finetune/finetune_retinanet_12k_voc0712_lr1.5e-2_wd5e-5/20221003_232534.log

2022-10-03 23:25:34,518 - 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 23:25:35,633 - mmdet - INFO - Distributed training: True
2022-10-03 23:25:36,764 - mmdet - INFO - Config:
model = dict(
    type='RetinaNet',
    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,
        start_level=1,
        add_extra_convs='on_input',
        num_outs=5,
        norm_cfg=dict(type='SyncBN', requires_grad=True)),
    bbox_head=dict(
        type='RetinaHead',
        num_classes=20,
        in_channels=256,
        stacked_convs=4,
        feat_channels=256,
        anchor_generator=dict(
            type='AnchorGenerator',
            octave_base_scale=4,
            scales_per_octave=3,
            ratios=[0.5, 1.0, 2.0],
            strides=[8, 16, 32, 64, 128]),
        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='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
    train_cfg=dict(
        assigner=dict(
            type='MaxIoUAssigner',
            pos_iou_thr=0.5,
            neg_iou_thr=0.4,
            min_pos_iou=0,
            ignore_iof_thr=-1),
        allowed_border=-1,
        pos_weight=-1,
        debug=False),
    test_cfg=dict(
        nms_pre=1000,
        min_bbox_size=0,
        score_thr=0.05,
        nms=dict(type='nms', iou_threshold=0.5),
        max_per_img=100))
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=0.015, 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_retinanet_mstrain-soft-teacher_sampler-2048_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_retinanet_12k_voc0712_lr1.5e-2_wd5e-5'
auto_resume = False
gpu_ids = range(0, 8)

2022-10-03 23:25:36,764 - mmdet - INFO - Set random seed to 42, deterministic: False
2022-10-03 23:25:37,080 - mmdet - INFO - initialize ResNet with init_cfg {'type': 'Pretrained', 'checkpoint': 'torchvision://resnet50'}
2022-10-03 23:25:46,723 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2022-10-03 23:25:46,781 - mmdet - INFO - initialize RetinaHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01, 'override': {'type': 'Normal', 'name': 'retina_cls', 'std': 0.01, 'bias_prob': 0.01}}
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, 512, 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 RetinaNet  

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

neck.lateral_convs.1.conv.weight - torch.Size([256, 1024, 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 RetinaNet  

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

neck.lateral_convs.2.conv.weight - torch.Size([256, 2048, 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 RetinaNet  

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

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 RetinaNet  

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

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 RetinaNet  

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

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 RetinaNet  

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

neck.fpn_convs.3.conv.weight - torch.Size([256, 2048, 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 RetinaNet  

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

neck.fpn_convs.4.conv.weight - torch.Size([256, 256, 3, 3]): 
XavierInit: gain=1, distribution=uniform, bias=0 

neck.fpn_convs.4.bn.weight - torch.Size([256]): 
The value is the same before and after calling `init_weights` of RetinaNet  

neck.fpn_convs.4.bn.bias - torch.Size([256]): 
The value is the same before and after calling `init_weights` of RetinaNet  

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

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

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

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

bbox_head.cls_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

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

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

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

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

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

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

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

bbox_head.reg_convs.2.conv.weight - torch.Size([256, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

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

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

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

bbox_head.retina_cls.weight - torch.Size([180, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=-4.59511985013459 

bbox_head.retina_cls.bias - torch.Size([180]): 
NormalInit: mean=0, std=0.01, bias=-4.59511985013459 

bbox_head.retina_reg.weight - torch.Size([36, 256, 3, 3]): 
NormalInit: mean=0, std=0.01, bias=0 

bbox_head.retina_reg.bias - torch.Size([36]): 
NormalInit: mean=0, std=0.01, bias=0 
2022-10-03 23:25:48,644 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
2022-10-03 23:25:49,424 - mmdet - INFO - load checkpoint from local path: pretrain/selfsup_retinanet_mstrain-soft-teacher_sampler-2048_temp0.5/final_model.pth
2022-10-03 23:25:49,532 - 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.fpn_convs.0.conv.bias, neck.fpn_convs.1.conv.bias, neck.fpn_convs.2.conv.bias, neck.fpn_convs.3.conv.bias, neck.fpn_convs.4.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.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, neck.fpn_convs.4.bn.weight, neck.fpn_convs.4.bn.bias, neck.fpn_convs.4.bn.running_mean, neck.fpn_convs.4.bn.running_var, bbox_head.retina_cls.weight, bbox_head.retina_cls.bias, bbox_head.retina_reg.weight, bbox_head.retina_reg.bias

