getting mAP 0.00, when changing retinanet model to faster_rcnn in swin transfomer backbone config file.

[DishantMewada]

Hi there,
I want to use faster_rcnn with swin transformer as a backbone. There is a config available for retinanet with swin backbone.
I changed the model to faster_rcnn and my full config file is as follows (dataset has 5 classes).
I wonder what I am doing wrong, or do I need to change the out_indices. And can you please tell me how to figure out indices when changing the backbone.

Config file is attached.

And my train output -

/home/puser/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmdet/utils/setup_env.py:39: UserWarning: Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
f'Setting OMP_NUM_THREADS environment variable for each process '
/home/puser/miniconda3/envs/mmdetection/lib/python3.7/site-packages/mmdet/utils/setup_env.py:49: UserWarning: Setting MKL_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
f'Setting MKL_NUM_THREADS environment variable for each process '
2023-02-20 11:08:28,499 - mmdet - INFO - Environment info:

sys.platform: linux
Python: 3.7.13 (default, Mar 29 2022, 02:18:16) [GCC 7.5.0]
CUDA available: True
GPU 0: NVIDIA GeForce RTX 3060
CUDA_HOME: /usr/local/cuda-11.4
NVCC: Cuda compilation tools, release 11.4, V11.4.48
GCC: gcc (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
PyTorch: 1.12.0
PyTorch compiling details: PyTorch built with:

• GCC 9.3
• C++ Version: 201402
• Intel(R) oneAPI Math Kernel Library Version 2021.4-Product Build 20210904 for Intel(R) 64 architecture applications
• Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815)
• OpenMP 201511 (a.k.a. OpenMP 4.5)
• LAPACK is enabled (usually provided by MKL)
• NNPACK is enabled
• CPU capability usage: AVX2
• CUDA Runtime 11.3
• NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_61,code=sm_61;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86;-gencode;arch=compute_37,code=compute_37
• CuDNN 8.3.2 (built against CUDA 11.5)
• Magma 2.5.2
• Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/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-unused-parameter -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 -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF,

TorchVision: 0.13.0
OpenCV: 4.6.0
MMCV: 1.5.3
MMCV Compiler: GCC 9.4
MMCV CUDA Compiler: 11.4
MMDetection: 2.25.0+178b9fd

2023-02-20 11:08:29,299 - mmdet - INFO - Distributed training: False
2023-02-20 11:08:30,153 - mmdet - INFO - Config:
model = dict(
type='FasterRCNN',
backbone=dict(
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(1, 2, 3),
with_cp=False,
convert_weights=True,
init_cfg=dict(
type='Pretrained',
checkpoint=
'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
)),
neck=dict(
type='FPN',
in_channels=[192, 384, 768],
out_channels=256,
num_outs=5,
start_level=0),
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='Shared2FCBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=5,
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))),
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=False,
ignore_iof_thr=-1),
sampler=dict(
type='RandomSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
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)))
dataset_type = 'COCODataset'
data_root = 'data/coco/'
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, 800), 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='CocoDataset',
ann_file='dataset/contamination_v2/train.json',
img_prefix='dataset/contamination_v2/train/',
pipeline=[
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), 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'])
],
classes=('Black Plastic', 'White Plastic', 'Clear Plastic',
'Polystyrene', 'Blue Bag')),
val=dict(
type='CocoDataset',
ann_file='dataset/contamination_v2/val.json',
img_prefix='dataset/contamination_v2/val/',
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'])
])
],
classes=('Black Plastic', 'White Plastic', 'Clear Plastic',
'Polystyrene', 'Blue Bag')),
test=dict(
type='CocoDataset',
ann_file='dataset/contamination_v2/test.json',
img_prefix='dataset/contamination_v2/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'])
])
],
classes=('Black Plastic', 'White Plastic', 'Clear Plastic',
'Polystyrene', 'Blue Bag')))
evaluation = dict(interval=1, metric='bbox')
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8, 11])
runner = dict(type='EpochBasedRunner', max_epochs=12)
checkpoint_config = dict(interval=1)
log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
custom_hooks = [dict(type='NumClassCheckHook')]
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
resume_from = None
workflow = [('train', 1), ('val', 1)]
opencv_num_threads = 0
mp_start_method = 'fork'
auto_scale_lr = dict(enable=False, base_batch_size=16)
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'
classes = ('Black Plastic', 'White Plastic', 'Clear Plastic', 'Polystyrene',
'Blue Bag')
work_dir = './work_dirs/SOTA_fasterrcnn_swin_1x_dropout_0_v2'
auto_resume = False
gpu_ids = [0]

