 1:10:54 time: 0.7112 data_time: 0.3880 memory: 17392 loss: 0.1975 decode.loss_ce: 0.1268 decode.acc_seg: 94.9643 aux.loss_ce: 0.0707 aux.acc_seg: 92.5659 2023/06/08 07:11:44 - mmengine - INFO - Iter(train) [114050/240000] lr: 5.6415e-03 eta: 1 day, 1:10:18 time: 0.7176 data_time: 0.3943 memory: 17392 loss: 0.2019 decode.loss_ce: 0.1287 decode.acc_seg: 93.1321 aux.loss_ce: 0.0732 aux.acc_seg: 91.4338 2023/06/08 07:12:20 - mmengine - INFO - Iter(train) [114100/240000] lr: 5.6395e-03 eta: 1 day, 1:09:41 time: 0.7108 data_time: 0.3876 memory: 17394 loss: 0.1975 decode.loss_ce: 0.1251 decode.acc_seg: 94.7913 aux.loss_ce: 0.0723 aux.acc_seg: 92.1629 2023/06/08 07:12:56 - mmengine - INFO - Iter(train) [114150/240000] lr: 5.6375e-03 eta: 1 day, 1:09:05 time: 0.7008 data_time: 0.3786 memory: 17394 loss: 0.2018 decode.loss_ce: 0.1280 decode.acc_seg: 95.1877 aux.loss_ce: 0.0738 aux.acc_seg: 91.5318 2023/06/08 07:13:31 - mmengine - INFO - Iter(train) [114200/240000] lr: 5.6355e-03 eta: 1 day, 1:08:29 time: 0.7171 data_time: 0.3943 memory: 17393 loss: 0.2177 decode.loss_ce: 0.1393 decode.acc_seg: 93.1472 aux.loss_ce: 0.0784 aux.acc_seg: 92.2297 2023/06/08 07:14:06 - mmengine - INFO - Iter(train) [114250/240000] lr: 5.6336e-03 eta: 1 day, 1:07:52 time: 0.7020 data_time: 0.3791 memory: 17394 loss: 0.2097 decode.loss_ce: 0.1340 decode.acc_seg: 93.2127 aux.loss_ce: 0.0757 aux.acc_seg: 90.0364 2023/06/08 07:14:42 - mmengine - INFO - Iter(train) [114300/240000] lr: 5.6316e-03 eta: 1 day, 1:07:15 time: 0.7018 data_time: 0.3515 memory: 17395 loss: 0.1991 decode.loss_ce: 0.1260 decode.acc_seg: 95.3399 aux.loss_ce: 0.0731 aux.acc_seg: 92.3309 2023/06/08 07:15:18 - mmengine - INFO - Iter(train) [114350/240000] lr: 5.6296e-03 eta: 1 day, 1:06:39 time: 0.7135 data_time: 0.3906 memory: 17393 loss: 0.1926 decode.loss_ce: 0.1231 decode.acc_seg: 93.3513 aux.loss_ce: 0.0695 aux.acc_seg: 91.4031 2023/06/08 07:15:53 - mmengine - INFO - Iter(train) [114400/240000] lr: 5.6276e-03 eta: 1 day, 1:06:03 time: 0.7030 data_time: 0.3800 memory: 17393 loss: 0.1949 decode.loss_ce: 0.1216 decode.acc_seg: 94.3911 aux.loss_ce: 0.0733 aux.acc_seg: 91.5560 2023/06/08 07:16:29 - mmengine - INFO - Iter(train) [114450/240000] lr: 5.6256e-03 eta: 1 day, 1:05:26 time: 0.7123 data_time: 0.3897 memory: 17392 loss: 0.1919 decode.loss_ce: 0.1203 decode.acc_seg: 94.3995 aux.loss_ce: 0.0715 aux.acc_seg: 92.9581 2023/06/08 07:17:05 - mmengine - INFO - Iter(train) [114500/240000] lr: 5.6236e-03 eta: 1 day, 1:04:50 time: 0.7091 data_time: 0.3858 memory: 17394 loss: 0.1938 decode.loss_ce: 0.1236 decode.acc_seg: 95.0086 aux.loss_ce: 0.0702 aux.acc_seg: 93.1439 2023/06/08 07:17:40 - mmengine - INFO - Iter(train) [114550/240000] lr: 5.6217e-03 eta: 1 day, 1:04:14 time: 0.7098 data_time: 0.3873 memory: 17395 loss: 0.1873 decode.loss_ce: 0.1171 decode.acc_seg: 93.0320 aux.loss_ce: 0.0702 aux.acc_seg: 90.2577 2023/06/08 07:18:16 - mmengine - INFO - Iter(train) [114600/240000] lr: 5.6197e-03 eta: 1 day, 1:03:37 time: 0.7158 data_time: 0.3926 memory: 17395 loss: 0.2101 decode.loss_ce: 0.1337 decode.acc_seg: 93.8912 aux.loss_ce: 0.0764 aux.acc_seg: 90.2277 2023/06/08 07:18:51 - mmengine - INFO - Iter(train) [114650/240000] lr: 5.6177e-03 eta: 1 day, 1:03:01 time: 0.7070 data_time: 0.3841 memory: 17395 loss: 0.1873 decode.loss_ce: 0.1171 decode.acc_seg: 94.1441 aux.loss_ce: 0.0702 aux.acc_seg: 91.6015 2023/06/08 07:19:27 - mmengine - INFO - Iter(train) [114700/240000] lr: 5.6157e-03 eta: 1 day, 1:02:24 time: 0.7205 data_time: 0.3976 memory: 17395 loss: 0.1932 decode.loss_ce: 0.1229 decode.acc_seg: 94.6534 aux.loss_ce: 0.0703 aux.acc_seg: 90.4713 2023/06/08 07:20:02 - mmengine - INFO - Iter(train) [114750/240000] lr: 5.6137e-03 eta: 1 day, 1:01:48 time: 0.7184 data_time: 0.3959 memory: 17392 loss: 0.1861 decode.loss_ce: 0.1182 decode.acc_seg: 94.8007 aux.loss_ce: 0.0679 aux.acc_seg: 92.8357 2023/06/08 07:20:38 - mmengine - INFO - Iter(train) [114800/240000] lr: 5.6118e-03 eta: 1 day, 1:01:11 time: 0.7105 data_time: 0.3876 memory: 17392 loss: 0.2164 decode.loss_ce: 0.1397 decode.acc_seg: 91.9691 aux.loss_ce: 0.0767 aux.acc_seg: 89.2532 2023/06/08 07:21:13 - mmengine - INFO - Iter(train) [114850/240000] lr: 5.6098e-03 eta: 1 day, 1:00:35 time: 0.7127 data_time: 0.3895 memory: 17394 loss: 0.2097 decode.loss_ce: 0.1324 decode.acc_seg: 95.7252 aux.loss_ce: 0.0773 aux.acc_seg: 93.2820 2023/06/08 07:21:49 - mmengine - INFO - Iter(train) [114900/240000] lr: 5.6078e-03 eta: 1 day, 0:59:58 time: 0.7189 data_time: 0.3956 memory: 17394 loss: 0.1894 decode.loss_ce: 0.1203 decode.acc_seg: 94.5506 aux.loss_ce: 0.0691 aux.acc_seg: 92.1966 2023/06/08 07:22:24 - mmengine - INFO - Iter(train) [114950/240000] lr: 5.6058e-03 eta: 1 day, 0:59:22 time: 0.7044 data_time: 0.3813 memory: 17396 loss: 0.1875 decode.loss_ce: 0.1201 decode.acc_seg: 94.4553 aux.loss_ce: 0.0674 aux.acc_seg: 92.5671 2023/06/08 07:22:59 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 07:22:59 - mmengine - INFO - Iter(train) [115000/240000] lr: 5.6038e-03 eta: 1 day, 0:58:45 time: 0.7108 data_time: 0.2712 memory: 17395 loss: 0.1864 decode.loss_ce: 0.1180 decode.acc_seg: 96.0097 aux.loss_ce: 0.0684 aux.acc_seg: 94.6514 2023/06/08 07:23:35 - mmengine - INFO - Iter(train) [115050/240000] lr: 5.6019e-03 eta: 1 day, 0:58:08 time: 0.7253 data_time: 0.2870 memory: 17395 loss: 0.2002 decode.loss_ce: 0.1259 decode.acc_seg: 94.5825 aux.loss_ce: 0.0744 aux.acc_seg: 91.2193 2023/06/08 07:24:10 - mmengine - INFO - Iter(train) [115100/240000] lr: 5.5999e-03 eta: 1 day, 0:57:32 time: 0.7068 data_time: 0.3144 memory: 17394 loss: 0.2012 decode.loss_ce: 0.1278 decode.acc_seg: 94.9503 aux.loss_ce: 0.0733 aux.acc_seg: 92.3150 2023/06/08 07:24:46 - mmengine - INFO - Iter(train) [115150/240000] lr: 5.5979e-03 eta: 1 day, 0:56:55 time: 0.7059 data_time: 0.3302 memory: 17393 loss: 0.2083 decode.loss_ce: 0.1342 decode.acc_seg: 95.5346 aux.loss_ce: 0.0741 aux.acc_seg: 93.0237 2023/06/08 07:25:22 - mmengine - INFO - Iter(train) [115200/240000] lr: 5.5959e-03 eta: 1 day, 0:56:19 time: 0.7023 data_time: 0.1397 memory: 17392 loss: 0.1905 decode.loss_ce: 0.1205 decode.acc_seg: 95.5483 aux.loss_ce: 0.0700 aux.acc_seg: 93.9340 2023/06/08 07:25:57 - mmengine - INFO - Iter(train) [115250/240000] lr: 5.5939e-03 eta: 1 day, 0:55:43 time: 0.7136 data_time: 0.1440 memory: 17393 loss: 0.1912 decode.loss_ce: 0.1192 decode.acc_seg: 95.9605 aux.loss_ce: 0.0720 aux.acc_seg: 94.2367 2023/06/08 07:26:32 - mmengine - INFO - Iter(train) [115300/240000] lr: 5.5919e-03 eta: 1 day, 0:55:06 time: 0.7106 data_time: 0.3637 memory: 17395 loss: 0.1919 decode.loss_ce: 0.1211 decode.acc_seg: 95.4184 aux.loss_ce: 0.0708 aux.acc_seg: 93.6096 2023/06/08 07:27:08 - mmengine - INFO - Iter(train) [115350/240000] lr: 5.5900e-03 eta: 1 day, 0:54:29 time: 0.7119 data_time: 0.3034 memory: 17396 loss: 0.2217 decode.loss_ce: 0.1401 decode.acc_seg: 90.7050 aux.loss_ce: 0.0815 aux.acc_seg: 87.5203 2023/06/08 07:27:43 - mmengine - INFO - Iter(train) [115400/240000] lr: 5.5880e-03 eta: 1 day, 0:53:53 time: 0.7081 data_time: 0.3852 memory: 17392 loss: 0.1834 decode.loss_ce: 0.1162 decode.acc_seg: 95.3525 aux.loss_ce: 0.0672 aux.acc_seg: 93.1444 2023/06/08 07:28:19 - mmengine - INFO - Iter(train) [115450/240000] lr: 5.5860e-03 eta: 1 day, 0:53:16 time: 0.7075 data_time: 0.2417 memory: 17395 loss: 0.1913 decode.loss_ce: 0.1216 decode.acc_seg: 95.1487 aux.loss_ce: 0.0697 aux.acc_seg: 92.7357 2023/06/08 07:28:55 - mmengine - INFO - Iter(train) [115500/240000] lr: 5.5840e-03 eta: 1 day, 0:52:40 time: 0.7077 data_time: 0.3742 memory: 17395 loss: 0.1935 decode.loss_ce: 0.1217 decode.acc_seg: 92.4359 aux.loss_ce: 0.0718 aux.acc_seg: 89.9935 2023/06/08 07:29:30 - mmengine - INFO - Iter(train) [115550/240000] lr: 5.5820e-03 eta: 1 day, 0:52:03 time: 0.7055 data_time: 0.3493 memory: 17395 loss: 0.1745 decode.loss_ce: 0.1103 decode.acc_seg: 94.4546 aux.loss_ce: 0.0642 aux.acc_seg: 91.6464 2023/06/08 07:30:05 - mmengine - INFO - Iter(train) [115600/240000] lr: 5.5801e-03 eta: 1 day, 0:51:27 time: 0.7099 data_time: 0.2970 memory: 17392 loss: 0.1875 decode.loss_ce: 0.1173 decode.acc_seg: 95.0602 aux.loss_ce: 0.0702 aux.acc_seg: 92.8045 2023/06/08 07:30:41 - mmengine - INFO - Iter(train) [115650/240000] lr: 5.5781e-03 eta: 1 day, 0:50:51 time: 0.7171 data_time: 0.1848 memory: 17395 loss: 0.2041 decode.loss_ce: 0.1287 decode.acc_seg: 93.4693 aux.loss_ce: 0.0754 aux.acc_seg: 90.4093 2023/06/08 07:31:16 - mmengine - INFO - Iter(train) [115700/240000] lr: 5.5761e-03 eta: 1 day, 0:50:14 time: 0.7139 data_time: 0.0166 memory: 17396 loss: 0.1945 decode.loss_ce: 0.1237 decode.acc_seg: 93.6230 aux.loss_ce: 0.0708 aux.acc_seg: 91.3046 2023/06/08 07:31:52 - mmengine - INFO - Iter(train) [115750/240000] lr: 5.5741e-03 eta: 1 day, 0:49:37 time: 0.7019 data_time: 0.1905 memory: 17395 loss: 0.1844 decode.loss_ce: 0.1164 decode.acc_seg: 93.8064 aux.loss_ce: 0.0681 aux.acc_seg: 91.3852 2023/06/08 07:32:27 - mmengine - INFO - Iter(train) [115800/240000] lr: 5.5721e-03 eta: 1 day, 0:49:01 time: 0.7110 data_time: 0.0213 memory: 17394 loss: 0.1977 decode.loss_ce: 0.1251 decode.acc_seg: 94.2444 aux.loss_ce: 0.0726 aux.acc_seg: 91.5034 2023/06/08 07:33:03 - mmengine - INFO - Iter(train) [115850/240000] lr: 5.5701e-03 eta: 1 day, 0:48:24 time: 0.7137 data_time: 0.0122 memory: 17395 loss: 0.2096 decode.loss_ce: 0.1348 decode.acc_seg: 95.8235 aux.loss_ce: 0.0748 aux.acc_seg: 94.0615 2023/06/08 07:33:38 - mmengine - INFO - Iter(train) [115900/240000] lr: 5.5682e-03 eta: 1 day, 0:47:48 time: 0.7043 data_time: 0.0272 memory: 17394 loss: 0.1922 decode.loss_ce: 0.1218 decode.acc_seg: 93.3445 aux.loss_ce: 0.0704 aux.acc_seg: 90.5794 2023/06/08 07:34:13 - mmengine - INFO - Iter(train) [115950/240000] lr: 5.5662e-03 eta: 1 day, 0:47:11 time: 0.6981 data_time: 0.3148 memory: 17392 loss: 0.1945 decode.loss_ce: 0.1231 decode.acc_seg: 92.6975 aux.loss_ce: 0.0714 aux.acc_seg: 89.5049 2023/06/08 07:34:49 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 07:34:49 - mmengine - INFO - Iter(train) [116000/240000] lr: 5.5642e-03 eta: 1 day, 0:46:35 time: 0.7055 data_time: 0.3824 memory: 17395 loss: 0.2041 decode.loss_ce: 0.1299 decode.acc_seg: 94.6540 aux.loss_ce: 0.0743 aux.acc_seg: 92.4844 2023/06/08 07:35:24 - mmengine - INFO - Iter(train) [116050/240000] lr: 5.5622e-03 eta: 1 day, 0:45:58 time: 0.7083 data_time: 0.3846 memory: 17392 loss: 0.1912 decode.loss_ce: 0.1224 decode.acc_seg: 95.5234 aux.loss_ce: 0.0688 aux.acc_seg: 92.4931 2023/06/08 07:36:00 - mmengine - INFO - Iter(train) [116100/240000] lr: 5.5602e-03 eta: 1 day, 0:45:22 time: 0.7152 data_time: 0.0121 memory: 17393 loss: 0.1954 decode.loss_ce: 0.1219 decode.acc_seg: 95.6529 aux.loss_ce: 0.0734 aux.acc_seg: 93.5248 2023/06/08 07:36:35 - mmengine - INFO - Iter(train) [116150/240000] lr: 5.5582e-03 eta: 1 day, 0:44:45 time: 0.6972 data_time: 0.0266 memory: 17394 loss: 0.1902 decode.loss_ce: 0.1193 decode.acc_seg: 93.4134 aux.loss_ce: 0.0709 aux.acc_seg: 90.3778 2023/06/08 07:37:11 - mmengine - INFO - Iter(train) [116200/240000] lr: 5.5563e-03 eta: 1 day, 0:44:09 time: 0.7077 data_time: 0.3555 memory: 17392 loss: 0.2316 decode.loss_ce: 0.1503 decode.acc_seg: 92.1093 aux.loss_ce: 0.0813 aux.acc_seg: 87.5414 2023/06/08 07:37:46 - mmengine - INFO - Iter(train) [116250/240000] lr: 5.5543e-03 eta: 1 day, 0:43:32 time: 0.7133 data_time: 0.1377 memory: 17395 loss: 0.1951 decode.loss_ce: 0.1230 decode.acc_seg: 94.7940 aux.loss_ce: 0.0721 aux.acc_seg: 91.2941 2023/06/08 07:38:22 - mmengine - INFO - Iter(train) [116300/240000] lr: 5.5523e-03 eta: 1 day, 0:42:56 time: 0.7122 data_time: 0.0121 memory: 17395 loss: 0.1862 decode.loss_ce: 0.1156 decode.acc_seg: 93.8163 aux.loss_ce: 0.0706 aux.acc_seg: 89.0348 2023/06/08 07:38:58 - mmengine - INFO - Iter(train) [116350/240000] lr: 5.5503e-03 eta: 1 day, 0:42:20 time: 0.7076 data_time: 0.0122 memory: 17393 loss: 0.1955 decode.loss_ce: 0.1233 decode.acc_seg: 95.6917 aux.loss_ce: 0.0722 aux.acc_seg: 93.2044 2023/06/08 07:39:33 - mmengine - INFO - Iter(train) [116400/240000] lr: 5.5483e-03 eta: 1 day, 0:41:43 time: 0.7117 data_time: 0.0121 memory: 17394 loss: 0.1956 decode.loss_ce: 0.1263 decode.acc_seg: 94.0171 aux.loss_ce: 0.0692 aux.acc_seg: 91.9395 2023/06/08 07:40:09 - mmengine - INFO - Iter(train) [116450/240000] lr: 5.5463e-03 eta: 1 day, 0:41:07 time: 0.7061 data_time: 0.0121 memory: 17394 loss: 0.1870 decode.loss_ce: 0.1194 decode.acc_seg: 94.2975 aux.loss_ce: 0.0676 aux.acc_seg: 92.0471 2023/06/08 07:40:45 - mmengine - INFO - Iter(train) [116500/240000] lr: 5.5444e-03 eta: 1 day, 0:40:31 time: 0.7230 data_time: 0.0124 memory: 17393 loss: 0.1751 decode.loss_ce: 0.1108 decode.acc_seg: 95.5541 aux.loss_ce: 0.0643 aux.acc_seg: 94.0054 2023/06/08 07:41:21 - mmengine - INFO - Iter(train) [116550/240000] lr: 5.5424e-03 eta: 1 day, 0:39:55 time: 0.7335 data_time: 0.0122 memory: 17395 loss: 0.2118 decode.loss_ce: 0.1354 decode.acc_seg: 94.7906 aux.loss_ce: 0.0765 aux.acc_seg: 92.6958 2023/06/08 07:41:57 - mmengine - INFO - Iter(train) [116600/240000] lr: 5.5404e-03 eta: 1 day, 0:39:19 time: 0.7541 data_time: 0.0125 memory: 17392 loss: 0.2017 decode.loss_ce: 0.1279 decode.acc_seg: 95.2163 aux.loss_ce: 0.0738 aux.acc_seg: 93.2659 2023/06/08 07:42:33 - mmengine - INFO - Iter(train) [116650/240000] lr: 5.5384e-03 eta: 1 day, 0:38:44 time: 0.7314 data_time: 0.0288 memory: 17393 loss: 0.2040 decode.loss_ce: 0.1306 decode.acc_seg: 94.1826 aux.loss_ce: 0.0734 aux.acc_seg: 91.6193 2023/06/08 07:43:10 - mmengine - INFO - Iter(train) [116700/240000] lr: 5.5364e-03 eta: 1 day, 0:38:08 time: 0.7265 data_time: 0.3906 memory: 17394 loss: 0.2052 decode.loss_ce: 0.1296 decode.acc_seg: 91.3415 aux.loss_ce: 0.0756 aux.acc_seg: 93.5818 2023/06/08 07:43:46 - mmengine - INFO - Iter(train) [116750/240000] lr: 5.5344e-03 eta: 1 day, 0:37:32 time: 0.7264 data_time: 0.2541 memory: 17394 loss: 0.2002 decode.loss_ce: 0.1246 decode.acc_seg: 95.2416 aux.loss_ce: 0.0756 aux.acc_seg: 90.5321 2023/06/08 07:44:22 - mmengine - INFO - Iter(train) [116800/240000] lr: 5.5325e-03 eta: 1 day, 0:36:56 time: 0.7129 data_time: 0.0989 memory: 17396 loss: 0.1819 decode.loss_ce: 0.1138 decode.acc_seg: 94.0943 aux.loss_ce: 0.0681 aux.acc_seg: 91.8510 2023/06/08 07:44:58 - mmengine - INFO - Iter(train) [116850/240000] lr: 5.5305e-03 eta: 1 day, 0:36:21 time: 0.7191 data_time: 0.0122 memory: 17396 loss: 0.2174 decode.loss_ce: 0.1389 decode.acc_seg: 94.2024 aux.loss_ce: 0.0785 aux.acc_seg: 92.0847 2023/06/08 07:45:35 - mmengine - INFO - Iter(train) [116900/240000] lr: 5.5285e-03 eta: 1 day, 0:35:45 time: 0.7389 data_time: 0.0126 memory: 17396 loss: 0.1872 decode.loss_ce: 0.1182 decode.acc_seg: 93.8545 aux.loss_ce: 0.0690 aux.acc_seg: 92.1328 2023/06/08 07:46:11 - mmengine - INFO - Iter(train) [116950/240000] lr: 5.5265e-03 eta: 1 day, 0:35:10 time: 0.7170 data_time: 0.0126 memory: 17393 loss: 0.2017 decode.loss_ce: 0.1283 decode.acc_seg: 94.9865 aux.loss_ce: 0.0734 aux.acc_seg: 92.7744 2023/06/08 07:46:47 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 07:46:47 - mmengine - INFO - Iter(train) [117000/240000] lr: 5.5245e-03 eta: 1 day, 0:34:34 time: 0.7098 data_time: 0.0122 memory: 17395 loss: 0.1965 decode.loss_ce: 0.1237 decode.acc_seg: 92.8399 aux.loss_ce: 0.0728 aux.acc_seg: 88.7248 2023/06/08 07:47:23 - mmengine - INFO - Iter(train) [117050/240000] lr: 5.5225e-03 eta: 1 day, 0:33:58 time: 0.7176 data_time: 0.0124 memory: 17395 loss: 0.2073 decode.loss_ce: 0.1308 decode.acc_seg: 93.8921 aux.loss_ce: 0.0765 aux.acc_seg: 90.8347 2023/06/08 07:47:58 - mmengine - INFO - Iter(train) [117100/240000] lr: 5.5206e-03 eta: 1 day, 0:33:22 time: 0.7149 data_time: 0.0124 memory: 17396 loss: 0.2051 decode.loss_ce: 0.1305 decode.acc_seg: 95.1272 aux.loss_ce: 0.0746 aux.acc_seg: 93.5724 2023/06/08 07:48:34 - mmengine - INFO - Iter(train) [117150/240000] lr: 5.5186e-03 eta: 1 day, 0:32:45 time: 0.7200 data_time: 0.0125 memory: 17394 loss: 0.1961 decode.loss_ce: 0.1245 decode.acc_seg: 94.5024 aux.loss_ce: 0.0716 aux.acc_seg: 91.2150 2023/06/08 07:49:11 - mmengine - INFO - Iter(train) [117200/240000] lr: 5.5166e-03 eta: 1 day, 0:32:10 time: 0.7184 data_time: 0.0124 memory: 17393 loss: 0.1847 decode.loss_ce: 0.1155 decode.acc_seg: 93.7967 aux.loss_ce: 0.0692 aux.acc_seg: 90.7038 2023/06/08 07:49:48 - mmengine - INFO - Iter(train) [117250/240000] lr: 5.5146e-03 eta: 1 day, 0:31:35 time: 0.7513 data_time: 0.0128 memory: 17395 loss: 0.2004 decode.loss_ce: 0.1265 decode.acc_seg: 94.5608 aux.loss_ce: 0.0739 aux.acc_seg: 91.0010 2023/06/08 07:50:24 - mmengine - INFO - Iter(train) [117300/240000] lr: 5.5126e-03 eta: 1 day, 0:30:59 time: 0.7426 data_time: 0.0127 memory: 17392 loss: 0.1927 decode.loss_ce: 0.1200 decode.acc_seg: 93.5088 aux.loss_ce: 0.0727 aux.acc_seg: 92.3004 2023/06/08 07:51:01 - mmengine - INFO - Iter(train) [117350/240000] lr: 5.5106e-03 eta: 1 day, 0:30:24 time: 0.7211 data_time: 0.0128 memory: 17395 loss: 0.2079 decode.loss_ce: 0.1345 decode.acc_seg: 94.7414 aux.loss_ce: 0.0734 aux.acc_seg: 92.9758 2023/06/08 07:51:36 - mmengine - INFO - Iter(train) [117400/240000] lr: 5.5086e-03 eta: 1 day, 0:29:48 time: 0.7027 data_time: 0.0122 memory: 17395 loss: 0.1895 decode.loss_ce: 0.1199 decode.acc_seg: 95.1871 aux.loss_ce: 0.0696 aux.acc_seg: 93.5492 2023/06/08 07:52:12 - mmengine - INFO - Iter(train) [117450/240000] lr: 5.5067e-03 eta: 1 day, 0:29:12 time: 0.7052 data_time: 0.0124 memory: 17396 loss: 0.1870 decode.loss_ce: 0.1188 decode.acc_seg: 95.1596 aux.loss_ce: 0.0682 aux.acc_seg: 93.1083 2023/06/08 07:52:48 - mmengine - INFO - Iter(train) [117500/240000] lr: 5.5047e-03 eta: 1 day, 0:28:36 time: 0.7123 data_time: 0.0300 memory: 17393 loss: 0.1923 decode.loss_ce: 0.1226 decode.acc_seg: 92.8689 aux.loss_ce: 0.0697 aux.acc_seg: 91.5668 2023/06/08 07:53:24 - mmengine - INFO - Iter(train) [117550/240000] lr: 5.5027e-03 eta: 1 day, 0:28:00 time: 0.7270 data_time: 0.0122 memory: 17394 loss: 0.1982 