2022-10-03 23:25:49,538 - mmdet - INFO - Start running, host: tiger@n136-144-086, work_dir: /home/tiger/code/mmdet/work_dirs/finetune_retinanet_12k_voc0712_lr1.5e-2_wd5e-5
2022-10-03 23:25:49,538 - 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 23:25:49,539 - mmdet - INFO - workflow: [('train', 1)], max: 12000 iters
2022-10-03 23:25:49,539 - mmdet - INFO - Checkpoints will be saved to /home/tiger/code/mmdet/work_dirs/finetune_retinanet_12k_voc0712_lr1.5e-2_wd5e-5 by HardDiskBackend.
2022-10-03 23:25:55,378 - mmdet - INFO - Iter [50/12000]	lr: 1.484e-03, eta: 0:17:55, time: 0.090, data_time: 0.006, memory: 3221, loss_cls: 1.1629, loss_bbox: 0.6614, loss: 1.8243
2022-10-03 23:25:59,656 - mmdet - INFO - Iter [100/12000]	lr: 2.982e-03, eta: 0:17:24, time: 0.086, data_time: 0.006, memory: 3222, loss_cls: 1.1584, loss_bbox: 0.5555, loss: 1.7139
2022-10-03 23:26:05,649 - mmdet - INFO - Iter [150/12000]	lr: 4.481e-03, eta: 0:19:26, time: 0.120, data_time: 0.005, memory: 3223, loss_cls: 1.0891, loss_bbox: 0.4517, loss: 1.5408
2022-10-03 23:26:09,824 - mmdet - INFO - Iter [200/12000]	lr: 5.979e-03, eta: 0:18:37, time: 0.083, data_time: 0.005, memory: 3223, loss_cls: 0.8258, loss_bbox: 0.4111, loss: 1.2369
2022-10-03 23:26:14,107 - mmdet - INFO - Iter [250/12000]	lr: 7.478e-03, eta: 0:18:11, time: 0.086, data_time: 0.006, memory: 3223, loss_cls: 0.7845, loss_bbox: 0.4073, loss: 1.1919
2022-10-03 23:26:18,252 - mmdet - INFO - Iter [300/12000]	lr: 8.976e-03, eta: 0:17:47, time: 0.083, data_time: 0.005, memory: 3223, loss_cls: 0.7312, loss_bbox: 0.3949, loss: 1.1262
2022-10-03 23:26:22,505 - mmdet - INFO - Iter [350/12000]	lr: 1.047e-02, eta: 0:17:32, time: 0.085, data_time: 0.006, memory: 3223, loss_cls: 0.6597, loss_bbox: 0.3889, loss: 1.0485
2022-10-03 23:26:26,620 - mmdet - INFO - Iter [400/12000]	lr: 1.197e-02, eta: 0:17:16, time: 0.082, data_time: 0.006, memory: 3223, loss_cls: 0.6312, loss_bbox: 0.3784, loss: 1.0096
2022-10-03 23:26:30,838 - mmdet - INFO - Iter [450/12000]	lr: 1.347e-02, eta: 0:17:05, time: 0.084, data_time: 0.005, memory: 3223, loss_cls: 0.8423, loss_bbox: 0.4101, loss: 1.2524
2022-10-03 23:26:34,914 - mmdet - INFO - Iter [500/12000]	lr: 1.497e-02, eta: 0:16:52, time: 0.081, data_time: 0.006, memory: 3223, loss_cls: 0.9053, loss_bbox: 0.4874, loss: 1.3928
2022-10-03 23:26:39,238 - mmdet - INFO - Iter [550/12000]	lr: 1.500e-02, eta: 0:16:46, time: 0.087, data_time: 0.006, memory: 3223, loss_cls: 0.7680, loss_bbox: 0.4204, loss: 1.1885
2022-10-03 23:26:43,449 - mmdet - INFO - Iter [600/12000]	lr: 1.500e-02, eta: 0:16:38, time: 0.084, data_time: 0.006, memory: 3223, loss_cls: 0.7605, loss_bbox: 0.4015, loss: 1.1619
2022-10-03 23:26:47,714 - mmdet - INFO - Iter [650/12000]	lr: 1.500e-02, eta: 0:16:32, time: 0.085, data_time: 0.006, memory: 3223, loss_cls: 0.8102, loss_bbox: 0.3966, loss: 1.2068
2022-10-03 23:26:51,904 - mmdet - INFO - Iter [700/12000]	lr: 1.500e-02, eta: 0:16:25, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.6460, loss_bbox: 0.3687, loss: 1.0146
2022-10-03 23:26:56,191 - mmdet - INFO - Iter [750/12000]	lr: 1.500e-02, eta: 0:16:19, time: 0.086, data_time: 0.006, memory: 3224, loss_cls: 0.6535, loss_bbox: 0.3552, loss: 1.0088
2022-10-03 23:27:00,473 - mmdet - INFO - Iter [800/12000]	lr: 1.500e-02, eta: 0:16:14, time: 0.086, data_time: 0.006, memory: 3224, loss_cls: 0.5858, loss_bbox: 0.3599, loss: 0.9457
2022-10-03 23:27:04,500 - mmdet - INFO - Iter [850/12000]	lr: 1.500e-02, eta: 0:16:05, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.5724, loss_bbox: 0.3407, loss: 0.9132
2022-10-03 23:27:08,607 - mmdet - INFO - Iter [900/12000]	lr: 1.500e-02, eta: 0:15:58, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.5498, loss_bbox: 0.3449, loss: 0.8947
2022-10-03 23:27:12,689 - mmdet - INFO - Iter [950/12000]	lr: 1.500e-02, eta: 0:15:51, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.5324, loss_bbox: 0.3411, loss: 0.8735
2022-10-03 23:27:16,695 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:27:16,696 - mmdet - INFO - Iter [1000/12000]	lr: 1.500e-02, eta: 0:15:43, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.5129, loss_bbox: 0.3193, loss: 0.8322
2022-10-03 23:27:20,708 - mmdet - INFO - Iter [1050/12000]	lr: 1.500e-02, eta: 0:15:36, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.5172, loss_bbox: 0.3283, loss: 0.8456
2022-10-03 23:27:24,758 - mmdet - INFO - Iter [1100/12000]	lr: 1.500e-02, eta: 0:15:30, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.4909, loss_bbox: 0.3207, loss: 0.8116
2022-10-03 23:27:29,067 - mmdet - INFO - Iter [1150/12000]	lr: 1.500e-02, eta: 0:15:26, time: 0.086, data_time: 0.006, memory: 3224, loss_cls: 0.5034, loss_bbox: 0.3272, loss: 0.8305
2022-10-03 23:27:33,084 - mmdet - INFO - Iter [1200/12000]	lr: 1.500e-02, eta: 0:15:19, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.4889, loss_bbox: 0.3240, loss: 0.8129
2022-10-03 23:27:38,387 - mmdet - INFO - Iter [1250/12000]	lr: 1.500e-02, eta: 0:15:24, time: 0.106, data_time: 0.006, memory: 3224, loss_cls: 0.4536, loss_bbox: 0.3185, loss: 0.7721
2022-10-03 23:27:42,429 - mmdet - INFO - Iter [1300/12000]	lr: 1.500e-02, eta: 0:15:18, time: 0.081, data_time: 0.005, memory: 3224, loss_cls: 0.4639, loss_bbox: 0.3065, loss: 0.7703
2022-10-03 23:27:46,548 - mmdet - INFO - Iter [1350/12000]	lr: 1.500e-02, eta: 0:15:12, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.4371, loss_bbox: 0.3044, loss: 0.7415
2022-10-03 23:27:50,692 - mmdet - INFO - Iter [1400/12000]	lr: 1.500e-02, eta: 0:15:07, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.4519, loss_bbox: 0.3134, loss: 0.7653
2022-10-03 23:27:54,677 - mmdet - INFO - Iter [1450/12000]	lr: 1.500e-02, eta: 0:15:00, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.4309, loss_bbox: 0.3055, loss: 0.7364
2022-10-03 23:27:58,762 - mmdet - INFO - Iter [1500/12000]	lr: 1.500e-02, eta: 0:14:55, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.4384, loss_bbox: 0.3167, loss: 0.7551
2022-10-03 23:28:02,790 - mmdet - INFO - Iter [1550/12000]	lr: 1.500e-02, eta: 0:14:49, time: 0.081, data_time: 0.005, memory: 3224, loss_cls: 0.4355, loss_bbox: 0.3069, loss: 0.7424
2022-10-03 23:28:06,777 - mmdet - INFO - Iter [1600/12000]	lr: 1.500e-02, eta: 0:14:43, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.4055, loss_bbox: 0.3089, loss: 0.7144
2022-10-03 23:28:10,767 - mmdet - INFO - Iter [1650/12000]	lr: 1.500e-02, eta: 0:14:37, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.4069, loss_bbox: 0.3050, loss: 0.7119