2023-02-20 11:08:30,154 - mmdet - INFO - Set random seed to 1694722566, deterministic: False
2023-02-20 11:08:30,483 - mmdet - INFO - load checkpoint from http path: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
2023-02-20 11:08:30,596 - mmdet - INFO - initialize FPN with init_cfg {'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}
2023-02-20 11:08:30,608 - mmdet - INFO - initialize RPNHead with init_cfg {'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}
2023-02-20 11:08:30,612 - mmdet - INFO - initialize Shared2FCBBoxHead 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'}]}]
loading annotations into memory...
Done (t=0.10s)
creating index...
index created!
loading annotations into memory...
Done (t=0.03s)
creating index...
index created!
2023-02-20 11:08:31,826 - mmdet - INFO - Automatic scaling of learning rate (LR) has been disabled.
loading annotations into memory...
Done (t=0.11s)
creating index...
index created!
2023-02-20 11:08:31,942 - mmdet - INFO - load checkpoint from local path: checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
2023-02-20 11:08:32,029 - mmdet - WARNING - The model and loaded state dict do not match exactly

size mismatch for neck.lateral_convs.0.conv.weight: copying a param with shape torch.Size([256, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 192, 1, 1]).
size mismatch for neck.lateral_convs.1.conv.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 384, 1, 1]).
size mismatch for neck.lateral_convs.2.conv.weight: copying a param with shape torch.Size([256, 1024, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 768, 1, 1]).
size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([6, 1024]).
size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([6]).
size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([20, 1024]).
size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([20]).
unexpected key in source state_dict: backbone.conv1.weight, backbone.bn1.weight, backbone.bn1.bias, backbone.bn1.running_mean, backbone.bn1.running_var, backbone.bn1.num_batches_tracked, backbone.layer1.0.conv1.weight, backbone.layer1.0.bn1.weight, backbone.layer1.0.bn1.bias, backbone.layer1.0.bn1.running_mean, backbone.layer1.0.bn1.running_var, backbone.layer1.0.bn1.num_batches_tracked, backbone.layer1.0.conv2.weight, backbone.layer1.0.bn2.weight, backbone.layer1.0.bn2.bias, backbone.layer1.0.bn2.running_mean, backbone.layer1.0.bn2.running_var, backbone.layer1.0.bn2.num_batches_tracked, backbone.layer1.0.conv3.weight, backbone.layer1.0.bn3.weight, backbone.layer1.0.bn3.bias, backbone.layer1.0.bn3.running_mean, backbone.layer1.0.bn3.running_var, backbone.layer1.0.bn3.num_batches_tracked, backbone.layer1.0.downsample.0.weight, backbone.layer1.0.downsample.1.weight, backbone.layer1.0.downsample.1.bias, backbone.layer1.0.downsample.1.running_mean, backbone.layer1.0.downsample.1.running_var, backbone.layer1.0.downsample.1.num_batches_tracked, backbone.layer1.1.conv1.weight, backbone.layer1.1.bn1.weight, backbone.layer1.1.bn1.bias, backbone.layer1.1.bn1.running_mean, backbone.layer1.1.bn1.running_var, backbone.layer1.1.bn1.num_batches_tracked, backbone.layer1.1.conv2.weight, backbone.layer1.1.bn2.weight, backbone.layer1.1.bn2.bias, backbone.layer1.1.bn2.running_mean, backbone.layer1.1.bn2.running_var, backbone.layer1.1.bn2.num_batches_tracked, backbone.layer1.1.conv3.weight, backbone.layer1.1.bn3.weight, backbone.layer1.1.bn3.bias, backbone.layer1.1.bn3.running_mean, backbone.layer1.1.bn3.running_var, backbone.layer1.1.bn3.num_batches_tracked, backbone.layer1.2.conv1.weight, backbone.layer1.2.bn1.weight, backbone.layer1.2.bn1.bias, backbone.layer1.2.bn1.running_