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