2022-10-03 23:28:14,828 - mmdet - INFO - Iter [1700/12000]	lr: 1.500e-02, eta: 0:14:32, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.4004, loss_bbox: 0.3055, loss: 0.7058
2022-10-03 23:28:19,091 - mmdet - INFO - Iter [1750/12000]	lr: 1.500e-02, eta: 0:14:28, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.4183, loss_bbox: 0.3042, loss: 0.7225
2022-10-03 23:28:23,263 - mmdet - INFO - Iter [1800/12000]	lr: 1.500e-02, eta: 0:14:23, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.4023, loss_bbox: 0.3023, loss: 0.7046
2022-10-03 23:28:27,267 - mmdet - INFO - Iter [1850/12000]	lr: 1.500e-02, eta: 0:14:17, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.3761, loss_bbox: 0.2956, loss: 0.6717
2022-10-03 23:28:31,555 - mmdet - INFO - Iter [1900/12000]	lr: 1.500e-02, eta: 0:14:14, time: 0.086, data_time: 0.006, memory: 3224, loss_cls: 0.4005, loss_bbox: 0.3044, loss: 0.7049
2022-10-03 23:28:35,749 - mmdet - INFO - Iter [1950/12000]	lr: 1.500e-02, eta: 0:14:09, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.3999, loss_bbox: 0.2954, loss: 0.6952
2022-10-03 23:28:39,943 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:28:39,944 - mmdet - INFO - Iter [2000/12000]	lr: 1.500e-02, eta: 0:14:05, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.3785, loss_bbox: 0.2902, loss: 0.6687
2022-10-03 23:28:43,964 - mmdet - INFO - Iter [2050/12000]	lr: 1.500e-02, eta: 0:14:00, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.3586, loss_bbox: 0.2902, loss: 0.6489
2022-10-03 23:28:48,052 - mmdet - INFO - Iter [2100/12000]	lr: 1.500e-02, eta: 0:13:55, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.3824, loss_bbox: 0.2926, loss: 0.6750
2022-10-03 23:28:52,112 - mmdet - INFO - Iter [2150/12000]	lr: 1.500e-02, eta: 0:13:50, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.3551, loss_bbox: 0.2795, loss: 0.6346
2022-10-03 23:28:56,312 - mmdet - INFO - Iter [2200/12000]	lr: 1.500e-02, eta: 0:13:45, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.3408, loss_bbox: 0.2830, loss: 0.6238
2022-10-03 23:29:00,350 - mmdet - INFO - Iter [2250/12000]	lr: 1.500e-02, eta: 0:13:40, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.3448, loss_bbox: 0.2823, loss: 0.6270
2022-10-03 23:29:04,412 - mmdet - INFO - Iter [2300/12000]	lr: 1.500e-02, eta: 0:13:36, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.3532, loss_bbox: 0.2838, loss: 0.6370
2022-10-03 23:29:08,485 - mmdet - INFO - Iter [2350/12000]	lr: 1.500e-02, eta: 0:13:31, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.3550, loss_bbox: 0.2873, loss: 0.6422
2022-10-03 23:29:12,494 - mmdet - INFO - Iter [2400/12000]	lr: 1.500e-02, eta: 0:13:26, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.3385, loss_bbox: 0.2863, loss: 0.6248
2022-10-03 23:29:16,498 - mmdet - INFO - Iter [2450/12000]	lr: 1.500e-02, eta: 0:13:21, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.3352, loss_bbox: 0.2791, loss: 0.6143
2022-10-03 23:29:20,492 - mmdet - INFO - Iter [2500/12000]	lr: 1.500e-02, eta: 0:13:16, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.3293, loss_bbox: 0.2816, loss: 0.6109
2022-10-03 23:29:24,513 - mmdet - INFO - Iter [2550/12000]	lr: 1.500e-02, eta: 0:13:11, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.3459, loss_bbox: 0.2878, loss: 0.6337
2022-10-03 23:29:28,596 - mmdet - INFO - Iter [2600/12000]	lr: 1.500e-02, eta: 0:13:07, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.3378, loss_bbox: 0.2843, loss: 0.6221
2022-10-03 23:29:32,760 - mmdet - INFO - Iter [2650/12000]	lr: 1.500e-02, eta: 0:13:02, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.3347, loss_bbox: 0.2786, loss: 0.6133
2022-10-03 23:29:36,728 - mmdet - INFO - Iter [2700/12000]	lr: 1.500e-02, eta: 0:12:57, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.3163, loss_bbox: 0.2768, loss: 0.5931
2022-10-03 23:29:40,777 - mmdet - INFO - Iter [2750/12000]	lr: 1.500e-02, eta: 0:12:53, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.3499, loss_bbox: 0.2751, loss: 0.6250
2022-10-03 23:29:44,893 - mmdet - INFO - Iter [2800/12000]	lr: 1.500e-02, eta: 0:12:48, time: 0.082, data_time: 0.005, memory: 3224, loss_cls: 0.3302, loss_bbox: 0.2774, loss: 0.6076
2022-10-03 23:29:48,991 - mmdet - INFO - Iter [2850/12000]	lr: 1.500e-02, eta: 0:12:44, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.3143, loss_bbox: 0.2723, loss: 0.5866
2022-10-03 23:29:53,020 - mmdet - INFO - Iter [2900/12000]	lr: 1.500e-02, eta: 0:12:39, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.3332, loss_bbox: 0.2821, loss: 0.6153
2022-10-03 23:29:57,164 - mmdet - INFO - Iter [2950/12000]	lr: 1.500e-02, eta: 0:12:35, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.3081, loss_bbox: 0.2870, loss: 0.5952
2022-10-03 23:30:01,178 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:30:01,178 - mmdet - INFO - Iter [3000/12000]	lr: 1.500e-02, eta: 0:12:30, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.3119, loss_bbox: 0.2731, loss: 0.5851
2022-10-03 23:30:05,167 - mmdet - INFO - Iter [3050/12000]	lr: 1.500e-02, eta: 0:12:26, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.3114, loss_bbox: 0.2781, loss: 0.5895
2022-10-03 23:30:09,289 - mmdet - INFO - Iter [3100/12000]	lr: 1.500e-02, eta: 0:12:21, time: 0.082, data_time: 0.005, memory: 3224, loss_cls: 0.2978, loss_bbox: 0.2683, loss: 0.5661
2022-10-03 23:30:13,342 - mmdet - INFO - Iter [3150/12000]	lr: 1.500e-02, eta: 0:12:17, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2990, loss_bbox: 0.2785, loss: 0.5775
2022-10-03 23:30:17,344 - mmdet - INFO - Iter [3200/12000]	lr: 1.500e-02, eta: 0:12:12, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.3079, loss_bbox: 0.2673, loss: 0.5753
2022-10-03 23:30:21,436 - mmdet - INFO - Iter [3250/12000]	lr: 1.500e-02, eta: 0:12:08, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.3240, loss_bbox: 0.2747, loss: 0.5987
2022-10-03 23:30:25,542 - mmdet - INFO - Iter [3300/12000]	lr: 1.500e-02, eta: 0:12:04, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.3110, loss_bbox: 0.2722, loss: 0.5833
2022-10-03 23:30:29,587 - mmdet - INFO - Iter [3350/12000]	lr: 1.500e-02, eta: 0:11:59, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2929, loss_bbox: 0.2691, loss: 0.5621
2022-10-03 23:30:33,639 - mmdet - INFO - Iter [3400/12000]	lr: 1.500e-02, eta: 0:11:55, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2892, loss_bbox: 0.2625, loss: 0.5517
2022-10-03 23:30:37,892 - mmdet - INFO - Iter [3450/12000]	lr: 1.500e-02, eta: 0:11:51, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.2942, loss_bbox: 0.2680, loss: 0.5622
