finetune/finetune_mask-rcnn_12k_voc0712_lr3e-2_wd5e-5/20221003_234250.log

2022-10-03 23:42:50,462 - mmdet - INFO - Environment info:
------------------------------------------------------------
sys.platform: linux
Python: 3.7.3 (default, Jan 22 2021, 20:04:44) [GCC 8.3.0]
CUDA available: True
GPU 0,1,2,3,4,5,6,7: A100-SXM-80GB
CUDA_HOME: /usr/local/cuda
NVCC: Cuda compilation tools, release 11.3, V11.3.109
GCC: x86_64-linux-gnu-gcc (Debian 8.3.0-6) 8.3.0
PyTorch: 1.10.0
PyTorch compiling details: PyTorch built with:
  - GCC 7.3
  - C++ Version: 201402
  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications
  - Intel(R) MKL-DNN v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
  - OpenMP 201511 (a.k.a. OpenMP 4.5)
  - LAPACK is enabled (usually provided by MKL)
  - NNPACK is enabled
  - CPU capability usage: AVX512
  - CUDA Runtime 11.3
  - NVCC architecture flags: -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 8.2
  - Magma 2.5.2
  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.2.0, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.10.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, 

TorchVision: 0.11.1+cu113
OpenCV: 4.6.0
MMCV: 1.6.1
MMCV Compiler: GCC 9.3
MMCV CUDA Compiler: 11.3
MMDetection: 2.25.2+87c120c
------------------------------------------------------------

2022-10-03 23:42:51,716 - mmdet - INFO - Distributed training: True
2022-10-03 23:42:52,833 - mmdet - INFO - Config:
model = dict(
    type='MaskRCNN',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5,
        norm_cfg=dict(type='SyncBN', requires_grad=True)),
    rpn_head=dict(
        type='RPNHead',
        in_channels=256,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            scales=[8],
            ratios=[0.5, 1.0, 2.0],
            strides=[4, 8, 16, 32, 64]),
        bbox_coder=dict(
            type='DeltaXYWHBBoxCoder',
            target_means=[0.0, 0.0, 0.0, 0.0],
            target_stds=[1.0, 1.0, 1.0, 1.0]),
        loss_cls=dict(
            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    roi_head=dict(
        type='StandardRoIHead',
        bbox_roi_extractor=dict(
            type='SingleRoIExtractor',
            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
            out_channels=256,
            featmap_strides=[4, 8, 16, 32]),
        bbox_head=dict(
            type='Shared4Conv1FCBBoxHead',
            in_channels=256,
            fc_out_channels=1024,
            roi_feat_size=7,
            num_classes=20,
            bbox_coder=dict(
                type='DeltaXYWHBBoxCoder',
                target_means=[0.0, 0.0, 0.0, 0.0],
                target_stds=[0.1, 0.1, 0.2, 0.2]),
            reg_class_agnostic=False,
            loss_cls=dict(
                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
        mask_roi_extractor=None,
        mask_head=None),
    train_cfg=dict(
        rpn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.7,
                neg_iou_thr=0.3,
                min_pos_iou=0.3,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=256,
                pos_fraction=0.5,
                neg_pos_ub=-1,
                add_gt_as_proposals=False),
            allowed_border=-1,
            pos_weight=-1,
            debug=False),
        rpn_proposal=dict(
            nms_pre=2000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            assigner=dict(
                type='MaxIoUAssigner',
                pos_iou_thr=0.5,
                neg_iou_thr=0.5,
                min_pos_iou=0.5,
                match_low_quality=True,
                ignore_iof_thr=-1),
            sampler=dict(
                type='RandomSampler',
                num=512,
                pos_fraction=0.25,
                neg_pos_ub=-1,
                add_gt_as_proposals=True),
            mask_size=28,
            pos_weight=-1,
            debug=False)),
    test_cfg=dict(
        rpn=dict(
            nms_pre=1000,
            max_per_img=1000,
            nms=dict(type='nms', iou_threshold=0.7),
            min_bbox_size=0),
        rcnn=dict(
            score_thr=0.05,
            nms=dict(type='nms', iou_threshold=0.5),
            max_per_img=100,
            mask_thr_binary=0.5)))
dataset_type = 'VOCDataset'
data_root = 'data/VOCdevkit/'
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='Resize',
        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),
                   (1333, 736), (1333, 768), (1333, 800)],
        multiscale_mode='value',
        keep_ratio=True),
    dict(type='RandomFlip', flip_ratio=0.5),
    dict(
        type='Normalize',
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        to_rgb=True),
    dict(type='Pad', size_divisor=32),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='RandomFlip'),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img'])
        ])
]
data = dict(
    samples_per_gpu=2,
    workers_per_gpu=2,
    train=dict(
        type='VOCDataset',
        ann_file=[
            'data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt',
            'data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt'
        ],
        img_prefix=['data/VOCdevkit/VOC2007/', 'data/VOCdevkit/VOC2012/'],
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(type='LoadAnnotations', with_bbox=True),
            dict(
                type='Resize',
                img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
                           (1333, 608), (1333, 640), (1333, 672), (1333, 704),
                           (1333, 736), (1333, 768), (1333, 800)],
                multiscale_mode='value',
                keep_ratio=True),
            dict(type='RandomFlip', flip_ratio=0.5),
            dict(
                type='Normalize',
                mean=[123.675, 116.28, 103.53],
                std=[58.395, 57.12, 57.375],
                to_rgb=True),
            dict(type='Pad', size_divisor=32),
            dict(type='DefaultFormatBundle'),
            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
        ]),
    val=dict(
        type='VOCDataset',
        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
        img_prefix='data/VOCdevkit/VOC2007/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]),
    test=dict(
        type='VOCDataset',
        ann_file='data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
        img_prefix='data/VOCdevkit/VOC2007/',
        pipeline=[
            dict(type='LoadImageFromFile'),
            dict(
                type='MultiScaleFlipAug',
                img_scale=(1333, 800),
                flip=False,
                transforms=[
                    dict(type='Resize', keep_ratio=True),
                    dict(type='RandomFlip'),
                    dict(
                        type='Normalize',
                        mean=[123.675, 116.28, 103.53],
                        std=[58.395, 57.12, 57.375],
                        to_rgb=True),
                    dict(type='Pad', size_divisor=32),
                    dict(type='ImageToTensor', keys=['img']),
                    dict(type='Collect', keys=['img'])
                ])
        ]))
evaluation = dict(interval=12000, metric='mAP', save_best='auto')
optimizer = dict(type='SGD', lr=0.03, momentum=0.9, weight_decay=5e-05)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
    policy='step',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=0.001,
    step=[9000, 11000],
    by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=12000)
checkpoint_config = dict(interval=12000)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [
    dict(type='NumClassCheckHook'),
    dict(
        type='MMDetWandbHook',
        init_kwargs=dict(project='I2B', group='finetune'),
        interval=50,
        num_eval_images=0,
        log_checkpoint=False)
]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth'
resume_from = None
workflow = [('train', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
custom_imports = None
norm_cfg = dict(type='SyncBN', requires_grad=True)
work_dir = 'work_dirs/finetune_mask-rcnn__12k_voc0712_lr3e-2_wd5e-5'
auto_resume = False
gpu_ids = range(0, 8)

2022-10-03 23:42:52,834 - mmdet - INFO - Set random seed to 42, deterministic: False
2022-10-03 23:42:53,163 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2022-10-03 23:43:09,336 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-10-03 23:43:09,359 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2022-10-03 23:43:09,369 - mmdet - INFO - initialize Shared4Conv1FCBBoxHead with init_cfg [{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]
Name of parameter - Initialization information

backbone.conv1.weight - torch.Size([64, 3, 7, 7]): 
PretrainedInit: load from torchvision://resnet50 

backbone.bn1.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.bn1.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.conv1.weight - torch.Size([64, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn1.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn1.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.conv2.weight - torch.Size([64, 64, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn2.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn2.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.conv3.weight - torch.Size([256, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn3.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.bn3.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.downsample.0.weight - torch.Size([256, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.downsample.1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.0.downsample.1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.conv1.weight - torch.Size([64, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn1.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn1.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.conv2.weight - torch.Size([64, 64, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn2.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn2.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.conv3.weight - torch.Size([256, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn3.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.1.bn3.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.conv1.weight - torch.Size([64, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn1.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn1.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.conv2.weight - torch.Size([64, 64, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn2.weight - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn2.bias - torch.Size([64]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.conv3.weight - torch.Size([256, 64, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn3.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer1.2.bn3.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.conv1.weight - torch.Size([128, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn1.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn1.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.conv2.weight - torch.Size([128, 128, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn2.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn2.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.conv3.weight - torch.Size([512, 128, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn3.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.bn3.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.downsample.0.weight - torch.Size([512, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.downsample.1.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.0.downsample.1.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.conv1.weight - torch.Size([128, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn1.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn1.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.conv2.weight - torch.Size([128, 128, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn2.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn2.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.conv3.weight - torch.Size([512, 128, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn3.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.1.bn3.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.conv1.weight - torch.Size([128, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn1.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn1.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.conv2.weight - torch.Size([128, 128, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn2.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn2.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.conv3.weight - torch.Size([512, 128, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn3.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.2.bn3.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.conv1.weight - torch.Size([128, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn1.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn1.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.conv2.weight - torch.Size([128, 128, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn2.weight - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn2.bias - torch.Size([128]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.conv3.weight - torch.Size([512, 128, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn3.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer2.3.bn3.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.conv1.weight - torch.Size([256, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.downsample.0.weight - torch.Size([1024, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.downsample.1.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.0.downsample.1.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.1.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.2.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.3.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.4.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.conv1.weight - torch.Size([256, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn1.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn1.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.conv2.weight - torch.Size([256, 256, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn2.weight - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn2.bias - torch.Size([256]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.conv3.weight - torch.Size([1024, 256, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn3.weight - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer3.5.bn3.bias - torch.Size([1024]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.conv1.weight - torch.Size([512, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn1.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn1.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.conv2.weight - torch.Size([512, 512, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn2.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn2.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.conv3.weight - torch.Size([2048, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn3.weight - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.bn3.bias - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.downsample.0.weight - torch.Size([2048, 1024, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.downsample.1.weight - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.0.downsample.1.bias - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.conv1.weight - torch.Size([512, 2048, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn1.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn1.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.conv2.weight - torch.Size([512, 512, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn2.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn2.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.conv3.weight - torch.Size([2048, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn3.weight - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.1.bn3.bias - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.conv1.weight - torch.Size([512, 2048, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn1.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn1.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.conv2.weight - torch.Size([512, 512, 3, 3]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn2.weight - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn2.bias - torch.Size([512]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.conv3.weight - torch.Size([2048, 512, 1, 1]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn3.weight - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

backbone.layer4.2.bn3.bias - torch.Size([2048]): 
PretrainedInit: load from torchvision://resnet50 