mean, backbone.layer1.2.bn1.running_var, backbone.layer1.2.bn1.num_batches_tracked, backbone.layer1.2.conv2.weight, backbone.layer1.2.bn2.weight, backbone.layer1.2.bn2.bias, backbone.layer1.2.bn2.running_mean, backbone.layer1.2.bn2.running_var, backbone.layer1.2.bn2.num_batches_tracked, backbone.layer1.2.conv3.weight, backbone.layer1.2.bn3.weight, backbone.layer1.2.bn3.bias, backbone.layer1.2.bn3.running_mean, backbone.layer1.2.bn3.running_var, backbone.layer1.2.bn3.num_batches_tracked, backbone.layer2.0.conv1.weight, backbone.layer2.0.bn1.weight, backbone.layer2.0.bn1.bias, backbone.layer2.0.bn1.running_mean, backbone.layer2.0.bn1.running_var, backbone.layer2.0.bn1.num_batches_tracked, backbone.layer2.0.conv2.weight, backbone.layer2.0.bn2.weight, backbone.layer2.0.bn2.bias, backbone.layer2.0.bn2.running_mean, backbone.layer2.0.bn2.running_var, backbone.layer2.0.bn2.num_batches_tracked, backbone.layer2.0.conv3.weight, backbone.layer2.0.bn3.weight, backbone.layer2.0.bn3.bias, backbone.layer2.0.bn3.running_mean, backbone.layer2.0.bn3.running_var, backbone.layer2.0.bn3.num_batches_tracked, backbone.layer2.0.downsample.0.weight, backbone.layer2.0.downsample.1.weight, backbone.layer2.0.downsample.1.bias, backbone.layer2.0.downsample.1.running_mean, backbone.layer2.0.downsample.1.running_var, backbone.layer2.0.downsample.1.num_batches_tracked, backbone.layer2.1.conv1.weight, backbone.layer2.1.bn1.weight, backbone.layer2.1.bn1.bias, backbone.layer2.1.bn1.running_mean, backbone.layer2.1.bn1.running_var, backbone.layer2.1.bn1.num_batches_tracked, backbone.layer2.1.conv2.weight, backbone.layer2.1.bn2.weight, backbone.layer2.1.bn2.bias, backbone.layer2.1.bn2.running_mean, backbone.layer2.1.bn2.running_var, backbone.layer2.1.bn2.num_batches_tracked, backbone.layer2.1.conv3.weight, backbone.layer2.1.bn3.weight, backbone.layer2.1.bn3.bias, backbone.layer2.1.bn3.running_mean, backbone.layer2.1.bn3.running_var, backbone.layer2.1.bn3.num_batches_tracked, backbone.layer2.2.conv1.weight, backbone.layer2.2.bn1.weight, backbone.layer2.2.bn1.bias, backbone.layer2.2.bn1.running_mean, backbone.layer2.2.bn1.running_var, backbone.layer2.2.bn1.num_batches_tracked, backbone.layer2.2.conv2.weight, backbone.layer2.2.bn2.weight, backbone.layer2.2.bn2.bias, backbone.layer2.2.bn2.running_mean, backbone.layer2.2.bn2.running_var, backbone.layer2.2.bn2.num_batches_tracked, backbone.layer2.2.conv3.weight, backbone.layer2.2.bn3.weight, backbone.layer2.2.bn3.bias, backbone.layer2.2.bn3.running_mean, backbone.layer2.2.bn3.running_var, backbone.layer2.2.bn3.num_batches_tracked, backbone.layer2.3.conv1.weight, backbone.layer2.3.bn1.weight, backbone.layer2.3.bn1.bias, backbone.layer2.3.bn1.running_mean, backbone.layer2.3.bn1.running_var, backbone.layer2.3.bn1.num_batches_tracked, backbone.layer2.3.conv2.weight, backbone.layer2.3.bn2.weight, backbone.layer2.3.bn2.bias, backbone.layer2.3.bn2.running_mean, backbone.layer2.3.bn2.running_var, backbone.layer2.3.bn2.num_batches_tracked, backbone.layer2.3.conv3.weight, backbone.layer2.3.bn3.weight, backbone.layer2.3.bn3.bias, backbone.layer2.3.bn3.running_mean, 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2023-02-20 11:08:32,037 - mmdet - INFO - Start running, host: puser@Confirm8fkc1m3, work_dir: /home/puser/Desktop/mm/work_dirs/SOTA_fasterrcnn_swin_1x_dropout_0_v2
2023-02-20 11:08:32,037 - mmdet - INFO - Hooks will be executed in the following order:
before_run:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

before_train_epoch:
(VERY_HIGH ) StepLrUpdaterHook
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

before_train_iter:
(VERY_HIGH ) StepLrUpdaterHook
(LOW ) IterTimerHook
(LOW ) EvalHook