decode.loss_ce: 0.1262 decode.acc_seg: 95.8916 aux.loss_ce: 0.0719 aux.acc_seg: 93.7323 2023/06/08 07:54:00 - mmengine - INFO - Iter(train) [117600/240000] lr: 5.5007e-03 eta: 1 day, 0:27:24 time: 0.7188 data_time: 0.0124 memory: 17397 loss: 0.1972 decode.loss_ce: 0.1255 decode.acc_seg: 95.1754 aux.loss_ce: 0.0718 aux.acc_seg: 93.5998 2023/06/08 07:54:36 - mmengine - INFO - Iter(train) [117650/240000] lr: 5.4987e-03 eta: 1 day, 0:26:48 time: 0.7000 data_time: 0.0122 memory: 17396 loss: 0.2020 decode.loss_ce: 0.1278 decode.acc_seg: 93.1423 aux.loss_ce: 0.0742 aux.acc_seg: 88.9687 2023/06/08 07:55:12 - mmengine - INFO - Iter(train) [117700/240000] lr: 5.4967e-03 eta: 1 day, 0:26:12 time: 0.7230 data_time: 0.0133 memory: 17395 loss: 0.2081 decode.loss_ce: 0.1344 decode.acc_seg: 93.1341 aux.loss_ce: 0.0736 aux.acc_seg: 90.3966 2023/06/08 07:55:48 - mmengine - INFO - Iter(train) [117750/240000] lr: 5.4947e-03 eta: 1 day, 0:25:36 time: 0.7355 data_time: 0.0123 memory: 17392 loss: 0.1939 decode.loss_ce: 0.1244 decode.acc_seg: 96.2105 aux.loss_ce: 0.0696 aux.acc_seg: 94.8458 2023/06/08 07:56:24 - mmengine - INFO - Iter(train) [117800/240000] lr: 5.4928e-03 eta: 1 day, 0:25:00 time: 0.7376 data_time: 0.0125 memory: 17393 loss: 0.1900 decode.loss_ce: 0.1205 decode.acc_seg: 95.8174 aux.loss_ce: 0.0696 aux.acc_seg: 92.7650 2023/06/08 07:57:00 - mmengine - INFO - Iter(train) [117850/240000] lr: 5.4908e-03 eta: 1 day, 0:24:24 time: 0.7296 data_time: 0.0127 memory: 17394 loss: 0.2058 decode.loss_ce: 0.1300 decode.acc_seg: 94.6468 aux.loss_ce: 0.0758 aux.acc_seg: 92.3759 2023/06/08 07:57:36 - mmengine - INFO - Iter(train) [117900/240000] lr: 5.4888e-03 eta: 1 day, 0:23:48 time: 0.7054 data_time: 0.0124 memory: 17395 loss: 0.2185 decode.loss_ce: 0.1382 decode.acc_seg: 95.0488 aux.loss_ce: 0.0803 aux.acc_seg: 92.1606 2023/06/08 07:58:12 - mmengine - INFO - Iter(train) [117950/240000] lr: 5.4868e-03 eta: 1 day, 0:23:12 time: 0.7127 data_time: 0.0124 memory: 17393 loss: 0.2119 decode.loss_ce: 0.1343 decode.acc_seg: 94.8588 aux.loss_ce: 0.0775 aux.acc_seg: 92.5001 2023/06/08 07:58:48 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 07:58:48 - mmengine - INFO - Iter(train) [118000/240000] lr: 5.4848e-03 eta: 1 day, 0:22:36 time: 0.7158 data_time: 0.0135 memory: 17394 loss: 0.1949 decode.loss_ce: 0.1263 decode.acc_seg: 95.3102 aux.loss_ce: 0.0686 aux.acc_seg: 93.9466 2023/06/08 07:59:23 - mmengine - INFO - Iter(train) [118050/240000] lr: 5.4828e-03 eta: 1 day, 0:21:59 time: 0.7035 data_time: 0.2749 memory: 17395 loss: 0.2288 decode.loss_ce: 0.1461 decode.acc_seg: 95.0116 aux.loss_ce: 0.0827 aux.acc_seg: 91.3939 2023/06/08 07:59:59 - mmengine - INFO - Iter(train) [118100/240000] lr: 5.4808e-03 eta: 1 day, 0:21:23 time: 0.7192 data_time: 0.0576 memory: 17392 loss: 0.2004 decode.loss_ce: 0.1292 decode.acc_seg: 92.6406 aux.loss_ce: 0.0713 aux.acc_seg: 90.6540 2023/06/08 08:00:35 - mmengine - INFO - Iter(train) [118150/240000] lr: 5.4789e-03 eta: 1 day, 0:20:48 time: 0.7370 data_time: 0.0126 memory: 17395 loss: 0.2183 decode.loss_ce: 0.1399 decode.acc_seg: 95.3021 aux.loss_ce: 0.0783 aux.acc_seg: 93.4324 2023/06/08 08:01:11 - mmengine - INFO - Iter(train) [118200/240000] lr: 5.4769e-03 eta: 1 day, 0:20:12 time: 0.7114 data_time: 0.0212 memory: 17395 loss: 0.1864 decode.loss_ce: 0.1188 decode.acc_seg: 95.4518 aux.loss_ce: 0.0675 aux.acc_seg: 93.5620 2023/06/08 08:01:47 - mmengine - INFO - Iter(train) [118250/240000] lr: 5.4749e-03 eta: 1 day, 0:19:36 time: 0.7238 data_time: 0.1551 memory: 17394 loss: 0.2149 decode.loss_ce: 0.1361 decode.acc_seg: 93.9990 aux.loss_ce: 0.0789 aux.acc_seg: 91.0711 2023/06/08 08:02:23 - mmengine - INFO - Iter(train) [118300/240000] lr: 5.4729e-03 eta: 1 day, 0:19:00 time: 0.7097 data_time: 0.1361 memory: 17395 loss: 0.2634 decode.loss_ce: 0.1700 decode.acc_seg: 92.9203 aux.loss_ce: 0.0934 aux.acc_seg: 89.2589 2023/06/08 08:02:59 - mmengine - INFO - Iter(train) [118350/240000] lr: 5.4709e-03 eta: 1 day, 0:18:23 time: 0.7083 data_time: 0.3213 memory: 17394 loss: 0.2307 decode.loss_ce: 0.1491 decode.acc_seg: 94.7466 aux.loss_ce: 0.0816 aux.acc_seg: 92.3405 2023/06/08 08:03:35 - mmengine - INFO - Iter(train) [118400/240000] lr: 5.4689e-03 eta: 1 day, 0:17:48 time: 0.7253 data_time: 0.3925 memory: 17397 loss: 0.2309 decode.loss_ce: 0.1442 decode.acc_seg: 95.3460 aux.loss_ce: 0.0867 aux.acc_seg: 93.0975 2023/06/08 08:04:11 - mmengine - INFO - Iter(train) [118450/240000] lr: 5.4669e-03 eta: 1 day, 0:17:12 time: 0.7201 data_time: 0.3826 memory: 17393 loss: 0.2039 decode.loss_ce: 0.1318 decode.acc_seg: 94.2341 aux.loss_ce: 0.0721 aux.acc_seg: 91.9549 2023/06/08 08:04:47 - mmengine - INFO - Iter(train) [118500/240000] lr: 5.4649e-03 eta: 1 day, 0:16:36 time: 0.7199 data_time: 0.3909 memory: 17396 loss: 0.2053 decode.loss_ce: 0.1275 decode.acc_seg: 94.0942 aux.loss_ce: 0.0778 aux.acc_seg: 90.8859 2023/06/08 08:05:24 - mmengine - INFO - Iter(train) [118550/240000] lr: 5.4630e-03 eta: 1 day, 0:16:01 time: 0.7588 data_time: 0.3948 memory: 17392 loss: 0.2045 decode.loss_ce: 0.1316 decode.acc_seg: 94.9736 aux.loss_ce: 0.0730 aux.acc_seg: 92.7623 2023/06/08 08:06:01 - mmengine - INFO - Iter(train) [118600/240000] lr: 5.4610e-03 eta: 1 day, 0:15:26 time: 0.7283 data_time: 0.4020 memory: 17395 loss: 0.2002 decode.loss_ce: 0.1271 decode.acc_seg: 92.6949 aux.loss_ce: 0.0731 aux.acc_seg: 90.2650 2023/06/08 08:06:37 - mmengine - INFO - Iter(train) [118650/240000] lr: 5.4590e-03 eta: 1 day, 0:14:50 time: 0.7247 data_time: 0.3948 memory: 17395 loss: 0.2018 decode.loss_ce: 0.1288 decode.acc_seg: 95.4347 aux.loss_ce: 0.0730 aux.acc_seg: 92.5668 2023/06/08 08:07:13 - mmengine - INFO - Iter(train) [118700/240000] lr: 5.4570e-03 eta: 1 day, 0:14:14 time: 0.7241 data_time: 0.3908 memory: 17394 loss: 0.1904 decode.loss_ce: 0.1197 decode.acc_seg: 94.4112 aux.loss_ce: 0.0707 aux.acc_seg: 92.3069 2023/06/08 08:07:49 - mmengine - INFO - Iter(train) [118750/240000] lr: 5.4550e-03 eta: 1 day, 0:13:38 time: 0.7089 data_time: 0.3764 memory: 17396 loss: 0.1918 decode.loss_ce: 0.1168 decode.acc_seg: 94.0611 aux.loss_ce: 0.0750 aux.acc_seg: 91.7073 2023/06/08 08:08:25 - mmengine - INFO - Iter(train) [118800/240000] lr: 5.4530e-03 eta: 1 day, 0:13:02 time: 0.7232 data_time: 0.3915 memory: 17394 loss: 0.1988 decode.loss_ce: 0.1265 decode.acc_seg: 95.2559 aux.loss_ce: 0.0723 aux.acc_seg: 93.4762 2023/06/08 08:09:01 - mmengine - INFO - Iter(train) [118850/240000] lr: 5.4510e-03 eta: 1 day, 0:12:26 time: 0.7361 data_time: 0.4081 memory: 17396 loss: 0.2128 decode.loss_ce: 0.1373 decode.acc_seg: 95.3511 aux.loss_ce: 0.0755 aux.acc_seg: 93.0322 2023/06/08 08:09:37 - mmengine - INFO - Iter(train) [118900/240000] lr: 5.4490e-03 eta: 1 day, 0:11:50 time: 0.7318 data_time: 0.3897 memory: 17394 loss: 0.2128 decode.loss_ce: 0.1361 decode.acc_seg: 94.6228 aux.loss_ce: 0.0767 aux.acc_seg: 91.1905 2023/06/08 08:10:13 - mmengine - INFO - Iter(train) [118950/240000] lr: 5.4471e-03 eta: 1 day, 0:11:14 time: 0.7177 data_time: 0.3915 memory: 17395 loss: 0.1911 decode.loss_ce: 0.1229 decode.acc_seg: 93.7020 aux.loss_ce: 0.0681 aux.acc_seg: 91.6850 2023/06/08 08:10:50 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 08:10:50 - mmengine - INFO - Iter(train) [119000/240000] lr: 5.4451e-03 eta: 1 day, 0:10:39 time: 0.7348 data_time: 0.3733 memory: 17393 loss: 0.2185 decode.loss_ce: 0.1409 decode.acc_seg: 89.7759 aux.loss_ce: 0.0776 aux.acc_seg: 88.0158 2023/06/08 08:11:28 - mmengine - INFO - Iter(train) [119050/240000] lr: 5.4431e-03 eta: 1 day, 0:10:05 time: 0.7566 data_time: 0.4060 memory: 17394 loss: 0.1840 decode.loss_ce: 0.1162 decode.acc_seg: 95.3272 aux.loss_ce: 0.0678 aux.acc_seg: 93.5207 2023/06/08 08:12:04 - mmengine - INFO - Iter(train) [119100/240000] lr: 5.4411e-03 eta: 1 day, 0:09:30 time: 0.7477 data_time: 0.4097 memory: 17394 loss: 0.1874 decode.loss_ce: 0.1194 decode.acc_seg: 94.7990 aux.loss_ce: 0.0681 aux.acc_seg: 92.8971 2023/06/08 08:12:39 - mmengine - INFO - Iter(train) [119150/240000] lr: 5.4391e-03 eta: 1 day, 0:08:53 time: 0.7079 data_time: 0.3833 memory: 17396 loss: 0.2078 decode.loss_ce: 0.1317 decode.acc_seg: 94.1064 aux.loss_ce: 0.0761 aux.acc_seg: 91.3046 2023/06/08 08:13:15 - mmengine - INFO - Iter(train) [119200/240000] lr: 5.4371e-03 eta: 1 day, 0:08:17 time: 0.7123 data_time: 0.3439 memory: 17397 loss: 0.1920 decode.loss_ce: 0.1213 decode.acc_seg: 95.6499 aux.loss_ce: 0.0708 aux.acc_seg: 93.1362 2023/06/08 08:13:50 - mmengine - INFO - Iter(train) [119250/240000] lr: 5.4351e-03 eta: 1 day, 0:07:40 time: 0.7086 data_time: 0.2750 memory: 17396 loss: 0.2093 decode.loss_ce: 0.1313 decode.acc_seg: 95.3520 aux.loss_ce: 0.0780 aux.acc_seg: 93.3943 2023/06/08 08:14:26 - mmengine - INFO - Iter(train) [119300/240000] lr: 5.4331e-03 eta: 1 day, 0:07:04 time: 0.7252 data_time: 0.0120 memory: 17393 loss: 0.2023 decode.loss_ce: 0.1287 decode.acc_seg: 93.7798 aux.loss_ce: 0.0735 aux.acc_seg: 91.3300 2023/06/08 08:15:02 - mmengine - INFO - Iter(train) [119350/240000] lr: 5.4312e-03 eta: 1 day, 0:06:28 time: 0.7153 data_time: 0.0120 memory: 17395 loss: 0.2070 decode.loss_ce: 0.1342 decode.acc_seg: 92.5714 aux.loss_ce: 0.0729 aux.acc_seg: 88.2852 2023/06/08 08:15:37 - mmengine - INFO - Iter(train) [119400/240000] lr: 5.4292e-03 eta: 1 day, 0:05:51 time: 0.7240 data_time: 0.0182 memory: 17394 loss: 0.1881 decode.loss_ce: 0.1202 decode.acc_seg: 93.2113 aux.loss_ce: 0.0679 aux.acc_seg: 90.8639 2023/06/08 08:16:13 - mmengine - INFO - Iter(train) [119450/240000] lr: 5.4272e-03 eta: 1 day, 0:05:15 time: 0.6988 data_time: 0.0121 memory: 17393 loss: 0.2081 decode.loss_ce: 0.1309 decode.acc_seg: 94.0379 aux.loss_ce: 0.0772 aux.acc_seg: 91.4515 2023/06/08 08:16:49 - mmengine - INFO - Iter(train) [119500/240000] lr: 5.4252e-03 eta: 1 day, 0:04:39 time: 0.7082 data_time: 0.0122 memory: 17394 loss: 0.2036 decode.loss_ce: 0.1283 decode.acc_seg: 94.2672 aux.loss_ce: 0.0752 aux.acc_seg: 90.8372 2023/06/08 08:17:24 - mmengine - INFO - Iter(train) [119550/240000] lr: 5.4232e-03 eta: 1 day, 0:04:02 time: 0.7280 data_time: 0.0124 memory: 17391 loss: 0.1983 decode.loss_ce: 0.1255 decode.acc_seg: 93.0950 aux.loss_ce: 0.0728 aux.acc_seg: 90.1034 2023/06/08 08:18:00 - mmengine - INFO - Iter(train) [119600/240000] lr: 5.4212e-03 eta: 1 day, 0:03:26 time: 0.7175 data_time: 0.0122 memory: 17393 loss: 0.1974 decode.loss_ce: 0.1242 decode.acc_seg: 93.8137 aux.loss_ce: 0.0733 aux.acc_seg: 90.6654 2023/06/08 08:18:36 - mmengine - INFO - Iter(train) [119650/240000] lr: 5.4192e-03 eta: 1 day, 0:02:50 time: 0.7103 data_time: 0.0123 memory: 17396 loss: 0.2213 decode.loss_ce: 0.1419 decode.acc_seg: 94.2928 aux.loss_ce: 0.0794 aux.acc_seg: 92.0036 2023/06/08 08:19:11 - mmengine - INFO - Iter(train) [119700/240000] lr: 5.4172e-03 eta: 1 day, 0:02:14 time: 0.7056 data_time: 0.0124 memory: 17395 loss: 0.2017 decode.loss_ce: 0.1293 decode.acc_seg: 87.4944 aux.loss_ce: 0.0723 aux.acc_seg: 85.9233 2023/06/08 08:19:47 - mmengine - INFO - Iter(train) [119750/240000] lr: 5.4152e-03 eta: 1 day, 0:01:38 time: 0.7199 data_time: 0.0124 memory: 17392 loss: 0.1780 decode.loss_ce: 0.1115 decode.acc_seg: 95.7341 aux.loss_ce: 0.0665 aux.acc_seg: 92.9945 2023/06/08 08:20:22 - mmengine - INFO - Iter(train) [119800/240000] lr: 5.4133e-03 eta: 1 day, 0:01:01 time: 0.7038 data_time: 0.0267 memory: 17394 loss: 0.1895 decode.loss_ce: 0.1201 decode.acc_seg: 94.6290 aux.loss_ce: 0.0693 aux.acc_seg: 92.7594 2023/06/08 08:20:58 - mmengine - INFO - Iter(train) [119850/240000] lr: 5.4113e-03 eta: 1 day, 0:00:24 time: 0.7156 data_time: 0.3914 memory: 17393 loss: 0.2034 decode.loss_ce: 0.1246 decode.acc_seg: 92.8959 aux.loss_ce: 0.0788 aux.acc_seg: 87.2368 2023/06/08 08:21:33 - mmengine - INFO - Iter(train) [119900/240000] lr: 5.4093e-03 eta: 23:59:48 time: 0.7109 data_time: 0.2417 memory: 17395 loss: 0.2123 decode.loss_ce: 0.1352 decode.acc_seg: 94.5067 aux.loss_ce: 0.0771 aux.acc_seg: 92.3502 2023/06/08 08:22:09 - mmengine - INFO - Iter(train) [119950/240000] lr: 5.4073e-03 eta: 23:59:11 time: 0.7002 data_time: 0.1481 memory: 17393 loss: 0.1995 decode.loss_ce: 0.1292 decode.acc_seg: 94.5778 aux.loss_ce: 0.0702 aux.acc_seg: 92.8446 2023/06/08 08:22:44 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 08:22:44 - mmengine - INFO - Iter(train) [120000/240000] lr: 5.4053e-03 eta: 23:58:35 time: 0.7127 data_time: 0.3883 memory: 17394 loss: 0.1833 decode.loss_ce: 0.1163 decode.acc_seg: 95.1598 aux.loss_ce: 0.0671 aux.acc_seg: 93.4500 2023/06/08 08:22:44 - mmengine - INFO - Saving checkpoint at 120000 iterations 2023/06/08 08:22:46 - mmengine - INFO - Iter(val) [ 50/1297] eta: 0:00:38 time: 0.0316 data_time: 0.0236 memory: 203 2023/06/08 08:22:48 - mmengine - INFO - Iter(val) [ 100/1297] eta: 0:00:36 time: 0.0232 data_time: 0.0151 memory: 203 2023/06/08 08:22:49 - mmengine - INFO - Iter(val) [ 150/1297] eta: 0:00:34 time: 0.0328 data_time: 0.0247 memory: 203 2023/06/08 08:22:50 - mmengine - INFO - Iter(val) [ 200/1297] eta: 0:00:31 time: 0.0172 data_time: 0.0091 memory: 203 2023/06/08 08:22:52 - mmengine - INFO - Iter(val) [ 250/1297] eta: 0:00:28 time: 0.0291 data_time: 0.0210 memory: 203 2023/06/08 08:22:53 - mmengine - INFO - Iter(val) [ 300/1297] eta: 0:00:26 time: 0.0206 data_time: 0.0125 memory: 203 2023/06/08 08:22:54 - mmengine - INFO - Iter(val) [ 350/1297] eta: 0:00:25 time: 0.0273 data_time: 0.0191 memory: 203 2023/06/08 08:22:55 - mmengine - INFO - Iter(val) [ 400/1297] eta: 0:00:23 time: 0.0200 data_time: 0.0118 memory: 203 2023/06/08 08:22:56 - mmengine - INFO - Iter(val) [ 450/1297] eta: 0:00:22 time: 0.0277 data_time: 0.0194 memory: 203 2023/06/08 08:22:58 - mmengine - INFO - Iter(val) [ 500/1297] eta: 0:00:20 time: 0.0219 data_time: 0.0137 memory: 203 2023/06/08 08:22:59 - mmengine - INFO - Iter(val) [ 550/1297] eta: 0:00:19 time: 0.0284 data_time: 0.0202 memory: 203 2023/06/08 08:23:00 - mmengine - INFO - Iter(val) [ 600/1297] eta: 0:00:17 time: 0.0186 data_time: 0.0105 memory: 203 2023/06/08 08:23:01 - mmengine - INFO - Iter(val) [ 650/1297] eta: 0:00:16 time: 0.0257 data_time: 0.0175 memory: 203 2023/06/08 08:23:02 - mmengine - INFO - Iter(val) [ 700/1297] eta: 0:00:15 time: 0.0210 data_time: 0.0129 memory: 203 2023/06/08 08:23:04 - mmengine - INFO - Iter(val) [ 750/1297] eta: 0:00:13 time: 0.0304 data_time: 0.0222 memory: 203 2023/06/08 08:23:05 - mmengine - INFO - Iter(val) [ 800/1297] eta: 0:00:12 time: 0.0220 data_time: 0.0139 memory: 203 2023/06/08 08:23:06 - mmengine - INFO - Iter(val) [ 850/1297] eta: 0:00:11 time: 0.0267 data_time: 0.0186 memory: 203 2023/06/08 08:23:07 - mmengine - INFO - Iter(val) [ 900/1297] eta: 0:00:09 time: 0.0205 data_time: 0.0124 memory: 203 2023/06/08 08:23:08 - mmengine - INFO - Iter(val) [ 950/1297] eta: 0:00:08 time: 0.0285 data_time: 0.0203 memory: 203 2023/06/08 08:23:10 - mmengine - INFO - Iter(val) [1000/1297] eta: 0:00:07 time: 0.0187 data_time: 0.0106 memory: 203 2023/06/08 08:23:11 - mmengine - INFO - Iter(val) [1050/1297] eta: 0:00:06 time: 0.0254 data_time: 0.0173 memory: 203 2023/06/08 08:23:12 - mmengine - INFO - Iter(val) [1100/1297] eta: 0:00:04 time: 0.0227 data_time: 0.0148 memory: 203 2023/06/08 08:23:13 - mmengine - INFO - Iter(val) [1150/1297] eta: 0:00:03 time: 0.0298 data_time: 0.0216 memory: 203 2023/06/08 08:23:14 - mmengine - INFO - Iter(val) [1200/1297] eta: 0:00:02 time: 0.0225 data_time: 0.0146 memory: 203 2023/06/08 08:23:15 - mmengine - INFO - Iter(val) [1250/1297] eta: 0:00:01 time: 0.0226 data_time: 0.0147 memory: 203 2023/06/08 08:23:17 - mmengine - INFO - per class results: 2023/06/08 08:23:17 - mmengine - INFO - +------------+-------+-------+ | Class | IoU | Acc | +------------+-------+-------+ | background | 91.99 | 96.31 | | obstacle | 87.66 | 92.8 | | human | 57.23 | 71.1 | +------------+-------+-------+ 2023/06/08 08:23:17 - mmengine - INFO - Iter(val) [1297/1297] aAcc: 94.6200 mIoU: 78.9600 mAcc: 86.7400 data_time: 0.0162 time: 0.0243 2023/06/08 08:23:51 - mmengine - INFO - Iter(train) [120050/240000] lr: 5.4033e-03 eta: 23:57:58 time: 0.7130 data_time: 0.3355 memory: 17397 loss: 0.1965 decode.loss_ce: 0.1237 decode.acc_seg: 95.5226 aux.loss_ce: 0.0727 aux.acc_seg: 93.0894 2023/06/08 08:24:27 - mmengine - INFO - Iter(train) [120100/240000] lr: 5.4013e-03 eta: 23:57:22 time: 0.7055 data_time: 0.3332 memory: 17391 loss: 0.1852 decode.loss_ce: 0.1180 decode.acc_seg: 94.3153 aux.loss_ce: 0.0672 aux.acc_seg: 91.5628 2023/06/08 08:25:02 - mmengine - INFO - Iter(train) [120150/240000] lr: 5.3993e-03 eta: 23:56:45 time: 0.7101 data_time: 0.2931 memory: 17394 loss: 0.1951 decode.loss_ce: 0.1238 decode.acc_seg: 93.2409 aux.loss_ce: 0.0713 aux.acc_seg: 90.4027 2023/06/08 08:25:38 - mmengine - INFO - Iter(train) [120200/240000] lr: 5.3973e-03 eta: 23:56:09 time: 0.7042 data_time: 0.1246 memory: 17392 loss: 0.1973 decode.loss_ce: 0.1264 decode.acc_seg: 94.9861 aux.loss_ce: 0.0710 aux.acc_seg: 93.1127 2023/06/08 08:26:13 - mmengine - INFO - Iter(train) [120250/240000] lr: 5.3954e-03 eta: 23:55:32 time: 0.7115 data_time: 0.1650 memory: 17395 loss: 0.2163 decode.loss_ce: 0.1376 decode.acc_seg: 94.6043 aux.loss_ce: 0.0787 aux.acc_seg: 93.0179 2023/06/08 08:26:49 - mmengine - INFO - Iter(train) [120300/240000] lr: 5.3934e-03 eta: 23:54:56 time: 0.7087 data_time: 0.2185 memory: 17395 loss: 0.2017 decode.loss_ce: 0.1296 decode.acc_seg: 96.1985 aux.loss_ce: 0.0721 aux.acc_seg: 94.4818 2023/06/08 08:27:25 - mmengine - INFO - Iter(train) [120350/240000] lr: 5.3914e-03 eta: 23:54:20 time: 0.7051 data_time: 0.0121 memory: 17395 loss: 0.1969 decode.loss_ce: 0.1229 decode.acc_seg: 95.3688 aux.loss_ce: 0.0740 aux.acc_seg: 93.5669 2023/06/08 08:28:00 - mmengine - INFO - Iter(train) [120400/240000] lr: 5.3894e-03 eta: 23:53:43 time: 0.7081 data_time: 0.0121 memory: 17393 loss: 0.2199 decode.loss_ce: 0.1407 decode.acc_seg: 93.7076 aux.loss_ce: 0.0793 aux.acc_seg: 91.9974 2023/06/08 08:28:36 - mmengine - INFO - Iter(train) [120450/240000] lr: 