neck.lateral_convs.0.conv.weight - torch.Size([256, 256, 1, 1]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.lateral_convs.0.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.lateral_convs.0.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.lateral_convs.1.conv.weight - torch.Size([256, 512, 1, 1]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.lateral_convs.1.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.lateral_convs.1.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.lateral_convs.2.conv.weight - torch.Size([256, 1024, 1, 1]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.lateral_convs.2.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.lateral_convs.2.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.lateral_convs.3.conv.weight - torch.Size([256, 2048, 1, 1]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.lateral_convs.3.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.lateral_convs.3.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.fpn_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.0.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.fpn_convs.0.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.fpn_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.1.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.fpn_convs.1.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.fpn_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.2.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.fpn_convs.2.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.fpn_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.3.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

neck.fpn_convs.3.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

rpn_head.rpn_conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

rpn_head.rpn_conv.bias - torch.Size([256]): 
NormalInit: mean=0, std=0.01, bias=0 

rpn_head.rpn_cls.weight - torch.Size([3, 256, 1, 1]): 
NormalInit: mean=0, std=0.01, bias=0 

rpn_head.rpn_cls.bias - torch.Size([3]): 
NormalInit: mean=0, std=0.01, bias=0 

rpn_head.rpn_reg.weight - torch.Size([12, 256, 1, 1]): 
NormalInit: mean=0, std=0.01, bias=0 

rpn_head.rpn_reg.bias - torch.Size([12]): 
NormalInit: mean=0, std=0.01, bias=0 

roi_head.bbox_head.fc_cls.weight - torch.Size([21, 1024]): 
NormalInit: mean=0, std=0.01, bias=0 

roi_head.bbox_head.fc_cls.bias - torch.Size([21]): 
NormalInit: mean=0, std=0.01, bias=0 

roi_head.bbox_head.fc_reg.weight - torch.Size([80, 1024]): 
NormalInit: mean=0, std=0.001, bias=0 

roi_head.bbox_head.fc_reg.bias - torch.Size([80]): 
NormalInit: mean=0, std=0.001, bias=0 

roi_head.bbox_head.shared_convs.0.conv.weight - torch.Size([256, 256, 3, 3]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

roi_head.bbox_head.shared_convs.0.conv.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

roi_head.bbox_head.shared_convs.1.conv.weight - torch.Size([256, 256, 3, 3]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

roi_head.bbox_head.shared_convs.1.conv.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

roi_head.bbox_head.shared_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

roi_head.bbox_head.shared_convs.2.conv.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

roi_head.bbox_head.shared_convs.3.conv.weight - torch.Size([256, 256, 3, 3]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

roi_head.bbox_head.shared_convs.3.conv.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of MaskRCNN  

roi_head.bbox_head.shared_fcs.0.weight - torch.Size([1024, 12544]): 
XavierInit: gain=1, distribution=uniform, bias=0 

roi_head.bbox_head.shared_fcs.0.bias - torch.Size([1024]): 
XavierInit: gain=1, distribution=uniform, bias=0 
2022-10-03 23:43:11,479 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
2022-10-03 23:43:12,257 - mmdet - INFO - load checkpoint from local path: pretrain/selfsup_mask-rcnn_mstrain-soft-teacher_sampler-4096_temp0.5/final_model.pth
2022-10-03 23:43:12,409 - mmdet - WARNING - The model and loaded state dict do not match exactly

unexpected key in source state_dict: neck.lateral_convs.0.conv.bias, neck.lateral_convs.1.conv.bias, neck.lateral_convs.2.conv.bias, neck.lateral_convs.3.conv.bias, neck.fpn_convs.0.conv.bias, neck.fpn_convs.1.conv.bias, neck.fpn_convs.2.conv.bias, neck.fpn_convs.3.conv.bias

missing keys in source state_dict: neck.lateral_convs.0.bn.weight, neck.lateral_convs.0.bn.bias, neck.lateral_convs.0.bn.running_mean, neck.lateral_convs.0.bn.running_var, neck.lateral_convs.1.bn.weight, neck.lateral_convs.1.bn.bias, neck.lateral_convs.1.bn.running_mean, neck.lateral_convs.1.bn.running_var, neck.lateral_convs.2.bn.weight, neck.lateral_convs.2.bn.bias, neck.lateral_convs.2.bn.running_mean, neck.lateral_convs.2.bn.running_var, neck.lateral_convs.3.bn.weight, neck.lateral_convs.3.bn.bias, neck.lateral_convs.3.bn.running_mean, neck.lateral_convs.3.bn.running_var, neck.fpn_convs.0.bn.weight, neck.fpn_convs.0.bn.bias, neck.fpn_convs.0.bn.running_mean, neck.fpn_convs.0.bn.running_var, neck.fpn_convs.1.bn.weight, neck.fpn_convs.1.bn.bias, neck.fpn_convs.1.bn.running_mean, neck.fpn_convs.1.bn.running_var, neck.fpn_convs.2.bn.weight, neck.fpn_convs.2.bn.bias, neck.fpn_convs.2.bn.running_mean, neck.fpn_convs.2.bn.running_var, neck.fpn_convs.3.bn.weight, neck.fpn_convs.3.bn.bias, neck.fpn_convs.3.bn.running_mean, neck.fpn_convs.3.bn.running_var, rpn_head.rpn_cls.weight, rpn_head.rpn_cls.bias, rpn_head.rpn_reg.weight, rpn_head.rpn_reg.bias, roi_head.bbox_head.fc_cls.weight, roi_head.bbox_head.fc_cls.bias, roi_head.bbox_head.fc_reg.weight, roi_head.bbox_head.fc_reg.bias