after_train_iter:
(ABOVE_NORMAL) OptimizerHook
(NORMAL ) CheckpointHook
(LOW ) IterTimerHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

after_train_epoch:
(NORMAL ) CheckpointHook
(LOW ) EvalHook
(VERY_LOW ) TextLoggerHook

before_val_epoch:
(NORMAL ) NumClassCheckHook
(LOW ) IterTimerHook
(VERY_LOW ) TextLoggerHook

before_val_iter:
(LOW ) IterTimerHook

after_val_iter:
(LOW ) IterTimerHook

after_val_epoch:
(VERY_LOW ) TextLoggerHook

after_run:
(VERY_LOW ) TextLoggerHook

2023-02-20 11:08:32,037 - mmdet - INFO - workflow: [('train', 1), ('val', 1)], max: 12 epochs
2023-02-20 11:08:32,037 - mmdet - INFO - Checkpoints will be saved to /home/puser/Desktop/mm/work_dirs/SOTA_fasterrcnn_swin_1x_dropout_0_v2 by HardDiskBackend.
2023-02-20 11:08:59,328 - mmdet - INFO - Epoch [1][50/999] lr: 9.890e-04, eta: 1:48:12, time: 0.544, data_time: 0.048, memory: 4898, loss_rpn_cls: 0.0832, loss_rpn_bbox: 0.0100, loss_cls: 0.4032, acc: 87.8281, loss_bbox: 0.0289, loss: 0.5254
2023-02-20 11:09:22,788 - mmdet - INFO - Epoch [1][100/999] lr: 1.988e-03, eta: 1:40:21, time: 0.469, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0487, loss_rpn_bbox: 0.0099, loss_cls: 0.1164, acc: 98.0957, loss_bbox: 0.0537, loss: 0.2287
2023-02-20 11:09:46,820 - mmdet - INFO - Epoch [1][150/999] lr: 2.987e-03, eta: 1:38:14, time: 0.481, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0418, loss_rpn_bbox: 0.0087, loss_cls: 0.1196, acc: 97.9062, loss_bbox: 0.0660, loss: 0.2360
2023-02-20 11:10:10,847 - mmdet - INFO - Epoch [1][200/999] lr: 3.986e-03, eta: 1:36:57, time: 0.481, data_time: 0.008, memory: 4898, loss_rpn_cls: 0.0377, loss_rpn_bbox: 0.0082, loss_cls: 0.1031, acc: 98.1309, loss_bbox: 0.0528, loss: 0.2019
2023-02-20 11:10:34,884 - mmdet - INFO - Epoch [1][250/999] lr: 4.985e-03, eta: 1:36:03, time: 0.481, data_time: 0.008, memory: 4898, loss_rpn_cls: 0.0414, loss_rpn_bbox: 0.0109, loss_cls: 0.1185, acc: 97.8105, loss_bbox: 0.0686, loss: 0.2394
2023-02-20 11:10:58,946 - mmdet - INFO - Epoch [1][300/999] lr: 5.984e-03, eta: 1:35:19, time: 0.481, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0472, loss_rpn_bbox: 0.0103, loss_cls: 0.1099, acc: 97.9980, loss_bbox: 0.0587, loss: 0.2262
2023-02-20 11:11:22,996 - mmdet - INFO - Epoch [1][350/999] lr: 6.983e-03, eta: 1:34:41, time: 0.481, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0445, loss_rpn_bbox: 0.0101, loss_cls: 0.1221, acc: 97.7949, loss_bbox: 0.0650, loss: 0.2418
2023-02-20 11:11:47,218 - mmdet - INFO - Epoch [1][400/999] lr: 7.982e-03, eta: 1:34:11, time: 0.484, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0414, loss_rpn_bbox: 0.0099, loss_cls: 0.1093, acc: 98.0176, loss_bbox: 0.0598, loss: 0.2204
2023-02-20 11:12:11,372 - mmdet - INFO - Epoch [1][450/999] lr: 8.981e-03, eta: 1:33:41, time: 0.483, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0331, loss_rpn_bbox: 0.0076, loss_cls: 0.1060, acc: 98.1816, loss_bbox: 0.0557, loss: 0.2025
2023-02-20 11:12:35,364 - mmdet - INFO - Epoch [1][500/999] lr: 9.980e-03, eta: 1:33:08, time: 0.480, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0358, loss_rpn_bbox: 0.0095, loss_cls: 0.1082, acc: 97.9746, loss_bbox: 0.0601, loss: 0.2136
2023-02-20 11:12:59,461 - mmdet - INFO - Epoch [1][550/999] lr: 1.000e-02, eta: 1:32:39, time: 0.482, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0364, loss_rpn_bbox: 0.0082, loss_cls: 0.1177, acc: 98.0449, loss_bbox: 0.0563, loss: 0.2186