2022-10-03 23:30:41,912 - mmdet - INFO - Iter [3500/12000]	lr: 1.500e-02, eta: 0:11:46, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2985, loss_bbox: 0.2610, loss: 0.5595
2022-10-03 23:30:45,927 - mmdet - INFO - Iter [3550/12000]	lr: 1.500e-02, eta: 0:11:42, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2857, loss_bbox: 0.2597, loss: 0.5454
2022-10-03 23:30:49,914 - mmdet - INFO - Iter [3600/12000]	lr: 1.500e-02, eta: 0:11:37, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2872, loss_bbox: 0.2613, loss: 0.5485
2022-10-03 23:30:53,850 - mmdet - INFO - Iter [3650/12000]	lr: 1.500e-02, eta: 0:11:33, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.2985, loss_bbox: 0.2699, loss: 0.5684
2022-10-03 23:30:57,902 - mmdet - INFO - Iter [3700/12000]	lr: 1.500e-02, eta: 0:11:28, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2794, loss_bbox: 0.2603, loss: 0.5397
2022-10-03 23:31:01,903 - mmdet - INFO - Iter [3750/12000]	lr: 1.500e-02, eta: 0:11:24, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2825, loss_bbox: 0.2614, loss: 0.5439
2022-10-03 23:31:05,932 - mmdet - INFO - Iter [3800/12000]	lr: 1.500e-02, eta: 0:11:19, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2832, loss_bbox: 0.2694, loss: 0.5527
2022-10-03 23:31:09,947 - mmdet - INFO - Iter [3850/12000]	lr: 1.500e-02, eta: 0:11:15, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2898, loss_bbox: 0.2653, loss: 0.5550
2022-10-03 23:31:13,985 - mmdet - INFO - Iter [3900/12000]	lr: 1.500e-02, eta: 0:11:11, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2617, loss_bbox: 0.2559, loss: 0.5176
2022-10-03 23:31:17,999 - mmdet - INFO - Iter [3950/12000]	lr: 1.500e-02, eta: 0:11:06, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2774, loss_bbox: 0.2668, loss: 0.5442
2022-10-03 23:31:21,989 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:31:21,990 - mmdet - INFO - Iter [4000/12000]	lr: 1.500e-02, eta: 0:11:02, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2797, loss_bbox: 0.2677, loss: 0.5474
2022-10-03 23:31:25,993 - mmdet - INFO - Iter [4050/12000]	lr: 1.500e-02, eta: 0:10:57, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2798, loss_bbox: 0.2619, loss: 0.5417
2022-10-03 23:31:30,069 - mmdet - INFO - Iter [4100/12000]	lr: 1.500e-02, eta: 0:10:53, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2771, loss_bbox: 0.2648, loss: 0.5419
2022-10-03 23:31:34,132 - mmdet - INFO - Iter [4150/12000]	lr: 1.500e-02, eta: 0:10:49, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2628, loss_bbox: 0.2520, loss: 0.5149
2022-10-03 23:31:38,088 - mmdet - INFO - Iter [4200/12000]	lr: 1.500e-02, eta: 0:10:44, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.2628, loss_bbox: 0.2463, loss: 0.5091
2022-10-03 23:31:42,091 - mmdet - INFO - Iter [4250/12000]	lr: 1.500e-02, eta: 0:10:40, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2621, loss_bbox: 0.2472, loss: 0.5094
2022-10-03 23:31:46,112 - mmdet - INFO - Iter [4300/12000]	lr: 1.500e-02, eta: 0:10:36, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2675, loss_bbox: 0.2574, loss: 0.5249
2022-10-03 23:31:50,251 - mmdet - INFO - Iter [4350/12000]	lr: 1.500e-02, eta: 0:10:31, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2620, loss_bbox: 0.2527, loss: 0.5147
2022-10-03 23:31:54,474 - mmdet - INFO - Iter [4400/12000]	lr: 1.500e-02, eta: 0:10:27, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.2705, loss_bbox: 0.2546, loss: 0.5251
2022-10-03 23:31:58,523 - mmdet - INFO - Iter [4450/12000]	lr: 1.500e-02, eta: 0:10:23, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2602, loss_bbox: 0.2542, loss: 0.5143
2022-10-03 23:32:02,530 - mmdet - INFO - Iter [4500/12000]	lr: 1.500e-02, eta: 0:10:19, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2608, loss_bbox: 0.2560, loss: 0.5168
2022-10-03 23:32:06,487 - mmdet - INFO - Iter [4550/12000]	lr: 1.500e-02, eta: 0:10:14, time: 0.079, data_time: 0.005, memory: 3224, loss_cls: 0.2590, loss_bbox: 0.2553, loss: 0.5143
2022-10-03 23:32:10,530 - mmdet - INFO - Iter [4600/12000]	lr: 1.500e-02, eta: 0:10:10, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2641, loss_bbox: 0.2479, loss: 0.5120
2022-10-03 23:32:14,604 - mmdet - INFO - Iter [4650/12000]	lr: 1.500e-02, eta: 0:10:06, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2534, loss_bbox: 0.2519, loss: 0.5053
2022-10-03 23:32:18,657 - mmdet - INFO - Iter [4700/12000]	lr: 1.500e-02, eta: 0:10:02, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2575, loss_bbox: 0.2584, loss: 0.5159
2022-10-03 23:32:22,641 - mmdet - INFO - Iter [4750/12000]	lr: 1.500e-02, eta: 0:09:57, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2542, loss_bbox: 0.2506, loss: 0.5048
2022-10-03 23:32:26,629 - mmdet - INFO - Iter [4800/12000]	lr: 1.500e-02, eta: 0:09:53, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2532, loss_bbox: 0.2537, loss: 0.5069
2022-10-03 23:32:30,627 - mmdet - INFO - Iter [4850/12000]	lr: 1.500e-02, eta: 0:09:49, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2557, loss_bbox: 0.2487, loss: 0.5044
2022-10-03 23:32:34,633 - mmdet - INFO - Iter [4900/12000]	lr: 1.500e-02, eta: 0:09:44, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2509, loss_bbox: 0.2476, loss: 0.4985
2022-10-03 23:32:38,691 - mmdet - INFO - Iter [4950/12000]	lr: 1.500e-02, eta: 0:09:40, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2586, loss_bbox: 0.2498, loss: 0.5084
2022-10-03 23:32:42,703 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:32:42,703 - mmdet - INFO - Iter [5000/12000]	lr: 1.500e-02, eta: 0:09:36, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2594, loss_bbox: 0.2504, loss: 0.5098
2022-10-03 23:32:46,796 - mmdet - INFO - Iter [5050/12000]	lr: 1.500e-02, eta: 0:09:32, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2681, loss_bbox: 0.2601, loss: 0.5282
2022-10-03 23:32:51,033 - mmdet - INFO - Iter [5100/12000]	lr: 1.500e-02, eta: 0:09:28, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.2681, loss_bbox: 0.2526, loss: 0.5208
2022-10-03 23:32:55,077 - mmdet - INFO - Iter [5150/12000]	lr: 1.500e-02, eta: 0:09:24, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2772, loss_bbox: 0.2604, loss: 0.5376
2022-10-03 23:32:59,205 - mmdet - INFO - Iter [5200/12000]	lr: 1.500e-02, eta: 0:09:20, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2518, loss_bbox: 0.2492, loss: 0.5010
2022-10-03 23:33:03,230 - mmdet - INFO - Iter [5250/12000]	lr: 1.500e-02, eta: 0:09:15, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2322, loss_bbox: 0.2438, loss: 0.4760