2022-10-03 23:43:12,417 - mmdet - INFO - Start running, host: tiger@n136-144-086, work_dir: /home/tiger/code/mmdet/work_dirs/finetune_mask-rcnn__12k_voc0712_lr3e-2_wd5e-5
2022-10-03 23:43:12,417 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) CheckpointHook                     
(NORMAL      ) MMDetWandbHook                     
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_epoch:
(VERY_HIGH   ) StepLrUpdaterHook                  
(NORMAL      ) NumClassCheckHook                  
(NORMAL      ) MMDetWandbHook                     
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_train_iter:
(VERY_HIGH   ) StepLrUpdaterHook                  
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
 -------------------- 
after_train_iter:
(ABOVE_NORMAL) OptimizerHook                      
(NORMAL      ) CheckpointHook                     
(NORMAL      ) MMDetWandbHook                     
(LOW         ) IterTimerHook                      
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_train_epoch:
(NORMAL      ) CheckpointHook                     
(NORMAL      ) MMDetWandbHook                     
(LOW         ) DistEvalHook                       
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_epoch:
(NORMAL      ) NumClassCheckHook                  
(NORMAL      ) MMDetWandbHook                     
(LOW         ) IterTimerHook                      
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
before_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_iter:
(LOW         ) IterTimerHook                      
 -------------------- 
after_val_epoch:
(NORMAL      ) MMDetWandbHook                     
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
after_run:
(NORMAL      ) MMDetWandbHook                     
(VERY_LOW    ) TextLoggerHook                     
 -------------------- 
2022-10-03 23:43:12,418 - mmdet - INFO - workflow: [('train', 1)], max: 12000 iters
2022-10-03 23:43:12,418 - mmdet - INFO - Checkpoints will be saved to /home/tiger/code/mmdet/work_dirs/finetune_mask-rcnn__12k_voc0712_lr3e-2_wd5e-5 by HardDiskBackend.
2022-10-03 23:43:19,589 - mmdet - INFO - Iter [50/12000]	lr: 2.967e-03, eta: 0:23:15, time: 0.117, data_time: 0.006, memory: 3991, loss_rpn_cls: 0.4104, loss_rpn_bbox: 0.0314, loss_cls: 1.0865, acc: 84.5825, loss_bbox: 0.0480, loss: 1.5764
2022-10-03 23:43:25,188 - mmdet - INFO - Iter [100/12000]	lr: 5.964e-03, eta: 0:22:41, time: 0.112, data_time: 0.006, memory: 3991, loss_rpn_cls: 0.1029, loss_rpn_bbox: 0.0304, loss_cls: 0.2291, acc: 95.9438, loss_bbox: 0.1588, loss: 0.5212
2022-10-03 23:43:30,809 - mmdet - INFO - Iter [150/12000]	lr: 8.961e-03, eta: 0:22:27, time: 0.112, data_time: 0.006, memory: 3992, loss_rpn_cls: 0.0698, loss_rpn_bbox: 0.0302, loss_cls: 0.2317, acc: 95.4901, loss_bbox: 0.1731, loss: 0.5048
2022-10-03 23:43:36,504 - mmdet - INFO - Iter [200/12000]	lr: 1.196e-02, eta: 0:22:22, time: 0.114, data_time: 0.005, memory: 3995, loss_rpn_cls: 0.0547, loss_rpn_bbox: 0.0268, loss_cls: 0.2299, acc: 95.1844, loss_bbox: 0.1777, loss: 0.4892
2022-10-03 23:43:42,118 - mmdet - INFO - Iter [250/12000]	lr: 1.496e-02, eta: 0:22:13, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0378, loss_rpn_bbox: 0.0261, loss_cls: 0.2575, acc: 94.1749, loss_bbox: 0.1868, loss: 0.5083
2022-10-03 23:43:47,802 - mmdet - INFO - Iter [300/12000]	lr: 1.795e-02, eta: 0:22:07, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0302, loss_rpn_bbox: 0.0268, loss_cls: 0.2374, acc: 94.2424, loss_bbox: 0.1747, loss: 0.4692
2022-10-03 23:43:53,431 - mmdet - INFO - Iter [350/12000]	lr: 2.095e-02, eta: 0:22:00, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0302, loss_rpn_bbox: 0.0255, loss_cls: 0.2147, acc: 94.5897, loss_bbox: 0.1620, loss: 0.4324
2022-10-03 23:43:59,072 - mmdet - INFO - Iter [400/12000]	lr: 2.395e-02, eta: 0:21:54, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0296, loss_rpn_bbox: 0.0263, loss_cls: 0.2066, acc: 94.5005, loss_bbox: 0.1646, loss: 0.4271
2022-10-03 23:44:04,659 - mmdet - INFO - Iter [450/12000]	lr: 2.694e-02, eta: 0:21:46, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0281, loss_rpn_bbox: 0.0259, loss_cls: 0.1956, acc: 94.6162, loss_bbox: 0.1612, loss: 0.4107
2022-10-03 23:44:10,194 - mmdet - INFO - Iter [500/12000]	lr: 2.994e-02, eta: 0:21:38, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0275, loss_rpn_bbox: 0.0265, loss_cls: 0.1976, acc: 94.5139, loss_bbox: 0.1637, loss: 0.4153
2022-10-03 23:44:15,852 - mmdet - INFO - Iter [550/12000]	lr: 3.000e-02, eta: 0:21:32, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0237, loss_cls: 0.1965, acc: 94.4979, loss_bbox: 0.1656, loss: 0.4120
2022-10-03 23:44:21,475 - mmdet - INFO - Iter [600/12000]	lr: 3.000e-02, eta: 0:21:26, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0262, loss_rpn_bbox: 0.0244, loss_cls: 0.1926, acc: 94.4954, loss_bbox: 0.1648, loss: 0.4080
2022-10-03 23:44:27,056 - mmdet - INFO - Iter [650/12000]	lr: 3.000e-02, eta: 0:21:19, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0253, loss_cls: 0.1795, acc: 94.8441, loss_bbox: 0.1639, loss: 0.3943
2022-10-03 23:44:32,708 - mmdet - INFO - Iter [700/12000]	lr: 3.000e-02, eta: 0:21:14, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0268, loss_rpn_bbox: 0.0249, loss_cls: 0.1821, acc: 94.7508, loss_bbox: 0.1626, loss: 0.3964
2022-10-03 23:44:38,420 - mmdet - INFO - Iter [750/12000]	lr: 3.000e-02, eta: 0:21:09, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0231, loss_rpn_bbox: 0.0238, loss_cls: 0.1784, acc: 94.8594, loss_bbox: 0.1554, loss: 0.3806
2022-10-03 23:44:44,063 - mmdet - INFO - Iter [800/12000]	lr: 3.000e-02, eta: 0:21:04, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0256, loss_rpn_bbox: 0.0250, loss_cls: 0.1712, acc: 95.0114, loss_bbox: 0.1568, loss: 0.3786
2022-10-03 23:44:49,742 - mmdet - INFO - Iter [850/12000]	lr: 3.000e-02, eta: 0:20:59, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0229, loss_rpn_bbox: 0.0245, loss_cls: 0.1749, acc: 94.8251, loss_bbox: 0.1608, loss: 0.3832
2022-10-03 23:44:55,379 - mmdet - INFO - Iter [900/12000]	lr: 3.000e-02, eta: 0:20:53, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0245, loss_cls: 0.1636, acc: 94.9949, loss_bbox: 0.1574, loss: 0.3709
2022-10-03 23:45:01,069 - mmdet - INFO - Iter [950/12000]	lr: 3.000e-02, eta: 0:20:48, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0235, loss_rpn_bbox: 0.0236, loss_cls: 0.1655, acc: 94.9069, loss_bbox: 0.1632, loss: 0.3759
2022-10-03 23:45:06,625 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-03 23:45:06,625 - mmdet - INFO - Iter [1000/12000]	lr: 3.000e-02, eta: 0:20:41, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0260, loss_rpn_bbox: 0.0228, loss_cls: 0.1518, acc: 95.3542, loss_bbox: 0.1476, loss: 0.3482
2022-10-03 23:45:12,284 - mmdet - INFO - Iter [1050/12000]	lr: 3.000e-02, eta: 0:20:36, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0223, loss_cls: 0.1549, acc: 95.1792, loss_bbox: 0.1526, loss: 0.3521
2022-10-03 23:45:17,918 - mmdet - INFO - Iter [1100/12000]	lr: 3.000e-02, eta: 0:20:30, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0217, loss_cls: 0.1577, acc: 94.9471, loss_bbox: 0.1592, loss: 0.3599
2022-10-03 23:45:23,584 - mmdet - INFO - Iter [1150/12000]	lr: 3.000e-02, eta: 0:20:24, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0213, loss_cls: 0.1472, acc: 95.3429, loss_bbox: 0.1488, loss: 0.3391
2022-10-03 23:45:29,260 - mmdet - INFO - Iter [1200/12000]	lr: 3.000e-02, eta: 0:20:19, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0235, loss_cls: 0.1632, acc: 94.9394, loss_bbox: 0.1600, loss: 0.3684
2022-10-03 23:45:34,896 - mmdet - INFO - Iter [1250/12000]	lr: 3.000e-02, eta: 0:20:13, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0213, loss_rpn_bbox: 0.0225, loss_cls: 0.1606, acc: 94.9053, loss_bbox: 0.1656, loss: 0.3700
2022-10-03 23:45:40,505 - mmdet - INFO - Iter [1300/12000]	lr: 3.000e-02, eta: 0:20:07, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0200, loss_rpn_bbox: 0.0205, loss_cls: 0.1446, acc: 95.5248, loss_bbox: 0.1461, loss: 0.3311
2022-10-03 23:45:46,039 - mmdet - INFO - Iter [1350/12000]	lr: 3.000e-02, eta: 0:20:01, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0218, loss_rpn_bbox: 0.0219, loss_cls: 0.1562, acc: 95.0852, loss_bbox: 0.1570, loss: 0.3569
2022-10-03 23:45:51,657 - mmdet - INFO - Iter [1400/12000]	lr: 3.000e-02, eta: 0:19:55, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0223, loss_rpn_bbox: 0.0215, loss_cls: 0.1555, acc: 95.1567, loss_bbox: 0.1561, loss: 0.3554
2022-10-03 23:45:57,206 - mmdet - INFO - Iter [1450/12000]	lr: 3.000e-02, eta: 0:19:49, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0231, loss_cls: 0.1552, acc: 95.0503, loss_bbox: 0.1592, loss: 0.3586
2022-10-03 23:46:02,815 - mmdet - INFO - Iter [1500/12000]	lr: 3.000e-02, eta: 0:19:43, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0224, loss_rpn_bbox: 0.0227, loss_cls: 0.1615, acc: 94.9043, loss_bbox: 0.1626, loss: 0.3691
2022-10-03 23:46:08,393 - mmdet - INFO - Iter [1550/12000]	lr: 3.000e-02, eta: 0:19:37, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0225, loss_cls: 0.1581, acc: 95.1424, loss_bbox: 0.1530, loss: 0.3548
2022-10-03 23:46:13,948 - mmdet - INFO - Iter [1600/12000]	lr: 3.000e-02, eta: 0:19:31, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0211, loss_rpn_bbox: 0.0223, loss_cls: 0.1542, acc: 95.0548, loss_bbox: 0.1601, loss: 0.3578
2022-10-03 23:46:19,536 - mmdet - INFO - Iter [1650/12000]	lr: 3.000e-02, eta: 0:19:25, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0186, loss_rpn_bbox: 0.0205, loss_cls: 0.1440, acc: 95.2990, loss_bbox: 0.1535, loss: 0.3366