5.3874e-03 eta: 23:53:07 time: 0.7023 data_time: 0.0123 memory: 17393 loss: 0.2128 decode.loss_ce: 0.1328 decode.acc_seg: 94.6486 aux.loss_ce: 0.0800 aux.acc_seg: 89.5159 2023/06/08 08:29:11 - mmengine - INFO - Iter(train) [120500/240000] lr: 5.3854e-03 eta: 23:52:31 time: 0.7052 data_time: 0.0117 memory: 17395 loss: 0.1768 decode.loss_ce: 0.1123 decode.acc_seg: 95.3484 aux.loss_ce: 0.0644 aux.acc_seg: 93.4863 2023/06/08 08:29:47 - mmengine - INFO - Iter(train) [120550/240000] lr: 5.3834e-03 eta: 23:51:54 time: 0.7065 data_time: 0.0171 memory: 17393 loss: 0.2007 decode.loss_ce: 0.1292 decode.acc_seg: 94.0888 aux.loss_ce: 0.0715 aux.acc_seg: 91.9284 2023/06/08 08:30:22 - mmengine - INFO - Iter(train) [120600/240000] lr: 5.3814e-03 eta: 23:51:18 time: 0.7049 data_time: 0.0119 memory: 17393 loss: 0.2096 decode.loss_ce: 0.1320 decode.acc_seg: 94.0665 aux.loss_ce: 0.0776 aux.acc_seg: 89.9386 2023/06/08 08:30:58 - mmengine - INFO - Iter(train) [120650/240000] lr: 5.3794e-03 eta: 23:50:41 time: 0.7071 data_time: 0.0123 memory: 17391 loss: 0.2100 decode.loss_ce: 0.1338 decode.acc_seg: 94.9593 aux.loss_ce: 0.0762 aux.acc_seg: 92.7263 2023/06/08 08:31:33 - mmengine - INFO - Iter(train) [120700/240000] lr: 5.3774e-03 eta: 23:50:05 time: 0.7066 data_time: 0.0121 memory: 17394 loss: 0.1941 decode.loss_ce: 0.1237 decode.acc_seg: 95.1646 aux.loss_ce: 0.0703 aux.acc_seg: 93.0091 2023/06/08 08:32:09 - mmengine - INFO - Iter(train) [120750/240000] lr: 5.3754e-03 eta: 23:49:28 time: 0.7165 data_time: 0.0123 memory: 17396 loss: 0.1978 decode.loss_ce: 0.1238 decode.acc_seg: 95.1452 aux.loss_ce: 0.0740 aux.acc_seg: 92.7940 2023/06/08 08:32:44 - mmengine - INFO - Iter(train) [120800/240000] lr: 5.3735e-03 eta: 23:48:52 time: 0.7183 data_time: 0.2314 memory: 17392 loss: 0.1831 decode.loss_ce: 0.1153 decode.acc_seg: 95.2113 aux.loss_ce: 0.0679 aux.acc_seg: 93.1022 2023/06/08 08:33:20 - mmengine - INFO - Iter(train) [120850/240000] lr: 5.3715e-03 eta: 23:48:16 time: 0.7088 data_time: 0.3852 memory: 17394 loss: 0.2009 decode.loss_ce: 0.1264 decode.acc_seg: 95.2366 aux.loss_ce: 0.0746 aux.acc_seg: 92.8846 2023/06/08 08:33:55 - mmengine - INFO - Iter(train) [120900/240000] lr: 5.3695e-03 eta: 23:47:39 time: 0.7085 data_time: 0.3854 memory: 17394 loss: 0.2130 decode.loss_ce: 0.1341 decode.acc_seg: 93.1350 aux.loss_ce: 0.0788 aux.acc_seg: 89.3370 2023/06/08 08:34:31 - mmengine - INFO - Iter(train) [120950/240000] lr: 5.3675e-03 eta: 23:47:03 time: 0.7167 data_time: 0.3929 memory: 17395 loss: 0.1799 decode.loss_ce: 0.1164 decode.acc_seg: 95.4885 aux.loss_ce: 0.0636 aux.acc_seg: 93.9271 2023/06/08 08:35:07 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 08:35:07 - mmengine - INFO - Iter(train) [121000/240000] lr: 5.3655e-03 eta: 23:46:27 time: 0.7184 data_time: 0.3278 memory: 17393 loss: 0.2107 decode.loss_ce: 0.1354 decode.acc_seg: 94.1084 aux.loss_ce: 0.0753 aux.acc_seg: 91.3968 2023/06/08 08:35:44 - mmengine - INFO - Iter(train) [121050/240000] lr: 5.3635e-03 eta: 23:45:52 time: 0.7315 data_time: 0.3858 memory: 17395 loss: 0.1885 decode.loss_ce: 0.1178 decode.acc_seg: 95.0581 aux.loss_ce: 0.0706 aux.acc_seg: 92.3289 2023/06/08 08:36:20 - mmengine - INFO - Iter(train) [121100/240000] lr: 5.3615e-03 eta: 23:45:16 time: 0.7354 data_time: 0.3113 memory: 17391 loss: 0.1959 decode.loss_ce: 0.1251 decode.acc_seg: 93.5483 aux.loss_ce: 0.0707 aux.acc_seg: 91.1946 2023/06/08 08:36:56 - mmengine - INFO - Iter(train) [121150/240000] lr: 5.3595e-03 eta: 23:44:40 time: 0.7211 data_time: 0.3901 memory: 17395 loss: 0.2071 decode.loss_ce: 0.1323 decode.acc_seg: 94.5387 aux.loss_ce: 0.0748 aux.acc_seg: 91.6991 2023/06/08 08:37:33 - mmengine - INFO - Iter(train) [121200/240000] lr: 5.3575e-03 eta: 23:44:05 time: 0.7214 data_time: 0.3612 memory: 17394 loss: 0.1970 decode.loss_ce: 0.1263 decode.acc_seg: 94.5150 aux.loss_ce: 0.0707 aux.acc_seg: 92.9858 2023/06/08 08:38:09 - mmengine - INFO - Iter(train) [121250/240000] lr: 5.3555e-03 eta: 23:43:29 time: 0.7360 data_time: 0.1979 memory: 17393 loss: 0.1917 decode.loss_ce: 0.1225 decode.acc_seg: 94.1688 aux.loss_ce: 0.0692 aux.acc_seg: 92.2079 2023/06/08 08:38:46 - mmengine - INFO - Iter(train) [121300/240000] lr: 5.3535e-03 eta: 23:42:54 time: 0.7275 data_time: 0.1750 memory: 17394 loss: 0.2036 decode.loss_ce: 0.1272 decode.acc_seg: 93.2800 aux.loss_ce: 0.0764 aux.acc_seg: 87.7427 2023/06/08 08:39:22 - mmengine - INFO - Iter(train) [121350/240000] lr: 5.3516e-03 eta: 23:42:18 time: 0.7254 data_time: 0.1424 memory: 17394 loss: 0.2043 decode.loss_ce: 0.1290 decode.acc_seg: 92.5541 aux.loss_ce: 0.0753 aux.acc_seg: 89.1075 2023/06/08 08:39:58 - mmengine - INFO - Iter(train) [121400/240000] lr: 5.3496e-03 eta: 23:41:43 time: 0.7376 data_time: 0.3954 memory: 17392 loss: 0.1880 decode.loss_ce: 0.1166 decode.acc_seg: 94.6336 aux.loss_ce: 0.0713 aux.acc_seg: 92.6015 2023/06/08 08:40:34 - mmengine - INFO - Iter(train) [121450/240000] lr: 5.3476e-03 eta: 23:41:07 time: 0.7194 data_time: 0.2634 memory: 17391 loss: 0.2050 decode.loss_ce: 0.1304 decode.acc_seg: 93.0993 aux.loss_ce: 0.0746 aux.acc_seg: 90.0166 2023/06/08 08:41:11 - mmengine - INFO - Iter(train) [121500/240000] lr: 5.3456e-03 eta: 23:40:31 time: 0.7200 data_time: 0.3202 memory: 17393 loss: 0.2032 decode.loss_ce: 0.1294 decode.acc_seg: 96.1333 aux.loss_ce: 0.0739 aux.acc_seg: 94.0034 2023/06/08 08:41:47 - mmengine - INFO - Iter(train) [121550/240000] lr: 5.3436e-03 eta: 23:39:56 time: 0.7326 data_time: 0.3739 memory: 17394 loss: 0.1905 decode.loss_ce: 0.1207 decode.acc_seg: 94.3860 aux.loss_ce: 0.0698 aux.acc_seg: 91.0515 2023/06/08 08:42:23 - mmengine - INFO - Iter(train) [121600/240000] lr: 5.3416e-03 eta: 23:39:20 time: 0.7157 data_time: 0.0325 memory: 17393 loss: 0.2040 decode.loss_ce: 0.1295 decode.acc_seg: 93.9011 aux.loss_ce: 0.0745 aux.acc_seg: 91.6938 2023/06/08 08:42:59 - mmengine - INFO - Iter(train) [121650/240000] lr: 5.3396e-03 eta: 23:38:44 time: 0.7122 data_time: 0.0122 memory: 17395 loss: 0.2182 decode.loss_ce: 0.1409 decode.acc_seg: 93.7342 aux.loss_ce: 0.0773 aux.acc_seg: 92.0717 2023/06/08 08:43:35 - mmengine - INFO - Iter(train) [121700/240000] lr: 5.3376e-03 eta: 23:38:07 time: 0.7086 data_time: 0.0124 memory: 17394 loss: 0.2094 decode.loss_ce: 0.1361 decode.acc_seg: 94.3483 aux.loss_ce: 0.0733 aux.acc_seg: 92.2190 2023/06/08 08:44:11 - mmengine - INFO - Iter(train) [121750/240000] lr: 5.3356e-03 eta: 23:37:32 time: 0.7341 data_time: 0.0125 memory: 17395 loss: 0.2114 decode.loss_ce: 0.1362 decode.acc_seg: 89.6635 aux.loss_ce: 0.0752 aux.acc_seg: 89.1076 2023/06/08 08:44:47 - mmengine - INFO - Iter(train) [121800/240000] lr: 5.3336e-03 eta: 23:36:56 time: 0.7254 data_time: 0.0127 memory: 17394 loss: 0.2037 decode.loss_ce: 0.1297 decode.acc_seg: 91.6618 aux.loss_ce: 0.0740 aux.acc_seg: 88.7005 2023/06/08 08:45:24 - mmengine - INFO - Iter(train) [121850/240000] lr: 5.3316e-03 eta: 23:36:21 time: 0.7146 data_time: 0.0127 memory: 17396 loss: 0.2080 decode.loss_ce: 0.1309 decode.acc_seg: 92.9044 aux.loss_ce: 0.0771 aux.acc_seg: 88.7681 2023/06/08 08:46:00 - mmengine - INFO - Iter(train) [121900/240000] lr: 5.3296e-03 eta: 23:35:45 time: 0.7248 data_time: 0.0340 memory: 17393 loss: 0.2076 decode.loss_ce: 0.1325 decode.acc_seg: 94.8835 aux.loss_ce: 0.0751 aux.acc_seg: 92.2733 2023/06/08 08:46:37 - mmengine - INFO - Iter(train) [121950/240000] lr: 5.3276e-03 eta: 23:35:10 time: 0.7333 data_time: 0.0124 memory: 17394 loss: 0.2411 decode.loss_ce: 0.1526 decode.acc_seg: 94.8529 aux.loss_ce: 0.0885 aux.acc_seg: 92.7741 2023/06/08 08:47:13 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 08:47:13 - mmengine - INFO - Iter(train) [122000/240000] lr: 5.3257e-03 eta: 23:34:34 time: 0.7196 data_time: 0.0124 memory: 17393 loss: 0.2266 decode.loss_ce: 0.1475 decode.acc_seg: 92.9519 aux.loss_ce: 0.0791 aux.acc_seg: 91.5134 2023/06/08 08:47:50 - mmengine - INFO - Iter(train) [122050/240000] lr: 5.3237e-03 eta: 23:33:59 time: 0.7715 data_time: 0.0138 memory: 17395 loss: 0.1968 decode.loss_ce: 0.1263 decode.acc_seg: 94.3079 aux.loss_ce: 0.0706 aux.acc_seg: 91.9363 2023/06/08 08:48:27 - mmengine - INFO - Iter(train) [122100/240000] lr: 5.3217e-03 eta: 23:33:24 time: 0.7226 data_time: 0.0128 memory: 17394 loss: 0.1793 decode.loss_ce: 0.1133 decode.acc_seg: 95.0935 aux.loss_ce: 0.0660 aux.acc_seg: 94.1971 2023/06/08 08:49:03 - mmengine - INFO - Iter(train) [122150/240000] lr: 5.3197e-03 eta: 23:32:49 time: 0.7223 data_time: 0.0124 memory: 17395 loss: 0.2052 decode.loss_ce: 0.1313 decode.acc_seg: 93.8179 aux.loss_ce: 0.0739 aux.acc_seg: 91.5548 2023/06/08 08:49:40 - mmengine - INFO - Iter(train) [122200/240000] lr: 5.3177e-03 eta: 23:32:13 time: 0.7195 data_time: 0.0124 memory: 17392 loss: 0.2035 decode.loss_ce: 0.1301 decode.acc_seg: 95.2678 aux.loss_ce: 0.0734 aux.acc_seg: 92.3001 2023/06/08 08:50:17 - mmengine - INFO - Iter(train) [122250/240000] lr: 5.3157e-03 eta: 23:31:38 time: 0.7100 data_time: 0.0126 memory: 17394 loss: 0.2030 decode.loss_ce: 