2022-10-03 23:33:07,247 - mmdet - INFO - Iter [5300/12000]	lr: 1.500e-02, eta: 0:09:11, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.2459, loss_bbox: 0.2391, loss: 0.4850
2022-10-03 23:33:11,416 - mmdet - INFO - Iter [5350/12000]	lr: 1.500e-02, eta: 0:09:07, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2367, loss_bbox: 0.2383, loss: 0.4750
2022-10-03 23:33:15,470 - mmdet - INFO - Iter [5400/12000]	lr: 1.500e-02, eta: 0:09:03, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2256, loss_bbox: 0.2401, loss: 0.4656
2022-10-03 23:33:19,550 - mmdet - INFO - Iter [5450/12000]	lr: 1.500e-02, eta: 0:08:59, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2442, loss_bbox: 0.2445, loss: 0.4888
2022-10-03 23:33:23,503 - mmdet - INFO - Iter [5500/12000]	lr: 1.500e-02, eta: 0:08:54, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.2401, loss_bbox: 0.2421, loss: 0.4822
2022-10-03 23:33:27,517 - mmdet - INFO - Iter [5550/12000]	lr: 1.500e-02, eta: 0:08:50, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2480, loss_bbox: 0.2451, loss: 0.4932
2022-10-03 23:33:31,526 - mmdet - INFO - Iter [5600/12000]	lr: 1.500e-02, eta: 0:08:46, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2328, loss_bbox: 0.2398, loss: 0.4726
2022-10-03 23:33:35,585 - mmdet - INFO - Iter [5650/12000]	lr: 1.500e-02, eta: 0:08:42, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2306, loss_bbox: 0.2366, loss: 0.4672
2022-10-03 23:33:39,590 - mmdet - INFO - Iter [5700/12000]	lr: 1.500e-02, eta: 0:08:38, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2448, loss_bbox: 0.2429, loss: 0.4877
2022-10-03 23:33:43,583 - mmdet - INFO - Iter [5750/12000]	lr: 1.500e-02, eta: 0:08:33, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2457, loss_bbox: 0.2471, loss: 0.4928
2022-10-03 23:33:47,547 - mmdet - INFO - Iter [5800/12000]	lr: 1.500e-02, eta: 0:08:29, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.2367, loss_bbox: 0.2458, loss: 0.4825
2022-10-03 23:33:51,620 - mmdet - INFO - Iter [5850/12000]	lr: 1.500e-02, eta: 0:08:25, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2548, loss_bbox: 0.2479, loss: 0.5027
2022-10-03 23:33:55,720 - mmdet - INFO - Iter [5900/12000]	lr: 1.500e-02, eta: 0:08:21, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2380, loss_bbox: 0.2433, loss: 0.4812
2022-10-03 23:33:59,801 - mmdet - INFO - Iter [5950/12000]	lr: 1.500e-02, eta: 0:08:17, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2395, loss_bbox: 0.2469, loss: 0.4864
2022-10-03 23:34:03,792 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:34:03,793 - mmdet - INFO - Iter [6000/12000]	lr: 1.500e-02, eta: 0:08:12, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.2369, loss_bbox: 0.2395, loss: 0.4764
2022-10-03 23:34:07,771 - mmdet - INFO - Iter [6050/12000]	lr: 1.500e-02, eta: 0:08:08, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2330, loss_bbox: 0.2465, loss: 0.4794
2022-10-03 23:34:11,820 - mmdet - INFO - Iter [6100/12000]	lr: 1.500e-02, eta: 0:08:04, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2389, loss_bbox: 0.2384, loss: 0.4773
2022-10-03 23:34:15,828 - mmdet - INFO - Iter [6150/12000]	lr: 1.500e-02, eta: 0:08:00, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2498, loss_bbox: 0.2481, loss: 0.4979
2022-10-03 23:34:19,887 - mmdet - INFO - Iter [6200/12000]	lr: 1.500e-02, eta: 0:07:56, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2274, loss_bbox: 0.2364, loss: 0.4638
2022-10-03 23:34:23,879 - mmdet - INFO - Iter [6250/12000]	lr: 1.500e-02, eta: 0:07:51, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2375, loss_bbox: 0.2369, loss: 0.4744
2022-10-03 23:34:27,922 - mmdet - INFO - Iter [6300/12000]	lr: 1.500e-02, eta: 0:07:47, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2253, loss_bbox: 0.2289, loss: 0.4542
2022-10-03 23:34:31,934 - mmdet - INFO - Iter [6350/12000]	lr: 1.500e-02, eta: 0:07:43, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2235, loss_bbox: 0.2355, loss: 0.4591
2022-10-03 23:34:36,158 - mmdet - INFO - Iter [6400/12000]	lr: 1.500e-02, eta: 0:07:39, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.2184, loss_bbox: 0.2300, loss: 0.4484
2022-10-03 23:34:40,332 - mmdet - INFO - Iter [6450/12000]	lr: 1.500e-02, eta: 0:07:35, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2324, loss_bbox: 0.2364, loss: 0.4689
2022-10-03 23:34:44,335 - mmdet - INFO - Iter [6500/12000]	lr: 1.500e-02, eta: 0:07:31, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2435, loss_bbox: 0.2395, loss: 0.4831
2022-10-03 23:34:48,486 - mmdet - INFO - Iter [6550/12000]	lr: 1.500e-02, eta: 0:07:27, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2383, loss_bbox: 0.2421, loss: 0.4804
2022-10-03 23:34:52,504 - mmdet - INFO - Iter [6600/12000]	lr: 1.500e-02, eta: 0:07:23, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.2283, loss_bbox: 0.2329, loss: 0.4611
2022-10-03 23:34:56,575 - mmdet - INFO - Iter [6650/12000]	lr: 1.500e-02, eta: 0:07:18, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2189, loss_bbox: 0.2293, loss: 0.4481
2022-10-03 23:35:00,702 - mmdet - INFO - Iter [6700/12000]	lr: 1.500e-02, eta: 0:07:14, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2155, loss_bbox: 0.2316, loss: 0.4471
2022-10-03 23:35:04,665 - mmdet - INFO - Iter [6750/12000]	lr: 1.500e-02, eta: 0:07:10, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.2328, loss_bbox: 0.2393, loss: 0.4722
2022-10-03 23:35:08,662 - mmdet - INFO - Iter [6800/12000]	lr: 1.500e-02, eta: 0:07:06, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.2150, loss_bbox: 0.2267, loss: 0.4417
2022-10-03 23:35:12,647 - mmdet - INFO - Iter [6850/12000]	lr: 1.500e-02, eta: 0:07:02, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2233, loss_bbox: 0.2365, loss: 0.4599
2022-10-03 23:35:16,806 - mmdet - INFO - Iter [6900/12000]	lr: 1.500e-02, eta: 0:06:58, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2348, loss_bbox: 0.2479, loss: 0.4827
2022-10-03 23:35:20,900 - mmdet - INFO - Iter [6950/12000]	lr: 1.500e-02, eta: 0:06:54, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2296, loss_bbox: 0.2341, loss: 0.4637
2022-10-03 23:35:24,930 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:35:24,930 - mmdet - INFO - Iter [7000/12000]	lr: 1.500e-02, eta: 0:06:50, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2317, loss_bbox: 0.2416, loss: 0.4734
2022-10-03 23:35:28,924 - mmdet - INFO - Iter [7050/12000]	lr: 1.500e-02, eta: 0:06:45, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2193, loss_bbox: 0.2366, loss: 0.4560
2022-10-03 23:35:33,144 - mmdet - INFO - Iter [7100/12000]	lr: 1.500e-02, eta: 0:06:41, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.2232, loss_bbox: 0.2417, loss: 0.4648