2022-10-03 23:46:25,199 - mmdet - INFO - Iter [1700/12000]	lr: 3.000e-02, eta: 0:19:19, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0189, loss_rpn_bbox: 0.0202, loss_cls: 0.1525, acc: 95.1043, loss_bbox: 0.1604, loss: 0.3520
2022-10-03 23:46:30,753 - mmdet - INFO - Iter [1750/12000]	lr: 3.000e-02, eta: 0:19:13, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0205, loss_rpn_bbox: 0.0223, loss_cls: 0.1487, acc: 95.2358, loss_bbox: 0.1545, loss: 0.3460
2022-10-03 23:46:36,436 - mmdet - INFO - Iter [1800/12000]	lr: 3.000e-02, eta: 0:19:08, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0214, loss_cls: 0.1544, acc: 95.0889, loss_bbox: 0.1575, loss: 0.3517
2022-10-03 23:46:42,090 - mmdet - INFO - Iter [1850/12000]	lr: 3.000e-02, eta: 0:19:02, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0207, loss_cls: 0.1487, acc: 95.2413, loss_bbox: 0.1569, loss: 0.3440
2022-10-03 23:46:47,674 - mmdet - INFO - Iter [1900/12000]	lr: 3.000e-02, eta: 0:18:57, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0220, loss_rpn_bbox: 0.0207, loss_cls: 0.1531, acc: 95.1487, loss_bbox: 0.1563, loss: 0.3521
2022-10-03 23:46:53,244 - mmdet - INFO - Iter [1950/12000]	lr: 3.000e-02, eta: 0:18:51, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0212, loss_rpn_bbox: 0.0237, loss_cls: 0.1556, acc: 94.9813, loss_bbox: 0.1580, loss: 0.3584
2022-10-03 23:46:58,940 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-03 23:46:58,941 - mmdet - INFO - Iter [2000/12000]	lr: 3.000e-02, eta: 0:18:45, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0185, loss_rpn_bbox: 0.0206, loss_cls: 0.1542, acc: 95.0780, loss_bbox: 0.1563, loss: 0.3497
2022-10-03 23:47:04,630 - mmdet - INFO - Iter [2050/12000]	lr: 3.000e-02, eta: 0:18:40, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0176, loss_rpn_bbox: 0.0204, loss_cls: 0.1509, acc: 94.9890, loss_bbox: 0.1654, loss: 0.3543
2022-10-03 23:47:10,238 - mmdet - INFO - Iter [2100/12000]	lr: 3.000e-02, eta: 0:18:34, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0210, loss_cls: 0.1450, acc: 95.1726, loss_bbox: 0.1593, loss: 0.3435
2022-10-03 23:47:15,938 - mmdet - INFO - Iter [2150/12000]	lr: 3.000e-02, eta: 0:18:29, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0196, loss_cls: 0.1377, acc: 95.3903, loss_bbox: 0.1503, loss: 0.3264
2022-10-03 23:47:21,612 - mmdet - INFO - Iter [2200/12000]	lr: 3.000e-02, eta: 0:18:24, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0198, loss_rpn_bbox: 0.0220, loss_cls: 0.1406, acc: 95.4827, loss_bbox: 0.1511, loss: 0.3334
2022-10-03 23:47:27,276 - mmdet - INFO - Iter [2250/12000]	lr: 3.000e-02, eta: 0:18:18, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0208, loss_cls: 0.1333, acc: 95.5823, loss_bbox: 0.1460, loss: 0.3179
2022-10-03 23:47:32,931 - mmdet - INFO - Iter [2300/12000]	lr: 3.000e-02, eta: 0:18:13, time: 0.113, data_time: 0.007, memory: 3995, loss_rpn_cls: 0.0182, loss_rpn_bbox: 0.0209, loss_cls: 0.1454, acc: 95.1852, loss_bbox: 0.1563, loss: 0.3409
2022-10-03 23:47:38,493 - mmdet - INFO - Iter [2350/12000]	lr: 3.000e-02, eta: 0:18:07, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0184, loss_rpn_bbox: 0.0210, loss_cls: 0.1454, acc: 95.1355, loss_bbox: 0.1529, loss: 0.3378
2022-10-03 23:47:44,109 - mmdet - INFO - Iter [2400/12000]	lr: 3.000e-02, eta: 0:18:01, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0179, loss_rpn_bbox: 0.0213, loss_cls: 0.1459, acc: 95.1606, loss_bbox: 0.1564, loss: 0.3415
2022-10-03 23:47:49,741 - mmdet - INFO - Iter [2450/12000]	lr: 3.000e-02, eta: 0:17:55, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0175, loss_rpn_bbox: 0.0205, loss_cls: 0.1405, acc: 95.2637, loss_bbox: 0.1547, loss: 0.3332
2022-10-03 23:47:55,369 - mmdet - INFO - Iter [2500/12000]	lr: 3.000e-02, eta: 0:17:50, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0204, loss_rpn_bbox: 0.0240, loss_cls: 0.1549, acc: 94.9365, loss_bbox: 0.1643, loss: 0.3635
2022-10-03 23:48:01,043 - mmdet - INFO - Iter [2550/12000]	lr: 3.000e-02, eta: 0:17:44, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0210, loss_cls: 0.1425, acc: 95.2530, loss_bbox: 0.1565, loss: 0.3362
2022-10-03 23:48:06,612 - mmdet - INFO - Iter [2600/12000]	lr: 3.000e-02, eta: 0:17:38, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0211, loss_cls: 0.1355, acc: 95.4700, loss_bbox: 0.1532, loss: 0.3286
2022-10-03 23:48:12,186 - mmdet - INFO - Iter [2650/12000]	lr: 3.000e-02, eta: 0:17:32, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0197, loss_rpn_bbox: 0.0208, loss_cls: 0.1456, acc: 95.0889, loss_bbox: 0.1586, loss: 0.3446
2022-10-03 23:48:17,793 - mmdet - INFO - Iter [2700/12000]	lr: 3.000e-02, eta: 0:17:27, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0207, loss_cls: 0.1334, acc: 95.5565, loss_bbox: 0.1490, loss: 0.3200
2022-10-03 23:48:23,439 - mmdet - INFO - Iter [2750/12000]	lr: 3.000e-02, eta: 0:17:21, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0192, loss_rpn_bbox: 0.0198, loss_cls: 0.1310, acc: 95.7559, loss_bbox: 0.1335, loss: 0.3036
2022-10-03 23:48:29,194 - mmdet - INFO - Iter [2800/12000]	lr: 3.000e-02, eta: 0:17:16, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0202, loss_cls: 0.1372, acc: 95.4866, loss_bbox: 0.1471, loss: 0.3213
2022-10-03 23:48:34,794 - mmdet - INFO - Iter [2850/12000]	lr: 3.000e-02, eta: 0:17:10, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0210, loss_cls: 0.1379, acc: 95.4589, loss_bbox: 0.1484, loss: 0.3239
2022-10-03 23:48:40,374 - mmdet - INFO - Iter [2900/12000]	lr: 3.000e-02, eta: 0:17:04, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0188, loss_rpn_bbox: 0.0212, loss_cls: 0.1336, acc: 95.5189, loss_bbox: 0.1512, loss: 0.3248
2022-10-03 23:48:45,958 - mmdet - INFO - Iter [2950/12000]	lr: 3.000e-02, eta: 0:16:59, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0187, loss_rpn_bbox: 0.0217, loss_cls: 0.1326, acc: 95.5110, loss_bbox: 0.1516, loss: 0.3246
2022-10-03 23:48:51,583 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-03 23:48:51,583 - mmdet - INFO - Iter [3000/12000]	lr: 3.000e-02, eta: 0:16:53, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0169, loss_rpn_bbox: 0.0201, loss_cls: 0.1329, acc: 95.5895, loss_bbox: 0.1476, loss: 0.3174
2022-10-03 23:48:57,169 - mmdet - INFO - Iter [3050/12000]	lr: 3.000e-02, eta: 0:16:47, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0215, loss_cls: 0.1418, acc: 95.2405, loss_bbox: 0.1567, loss: 0.3365
2022-10-03 23:49:02,858 - mmdet - INFO - Iter [3100/12000]	lr: 3.000e-02, eta: 0:16:42, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0184, loss_cls: 0.1265, acc: 95.6736, loss_bbox: 0.1416, loss: 0.3011
2022-10-03 23:49:08,489 - mmdet - INFO - Iter [3150/12000]	lr: 3.000e-02, eta: 0:16:36, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0211, loss_cls: 0.1223, acc: 95.8212, loss_bbox: 0.1463, loss: 0.3051
2022-10-03 23:49:14,117 - mmdet - INFO - Iter [3200/12000]	lr: 3.000e-02, eta: 0:16:30, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0195, loss_cls: 0.1316, acc: 95.5488, loss_bbox: 0.1518, loss: 0.3186
2022-10-03 23:49:19,875 - mmdet - INFO - Iter [3250/12000]	lr: 3.000e-02, eta: 0:16:25, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0200, loss_cls: 0.1334, acc: 95.4619, loss_bbox: 0.1478, loss: 0.3183
2022-10-03 23:49:25,438 - mmdet - INFO - Iter [3300/12000]	lr: 3.000e-02, eta: 0:16:19, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0178, loss_rpn_bbox: 0.0207, loss_cls: 0.1467, acc: 95.1052, loss_bbox: 0.1564, loss: 0.3416
2022-10-03 23:49:31,087 - mmdet - INFO - Iter [3350/12000]	lr: 3.000e-02, eta: 0:16:14, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0171, loss_rpn_bbox: 0.0215, loss_cls: 0.1389, acc: 95.4212, loss_bbox: 0.1536, loss: 0.3311
2022-10-03 23:49:36,616 - mmdet - INFO - Iter [3400/12000]	lr: 3.000e-02, eta: 0:16:08, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0204, loss_cls: 0.1328, acc: 95.5188, loss_bbox: 0.1501, loss: 0.3206
2022-10-03 23:49:42,258 - mmdet - INFO - Iter [3450/12000]	lr: 3.000e-02, eta: 0:16:02, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0181, loss_cls: 0.1304, acc: 95.6438, loss_bbox: 0.1445, loss: 0.3090
2022-10-03 23:49:47,970 - mmdet - INFO - Iter [3500/12000]	lr: 3.000e-02, eta: 0:15:57, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0173, loss_rpn_bbox: 0.0196, loss_cls: 0.1287, acc: 95.6164, loss_bbox: 0.1464, loss: 0.3120
2022-10-03 23:49:53,573 - mmdet - INFO - Iter [3550/12000]	lr: 3.000e-02, eta: 0:15:51, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0185, loss_cls: 0.1235, acc: 95.9135, loss_bbox: 0.1348, loss: 0.2924
2022-10-03 23:49:59,163 - mmdet - INFO - Iter [3600/12000]	lr: 3.000e-02, eta: 0:15:45, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0188, loss_cls: 0.1240, acc: 95.7700, loss_bbox: 0.1442, loss: 0.3019
2022-10-03 23:50:04,720 - mmdet - INFO - Iter [3650/12000]	lr: 3.000e-02, eta: 0:15:40, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0161, loss_rpn_bbox: 0.0197, loss_cls: 0.1312, acc: 95.5571, loss_bbox: 0.1466, loss: 0.3136
2022-10-03 23:50:10,376 - mmdet - INFO - Iter [3700/12000]	lr: 3.000e-02, eta: 0:15:34, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0198, loss_cls: 0.1330, acc: 95.4290, loss_bbox: 0.1535, loss: 0.3211
2022-10-03 23:50:15,954 - mmdet - INFO - Iter [3750/12000]	lr: 3.000e-02, eta: 0:15:28, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0190, loss_cls: 0.1274, acc: 95.6029, loss_bbox: 0.1455, loss: 0.3072
2022-10-03 23:50:21,701 - mmdet - INFO - Iter [3800/12000]	lr: 3.000e-02, eta: 0:15:23, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0159, loss_rpn_bbox: 0.0212, loss_cls: 0.1325, acc: 95.4672, loss_bbox: 0.1503, loss: 0.3198