0.1259 decode.acc_seg: 96.4418 aux.loss_ce: 0.0770 aux.acc_seg: 92.5942 2023/06/08 08:50:52 - mmengine - INFO - Iter(train) [122300/240000] lr: 5.3137e-03 eta: 23:31:02 time: 0.7314 data_time: 0.2126 memory: 17395 loss: 0.1961 decode.loss_ce: 0.1237 decode.acc_seg: 95.0086 aux.loss_ce: 0.0724 aux.acc_seg: 93.0687 2023/06/08 08:51:29 - mmengine - INFO - Iter(train) [122350/240000] lr: 5.3117e-03 eta: 23:30:26 time: 0.7340 data_time: 0.0204 memory: 17395 loss: 0.2091 decode.loss_ce: 0.1325 decode.acc_seg: 93.0942 aux.loss_ce: 0.0766 aux.acc_seg: 89.7915 2023/06/08 08:52:05 - mmengine - INFO - Iter(train) [122400/240000] lr: 5.3097e-03 eta: 23:29:51 time: 0.7272 data_time: 0.0553 memory: 17394 loss: 0.1996 decode.loss_ce: 0.1279 decode.acc_seg: 94.0805 aux.loss_ce: 0.0717 aux.acc_seg: 91.0614 2023/06/08 08:52:42 - mmengine - INFO - Iter(train) [122450/240000] lr: 5.3077e-03 eta: 23:29:15 time: 0.7565 data_time: 0.0630 memory: 17395 loss: 0.1737 decode.loss_ce: 0.1097 decode.acc_seg: 95.1821 aux.loss_ce: 0.0641 aux.acc_seg: 93.5827 2023/06/08 08:53:18 - mmengine - INFO - Iter(train) [122500/240000] lr: 5.3057e-03 eta: 23:28:40 time: 0.7485 data_time: 0.3049 memory: 17395 loss: 0.2067 decode.loss_ce: 0.1314 decode.acc_seg: 90.5988 aux.loss_ce: 0.0753 aux.acc_seg: 87.6412 2023/06/08 08:53:55 - mmengine - INFO - Iter(train) [122550/240000] lr: 5.3037e-03 eta: 23:28:05 time: 0.7309 data_time: 0.1125 memory: 17396 loss: 0.1919 decode.loss_ce: 0.1224 decode.acc_seg: 95.8144 aux.loss_ce: 0.0695 aux.acc_seg: 92.9013 2023/06/08 08:54:32 - mmengine - INFO - Iter(train) [122600/240000] lr: 5.3017e-03 eta: 23:27:29 time: 0.7473 data_time: 0.0955 memory: 17394 loss: 0.1919 decode.loss_ce: 0.1214 decode.acc_seg: 95.0042 aux.loss_ce: 0.0706 aux.acc_seg: 92.0770 2023/06/08 08:55:08 - mmengine - INFO - Iter(train) [122650/240000] lr: 5.2997e-03 eta: 23:26:54 time: 0.7328 data_time: 0.0117 memory: 17393 loss: 0.1964 decode.loss_ce: 0.1259 decode.acc_seg: 95.2065 aux.loss_ce: 0.0705 aux.acc_seg: 93.3415 2023/06/08 08:55:45 - mmengine - INFO - Iter(train) [122700/240000] lr: 5.2977e-03 eta: 23:26:19 time: 0.7697 data_time: 0.0129 memory: 17393 loss: 0.2019 decode.loss_ce: 0.1272 decode.acc_seg: 95.2480 aux.loss_ce: 0.0747 aux.acc_seg: 92.8995 2023/06/08 08:56:22 - mmengine - INFO - Iter(train) [122750/240000] lr: 5.2958e-03 eta: 23:25:44 time: 0.7403 data_time: 0.0126 memory: 17393 loss: 0.1868 decode.loss_ce: 0.1161 decode.acc_seg: 93.4151 aux.loss_ce: 0.0707 aux.acc_seg: 92.1207 2023/06/08 08:56:59 - mmengine - INFO - Iter(train) [122800/240000] lr: 5.2938e-03 eta: 23:25:08 time: 0.7344 data_time: 0.0125 memory: 17394 loss: 0.1921 decode.loss_ce: 0.1213 decode.acc_seg: 94.5477 aux.loss_ce: 0.0708 aux.acc_seg: 91.8902 2023/06/08 08:57:36 - mmengine - INFO - Iter(train) [122850/240000] lr: 5.2918e-03 eta: 23:24:34 time: 0.7510 data_time: 0.0124 memory: 17395 loss: 0.2000 decode.loss_ce: 0.1254 decode.acc_seg: 95.1090 aux.loss_ce: 0.0746 aux.acc_seg: 92.6297 2023/06/08 08:58:13 - mmengine - INFO - Iter(train) [122900/240000] lr: 5.2898e-03 eta: 23:23:59 time: 0.7256 data_time: 0.0126 memory: 17395 loss: 0.1949 decode.loss_ce: 0.1241 decode.acc_seg: 93.7965 aux.loss_ce: 0.0708 aux.acc_seg: 90.7544 2023/06/08 08:58:50 - mmengine - INFO - Iter(train) [122950/240000] lr: 5.2878e-03 eta: 23:23:24 time: 0.7369 data_time: 0.0125 memory: 17393 loss: 0.1963 decode.loss_ce: 0.1249 decode.acc_seg: 95.4901 aux.loss_ce: 0.0713 aux.acc_seg: 93.7519 2023/06/08 08:59:27 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 08:59:27 - mmengine - INFO - Iter(train) [123000/240000] lr: 5.2858e-03 eta: 23:22:49 time: 0.7536 data_time: 0.0130 memory: 17395 loss: 0.2104 decode.loss_ce: 0.1330 decode.acc_seg: 95.2725 aux.loss_ce: 0.0774 aux.acc_seg: 91.6006 2023/06/08 09:00:05 - mmengine - INFO - Iter(train) [123050/240000] lr: 5.2838e-03 eta: 23:22:15 time: 0.7630 data_time: 0.0135 memory: 17394 loss: 0.1900 decode.loss_ce: 0.1215 decode.acc_seg: 94.5798 aux.loss_ce: 0.0685 aux.acc_seg: 92.9047 2023/06/08 09:00:43 - mmengine - INFO - Iter(train) [123100/240000] lr: 5.2818e-03 eta: 23:21:41 time: 0.7439 data_time: 0.0129 memory: 17395 loss: 0.1820 decode.loss_ce: 0.1152 decode.acc_seg: 95.2734 aux.loss_ce: 0.0668 aux.acc_seg: 93.3945 2023/06/08 09:01:21 - mmengine - INFO - Iter(train) [123150/240000] lr: 5.2798e-03 eta: 23:21:07 time: 0.7500 data_time: 0.0129 memory: 17394 loss: 0.2037 decode.loss_ce: 0.1301 decode.acc_seg: 92.1083 aux.loss_ce: 0.0736 aux.acc_seg: 89.0111 2023/06/08 09:01:59 - mmengine - INFO - Iter(train) [123200/240000] lr: 5.2778e-03 eta: 23:20:33 time: 0.7603 data_time: 0.0134 memory: 17395 loss: 0.2024 decode.loss_ce: 0.1278 decode.acc_seg: 94.1675 aux.loss_ce: 0.0746 aux.acc_seg: 91.1757 2023/06/08 09:02:37 - mmengine - INFO - Iter(train) [123250/240000] lr: 5.2758e-03 eta: 23:19:58 time: 0.7578 data_time: 0.1829 memory: 17397 loss: 0.1853 decode.loss_ce: 0.1178 decode.acc_seg: 90.7579 aux.loss_ce: 0.0675 aux.acc_seg: 88.3432 2023/06/08 09:03:14 - mmengine - INFO - Iter(train) [123300/240000] lr: 5.2738e-03 eta: 23:19:24 time: 0.7651 data_time: 0.1118 memory: 17395 loss: 0.2095 decode.loss_ce: 0.1324 decode.acc_seg: 92.6308 aux.loss_ce: 0.0770 aux.acc_seg: 89.0976 2023/06/08 09:03:52 - mmengine - INFO - Iter(train) [123350/240000] lr: 5.2718e-03 eta: 23:18:49 time: 0.7245 data_time: 0.2910 memory: 17393 loss: 0.1903 decode.loss_ce: 0.1196 decode.acc_seg: 94.3088 aux.loss_ce: 0.0707 aux.acc_seg: 91.7266 2023/06/08 09:04:29 - mmengine - INFO - Iter(train) [123400/240000] lr: 5.2698e-03 eta: 23:18:14 time: 0.7299 data_time: 0.3624 memory: 17394 loss: 0.2133 decode.loss_ce: 0.1382 decode.acc_seg: 92.8727 aux.loss_ce: 0.0751 aux.acc_seg: 89.6943 2023/06/08 09:05:05 - mmengine - INFO - Iter(train) [123450/240000] lr: 5.2678e-03 eta: 23:17:39 time: 0.7111 data_time: 0.3733 memory: 17393 loss: 0.1961 decode.loss_ce: 0.1244 decode.acc_seg: 94.4525 aux.loss_ce: 0.0717 aux.acc_seg: 92.1118 2023/06/08 09:05:42 - mmengine - INFO - Iter(train) [123500/240000] lr: 5.2658e-03 eta: 23:17:03 time: 0.7377 data_time: 0.1301 memory: 17393 loss: 0.2030 decode.loss_ce: 0.1300 decode.acc_seg: 94.4969 aux.loss_ce: 0.0730 aux.acc_seg: 91.5045 2023/06/08 09:06:18 - mmengine - INFO - Iter(train) [123550/240000] lr: 5.2638e-03 eta: 23:16:28 time: 0.7218 data_time: 0.0126 memory: 17393 loss: 0.1867 decode.loss_ce: 0.1175 decode.acc_seg: 93.9147 aux.loss_ce: 0.0692 aux.acc_seg: 91.0954 2023/06/08 09:06:54 - mmengine - INFO - Iter(train) [123600/240000] lr: 5.2618e-03 eta: 23:15:52 time: 0.7233 data_time: 0.0129 memory: 17394 loss: 0.1936 decode.loss_ce: 0.1230 decode.acc_seg: 93.8585 aux.loss_ce: 0.0706 aux.acc_seg: 90.3513 2023/06/08 09:07:31 - mmengine - INFO - Iter(train) [123650/240000] lr: 5.2598e-03 eta: 23:15:17 time: 0.7389 data_time: 0.0134 memory: 17394 loss: 0.2332 decode.loss_ce: 0.1484 decode.acc_seg: 90.8038 aux.loss_ce: 0.0847 aux.acc_seg: 90.4644 2023/06/08 09:08:08 - mmengine - INFO - Iter(train) [123700/240000] lr: 5.2578e-03 eta: 23:14:41 time: 0.7117 data_time: 0.0123 memory: 17395 loss: 0.2233 decode.loss_ce: 0.1438 decode.acc_seg: 95.4589 aux.loss_ce: 0.0795 aux.acc_seg: 93.5250 2023/06/08 09:08:44 - mmengine - INFO - Iter(train) [123750/240000] lr: 5.2559e-03 eta: 23:14:05 time: 0.7054 data_time: 0.0125 memory: 17394 loss: 0.1919 decode.loss_ce: 0.1211 decode.acc_seg: 94.7895 aux.loss_ce: 0.0708 aux.acc_seg: 92.7240 2023/06/08 09:09:19 - mmengine - INFO - Iter(train) [123800/240000] lr: 5.2539e-03 eta: 23:13:29 time: 0.7136 data_time: 0.0124 memory: 17396 loss: 0.1885 decode.loss_ce: 0.1194 decode.acc_seg: 94.1480 aux.loss_ce: 0.0691 aux.acc_seg: 91.5115 2023/06/08 09:09:55 - mmengine - INFO - Iter(train) [123850/240000] lr: 5.2519e-03 eta: 23:12:52 time: 0.7087 data_time: 0.1828 memory: 17395 loss: 0.2089 decode.loss_ce: 0.1336 decode.acc_seg: 94.8284 aux.loss_ce: 0.0753 aux.acc_seg: 91.9305 2023/06/08 09:10:30 - mmengine - INFO - Iter(train) [123900/240000] lr: 5.2499e-03 eta: 23:12:16 time: 0.7109 data_time: 0.0685 memory: 17397 loss: 0.1929 decode.loss_ce: 0.1220 decode.acc_seg: 94.2372 aux.loss_ce: 0.0708 aux.acc_seg: 90.9934 2023/06/08 09:11:06 - mmengine - INFO - Iter(train) [123950/240000] lr: 5.2479e-03 eta: 23:11:40 time: 0.7137 data_time: 0.2708 memory: 17392 loss: 0.1945 decode.loss_ce: 0.1247 decode.acc_seg: 93.6698 aux.loss_ce: 0.0698 aux.acc_seg: 91.5098 2023/06/08 09:11:41 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 09:11:41 - mmengine - INFO - Iter(train) [124000/240000] lr: 5.2459e-03 eta: 23:11:03 time: 0.6994 data_time: 0.3748 memory: 17394 loss: 0.1983 decode.loss_ce: 0.1249 decode.acc_seg: 95.1457 aux.loss_ce: 0.0734 aux.acc_seg: 92.6272 2023/06/08 09:12:17 - mmengine - INFO - Iter(train) [124050/240000] lr: 5.2439e-03 eta: 23:10:27 time: 0.7325 data_time: 0.4077 memory: 17392 loss: 0.1913 decode.loss_ce: 0.1184 decode.acc_seg: 93.7190 aux.loss_ce: 0.0729 aux.acc_seg: 91.4296 2023/06/08 09:12:53 - mmengine - INFO - Iter(train) [124100/240000] lr: 5.2419e-03 eta: 23:09:51 time: 0.7069 data_time: 0.3819 memory: 17393 loss: 0.2069 decode.loss_ce: 0.1324 decode.acc_seg: 93.5348 aux.loss_ce: 0.0745 aux.acc_seg: 90.9898 2023/06/08 09:13:29 - mmengine - INFO - Iter(train) [124150/240000] lr: 5.2399e-03 eta: 23:09:15 time: 0.7129 data_time: 0.3879 memory: 17394 loss: 0.1921 decode.loss_ce: 0.1214 decode.acc_seg: 94.9681 aux.loss_ce: 0.0707 aux.acc_seg: 91.8561 2023/06/08 09:14:06 - mmengine - INFO - Iter(train) [124200/240000] lr: 5.2379e-03 eta: 23:08:40 time: 0.7417 data_time: 0.3838 memory: 17393 loss: 0.2063 decode.loss_ce: 0.1314 decode.acc_seg: 94.7045 aux.loss_ce: 0.0749 aux.acc_seg: 92.7099 2023/06/08 09:14:42 - mmengine - INFO - Iter(train) [124250/240000] lr: 5.2359e-03 eta: 23:08:04 time: 0.7147 data_time: 0.3701 memory: 17395 loss: 0.1939 decode.loss_ce: 0.1229 decode.acc_seg: 94.2343 aux.loss_ce: 0.0710 aux.acc_seg: 91.8834 2023/06/08 09:15:19 - mmengine - INFO - Iter(train) [124300/240000] lr: 5.2339e-03 eta: 23:07:28 time: 0.7251 data_time: 0.3948 memory: 17393 loss: 0.1804 decode.loss_ce: 0.1137 decode.acc_seg: 94.5540 aux.loss_ce: 0.0667 aux.acc_seg: 92.7458 2023/06/08 09:15:55 - mmengine - INFO - Iter(train) [124350/240000] lr: 5.2319e-03 eta: 23:06:53 time: 0.7277 data_time: 0.3838 memory: 17393 loss: 0.1888 decode.loss_ce: 0.1183 decode.acc_seg: 94.5312 aux.loss_ce: 0.0705 aux.acc_seg: 91.7217 2023/06/08 09:16:31 - mmengine - INFO - Iter(train) [124400/240000] lr: 5.2299e-03 eta: 23:06:17 time: 0.7114 data_time: 0.3626 memory: 17394 loss: 0.2279 decode.loss_ce: 0.1448 decode.acc_seg: 94.0499 aux.loss_ce: 0.0831 aux.acc_seg: 91.3273 2023/06/08 09:17:07 - mmengine - INFO - Iter(train) [124450/240000] lr: 5.2279e-03 eta: 23:05:41 time: 0.7112 data_time: 0.3637 memory: 17395 loss: 0.2054 decode.loss_ce: 0.1308 decode.acc_seg: 95.3353 aux.loss_ce: 0.0745 aux.acc_seg: 92.7519 2023/06/08 09:17:44 - mmengine - INFO - Iter(train) [124500/240000] lr: 5.2259e-03 eta: 23:05:06 time: 0.7358 data_time: 0.2695 memory: 17394 loss: 0.1963 decode.loss_ce: 0.1271 decode.acc_seg: 94.6227 aux.loss_ce: 0.0693 aux.acc_seg: 92.3546 2023/06/08 09:18:20 - mmengine - INFO - Iter(train) [124550/240000] lr: 5.2239e-03 eta: 23:04:30 time: 0.7157 data_time: 0.3494 memory: 17395 loss: 0.1980 decode.loss_ce: 0.1257 decode.acc_seg: 94.9315 aux.loss_ce: 0.0723 aux.acc_seg: 92.7919 2023/06/08 09:18:56 - mmengine - INFO - Iter(train) [124600/240000] lr: 5.2219e-03 eta: 23:03:54 time: 0.7064 data_time: 0.3713 memory: 17393 loss: 0.1877 decode.loss_ce: 0.1183 decode.acc_seg: 95.5570 aux.loss_ce: 0.0694 aux.acc_seg: 92.8979 2023/06/08 09:19:31 - mmengine - INFO - Iter(train) [124650/240000] lr: 5.2199e-03 eta: 23:03:17 time: 0.7131 data_time: 0.3890 memory: 17392 loss: 0.1927 decode.loss_ce: 0.1211 decode.acc_seg: 94.8897 aux.loss_ce: 0.0716 aux.acc_seg: 91.4590 2023/06/08 09:20:07 - mmengine - INFO - Iter(train) [124700/240000] lr: 5.2179e-03 eta: 23:02:41 time: 0.7094 data_time: 0.0787 memory: 17397 loss: 0.1890 decode.loss_ce: 0.1216 decode.acc_seg: 95.2425 aux.loss_ce: 0.0674 aux.acc_seg: 93.5084 2023/06/08 09:20:43 - mmengine - INFO - Iter(train) [124750/240000] lr: 5.2159e-03 eta: 23:02:05 time: 0.7115 data_time: 0.1670 memory: 17393 loss: 0.1933 decode.loss_ce: 0.1228 decode.acc_seg: 95.7070 aux.loss_ce: 0.0705 aux.acc_seg: 93.2876 2023/06/08 09:21:18 - mmengine - INFO - Iter(train) [124800/240000] lr: 5.2139e-03 eta: 23:01:29 time: 0.7108 data_time: 0.3707 memory: 17395 loss: 0.2150 decode.loss_ce: 0.1392 decode.acc_seg: 93.7411 aux.loss_ce: 0.0758 aux.acc_seg: 91.8430 2023/06/08 09:21:54 - mmengine - INFO - Iter(train) [124850/240000] lr: 5.2119e-03 eta: 23:00:52 time: 0.7086 data_time: 0.3840 memory: 17393 loss: 0.2069 decode.loss_ce: 0.1309 decode.acc_seg: 94.8223 aux.loss_ce: 0.0759 aux.acc_seg: 91.6246 2023/06/08 09:22:30 - mmengine - INFO - Iter(train) [124900/240000] lr: 5.2099e-03 eta: 23:00:16 time: 0.7424 data_time: 0.4172 memory: 17392 loss: 0.2040 decode.loss_ce: 0.1300 decode.acc_seg: 93.4400 aux.loss_ce: 0.0740 aux.acc_seg: 90.3138 2023/06/08 09:23:06 - mmengine - INFO - Iter(train) [124950/240000] lr: 5.2079e-03 eta: 22:59:40 time: 0.7165 data_time: 0.3913 memory: 17392 loss: 0.2043 decode.loss_ce: 0.1286 decode.acc_seg: 94.4608 aux.loss_ce: 0.0757 aux.acc_seg: 91.7319 2023/06/08 09:23:41 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 09:23:41 - mmengine - INFO - Iter(train) [125000/240000] lr: 5.2059e-03 eta: 22:59:04 time: 0.7175 data_time: 0.3923 memory: 17395 loss: 0.2001 decode.loss_ce: 0.1269 decode.acc_seg: 94.8090 aux.loss_ce: 0.0732 aux.acc_seg: 92.0792 2023/06/08 09:24:17 - mmengine - INFO - Iter(train) [125050/240000] lr: 5.2039e-03 eta: 22:58:28 time: 0.7718 data_time: 0.4206 memory: 17393 loss: 0.2054 decode.loss_ce: 0.1328 decode.acc_seg: 94.8024 aux.loss_ce: 0.0725 aux.acc_seg: 91.8064 2023/06/08 09:24:54 - mmengine - INFO - Iter(train) [125100/240000] lr: 5.2019e-03 eta: 22:57:52 time: 0.7255 data_time: 0.3931 memory: 17395 loss: 0.1904 decode.loss_ce: 0.1227 decode.acc_seg: 95.4357 aux.loss_ce: 0.0676 aux.acc_seg: 94.1714 2023/06/08 09:25:30 - mmengine - INFO - Iter(train) [125150/240000] lr: 5.1999e-03 eta: 22:57:17 time: 0.7204 data_time: 0.3857 memory: 17392 loss: 0.1974 decode.loss_ce: 0.1249 decode.acc_seg: 95.0088 aux.loss_ce: 0.0725 aux.acc_seg: 93.1428 2023/06/08 09:26:07 - mmengine - INFO - Iter(train) [125200/240000] lr: 5.1979e-03 eta: 22:56:41 time: 0.7403 data_time: 0.4084 memory: 17393 loss: 0.1764 decode.loss_ce: 0.1108 decode.acc_seg: 95.7025 aux.loss_ce: 0.0656 aux.acc_seg: 93.5337 2023/06/08 09:26:43 - mmengine - INFO - Iter(train) [125250/240000] lr: 5.1959e-03 eta: 22:56:05 time: 0.7304 data_time: 0.3951 memory: 17396 loss: 0.1934 decode.loss_ce: 0.1232 decode.acc_seg: 95.3087 aux.loss_ce: 0.0702 aux.acc_seg: 92.9342 2023/06/08 09:27:19 - mmengine - INFO - Iter(train) [125300/240000] lr: 5.1939e-03 eta: 22:55:30 time: 0.7230 data_time: 0.3898 memory: 17393 loss: 0.1815 decode.loss_ce: 0.1143 decode.acc_seg: 95.4133 aux.loss_ce: 0.0673 aux.acc_seg: 92.9733 2023/06/08 09:27:56 - mmengine - INFO - Iter(train) [125350/240000] lr: 5.1919e-03 eta: 22:54:54 time: 0.7355 data_time: 0.4018 memory: 17395 loss: 0.1954 decode.loss_ce: 0.1211 decode.acc_seg: 94.2392 aux.loss_ce: 0.0744 aux.acc_seg: 90.3773 2023/06/08 09:28:32 - mmengine - INFO - Iter(train) [125400/240000] lr: 5.1899e-03 eta: 22:54:19 time: 0.7306 data_time: 0.3920 memory: 17393 loss: 0.2010 decode.loss_ce: 0.1256 decode.acc_seg: 94.5333 aux.loss_ce: 0.0754 aux.acc_seg: 92.6245 2023/06/08 09:29:09 - mmengine - INFO - Iter(train) [125450/240000] lr: 5.1879e-03 eta: 22:53:43 time: 0.7253 data_time: 0.3818 memory: 17393 loss: 0.1928 decode.loss_ce: 0.1225 decode.acc_seg: 95.2210 aux.loss_ce: 0.0702 aux.acc_seg: 93.2557 2023/06/08 09:29:45 - mmengine - INFO - Iter(train) [125500/240000] lr: 5.1859e-03 eta: 22:53:07 time: 0.7215 data_time: 0.3804 memory: 17394 loss: 0.1958 decode.loss_ce: 0.1240 decode.acc_seg: 93.6669 aux.loss_ce: 0.0718 aux.acc_seg: 91.7027 2023/06/08 09:30:21 - mmengine - INFO - Iter(train) [125550/240000] lr: 5.1839e-03 eta: 22:52:32 time: 0.7131 data_time: 0.3892 memory: 17393 loss: 0.1834 decode.loss_ce: 0.1158 decode.acc_seg: 94.4289 aux.loss_ce: 0.0675 aux.acc_seg: 92.0346 2023/06/08 09:30:57 - mmengine - INFO - Iter(train) [125600/240000] lr: 5.1819e-03 eta: 22:51:55 time: 0.7153 data_time: 0.3907 memory: 17394 loss: 0.1920 decode.loss_ce: 0.1222 decode.acc_seg: 93.3855 aux.loss_ce: 0.0698 aux.acc_seg: 91.2338 2023/06/08 09:31:32 - mmengine - INFO - Iter(train) [125650/240000] lr: 5.1799e-03 eta: 22:51:19 time: 0.7069 data_time: 0.3825 memory: 17394 loss: 0.1885 decode.loss_ce: 0.1204 decode.acc_seg: 94.0694 aux.loss_ce: 0.0681 aux.acc_seg: 91.4592 2023/06/08 09:32:08 - mmengine - INFO - Iter(train) [125700/240000] lr: 5.1779e-03 eta: 22:50:43 time: 0.7002 data_time: 0.3748 memory: 17394 loss: 0.2112 decode.loss_ce: 0.1340 decode.acc_seg: 93.9384 aux.loss_ce: 0.0772 aux.acc_seg: 90.7584 2023/06/08 09:32:45 - mmengine - INFO - Iter(train) [125750/240000] lr: 5.1759e-03 eta: 22:50:08 time: 0.7363 data_time: 0.4065 memory: 17394 loss: 0.1942 decode.loss_ce: 0.1247 decode.acc_seg: 94.5913 aux.loss_ce: 0.0696 aux.acc_seg: 92.3220 2023/06/08 09:33:21 - mmengine - INFO - Iter(train) [125800/240000] lr: 5.1740e-03 eta: 22:49:32 time: 0.7471 data_time: 0.4062 memory: 17394 loss: 0.1812 decode.loss_ce: 0.1144 decode.acc_seg: 90.8656 aux.loss_ce: 0.0667 aux.acc_seg: 88.9179 2023/06/08 09:33:59 - mmengine - INFO - Iter(train) [125850/240000] lr: 5.1720e-03 eta: 22:48:57 time: 0.7328 data_time: 0.4026 memory: 17397 loss: 0.1996 decode.loss_ce: 0.1274 decode.acc_seg: 91.8132 aux.loss_ce: 0.0722 aux.acc_seg: 90.0279 2023/06/08 09:34:35 - mmengine - INFO - Iter(train) [125900/240000] lr: 5.1700e-03 eta: 22:48:21 time: 0.7178 data_time: 0.3908 memory: 17396 loss: 0.2116 decode.loss_ce: 0.1333 