2022-10-03 23:35:37,317 - mmdet - INFO - Iter [7150/12000]	lr: 1.500e-02, eta: 0:06:37, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2292, loss_bbox: 0.2381, loss: 0.4673
2022-10-03 23:35:41,335 - mmdet - INFO - Iter [7200/12000]	lr: 1.500e-02, eta: 0:06:33, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2321, loss_bbox: 0.2379, loss: 0.4700
2022-10-03 23:35:45,276 - mmdet - INFO - Iter [7250/12000]	lr: 1.500e-02, eta: 0:06:29, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.2142, loss_bbox: 0.2347, loss: 0.4490
2022-10-03 23:35:49,461 - mmdet - INFO - Iter [7300/12000]	lr: 1.500e-02, eta: 0:06:25, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.2111, loss_bbox: 0.2279, loss: 0.4390
2022-10-03 23:35:53,549 - mmdet - INFO - Iter [7350/12000]	lr: 1.500e-02, eta: 0:06:21, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2049, loss_bbox: 0.2236, loss: 0.4284
2022-10-03 23:35:57,655 - mmdet - INFO - Iter [7400/12000]	lr: 1.500e-02, eta: 0:06:17, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2121, loss_bbox: 0.2243, loss: 0.4365
2022-10-03 23:36:01,893 - mmdet - INFO - Iter [7450/12000]	lr: 1.500e-02, eta: 0:06:13, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.2258, loss_bbox: 0.2258, loss: 0.4517
2022-10-03 23:36:06,160 - mmdet - INFO - Iter [7500/12000]	lr: 1.500e-02, eta: 0:06:09, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.2130, loss_bbox: 0.2297, loss: 0.4427
2022-10-03 23:36:10,458 - mmdet - INFO - Iter [7550/12000]	lr: 1.500e-02, eta: 0:06:05, time: 0.086, data_time: 0.006, memory: 3224, loss_cls: 0.2112, loss_bbox: 0.2282, loss: 0.4394
2022-10-03 23:36:14,596 - mmdet - INFO - Iter [7600/12000]	lr: 1.500e-02, eta: 0:06:01, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.2162, loss_bbox: 0.2296, loss: 0.4459
2022-10-03 23:36:18,558 - mmdet - INFO - Iter [7650/12000]	lr: 1.500e-02, eta: 0:05:56, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.2151, loss_bbox: 0.2276, loss: 0.4427
2022-10-03 23:36:22,654 - mmdet - INFO - Iter [7700/12000]	lr: 1.500e-02, eta: 0:05:52, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2154, loss_bbox: 0.2197, loss: 0.4351
2022-10-03 23:36:26,888 - mmdet - INFO - Iter [7750/12000]	lr: 1.500e-02, eta: 0:05:48, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.2141, loss_bbox: 0.2336, loss: 0.4477
2022-10-03 23:36:31,091 - mmdet - INFO - Iter [7800/12000]	lr: 1.500e-02, eta: 0:05:44, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.2094, loss_bbox: 0.2219, loss: 0.4313
2022-10-03 23:36:35,109 - mmdet - INFO - Iter [7850/12000]	lr: 1.500e-02, eta: 0:05:40, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2152, loss_bbox: 0.2270, loss: 0.4422
2022-10-03 23:36:39,058 - mmdet - INFO - Iter [7900/12000]	lr: 1.500e-02, eta: 0:05:36, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.2235, loss_bbox: 0.2284, loss: 0.4519
2022-10-03 23:36:43,089 - mmdet - INFO - Iter [7950/12000]	lr: 1.500e-02, eta: 0:05:32, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2086, loss_bbox: 0.2243, loss: 0.4329
2022-10-03 23:36:47,082 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:36:47,082 - mmdet - INFO - Iter [8000/12000]	lr: 1.500e-02, eta: 0:05:28, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2060, loss_bbox: 0.2271, loss: 0.4331
2022-10-03 23:36:51,090 - mmdet - INFO - Iter [8050/12000]	lr: 1.500e-02, eta: 0:05:23, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2273, loss_bbox: 0.2295, loss: 0.4568
2022-10-03 23:36:55,155 - mmdet - INFO - Iter [8100/12000]	lr: 1.500e-02, eta: 0:05:19, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2213, loss_bbox: 0.2310, loss: 0.4522
2022-10-03 23:36:59,214 - mmdet - INFO - Iter [8150/12000]	lr: 1.500e-02, eta: 0:05:15, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2237, loss_bbox: 0.2350, loss: 0.4587
2022-10-03 23:37:03,175 - mmdet - INFO - Iter [8200/12000]	lr: 1.500e-02, eta: 0:05:11, time: 0.079, data_time: 0.005, memory: 3224, loss_cls: 0.2133, loss_bbox: 0.2278, loss: 0.4411
2022-10-03 23:37:07,150 - mmdet - INFO - Iter [8250/12000]	lr: 1.500e-02, eta: 0:05:07, time: 0.079, data_time: 0.005, memory: 3224, loss_cls: 0.2180, loss_bbox: 0.2319, loss: 0.4499
2022-10-03 23:37:11,356 - mmdet - INFO - Iter [8300/12000]	lr: 1.500e-02, eta: 0:05:03, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.2139, loss_bbox: 0.2265, loss: 0.4404
2022-10-03 23:37:15,482 - mmdet - INFO - Iter [8350/12000]	lr: 1.500e-02, eta: 0:04:59, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.1983, loss_bbox: 0.2135, loss: 0.4118
2022-10-03 23:37:19,702 - mmdet - INFO - Iter [8400/12000]	lr: 1.500e-02, eta: 0:04:55, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.1954, loss_bbox: 0.2159, loss: 0.4113
2022-10-03 23:37:23,798 - mmdet - INFO - Iter [8450/12000]	lr: 1.500e-02, eta: 0:04:51, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2016, loss_bbox: 0.2227, loss: 0.4243
2022-10-03 23:37:27,846 - mmdet - INFO - Iter [8500/12000]	lr: 1.500e-02, eta: 0:04:46, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.2044, loss_bbox: 0.2293, loss: 0.4337
2022-10-03 23:37:31,839 - mmdet - INFO - Iter [8550/12000]	lr: 1.500e-02, eta: 0:04:42, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1912, loss_bbox: 0.2162, loss: 0.4074
2022-10-03 23:37:35,786 - mmdet - INFO - Iter [8600/12000]	lr: 1.500e-02, eta: 0:04:38, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.1917, loss_bbox: 0.2146, loss: 0.4063
2022-10-03 23:37:39,875 - mmdet - INFO - Iter [8650/12000]	lr: 1.500e-02, eta: 0:04:34, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.1972, loss_bbox: 0.2203, loss: 0.4175
2022-10-03 23:37:43,892 - mmdet - INFO - Iter [8700/12000]	lr: 1.500e-02, eta: 0:04:30, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2027, loss_bbox: 0.2158, loss: 0.4185
2022-10-03 23:37:47,911 - mmdet - INFO - Iter [8750/12000]	lr: 1.500e-02, eta: 0:04:26, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2064, loss_bbox: 0.2343, loss: 0.4407
2022-10-03 23:37:52,017 - mmdet - INFO - Iter [8800/12000]	lr: 1.500e-02, eta: 0:04:22, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.1968, loss_bbox: 0.2174, loss: 0.4142
2022-10-03 23:37:56,011 - mmdet - INFO - Iter [8850/12000]	lr: 1.500e-02, eta: 0:04:18, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1885, loss_bbox: 0.2162, loss: 0.4047
2022-10-03 23:38:00,134 - mmdet - INFO - Iter [8900/12000]	lr: 1.500e-02, eta: 0:04:13, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.2002, loss_bbox: 0.2231, loss: 0.4233