2022-10-03 23:50:27,426 - mmdet - INFO - Iter [3850/12000]	lr: 3.000e-02, eta: 0:15:18, time: 0.114, data_time: 0.007, memory: 3995, loss_rpn_cls: 0.0162, loss_rpn_bbox: 0.0200, loss_cls: 0.1276, acc: 95.7168, loss_bbox: 0.1437, loss: 0.3075
2022-10-03 23:50:33,157 - mmdet - INFO - Iter [3900/12000]	lr: 3.000e-02, eta: 0:15:12, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0168, loss_rpn_bbox: 0.0206, loss_cls: 0.1303, acc: 95.4753, loss_bbox: 0.1519, loss: 0.3196
2022-10-03 23:50:38,765 - mmdet - INFO - Iter [3950/12000]	lr: 3.000e-02, eta: 0:15:06, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0170, loss_rpn_bbox: 0.0205, loss_cls: 0.1300, acc: 95.5183, loss_bbox: 0.1515, loss: 0.3190
2022-10-03 23:50:44,386 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-03 23:50:44,386 - mmdet - INFO - Iter [4000/12000]	lr: 3.000e-02, eta: 0:15:01, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0202, loss_cls: 0.1273, acc: 95.7033, loss_bbox: 0.1470, loss: 0.3112
2022-10-03 23:50:49,952 - mmdet - INFO - Iter [4050/12000]	lr: 3.000e-02, eta: 0:14:55, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0213, loss_cls: 0.1277, acc: 95.6313, loss_bbox: 0.1474, loss: 0.3115
2022-10-03 23:50:55,592 - mmdet - INFO - Iter [4100/12000]	lr: 3.000e-02, eta: 0:14:49, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0205, loss_cls: 0.1364, acc: 95.3787, loss_bbox: 0.1520, loss: 0.3242
2022-10-03 23:51:01,315 - mmdet - INFO - Iter [4150/12000]	lr: 3.000e-02, eta: 0:14:44, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0166, loss_rpn_bbox: 0.0197, loss_cls: 0.1173, acc: 95.9214, loss_bbox: 0.1373, loss: 0.2910
2022-10-03 23:51:06,868 - mmdet - INFO - Iter [4200/12000]	lr: 3.000e-02, eta: 0:14:38, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0189, loss_cls: 0.1159, acc: 96.0770, loss_bbox: 0.1351, loss: 0.2832
2022-10-03 23:51:12,543 - mmdet - INFO - Iter [4250/12000]	lr: 3.000e-02, eta: 0:14:33, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0141, loss_rpn_bbox: 0.0194, loss_cls: 0.1195, acc: 95.7892, loss_bbox: 0.1404, loss: 0.2934
2022-10-03 23:51:18,213 - mmdet - INFO - Iter [4300/12000]	lr: 3.000e-02, eta: 0:14:27, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0140, loss_rpn_bbox: 0.0194, loss_cls: 0.1243, acc: 95.6846, loss_bbox: 0.1412, loss: 0.2989
2022-10-03 23:51:23,736 - mmdet - INFO - Iter [4350/12000]	lr: 3.000e-02, eta: 0:14:21, time: 0.110, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0187, loss_cls: 0.1155, acc: 95.9397, loss_bbox: 0.1375, loss: 0.2865
2022-10-03 23:51:29,366 - mmdet - INFO - Iter [4400/12000]	lr: 3.000e-02, eta: 0:14:16, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0197, loss_cls: 0.1266, acc: 95.6837, loss_bbox: 0.1386, loss: 0.2994
2022-10-03 23:51:35,030 - mmdet - INFO - Iter [4450/12000]	lr: 3.000e-02, eta: 0:14:10, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0176, loss_cls: 0.1150, acc: 96.0668, loss_bbox: 0.1341, loss: 0.2799
2022-10-03 23:51:40,659 - mmdet - INFO - Iter [4500/12000]	lr: 3.000e-02, eta: 0:14:04, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0191, loss_cls: 0.1227, acc: 95.8099, loss_bbox: 0.1409, loss: 0.2960
2022-10-03 23:51:46,201 - mmdet - INFO - Iter [4550/12000]	lr: 3.000e-02, eta: 0:13:59, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0190, loss_cls: 0.1186, acc: 95.7950, loss_bbox: 0.1428, loss: 0.2935
2022-10-03 23:51:51,867 - mmdet - INFO - Iter [4600/12000]	lr: 3.000e-02, eta: 0:13:53, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0150, loss_rpn_bbox: 0.0196, loss_cls: 0.1239, acc: 95.7886, loss_bbox: 0.1413, loss: 0.2998
2022-10-03 23:51:57,500 - mmdet - INFO - Iter [4650/12000]	lr: 3.000e-02, eta: 0:13:47, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0203, loss_cls: 0.1270, acc: 95.5702, loss_bbox: 0.1487, loss: 0.3107
2022-10-03 23:52:03,142 - mmdet - INFO - Iter [4700/12000]	lr: 3.000e-02, eta: 0:13:42, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0154, loss_rpn_bbox: 0.0211, loss_cls: 0.1237, acc: 95.6850, loss_bbox: 0.1462, loss: 0.3065
2022-10-03 23:52:08,681 - mmdet - INFO - Iter [4750/12000]	lr: 3.000e-02, eta: 0:13:36, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0153, loss_rpn_bbox: 0.0197, loss_cls: 0.1286, acc: 95.6023, loss_bbox: 0.1456, loss: 0.3092
2022-10-03 23:52:14,314 - mmdet - INFO - Iter [4800/12000]	lr: 3.000e-02, eta: 0:13:30, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0191, loss_cls: 0.1233, acc: 95.6651, loss_bbox: 0.1464, loss: 0.3034
2022-10-03 23:52:19,940 - mmdet - INFO - Iter [4850/12000]	lr: 3.000e-02, eta: 0:13:25, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0182, loss_cls: 0.1202, acc: 95.9275, loss_bbox: 0.1374, loss: 0.2897
2022-10-03 23:52:25,748 - mmdet - INFO - Iter [4900/12000]	lr: 3.000e-02, eta: 0:13:19, time: 0.116, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0201, loss_cls: 0.1216, acc: 95.7214, loss_bbox: 0.1471, loss: 0.3039
2022-10-03 23:52:31,476 - mmdet - INFO - Iter [4950/12000]	lr: 3.000e-02, eta: 0:13:14, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0203, loss_cls: 0.1228, acc: 95.7175, loss_bbox: 0.1454, loss: 0.3035
2022-10-03 23:52:37,044 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-03 23:52:37,044 - mmdet - INFO - Iter [5000/12000]	lr: 3.000e-02, eta: 0:13:08, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0200, loss_cls: 0.1202, acc: 95.8923, loss_bbox: 0.1366, loss: 0.2915
2022-10-03 23:52:42,754 - mmdet - INFO - Iter [5050/12000]	lr: 3.000e-02, eta: 0:13:03, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0167, loss_rpn_bbox: 0.0221, loss_cls: 0.1301, acc: 95.4080, loss_bbox: 0.1558, loss: 0.3247
2022-10-03 23:52:48,464 - mmdet - INFO - Iter [5100/12000]	lr: 3.000e-02, eta: 0:12:57, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0207, loss_cls: 0.1241, acc: 95.6823, loss_bbox: 0.1432, loss: 0.3030
2022-10-03 23:52:54,112 - mmdet - INFO - Iter [5150/12000]	lr: 3.000e-02, eta: 0:12:51, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0155, loss_rpn_bbox: 0.0202, loss_cls: 0.1285, acc: 95.4983, loss_bbox: 0.1487, loss: 0.3128
2022-10-03 23:52:59,764 - mmdet - INFO - Iter [5200/12000]	lr: 3.000e-02, eta: 0:12:46, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0203, loss_cls: 0.1229, acc: 95.6545, loss_bbox: 0.1467, loss: 0.3035
2022-10-03 23:53:05,330 - mmdet - INFO - Iter [5250/12000]	lr: 3.000e-02, eta: 0:12:40, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0195, loss_cls: 0.1188, acc: 95.7704, loss_bbox: 0.1462, loss: 0.2982
2022-10-03 23:53:10,877 - mmdet - INFO - Iter [5300/12000]	lr: 3.000e-02, eta: 0:12:34, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0187, loss_cls: 0.1166, acc: 95.8866, loss_bbox: 0.1374, loss: 0.2875
2022-10-03 23:53:16,489 - mmdet - INFO - Iter [5350/12000]	lr: 3.000e-02, eta: 0:12:29, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0144, loss_rpn_bbox: 0.0200, loss_cls: 0.1139, acc: 95.9493, loss_bbox: 0.1375, loss: 0.2859
2022-10-03 23:53:22,230 - mmdet - INFO - Iter [5400/12000]	lr: 3.000e-02, eta: 0:12:23, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0152, loss_rpn_bbox: 0.0200, loss_cls: 0.1153, acc: 95.9543, loss_bbox: 0.1442, loss: 0.2947
2022-10-03 23:53:27,901 - mmdet - INFO - Iter [5450/12000]	lr: 3.000e-02, eta: 0:12:18, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0134, loss_rpn_bbox: 0.0185, loss_cls: 0.1233, acc: 95.6227, loss_bbox: 0.1463, loss: 0.3014
2022-10-03 23:53:33,619 - mmdet - INFO - Iter [5500/12000]	lr: 3.000e-02, eta: 0:12:12, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0147, loss_rpn_bbox: 0.0212, loss_cls: 0.1223, acc: 95.7635, loss_bbox: 0.1451, loss: 0.3033
2022-10-03 23:53:39,200 - mmdet - INFO - Iter [5550/12000]	lr: 3.000e-02, eta: 0:12:06, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0194, loss_cls: 0.1187, acc: 95.9070, loss_bbox: 0.1387, loss: 0.2918
2022-10-03 23:53:44,929 - mmdet - INFO - Iter [5600/12000]	lr: 3.000e-02, eta: 0:12:01, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0191, loss_cls: 0.1139, acc: 95.9489, loss_bbox: 0.1340, loss: 0.2795
2022-10-03 23:53:50,538 - mmdet - INFO - Iter [5650/12000]	lr: 3.000e-02, eta: 0:11:55, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0183, loss_cls: 0.1101, acc: 96.0837, loss_bbox: 0.1407, loss: 0.2813
2022-10-03 23:53:56,170 - mmdet - INFO - Iter [5700/12000]	lr: 3.000e-02, eta: 0:11:50, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0145, loss_rpn_bbox: 0.0190, loss_cls: 0.1183, acc: 95.8934, loss_bbox: 0.1403, loss: 0.2921
2022-10-03 23:54:01,871 - mmdet - INFO - Iter [5750/12000]	lr: 3.000e-02, eta: 0:11:44, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0160, loss_rpn_bbox: 0.0197, loss_cls: 0.1250, acc: 95.6539, loss_bbox: 0.1475, loss: 0.3081
2022-10-03 23:54:07,549 - mmdet - INFO - Iter [5800/12000]	lr: 3.000e-02, eta: 0:11:38, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0184, loss_cls: 0.1139, acc: 95.8792, loss_bbox: 0.1417, loss: 0.2872
2022-10-03 23:54:13,125 - mmdet - INFO - Iter [5850/12000]	lr: 3.000e-02, eta: 0:11:33, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0180, loss_cls: 0.1195, acc: 95.9167, loss_bbox: 0.1370, loss: 0.2875
2022-10-03 23:54:18,863 - mmdet - INFO - Iter [5900/12000]	lr: 3.000e-02, eta: 0:11:27, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0190, loss_cls: 0.1134, acc: 96.0131, loss_bbox: 0.1383, loss: 0.2836
2022-10-03 23:54:24,496 - mmdet - INFO - Iter [5950/12000]	lr: 3.000e-02, eta: 0:11:21, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0133, loss_rpn_bbox: 0.0197, loss_cls: 0.1140, acc: 95.8813, loss_bbox: 0.1387, loss: 0.2856
2022-10-03 23:54:30,096 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-03 23:54:30,097 - mmdet - INFO - Iter [6000/12000]	lr: 3.000e-02, eta: 0:11:16, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0191, loss_cls: 0.1150, acc: 95.9385, loss_bbox: 0.1386, loss: 0.2847