decode.acc_seg: 93.6474 aux.loss_ce: 0.0783 aux.acc_seg: 91.0004 2023/06/08 09:35:11 - mmengine - INFO - Iter(train) [125950/240000] lr: 5.1680e-03 eta: 22:47:45 time: 0.7247 data_time: 0.4006 memory: 17396 loss: 0.1994 decode.loss_ce: 0.1281 decode.acc_seg: 94.9575 aux.loss_ce: 0.0713 aux.acc_seg: 93.1695 2023/06/08 09:35:47 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 09:35:47 - mmengine - INFO - Iter(train) [126000/240000] lr: 5.1660e-03 eta: 22:47:09 time: 0.7085 data_time: 0.3840 memory: 17397 loss: 0.2023 decode.loss_ce: 0.1293 decode.acc_seg: 93.2225 aux.loss_ce: 0.0731 aux.acc_seg: 90.7573 2023/06/08 09:36:22 - mmengine - INFO - Iter(train) [126050/240000] lr: 5.1640e-03 eta: 22:46:33 time: 0.7143 data_time: 0.3900 memory: 17394 loss: 0.1921 decode.loss_ce: 0.1201 decode.acc_seg: 94.6810 aux.loss_ce: 0.0720 aux.acc_seg: 92.2198 2023/06/08 09:36:58 - mmengine - INFO - Iter(train) [126100/240000] lr: 5.1620e-03 eta: 22:45:57 time: 0.7072 data_time: 0.3819 memory: 17396 loss: 0.1929 decode.loss_ce: 0.1199 decode.acc_seg: 94.5673 aux.loss_ce: 0.0730 aux.acc_seg: 91.3118 2023/06/08 09:37:34 - mmengine - INFO - Iter(train) [126150/240000] lr: 5.1600e-03 eta: 22:45:20 time: 0.7153 data_time: 0.3915 memory: 17391 loss: 0.1916 decode.loss_ce: 0.1189 decode.acc_seg: 95.4948 aux.loss_ce: 0.0727 aux.acc_seg: 92.2646 2023/06/08 09:38:09 - mmengine - INFO - Iter(train) [126200/240000] lr: 5.1580e-03 eta: 22:44:44 time: 0.7204 data_time: 0.3953 memory: 17395 loss: 0.1862 decode.loss_ce: 0.1172 decode.acc_seg: 93.6275 aux.loss_ce: 0.0690 aux.acc_seg: 91.0900 2023/06/08 09:38:45 - mmengine - INFO - Iter(train) [126250/240000] lr: 5.1560e-03 eta: 22:44:08 time: 0.7269 data_time: 0.4024 memory: 17394 loss: 0.1996 decode.loss_ce: 0.1264 decode.acc_seg: 96.2446 aux.loss_ce: 0.0733 aux.acc_seg: 94.0832 2023/06/08 09:39:21 - mmengine - INFO - Iter(train) [126300/240000] lr: 5.1540e-03 eta: 22:43:32 time: 0.7229 data_time: 0.3981 memory: 17393 loss: 0.1828 decode.loss_ce: 0.1157 decode.acc_seg: 95.4221 aux.loss_ce: 0.0672 aux.acc_seg: 92.7145 2023/06/08 09:39:57 - mmengine - INFO - Iter(train) [126350/240000] lr: 5.1520e-03 eta: 22:42:56 time: 0.7279 data_time: 0.4035 memory: 17396 loss: 0.1898 decode.loss_ce: 0.1216 decode.acc_seg: 94.3234 aux.loss_ce: 0.0682 aux.acc_seg: 92.2880 2023/06/08 09:40:32 - mmengine - INFO - Iter(train) [126400/240000] lr: 5.1500e-03 eta: 22:42:20 time: 0.7300 data_time: 0.4028 memory: 17393 loss: 0.1979 decode.loss_ce: 0.1246 decode.acc_seg: 95.0524 aux.loss_ce: 0.0733 aux.acc_seg: 92.9650 2023/06/08 09:41:09 - mmengine - INFO - Iter(train) [126450/240000] lr: 5.1480e-03 eta: 22:41:44 time: 0.7381 data_time: 0.3914 memory: 17394 loss: 0.1948 decode.loss_ce: 0.1251 decode.acc_seg: 94.8073 aux.loss_ce: 0.0697 aux.acc_seg: 92.8060 2023/06/08 09:41:46 - mmengine - INFO - Iter(train) [126500/240000] lr: 5.1460e-03 eta: 22:41:09 time: 0.7329 data_time: 0.3990 memory: 17395 loss: 0.1935 decode.loss_ce: 0.1237 decode.acc_seg: 95.0378 aux.loss_ce: 0.0698 aux.acc_seg: 92.2698 2023/06/08 09:42:22 - mmengine - INFO - Iter(train) [126550/240000] lr: 5.1439e-03 eta: 22:40:33 time: 0.7278 data_time: 0.3981 memory: 17393 loss: 0.1795 decode.loss_ce: 0.1129 decode.acc_seg: 94.1430 aux.loss_ce: 0.0665 aux.acc_seg: 90.7013 2023/06/08 09:42:58 - mmengine - INFO - Iter(train) [126600/240000] lr: 5.1419e-03 eta: 22:39:57 time: 0.7221 data_time: 0.3795 memory: 17396 loss: 0.2091 decode.loss_ce: 0.1327 decode.acc_seg: 94.5562 aux.loss_ce: 0.0764 aux.acc_seg: 91.7304 2023/06/08 09:43:34 - mmengine - INFO - Iter(train) [126650/240000] lr: 5.1399e-03 eta: 22:39:22 time: 0.7168 data_time: 0.2535 memory: 17394 loss: 0.1964 decode.loss_ce: 0.1234 decode.acc_seg: 95.8467 aux.loss_ce: 0.0730 aux.acc_seg: 94.6848 2023/06/08 09:44:10 - mmengine - INFO - Iter(train) [126700/240000] lr: 5.1379e-03 eta: 22:38:46 time: 0.7149 data_time: 0.3510 memory: 17394 loss: 0.1978 decode.loss_ce: 0.1254 decode.acc_seg: 94.6823 aux.loss_ce: 0.0724 aux.acc_seg: 93.3192 2023/06/08 09:44:47 - mmengine - INFO - Iter(train) [126750/240000] lr: 5.1359e-03 eta: 22:38:10 time: 0.7582 data_time: 0.4113 memory: 17394 loss: 0.1972 decode.loss_ce: 0.1260 decode.acc_seg: 92.7655 aux.loss_ce: 0.0713 aux.acc_seg: 91.0858 2023/06/08 09:45:23 - mmengine - INFO - Iter(train) [126800/240000] lr: 5.1339e-03 eta: 22:37:35 time: 0.7257 data_time: 0.3977 memory: 17394 loss: 0.2060 decode.loss_ce: 0.1314 decode.acc_seg: 95.0434 aux.loss_ce: 0.0746 aux.acc_seg: 92.7139 2023/06/08 09:45:59 - mmengine - INFO - Iter(train) [126850/240000] lr: 5.1319e-03 eta: 22:36:59 time: 0.7112 data_time: 0.3823 memory: 17393 loss: 0.1933 decode.loss_ce: 0.1219 decode.acc_seg: 94.8028 aux.loss_ce: 0.0714 aux.acc_seg: 92.3874 2023/06/08 09:46:36 - mmengine - INFO - Iter(train) [126900/240000] lr: 5.1299e-03 eta: 22:36:23 time: 0.7383 data_time: 0.4055 memory: 17394 loss: 0.1650 decode.loss_ce: 0.1050 decode.acc_seg: 95.5343 aux.loss_ce: 0.0600 aux.acc_seg: 93.0853 2023/06/08 09:47:11 - mmengine - INFO - Iter(train) [126950/240000] lr: 5.1279e-03 eta: 22:35:47 time: 0.7188 data_time: 0.3839 memory: 17393 loss: 0.1827 decode.loss_ce: 0.1143 decode.acc_seg: 95.1282 aux.loss_ce: 0.0685 aux.acc_seg: 93.0606 2023/06/08 09:47:48 - mmengine - INFO - Exp name: mobilenet_deeplab_drone_20230607_082146 2023/06/08 09:47:48 - mmengine - INFO - Iter(train) [127000/240000] lr: 5.1259e-03 eta: 22:35:11 time: 0.7467 data_time: 0.4031 memory: 17396 loss: 0.1803 decode.loss_ce: 0.1133 decode.acc_seg: 94.8795 aux.loss_ce: 0.0670 aux.acc_seg: 92.9219 2023/06/08 09:48:24 - mmengine - INFO - Iter(train) [127050/240000] lr: 5.1239e-03 eta: 22:34:36 time: 0.7297 data_time: 0.3992 memory: 17396 loss: 0.1875 decode.loss_ce: 0.1189 decode.acc_seg: 94.4246 aux.loss_ce: 0.0687 aux.acc_seg: 90.5810 2023/06/08 09:49:01 - mmengine - INFO - Iter(train) [127100/240000] lr: 5.1219e-03 eta: 22:34:00 time: 0.7284 data_time: 0.4002 memory: 17394 loss: 0.1811 decode.loss_ce: 0.1128 decode.acc_seg: 94.8924 aux.loss_ce: 0.0683 aux.acc_seg: 90.4458 2023/06/08 09:49:37 - mmengine - INFO - Iter(train) [127150/240000] lr: 5.1199e-03 eta: 22:33:24 time: 0.7240 data_time: 0.3865 memory: 17395 loss: 0.1910 decode.loss_ce: 0.1205 decode.acc_seg: 94.6369 aux.loss_ce: 0.0705 aux.acc_seg: 92.7772 2023/06/08 09:50:13 - mmengine - INFO - Iter(train) [127200/240000] lr: 5.1179e-03 eta: 22:32:48 time: 0.7123 data_time: 0.3657 memory: 17394 loss: 0.1913 decode.loss_ce: 0.1198 decode.acc_seg: 94.0758 aux.loss_ce: 0.0715 aux.acc_seg: 90.1435 2023/06/08 09:50:50 - mmengine - INFO - Iter(train) [127250/240000] lr: 5.1159e-03 eta: 22:32:13 time: 0.7271 data_time: 0.3928 memory: 17393 loss: 0.2028 decode.loss_ce: 0.1304 decode.acc_seg: 94.1226 aux.loss_ce: 0.0724 aux.acc_seg: 90.2714 2023/06/08 09:51:26 - mmengine - INFO - Iter(train) [127300/240000] lr: 5.1139e-03 eta: 22:31:37 time: 0.7437 data_time: 0.4023 memory: 17392 loss: 0.1914 decode.loss_ce: 0.1222 decode.acc_seg: 92.0084 aux.loss_ce: 0.0692 aux.acc_seg: 90.1926 2023/06/08 09:52:02 - mmengine - INFO - Iter(train) [127350/240000] lr: 5.1119e-03 eta: 22:31:02 time: 0.7153 data_time: 0.3468 memory: 17395 loss: 0.2036 decode.loss_ce: 0.1291 decode.acc_seg: 94.2951 aux.loss_ce: 0.0746 aux.acc_seg: 92.6446 2023/06/08 09:52:38 - mmengine - INFO - Iter(train) [127400/240000] lr: 5.1099e-03 eta: 22:30:26 time: 0.7209 data_time: 0.0122 memory: 17395 loss: 0.1893 decode.loss_ce: 0.1198 decode.acc_seg: 95.7799 aux.loss_ce: 0.0695 aux.acc_seg: 93.8502 2023/06/08 09:53:14 - mmengine - INFO - Iter(train) [127450/240000] lr: 5.1079e-03 eta: 22:29:50 time: 0.7115 data_time: 0.0124 memory: 17393 loss: 0.1884 decode.loss_ce: 0.1177 decode.acc_seg: 94.2067 aux.loss_ce: 0.0707 aux.acc_seg: 91.1305 2023/06/08 09:53:51 - mmengine - INFO - Iter(train) [127500/240000] lr: 5.1059e-03 eta: 22:29:14 time: 0.7663 data_time: 0.0137 memory: 17392 loss: 0.1926 decode.loss_ce: 0.1225 decode.acc_seg: 94.0948 aux.loss_ce: 0.0701 aux.acc_seg: 92.0916 2023/06/08 09:54:28 - mmengine - INFO - Iter(train) [127550/240000] lr: 5.1039e-03 eta: 22:28:39 time: 0.7201 data_time: 0.0127 memory: 17395 loss: 0.1839 decode.loss_ce: 0.1150 decode.acc_seg: 95.3896 aux.loss_ce: 0.0689 aux.acc_seg: 92.9974 2023/06/08 09:55:04 - mmengine - INFO - Iter(train) [127600/240000] lr: 5.1019e-03 eta: 22:28:03 time: 0.7160 data_time: 0.0122 memory: 17396 loss: 0.1886 decode.loss_ce: 0.1200 decode.acc_seg: 95.0597 aux.loss_ce: 0.0687 aux.acc_seg: 93.3967 2023/06/08 09:55:41 - mmengine - INFO - Iter(train) [127650/240000] lr: 5.0999e-03 eta: 22:27:28 time: 0.7219 data_time: 0.0126 memory: 17394 loss: 0.1954 decode.loss_ce: 0.1219 decode.acc_seg: 95.3294 aux.loss_ce: 0.0735 aux.acc_seg: 92.0842 2023/06/08 09:56:17 - mmengine - INFO - Iter(train) [127700/240000] lr: 5.0979e-03 eta: 22:26:52 time: 0.7394 data_time: 0.0127 memory: 17396 loss: 0.1832 decode.loss_ce: 0.1168 decode.acc_seg: 94.8025 aux.loss_ce: 0.0664 aux.acc_seg: 93.0837