2022-10-03 23:38:04,124 - mmdet - INFO - Iter [8950/12000]	lr: 1.500e-02, eta: 0:04:09, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.2095, loss_bbox: 0.2255, loss: 0.4350
2022-10-03 23:38:08,120 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:38:08,120 - mmdet - INFO - Iter [9000/12000]	lr: 1.500e-02, eta: 0:04:05, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.2179, loss_bbox: 0.2354, loss: 0.4533
2022-10-03 23:38:12,120 - mmdet - INFO - Iter [9050/12000]	lr: 1.500e-03, eta: 0:04:01, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1897, loss_bbox: 0.2153, loss: 0.4050
2022-10-03 23:38:16,101 - mmdet - INFO - Iter [9100/12000]	lr: 1.500e-03, eta: 0:03:57, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1712, loss_bbox: 0.2062, loss: 0.3774
2022-10-03 23:38:20,219 - mmdet - INFO - Iter [9150/12000]	lr: 1.500e-03, eta: 0:03:53, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.1705, loss_bbox: 0.2064, loss: 0.3768
2022-10-03 23:38:24,213 - mmdet - INFO - Iter [9200/12000]	lr: 1.500e-03, eta: 0:03:49, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1665, loss_bbox: 0.1998, loss: 0.3663
2022-10-03 23:38:28,257 - mmdet - INFO - Iter [9250/12000]	lr: 1.500e-03, eta: 0:03:45, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1659, loss_bbox: 0.2035, loss: 0.3695
2022-10-03 23:38:32,538 - mmdet - INFO - Iter [9300/12000]	lr: 1.500e-03, eta: 0:03:41, time: 0.086, data_time: 0.006, memory: 3224, loss_cls: 0.1672, loss_bbox: 0.1965, loss: 0.3637
2022-10-03 23:38:36,700 - mmdet - INFO - Iter [9350/12000]	lr: 1.500e-03, eta: 0:03:37, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.1513, loss_bbox: 0.1907, loss: 0.3420
2022-10-03 23:38:40,755 - mmdet - INFO - Iter [9400/12000]	lr: 1.500e-03, eta: 0:03:32, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1528, loss_bbox: 0.1907, loss: 0.3435
2022-10-03 23:38:44,827 - mmdet - INFO - Iter [9450/12000]	lr: 1.500e-03, eta: 0:03:28, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1436, loss_bbox: 0.1825, loss: 0.3260
2022-10-03 23:38:48,828 - mmdet - INFO - Iter [9500/12000]	lr: 1.500e-03, eta: 0:03:24, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1446, loss_bbox: 0.1885, loss: 0.3330
2022-10-03 23:38:53,071 - mmdet - INFO - Iter [9550/12000]	lr: 1.500e-03, eta: 0:03:20, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.1454, loss_bbox: 0.1821, loss: 0.3275
2022-10-03 23:38:57,363 - mmdet - INFO - Iter [9600/12000]	lr: 1.500e-03, eta: 0:03:16, time: 0.086, data_time: 0.006, memory: 3224, loss_cls: 0.1452, loss_bbox: 0.1871, loss: 0.3324
2022-10-03 23:39:01,565 - mmdet - INFO - Iter [9650/12000]	lr: 1.500e-03, eta: 0:03:12, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.1359, loss_bbox: 0.1872, loss: 0.3231
2022-10-03 23:39:05,566 - mmdet - INFO - Iter [9700/12000]	lr: 1.500e-03, eta: 0:03:08, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1465, loss_bbox: 0.1884, loss: 0.3349
2022-10-03 23:39:09,749 - mmdet - INFO - Iter [9750/12000]	lr: 1.500e-03, eta: 0:03:04, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.1463, loss_bbox: 0.1783, loss: 0.3245
2022-10-03 23:39:13,983 - mmdet - INFO - Iter [9800/12000]	lr: 1.500e-03, eta: 0:03:00, time: 0.085, data_time: 0.006, memory: 3224, loss_cls: 0.1454, loss_bbox: 0.1846, loss: 0.3300
2022-10-03 23:39:18,054 - mmdet - INFO - Iter [9850/12000]	lr: 1.500e-03, eta: 0:02:56, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1478, loss_bbox: 0.1887, loss: 0.3366
2022-10-03 23:39:22,168 - mmdet - INFO - Iter [9900/12000]	lr: 1.500e-03, eta: 0:02:52, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.1466, loss_bbox: 0.1900, loss: 0.3366
2022-10-03 23:39:26,300 - mmdet - INFO - Iter [9950/12000]	lr: 1.500e-03, eta: 0:02:47, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.1438, loss_bbox: 0.1887, loss: 0.3325
2022-10-03 23:39:30,376 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:39:30,377 - mmdet - INFO - Iter [10000/12000]	lr: 1.500e-03, eta: 0:02:43, time: 0.081, data_time: 0.005, memory: 3224, loss_cls: 0.1425, loss_bbox: 0.1841, loss: 0.3266
2022-10-03 23:39:34,388 - mmdet - INFO - Iter [10050/12000]	lr: 1.500e-03, eta: 0:02:39, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1405, loss_bbox: 0.1827, loss: 0.3233
2022-10-03 23:39:38,375 - mmdet - INFO - Iter [10100/12000]	lr: 1.500e-03, eta: 0:02:35, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1465, loss_bbox: 0.1857, loss: 0.3322
2022-10-03 23:39:42,580 - mmdet - INFO - Iter [10150/12000]	lr: 1.500e-03, eta: 0:02:31, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.1381, loss_bbox: 0.1795, loss: 0.3176
2022-10-03 23:39:46,711 - mmdet - INFO - Iter [10200/12000]	lr: 1.500e-03, eta: 0:02:27, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.1452, loss_bbox: 0.1838, loss: 0.3290
2022-10-03 23:39:50,718 - mmdet - INFO - Iter [10250/12000]	lr: 1.500e-03, eta: 0:02:23, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1435, loss_bbox: 0.1849, loss: 0.3284
2022-10-03 23:39:54,727 - mmdet - INFO - Iter [10300/12000]	lr: 1.500e-03, eta: 0:02:19, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1427, loss_bbox: 0.1860, loss: 0.3287
2022-10-03 23:39:58,749 - mmdet - INFO - Iter [10350/12000]	lr: 1.500e-03, eta: 0:02:15, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1485, loss_bbox: 0.1881, loss: 0.3366
2022-10-03 23:40:02,757 - mmdet - INFO - Iter [10400/12000]	lr: 1.500e-03, eta: 0:02:11, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1377, loss_bbox: 0.1817, loss: 0.3194
2022-10-03 23:40:06,778 - mmdet - INFO - Iter [10450/12000]	lr: 1.500e-03, eta: 0:02:06, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1335, loss_bbox: 0.1789, loss: 0.3124
2022-10-03 23:40:10,973 - mmdet - INFO - Iter [10500/12000]	lr: 1.500e-03, eta: 0:02:02, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.1336, loss_bbox: 0.1866, loss: 0.3202
2022-10-03 23:40:15,026 - mmdet - INFO - Iter [10550/12000]	lr: 1.500e-03, eta: 0:01:58, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1347, loss_bbox: 0.1769, loss: 0.3116
2022-10-03 23:40:19,063 - mmdet - INFO - Iter [10600/12000]	lr: 1.500e-03, eta: 0:01:54, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1328, loss_bbox: 0.1807, loss: 0.3135
2022-10-03 23:40:23,236 - mmdet - INFO - Iter [10650/12000]	lr: 1.500e-03, eta: 0:01:50, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.1388, loss_bbox: 0.1812, loss: 0.3200
2022-10-03 23:40:27,364 - mmdet - INFO - Iter [10700/12000]	lr: 1.500e-03, eta: 0:01:46, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.1409, loss_bbox: 0.1863, loss: 0.3272