2022-10-03 23:54:35,745 - mmdet - INFO - Iter [6050/12000]	lr: 3.000e-02, eta: 0:11:10, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0149, loss_rpn_bbox: 0.0193, loss_cls: 0.1178, acc: 95.8979, loss_bbox: 0.1428, loss: 0.2950
2022-10-03 23:54:41,501 - mmdet - INFO - Iter [6100/12000]	lr: 3.000e-02, eta: 0:11:05, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0192, loss_cls: 0.1122, acc: 96.1001, loss_bbox: 0.1320, loss: 0.2756
2022-10-03 23:54:47,105 - mmdet - INFO - Iter [6150/12000]	lr: 3.000e-02, eta: 0:10:59, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0197, loss_cls: 0.1169, acc: 95.8581, loss_bbox: 0.1409, loss: 0.2898
2022-10-03 23:54:52,683 - mmdet - INFO - Iter [6200/12000]	lr: 3.000e-02, eta: 0:10:53, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0195, loss_cls: 0.1119, acc: 96.0111, loss_bbox: 0.1372, loss: 0.2807
2022-10-03 23:54:58,284 - mmdet - INFO - Iter [6250/12000]	lr: 3.000e-02, eta: 0:10:48, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0190, loss_cls: 0.1121, acc: 96.0743, loss_bbox: 0.1347, loss: 0.2778
2022-10-03 23:55:03,871 - mmdet - INFO - Iter [6300/12000]	lr: 3.000e-02, eta: 0:10:42, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0177, loss_cls: 0.1027, acc: 96.3046, loss_bbox: 0.1272, loss: 0.2594
2022-10-03 23:55:09,570 - mmdet - INFO - Iter [6350/12000]	lr: 3.000e-02, eta: 0:10:36, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0200, loss_cls: 0.1151, acc: 95.9116, loss_bbox: 0.1377, loss: 0.2858
2022-10-03 23:55:15,244 - mmdet - INFO - Iter [6400/12000]	lr: 3.000e-02, eta: 0:10:31, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0174, loss_cls: 0.1046, acc: 96.3061, loss_bbox: 0.1296, loss: 0.2637
2022-10-03 23:55:20,894 - mmdet - INFO - Iter [6450/12000]	lr: 3.000e-02, eta: 0:10:25, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0132, loss_rpn_bbox: 0.0185, loss_cls: 0.1144, acc: 95.8736, loss_bbox: 0.1427, loss: 0.2888
2022-10-03 23:55:26,502 - mmdet - INFO - Iter [6500/12000]	lr: 3.000e-02, eta: 0:10:19, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0136, loss_rpn_bbox: 0.0192, loss_cls: 0.1208, acc: 95.7167, loss_bbox: 0.1458, loss: 0.2993
2022-10-03 23:55:32,185 - mmdet - INFO - Iter [6550/12000]	lr: 3.000e-02, eta: 0:10:14, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0130, loss_rpn_bbox: 0.0194, loss_cls: 0.1104, acc: 96.0889, loss_bbox: 0.1322, loss: 0.2751
2022-10-03 23:55:37,687 - mmdet - INFO - Iter [6600/12000]	lr: 3.000e-02, eta: 0:10:08, time: 0.110, data_time: 0.005, memory: 3995, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0193, loss_cls: 0.1071, acc: 96.2001, loss_bbox: 0.1338, loss: 0.2728
2022-10-03 23:55:43,309 - mmdet - INFO - Iter [6650/12000]	lr: 3.000e-02, eta: 0:10:03, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0186, loss_cls: 0.1150, acc: 95.9251, loss_bbox: 0.1375, loss: 0.2850
2022-10-03 23:55:48,973 - mmdet - INFO - Iter [6700/12000]	lr: 3.000e-02, eta: 0:09:57, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0193, loss_cls: 0.1060, acc: 96.2937, loss_bbox: 0.1269, loss: 0.2660
2022-10-03 23:55:54,614 - mmdet - INFO - Iter [6750/12000]	lr: 3.000e-02, eta: 0:09:51, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0148, loss_rpn_bbox: 0.0208, loss_cls: 0.1185, acc: 95.8574, loss_bbox: 0.1428, loss: 0.2968
2022-10-03 23:56:00,333 - mmdet - INFO - Iter [6800/12000]	lr: 3.000e-02, eta: 0:09:46, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0180, loss_cls: 0.1064, acc: 96.1913, loss_bbox: 0.1334, loss: 0.2701
2022-10-03 23:56:06,036 - mmdet - INFO - Iter [6850/12000]	lr: 3.000e-02, eta: 0:09:40, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0186, loss_cls: 0.1119, acc: 96.0418, loss_bbox: 0.1338, loss: 0.2766
2022-10-03 23:56:11,687 - mmdet - INFO - Iter [6900/12000]	lr: 3.000e-02, eta: 0:09:34, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0197, loss_cls: 0.1156, acc: 95.8246, loss_bbox: 0.1482, loss: 0.2954
2022-10-03 23:56:17,304 - mmdet - INFO - Iter [6950/12000]	lr: 3.000e-02, eta: 0:09:29, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0196, loss_cls: 0.1139, acc: 95.9733, loss_bbox: 0.1381, loss: 0.2844
2022-10-03 23:56:23,052 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-03 23:56:23,052 - mmdet - INFO - Iter [7000/12000]	lr: 3.000e-02, eta: 0:09:23, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0131, loss_rpn_bbox: 0.0198, loss_cls: 0.1158, acc: 95.9419, loss_bbox: 0.1371, loss: 0.2858
2022-10-03 23:56:28,757 - mmdet - INFO - Iter [7050/12000]	lr: 3.000e-02, eta: 0:09:18, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0172, loss_cls: 0.1173, acc: 95.8434, loss_bbox: 0.1393, loss: 0.2857
2022-10-03 23:56:34,365 - mmdet - INFO - Iter [7100/12000]	lr: 3.000e-02, eta: 0:09:12, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0126, loss_rpn_bbox: 0.0173, loss_cls: 0.1094, acc: 96.1531, loss_bbox: 0.1327, loss: 0.2720
2022-10-03 23:56:40,036 - mmdet - INFO - Iter [7150/12000]	lr: 3.000e-02, eta: 0:09:06, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0197, loss_cls: 0.1164, acc: 95.9240, loss_bbox: 0.1387, loss: 0.2875
2022-10-03 23:56:45,652 - mmdet - INFO - Iter [7200/12000]	lr: 3.000e-02, eta: 0:09:01, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0123, loss_rpn_bbox: 0.0182, loss_cls: 0.1127, acc: 96.0151, loss_bbox: 0.1360, loss: 0.2790
2022-10-03 23:56:51,123 - mmdet - INFO - Iter [7250/12000]	lr: 3.000e-02, eta: 0:08:55, time: 0.109, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0120, loss_rpn_bbox: 0.0181, loss_cls: 0.1144, acc: 95.9041, loss_bbox: 0.1400, loss: 0.2845
2022-10-03 23:56:56,743 - mmdet - INFO - Iter [7300/12000]	lr: 3.000e-02, eta: 0:08:49, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0118, loss_rpn_bbox: 0.0180, loss_cls: 0.1102, acc: 96.0014, loss_bbox: 0.1405, loss: 0.2806
2022-10-03 23:57:02,405 - mmdet - INFO - Iter [7350/12000]	lr: 3.000e-02, eta: 0:08:44, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0119, loss_rpn_bbox: 0.0190, loss_cls: 0.1082, acc: 96.1205, loss_bbox: 0.1333, loss: 0.2724
2022-10-03 23:57:08,047 - mmdet - INFO - Iter [7400/12000]	lr: 3.000e-02, eta: 0:08:38, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0118, loss_rpn_bbox: 0.0190, loss_cls: 0.1086, acc: 96.0117, loss_bbox: 0.1372, loss: 0.2766
2022-10-03 23:57:13,761 - mmdet - INFO - Iter [7450/12000]	lr: 3.000e-02, eta: 0:08:33, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0143, loss_rpn_bbox: 0.0192, loss_cls: 0.1051, acc: 96.2713, loss_bbox: 0.1299, loss: 0.2686
2022-10-03 23:57:19,472 - mmdet - INFO - Iter [7500/12000]	lr: 3.000e-02, eta: 0:08:27, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0185, loss_cls: 0.1093, acc: 96.1603, loss_bbox: 0.1313, loss: 0.2713
2022-10-03 23:57:25,088 - mmdet - INFO - Iter [7550/12000]	lr: 3.000e-02, eta: 0:08:21, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0118, loss_rpn_bbox: 0.0180, loss_cls: 0.1032, acc: 96.2725, loss_bbox: 0.1302, loss: 0.2632
2022-10-03 23:57:30,678 - mmdet - INFO - Iter [7600/12000]	lr: 3.000e-02, eta: 0:08:16, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0110, loss_rpn_bbox: 0.0185, loss_cls: 0.1059, acc: 96.1703, loss_bbox: 0.1313, loss: 0.2667
2022-10-03 23:57:36,200 - mmdet - INFO - Iter [7650/12000]	lr: 3.000e-02, eta: 0:08:10, time: 0.110, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0182, loss_cls: 0.1079, acc: 96.1164, loss_bbox: 0.1345, loss: 0.2729
2022-10-03 23:57:41,807 - mmdet - INFO - Iter [7700/12000]	lr: 3.000e-02, eta: 0:08:04, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0167, loss_cls: 0.1048, acc: 96.2260, loss_bbox: 0.1240, loss: 0.2582
2022-10-03 23:57:47,436 - mmdet - INFO - Iter [7750/12000]	lr: 3.000e-02, eta: 0:07:59, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0183, loss_cls: 0.1073, acc: 96.0892, loss_bbox: 0.1380, loss: 0.2759
2022-10-03 23:57:53,039 - mmdet - INFO - Iter [7800/12000]	lr: 3.000e-02, eta: 0:07:53, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0107, loss_rpn_bbox: 0.0175, loss_cls: 0.1021, acc: 96.3004, loss_bbox: 0.1283, loss: 0.2585
2022-10-03 23:57:58,644 - mmdet - INFO - Iter [7850/12000]	lr: 3.000e-02, eta: 0:07:47, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0121, loss_rpn_bbox: 0.0204, loss_cls: 0.1057, acc: 96.1150, loss_bbox: 0.1342, loss: 0.2724
2022-10-03 23:58:04,203 - mmdet - INFO - Iter [7900/12000]	lr: 3.000e-02, eta: 0:07:42, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0138, loss_rpn_bbox: 0.0168, loss_cls: 0.1089, acc: 96.1435, loss_bbox: 0.1316, loss: 0.2710
2022-10-03 23:58:09,903 - mmdet - INFO - Iter [7950/12000]	lr: 3.000e-02, eta: 0:07:36, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0117, loss_rpn_bbox: 0.0180, loss_cls: 0.1095, acc: 96.0830, loss_bbox: 0.1307, loss: 0.2698
2022-10-03 23:58:15,504 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-03 23:58:15,504 - mmdet - INFO - Iter [8000/12000]	lr: 3.000e-02, eta: 0:07:30, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0190, loss_cls: 0.1071, acc: 96.0735, loss_bbox: 0.1348, loss: 0.2724
2022-10-03 23:58:21,168 - mmdet - INFO - Iter [8050/12000]	lr: 3.000e-02, eta: 0:07:25, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0129, loss_rpn_bbox: 0.0215, loss_cls: 0.1140, acc: 95.8926, loss_bbox: 0.1391, loss: 0.2875
2022-10-03 23:58:26,816 - mmdet - INFO - Iter [8100/12000]	lr: 3.000e-02, eta: 0:07:19, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0183, loss_cls: 0.1071, acc: 96.1745, loss_bbox: 0.1332, loss: 0.2714
2022-10-03 23:58:32,557 - mmdet - INFO - Iter [8150/12000]	lr: 3.000e-02, eta: 0:07:14, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0122, loss_rpn_bbox: 0.0195, loss_cls: 0.1091, acc: 96.1495, loss_bbox: 0.1303, loss: 0.2711
2022-10-03 23:58:38,151 - mmdet - INFO - Iter [8200/12000]	lr: 3.000e-02, eta: 0:07:08, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0185, loss_cls: 0.1069, acc: 96.1385, loss_bbox: 0.1340, loss: 0.2719
2022-10-03 23:58:43,725 - mmdet - INFO - Iter [8250/12000]	lr: 3.000e-02, eta: 0:07:02, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0137, loss_rpn_bbox: 0.0186, loss_cls: 0.1195, acc: 95.8491, loss_bbox: 0.1420, loss: 0.2939
2022-10-03 23:58:49,392 - mmdet - INFO - Iter [8300/12000]	lr: 3.000e-02, eta: 0:06:57, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0124, loss_rpn_bbox: 0.0199, loss_cls: 0.1093, acc: 96.0474, loss_bbox: 0.1377, loss: 0.2793
2022-10-03 23:58:55,163 - mmdet - INFO - Iter [8350/12000]	lr: 3.000e-02, eta: 0:06:51, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0114, loss_rpn_bbox: 0.0171, loss_cls: 0.0991, acc: 96.4081, loss_bbox: 0.1218, loss: 0.2494
2022-10-03 23:59:00,831 - mmdet - INFO - Iter [8400/12000]	lr: 3.000e-02, eta: 0:06:45, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0114, loss_rpn_bbox: 0.0183, loss_cls: 0.1031, acc: 96.2626, loss_bbox: 0.1299, loss: 0.2628
2022-10-03 23:59:06,402 - mmdet - INFO - Iter [8450/12000]	lr: 3.000e-02, eta: 0:06:40, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0196, loss_cls: 0.1046, acc: 96.2140, loss_bbox: 0.1335, loss: 0.2685
2022-10-03 23:59:12,065 - mmdet - INFO - Iter [8500/12000]	lr: 3.000e-02, eta: 0:06:34, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0116, loss_rpn_bbox: 0.0184, loss_cls: 0.1098, acc: 95.9487, loss_bbox: 0.1386, loss: 0.2783
2022-10-03 23:59:17,658 - mmdet - INFO - Iter [8550/12000]	lr: 3.000e-02, eta: 0:06:28, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0181, loss_cls: 0.1046, acc: 96.2415, loss_bbox: 0.1297, loss: 0.2633
2022-10-03 23:59:23,257 - mmdet - INFO - Iter [8600/12000]	lr: 3.000e-02, eta: 0:06:23, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0108, loss_rpn_bbox: 0.0182, loss_cls: 0.1029, acc: 96.2724, loss_bbox: 0.1299, loss: 0.2618
2022-10-03 23:59:29,002 - mmdet - INFO - Iter [8650/12000]	lr: 3.000e-02, eta: 0:06:17, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0105, loss_rpn_bbox: 0.0167, loss_cls: 0.1001, acc: 96.3317, loss_bbox: 0.1249, loss: 0.2522
2022-10-03 23:59:34,616 - mmdet - INFO - Iter [8700/12000]	lr: 3.000e-02, eta: 0:06:12, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0201, loss_cls: 0.1087, acc: 96.1480, loss_bbox: 0.1315, loss: 0.2741
2022-10-03 23:59:40,216 - mmdet - INFO - Iter [8750/12000]	lr: 3.000e-02, eta: 0:06:06, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0135, loss_rpn_bbox: 0.0191, loss_cls: 0.1112, acc: 96.1343, loss_bbox: 0.1363, loss: 0.2800
2022-10-03 23:59:45,866 - mmdet - INFO - Iter [8800/12000]	lr: 3.000e-02, eta: 0:06:00, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0111, loss_rpn_bbox: 0.0176, loss_cls: 0.1043, acc: 96.2478, loss_bbox: 0.1309, loss: 0.2638
2022-10-03 23:59:51,590 - mmdet - INFO - Iter [8850/12000]	lr: 3.000e-02, eta: 0:05:55, time: 0.114, data_time: 0.007, memory: 3995, loss_rpn_cls: 0.0110, loss_rpn_bbox: 0.0175, loss_cls: 0.1020, acc: 96.3712, loss_bbox: 0.1280, loss: 0.2586
2022-10-03 23:59:57,300 - mmdet - INFO - Iter [8900/12000]	lr: 3.000e-02, eta: 0:05:49, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0098, loss_rpn_bbox: 0.0172, loss_cls: 0.1035, acc: 96.2627, loss_bbox: 0.1272, loss: 0.2578
2022-10-04 00:00:02,924 - mmdet - INFO - Iter [8950/12000]	lr: 3.000e-02, eta: 0:05:43, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0139, loss_rpn_bbox: 0.0182, loss_cls: 0.1100, acc: 96.0888, loss_bbox: 0.1323, loss: 0.2743
2022-10-04 00:00:08,558 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-04 00:00:08,558 - mmdet - INFO - Iter [9000/12000]	lr: 3.000e-02, eta: 0:05:38, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0125, loss_rpn_bbox: 0.0187, loss_cls: 0.1127, acc: 95.9687, loss_bbox: 0.1353, loss: 0.2792
2022-10-04 00:00:14,149 - mmdet - INFO - Iter [9050/12000]	lr: 3.000e-03, eta: 0:05:32, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0101, loss_rpn_bbox: 0.0175, loss_cls: 0.1002, acc: 96.2962, loss_bbox: 0.1311, loss: 0.2589
2022-10-04 00:00:19,770 - mmdet - INFO - Iter [9100/12000]	lr: 3.000e-03, eta: 0:05:26, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0096, loss_rpn_bbox: 0.0177, loss_cls: 0.0951, acc: 96.5305, loss_bbox: 0.1257, loss: 0.2481
2022-10-04 00:00:25,484 - mmdet - INFO - Iter [9150/12000]	lr: 3.000e-03, eta: 0:05:21, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0104, loss_rpn_bbox: 0.0172, loss_cls: 0.0917, acc: 96.6564, loss_bbox: 0.1208, loss: 0.2401
2022-10-04 00:00:31,170 - mmdet - INFO - Iter [9200/12000]	lr: 3.000e-03, eta: 0:05:15, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0097, loss_rpn_bbox: 0.0165, loss_cls: 0.0930, acc: 96.5720, loss_bbox: 0.1252, loss: 0.2444
2022-10-04 00:00:36,759 - mmdet - INFO - Iter [9250/12000]	lr: 3.000e-03, eta: 0:05:10, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0092, loss_rpn_bbox: 0.0169, loss_cls: 0.0934, acc: 96.5176, loss_bbox: 0.1277, loss: 0.2472
2022-10-04 00:00:42,484 - mmdet - INFO - Iter [9300/12000]	lr: 3.000e-03, eta: 0:05:04, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0092, loss_rpn_bbox: 0.0154, loss_cls: 0.0913, acc: 96.6136, loss_bbox: 0.1214, loss: 0.2372
2022-10-04 00:00:48,020 - mmdet - INFO - Iter [9350/12000]	lr: 3.000e-03, eta: 0:04:58, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0085, loss_rpn_bbox: 0.0146, loss_cls: 0.0815, acc: 96.9793, loss_bbox: 0.1129, loss: 0.2175
2022-10-04 00:00:53,742 - mmdet - INFO - Iter [9400/12000]	lr: 3.000e-03, eta: 0:04:53, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0162, loss_cls: 0.0825, acc: 96.8791, loss_bbox: 0.1188, loss: 0.2255
2022-10-04 00:00:59,493 - mmdet - INFO - Iter [9450/12000]	lr: 3.000e-03, eta: 0:04:47, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0097, loss_rpn_bbox: 0.0167, loss_cls: 0.0828, acc: 96.7928, loss_bbox: 0.1176, loss: 0.2268
2022-10-04 00:01:05,139 - mmdet - INFO - Iter [9500/12000]	lr: 3.000e-03, eta: 0:04:41, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0154, loss_cls: 0.0843, acc: 96.7783, loss_bbox: 0.1189, loss: 0.2269
2022-10-04 00:01:10,836 - mmdet - INFO - Iter [9550/12000]	lr: 3.000e-03, eta: 0:04:36, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0165, loss_cls: 0.0800, acc: 96.9097, loss_bbox: 0.1117, loss: 0.2161
2022-10-04 00:01:16,449 - mmdet - INFO - Iter [9600/12000]	lr: 3.000e-03, eta: 0:04:30, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0085, loss_rpn_bbox: 0.0173, loss_cls: 0.0813, acc: 96.9258, loss_bbox: 0.1180, loss: 0.2251
2022-10-04 00:01:21,986 - mmdet - INFO - Iter [9650/12000]	lr: 3.000e-03, eta: 0:04:24, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0159, loss_cls: 0.0780, acc: 96.9267, loss_bbox: 0.1153, loss: 0.2170
2022-10-04 00:01:27,556 - mmdet - INFO - Iter [9700/12000]	lr: 3.000e-03, eta: 0:04:19, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0081, loss_rpn_bbox: 0.0164, loss_cls: 0.0830, acc: 96.8487, loss_bbox: 0.1197, loss: 0.2272
2022-10-04 00:01:33,218 - mmdet - INFO - Iter [9750/12000]	lr: 3.000e-03, eta: 0:04:13, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0076, loss_rpn_bbox: 0.0168, loss_cls: 0.0766, acc: 97.0691, loss_bbox: 0.1046, loss: 0.2057
2022-10-04 00:01:38,887 - mmdet - INFO - Iter [9800/12000]	lr: 3.000e-03, eta: 0:04:08, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0086, loss_rpn_bbox: 0.0159, loss_cls: 0.0796, acc: 96.9885, loss_bbox: 0.1114, loss: 0.2156
2022-10-04 00:01:44,578 - mmdet - INFO - Iter [9850/12000]	lr: 3.000e-03, eta: 0:04:02, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0160, loss_cls: 0.0808, acc: 96.9010, loss_bbox: 0.1154, loss: 0.2192
2022-10-04 00:01:50,284 - mmdet - INFO - Iter [9900/12000]	lr: 3.000e-03, eta: 0:03:56, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0169, loss_cls: 0.0850, acc: 96.7120, loss_bbox: 0.1222, loss: 0.2322
2022-10-04 00:01:55,902 - mmdet - INFO - Iter [9950/12000]	lr: 3.000e-03, eta: 0:03:51, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0166, loss_cls: 0.0829, acc: 96.8256, loss_bbox: 0.1182, loss: 0.2259
2022-10-04 00:02:01,470 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-04 00:02:01,470 - mmdet - INFO - Iter [10000/12000]	lr: 3.000e-03, eta: 0:03:45, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0086, loss_rpn_bbox: 0.0167, loss_cls: 0.0842, acc: 96.8154, loss_bbox: 0.1170, loss: 0.2265
2022-10-04 00:02:07,093 - mmdet - INFO - Iter [10050/12000]	lr: 3.000e-03, eta: 0:03:39, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0088, loss_rpn_bbox: 0.0175, loss_cls: 0.0804, acc: 96.9459, loss_bbox: 0.1110, loss: 0.2177
2022-10-04 00:02:12,673 - mmdet - INFO - Iter [10100/12000]	lr: 3.000e-03, eta: 0:03:34, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0087, loss_rpn_bbox: 0.0167, loss_cls: 0.0832, acc: 96.8476, loss_bbox: 0.1198, loss: 0.2284
2022-10-04 00:02:18,428 - mmdet - INFO - Iter [10150/12000]	lr: 3.000e-03, eta: 0:03:28, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0160, loss_cls: 0.0791, acc: 96.9894, loss_bbox: 0.1139, loss: 0.2166
2022-10-04 00:02:24,065 - mmdet - INFO - Iter [10200/12000]	lr: 3.000e-03, eta: 0:03:22, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0085, loss_rpn_bbox: 0.0169, loss_cls: 0.0783, acc: 96.9489, loss_bbox: 0.1114, loss: 0.2150
2022-10-04 00:02:29,846 - mmdet - INFO - Iter [10250/12000]	lr: 3.000e-03, eta: 0:03:17, time: 0.116, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0087, loss_rpn_bbox: 0.0168, loss_cls: 0.0788, acc: 96.9985, loss_bbox: 0.1124, loss: 0.2167