2022-10-03 23:40:31,421 - mmdet - INFO - Iter [10750/12000]	lr: 1.500e-03, eta: 0:01:42, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1357, loss_bbox: 0.1779, loss: 0.3136
2022-10-03 23:40:35,447 - mmdet - INFO - Iter [10800/12000]	lr: 1.500e-03, eta: 0:01:38, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.1373, loss_bbox: 0.1832, loss: 0.3205
2022-10-03 23:40:39,435 - mmdet - INFO - Iter [10850/12000]	lr: 1.500e-03, eta: 0:01:34, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1369, loss_bbox: 0.1763, loss: 0.3132
2022-10-03 23:40:43,619 - mmdet - INFO - Iter [10900/12000]	lr: 1.500e-03, eta: 0:01:30, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.1321, loss_bbox: 0.1768, loss: 0.3089
2022-10-03 23:40:47,784 - mmdet - INFO - Iter [10950/12000]	lr: 1.500e-03, eta: 0:01:25, time: 0.083, data_time: 0.006, memory: 3224, loss_cls: 0.1333, loss_bbox: 0.1819, loss: 0.3152
2022-10-03 23:40:51,741 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:40:51,742 - mmdet - INFO - Iter [11000/12000]	lr: 1.500e-03, eta: 0:01:21, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.1382, loss_bbox: 0.1829, loss: 0.3211
2022-10-03 23:40:55,915 - mmdet - INFO - Iter [11050/12000]	lr: 1.500e-04, eta: 0:01:17, time: 0.084, data_time: 0.006, memory: 3224, loss_cls: 0.1316, loss_bbox: 0.1733, loss: 0.3049
2022-10-03 23:40:59,957 - mmdet - INFO - Iter [11100/12000]	lr: 1.500e-04, eta: 0:01:13, time: 0.081, data_time: 0.005, memory: 3224, loss_cls: 0.1339, loss_bbox: 0.1824, loss: 0.3163
2022-10-03 23:41:03,948 - mmdet - INFO - Iter [11150/12000]	lr: 1.500e-04, eta: 0:01:09, time: 0.080, data_time: 0.005, memory: 3224, loss_cls: 0.1324, loss_bbox: 0.1758, loss: 0.3082
2022-10-03 23:41:07,959 - mmdet - INFO - Iter [11200/12000]	lr: 1.500e-04, eta: 0:01:05, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1334, loss_bbox: 0.1796, loss: 0.3130
2022-10-03 23:41:11,955 - mmdet - INFO - Iter [11250/12000]	lr: 1.500e-04, eta: 0:01:01, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1316, loss_bbox: 0.1796, loss: 0.3112
2022-10-03 23:41:15,974 - mmdet - INFO - Iter [11300/12000]	lr: 1.500e-04, eta: 0:00:57, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1321, loss_bbox: 0.1769, loss: 0.3091
2022-10-03 23:41:20,077 - mmdet - INFO - Iter [11350/12000]	lr: 1.500e-04, eta: 0:00:53, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.1374, loss_bbox: 0.1751, loss: 0.3125
2022-10-03 23:41:24,078 - mmdet - INFO - Iter [11400/12000]	lr: 1.500e-04, eta: 0:00:49, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1315, loss_bbox: 0.1763, loss: 0.3078
2022-10-03 23:41:28,124 - mmdet - INFO - Iter [11450/12000]	lr: 1.500e-04, eta: 0:00:45, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1331, loss_bbox: 0.1792, loss: 0.3123
2022-10-03 23:41:32,144 - mmdet - INFO - Iter [11500/12000]	lr: 1.500e-04, eta: 0:00:40, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1207, loss_bbox: 0.1679, loss: 0.2886
2022-10-03 23:41:36,142 - mmdet - INFO - Iter [11550/12000]	lr: 1.500e-04, eta: 0:00:36, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1375, loss_bbox: 0.1823, loss: 0.3198
2022-10-03 23:41:40,174 - mmdet - INFO - Iter [11600/12000]	lr: 1.500e-04, eta: 0:00:32, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1275, loss_bbox: 0.1758, loss: 0.3033
2022-10-03 23:41:44,256 - mmdet - INFO - Iter [11650/12000]	lr: 1.500e-04, eta: 0:00:28, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.1376, loss_bbox: 0.1794, loss: 0.3171
2022-10-03 23:41:48,314 - mmdet - INFO - Iter [11700/12000]	lr: 1.500e-04, eta: 0:00:24, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1246, loss_bbox: 0.1731, loss: 0.2976
2022-10-03 23:41:52,368 - mmdet - INFO - Iter [11750/12000]	lr: 1.500e-04, eta: 0:00:20, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1330, loss_bbox: 0.1800, loss: 0.3130
2022-10-03 23:41:56,339 - mmdet - INFO - Iter [11800/12000]	lr: 1.500e-04, eta: 0:00:16, time: 0.079, data_time: 0.006, memory: 3224, loss_cls: 0.1323, loss_bbox: 0.1737, loss: 0.3060
2022-10-03 23:42:00,341 - mmdet - INFO - Iter [11850/12000]	lr: 1.500e-04, eta: 0:00:12, time: 0.080, data_time: 0.006, memory: 3224, loss_cls: 0.1308, loss_bbox: 0.1803, loss: 0.3111
2022-10-03 23:42:04,373 - mmdet - INFO - Iter [11900/12000]	lr: 1.500e-04, eta: 0:00:08, time: 0.081, data_time: 0.006, memory: 3224, loss_cls: 0.1270, loss_bbox: 0.1711, loss: 0.2981
2022-10-03 23:42:08,494 - mmdet - INFO - Iter [11950/12000]	lr: 1.500e-04, eta: 0:00:04, time: 0.082, data_time: 0.006, memory: 3224, loss_cls: 0.1334, loss_bbox: 0.1811, loss: 0.3145
2022-10-03 23:42:12,730 - mmdet - INFO - Saving checkpoint at 12000 iterations
2022-10-03 23:42:13,292 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:42:13,293 - mmdet - INFO - Iter [12000/12000]	lr: 1.500e-04, eta: 0:00:00, time: 0.096, data_time: 0.006, memory: 3224, loss_cls: 0.1328, loss_bbox: 0.1773, loss: 0.3101
2022-10-03 23:42:35,293 - mmdet - INFO - 
+-------------+------+-------+--------+-------+
| class       | gts  | dets  | recall | ap    |
+-------------+------+-------+--------+-------+
| aeroplane   | 285  | 4350  | 0.982  | 0.858 |
| bicycle     | 337  | 7182  | 0.982  | 0.852 |
| bird        | 459  | 6594  | 0.976  | 0.843 |
| boat        | 263  | 10306 | 0.977  | 0.748 |
| bottle      | 469  | 15005 | 0.949  | 0.708 |
| bus         | 213  | 5621  | 0.991  | 0.854 |
| car         | 1201 | 18728 | 0.989  | 0.884 |
| cat         | 358  | 3995  | 0.989  | 0.890 |
| chair       | 756  | 27501 | 0.963  | 0.659 |
| cow         | 244  | 4390  | 0.996  | 0.835 |
| diningtable | 206  | 13269 | 0.956  | 0.696 |
| dog         | 489  | 5970  | 0.990  | 0.867 |
| horse       | 348  | 5822  | 0.997  | 0.858 |
| motorbike   | 325  | 7104  | 0.985  | 0.842 |
| person      | 4528 | 52884 | 0.988  | 0.854 |
| pottedplant | 480  | 13789 | 0.944  | 0.582 |
| sheep       | 242  | 4454  | 0.988  | 0.819 |
| sofa        | 239  | 8932  | 0.983  | 0.758 |
| train       | 282  | 5723  | 0.986  | 0.855 |
| tvmonitor   | 308  | 7857  | 0.971  | 0.820 |
+-------------+------+-------+--------+-------+
| mAP         |      |       |        | 0.804 |
+-------------+------+-------+--------+-------+
2022-10-03 23:42:35,884 - mmdet - INFO - Now best checkpoint is saved as best_mAP_iter_12000.pth.
2022-10-03 23:42:35,885 - mmdet - INFO - Best mAP is 0.8042 at 12000 iter.
2022-10-03 23:42:35,885 - mmdet - INFO - Exp name: retinanet_mstrain_12k_voc0712.py
2022-10-03 23:42:35,885 - mmdet - INFO - Iter(val) [619]	mAP: 0.8042, AP50: 0.8040