2022-10-04 00:02:35,415 - mmdet - INFO - Iter [10300/12000]	lr: 3.000e-03, eta: 0:03:11, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0164, loss_cls: 0.0808, acc: 97.0089, loss_bbox: 0.1143, loss: 0.2198
2022-10-04 00:02:41,101 - mmdet - INFO - Iter [10350/12000]	lr: 3.000e-03, eta: 0:03:06, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0083, loss_rpn_bbox: 0.0161, loss_cls: 0.0824, acc: 96.8081, loss_bbox: 0.1169, loss: 0.2237
2022-10-04 00:02:46,716 - mmdet - INFO - Iter [10400/12000]	lr: 3.000e-03, eta: 0:03:00, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0091, loss_rpn_bbox: 0.0182, loss_cls: 0.0794, acc: 96.9293, loss_bbox: 0.1158, loss: 0.2225
2022-10-04 00:02:52,415 - mmdet - INFO - Iter [10450/12000]	lr: 3.000e-03, eta: 0:02:54, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0072, loss_rpn_bbox: 0.0144, loss_cls: 0.0774, acc: 97.0588, loss_bbox: 0.1105, loss: 0.2095
2022-10-04 00:02:58,053 - mmdet - INFO - Iter [10500/12000]	lr: 3.000e-03, eta: 0:02:49, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0167, loss_cls: 0.0799, acc: 96.9442, loss_bbox: 0.1197, loss: 0.2241
2022-10-04 00:03:03,783 - mmdet - INFO - Iter [10550/12000]	lr: 3.000e-03, eta: 0:02:43, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0154, loss_cls: 0.0765, acc: 97.0360, loss_bbox: 0.1110, loss: 0.2107
2022-10-04 00:03:09,429 - mmdet - INFO - Iter [10600/12000]	lr: 3.000e-03, eta: 0:02:37, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0165, loss_cls: 0.0733, acc: 97.2044, loss_bbox: 0.1080, loss: 0.2054
2022-10-04 00:03:15,100 - mmdet - INFO - Iter [10650/12000]	lr: 3.000e-03, eta: 0:02:32, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0147, loss_cls: 0.0764, acc: 97.0199, loss_bbox: 0.1134, loss: 0.2122
2022-10-04 00:03:20,692 - mmdet - INFO - Iter [10700/12000]	lr: 3.000e-03, eta: 0:02:26, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0157, loss_cls: 0.0811, acc: 96.9140, loss_bbox: 0.1151, loss: 0.2200
2022-10-04 00:03:26,424 - mmdet - INFO - Iter [10750/12000]	lr: 3.000e-03, eta: 0:02:21, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0084, loss_rpn_bbox: 0.0155, loss_cls: 0.0756, acc: 97.0987, loss_bbox: 0.1093, loss: 0.2089
2022-10-04 00:03:32,214 - mmdet - INFO - Iter [10800/12000]	lr: 3.000e-03, eta: 0:02:15, time: 0.116, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0075, loss_rpn_bbox: 0.0160, loss_cls: 0.0809, acc: 96.8699, loss_bbox: 0.1135, loss: 0.2179
2022-10-04 00:03:37,801 - mmdet - INFO - Iter [10850/12000]	lr: 3.000e-03, eta: 0:02:09, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0068, loss_rpn_bbox: 0.0144, loss_cls: 0.0763, acc: 97.0386, loss_bbox: 0.1119, loss: 0.2095
2022-10-04 00:03:43,431 - mmdet - INFO - Iter [10900/12000]	lr: 3.000e-03, eta: 0:02:04, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0159, loss_cls: 0.0762, acc: 97.0788, loss_bbox: 0.1131, loss: 0.2134
2022-10-04 00:03:49,082 - mmdet - INFO - Iter [10950/12000]	lr: 3.000e-03, eta: 0:01:58, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0153, loss_cls: 0.0783, acc: 97.0010, loss_bbox: 0.1126, loss: 0.2136
2022-10-04 00:03:54,639 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-04 00:03:54,640 - mmdet - INFO - Iter [11000/12000]	lr: 3.000e-03, eta: 0:01:52, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0154, loss_cls: 0.0821, acc: 96.8244, loss_bbox: 0.1179, loss: 0.2236
2022-10-04 00:04:00,351 - mmdet - INFO - Iter [11050/12000]	lr: 3.000e-04, eta: 0:01:47, time: 0.114, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0152, loss_cls: 0.0714, acc: 97.2684, loss_bbox: 0.1036, loss: 0.1973
2022-10-04 00:04:05,916 - mmdet - INFO - Iter [11100/12000]	lr: 3.000e-04, eta: 0:01:41, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0081, loss_rpn_bbox: 0.0167, loss_cls: 0.0801, acc: 96.9491, loss_bbox: 0.1147, loss: 0.2196
2022-10-04 00:04:11,585 - mmdet - INFO - Iter [11150/12000]	lr: 3.000e-04, eta: 0:01:35, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0157, loss_cls: 0.0738, acc: 97.1848, loss_bbox: 0.1058, loss: 0.2030
2022-10-04 00:04:17,190 - mmdet - INFO - Iter [11200/12000]	lr: 3.000e-04, eta: 0:01:30, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0159, loss_cls: 0.0755, acc: 97.0455, loss_bbox: 0.1115, loss: 0.2108
2022-10-04 00:04:22,810 - mmdet - INFO - Iter [11250/12000]	lr: 3.000e-04, eta: 0:01:24, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0148, loss_cls: 0.0757, acc: 97.0582, loss_bbox: 0.1135, loss: 0.2117
2022-10-04 00:04:28,447 - mmdet - INFO - Iter [11300/12000]	lr: 3.000e-04, eta: 0:01:18, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0074, loss_rpn_bbox: 0.0165, loss_cls: 0.0737, acc: 97.1729, loss_bbox: 0.1063, loss: 0.2040
2022-10-04 00:04:34,097 - mmdet - INFO - Iter [11350/12000]	lr: 3.000e-04, eta: 0:01:13, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0083, loss_rpn_bbox: 0.0161, loss_cls: 0.0773, acc: 97.0578, loss_bbox: 0.1097, loss: 0.2114
2022-10-04 00:04:39,758 - mmdet - INFO - Iter [11400/12000]	lr: 3.000e-04, eta: 0:01:07, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0080, loss_rpn_bbox: 0.0154, loss_cls: 0.0739, acc: 97.1705, loss_bbox: 0.1101, loss: 0.2073
2022-10-04 00:04:45,405 - mmdet - INFO - Iter [11450/12000]	lr: 3.000e-04, eta: 0:01:02, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0177, loss_cls: 0.0768, acc: 97.0169, loss_bbox: 0.1146, loss: 0.2170
2022-10-04 00:04:50,990 - mmdet - INFO - Iter [11500/12000]	lr: 3.000e-04, eta: 0:00:56, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0068, loss_rpn_bbox: 0.0150, loss_cls: 0.0693, acc: 97.3253, loss_bbox: 0.1013, loss: 0.1925
2022-10-04 00:04:56,538 - mmdet - INFO - Iter [11550/12000]	lr: 3.000e-04, eta: 0:00:50, time: 0.111, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0076, loss_rpn_bbox: 0.0164, loss_cls: 0.0780, acc: 97.0086, loss_bbox: 0.1149, loss: 0.2169
2022-10-04 00:05:02,121 - mmdet - INFO - Iter [11600/12000]	lr: 3.000e-04, eta: 0:00:45, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0068, loss_rpn_bbox: 0.0157, loss_cls: 0.0743, acc: 97.1241, loss_bbox: 0.1100, loss: 0.2068
2022-10-04 00:05:07,742 - mmdet - INFO - Iter [11650/12000]	lr: 3.000e-04, eta: 0:00:39, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0077, loss_rpn_bbox: 0.0152, loss_cls: 0.0764, acc: 97.0646, loss_bbox: 0.1104, loss: 0.2097
2022-10-04 00:05:13,505 - mmdet - INFO - Iter [11700/12000]	lr: 3.000e-04, eta: 0:00:33, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0072, loss_rpn_bbox: 0.0144, loss_cls: 0.0710, acc: 97.2821, loss_bbox: 0.1069, loss: 0.1995
2022-10-04 00:05:19,253 - mmdet - INFO - Iter [11750/12000]	lr: 3.000e-04, eta: 0:00:28, time: 0.115, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0168, loss_cls: 0.0775, acc: 96.9831, loss_bbox: 0.1178, loss: 0.2199
2022-10-04 00:05:24,926 - mmdet - INFO - Iter [11800/12000]	lr: 3.000e-04, eta: 0:00:22, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0069, loss_rpn_bbox: 0.0158, loss_cls: 0.0750, acc: 97.0958, loss_bbox: 0.1082, loss: 0.2059
2022-10-04 00:05:30,532 - mmdet - INFO - Iter [11850/12000]	lr: 3.000e-04, eta: 0:00:16, time: 0.112, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0163, loss_cls: 0.0756, acc: 97.0812, loss_bbox: 0.1115, loss: 0.2113
2022-10-04 00:05:36,162 - mmdet - INFO - Iter [11900/12000]	lr: 3.000e-04, eta: 0:00:11, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0067, loss_rpn_bbox: 0.0163, loss_cls: 0.0734, acc: 97.1910, loss_bbox: 0.1065, loss: 0.2029
2022-10-04 00:05:41,809 - mmdet - INFO - Iter [11950/12000]	lr: 3.000e-04, eta: 0:00:05, time: 0.113, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0079, loss_rpn_bbox: 0.0163, loss_cls: 0.0778, acc: 96.9836, loss_bbox: 0.1134, loss: 0.2155
2022-10-04 00:05:47,375 - mmdet - INFO - Saving checkpoint at 12000 iterations
2022-10-04 00:05:48,067 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-04 00:05:48,067 - mmdet - INFO - Iter [12000/12000]	lr: 3.000e-04, eta: 0:00:00, time: 0.125, data_time: 0.006, memory: 3995, loss_rpn_cls: 0.0072, loss_rpn_bbox: 0.0156, loss_cls: 0.0742, acc: 97.1443, loss_bbox: 0.1089, loss: 0.2059
2022-10-04 00:06:10,816 - mmdet - INFO - 
+-------------+------+-------+--------+-------+
| class       | gts  | dets  | recall | ap    |
+-------------+------+-------+--------+-------+
| aeroplane   | 285  | 779   | 0.965  | 0.889 |
| bicycle     | 337  | 1011  | 0.961  | 0.876 |
| bird        | 459  | 1206  | 0.932  | 0.866 |
| boat        | 263  | 1402  | 0.928  | 0.753 |
| bottle      | 469  | 1731  | 0.878  | 0.737 |
| bus         | 213  | 714   | 0.967  | 0.867 |
| car         | 1201 | 3217  | 0.976  | 0.894 |
| cat         | 358  | 983   | 0.972  | 0.889 |
| chair       | 756  | 4198  | 0.911  | 0.720 |
| cow         | 244  | 895   | 0.967  | 0.869 |
| diningtable | 206  | 1500  | 0.927  | 0.767 |
| dog         | 489  | 1425  | 0.984  | 0.873 |
| horse       | 348  | 1014  | 0.968  | 0.875 |
| motorbike   | 325  | 996   | 0.966  | 0.873 |
| person      | 4528 | 11730 | 0.961  | 0.875 |
| pottedplant | 480  | 2050  | 0.837  | 0.625 |
| sheep       | 242  | 680   | 0.946  | 0.853 |
| sofa        | 239  | 1313  | 0.967  | 0.799 |
| train       | 282  | 895   | 0.940  | 0.853 |
| tvmonitor   | 308  | 941   | 0.932  | 0.830 |
+-------------+------+-------+--------+-------+
| mAP         |      |       |        | 0.829 |
+-------------+------+-------+--------+-------+
2022-10-04 00:06:11,575 - mmdet - INFO - Now best checkpoint is saved as best_mAP_iter_12000.pth.
2022-10-04 00:06:11,576 - mmdet - INFO - Best mAP is 0.8292 at 12000 iter.
2022-10-04 00:06:11,576 - mmdet - INFO - Exp name: mask_rcnn_mstrain_12k_voc0712.py
2022-10-04 00:06:11,576 - mmdet - INFO - Iter(val) [619]	mAP: 0.8292, AP50: 0.8290