2023-02-20 11:13:23,589 - mmdet - INFO - Epoch [1][600/999] lr: 1.000e-02, eta: 1:32:11, time: 0.483, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0391, loss_rpn_bbox: 0.0079, loss_cls: 0.0941, acc: 98.2441, loss_bbox: 0.0510, loss: 0.1921
2023-02-20 11:13:47,746 - mmdet - INFO - Epoch [1][650/999] lr: 1.000e-02, eta: 1:31:45, time: 0.483, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0459, loss_rpn_bbox: 0.0113, loss_cls: 0.1343, acc: 97.5957, loss_bbox: 0.0748, loss: 0.2662
2023-02-20 11:14:11,477 - mmdet - INFO - Epoch [1][700/999] lr: 1.000e-02, eta: 1:31:12, time: 0.475, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0436, loss_rpn_bbox: 0.0100, loss_cls: 0.1269, acc: 97.6895, loss_bbox: 0.0705, loss: 0.2510
2023-02-20 11:14:35,457 - mmdet - INFO - Epoch [1][750/999] lr: 1.000e-02, eta: 1:30:43, time: 0.480, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0396, loss_rpn_bbox: 0.0091, loss_cls: 0.1300, acc: 97.8535, loss_bbox: 0.0648, loss: 0.2435
2023-02-20 11:14:59,463 - mmdet - INFO - Epoch [1][800/999] lr: 1.000e-02, eta: 1:30:16, time: 0.480, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0404, loss_rpn_bbox: 0.0112, loss_cls: 0.1308, acc: 97.5430, loss_bbox: 0.0768, loss: 0.2593
2023-02-20 11:15:23,435 - mmdet - INFO - Epoch [1][850/999] lr: 1.000e-02, eta: 1:29:49, time: 0.479, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0343, loss_rpn_bbox: 0.0076, loss_cls: 0.1035, acc: 98.0918, loss_bbox: 0.0576, loss: 0.2029
2023-02-20 11:15:47,620 - mmdet - INFO - Epoch [1][900/999] lr: 1.000e-02, eta: 1:29:25, time: 0.484, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0355, loss_rpn_bbox: 0.0075, loss_cls: 0.0957, acc: 98.2988, loss_bbox: 0.0478, loss: 0.1864
2023-02-20 11:16:11,622 - mmdet - INFO - Epoch [1][950/999] lr: 1.000e-02, eta: 1:28:58, time: 0.480, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0340, loss_rpn_bbox: 0.0064, loss_cls: 0.1156, acc: 98.0488, loss_bbox: 0.0605, loss: 0.2166
2023-02-20 11:16:35,155 - mmdet - INFO - Saving checkpoint at 1 epochs
[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 523/523, 10.9 task/s, elapsed: 48s, ETA: 0s2023-02-20 11:17:24,068 - mmdet - INFO - Evaluating bbox...
Loading and preparing results...
DONE (t=0.11s)
creating index...
index created!
Running per image evaluation...
Evaluate annotation type bbox
DONE (t=0.92s).
Accumulating evaluation results...
DONE (t=0.22s).
2023-02-20 11:17:25,365 - mmdet - INFO -
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.000
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.000
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.000
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.005
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.005
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.005
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = -1.000
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.001
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.010

2023-02-20 11:17:25,376 - mmdet - INFO - Exp name: SOTA_fasterrcnn_swin_1x_dropout_0_v2.py
2023-02-20 11:17:25,376 - mmdet - INFO - Epoch(val) [1][523] bbox_mAP: 0.0000, bbox_mAP_50: 0.0000, bbox_mAP_75: 0.0000, bbox_mAP_s: -1.0000, bbox_mAP_m: 0.0000, bbox_mAP_l: 0.0000, bbox_mAP_copypaste: 0.000 0.000 0.000 -1.000 0.000 0.000
2023-02-20 11:18:14,226 - mmdet - INFO - Exp name: SOTA_fasterrcnn_swin_1x_dropout_0_v2.py
2023-02-20 11:18:14,226 - mmdet - INFO - Epoch(val) [1][262] loss_rpn_cls: 0.0412, loss_rpn_bbox: 0.0093, loss_cls: 0.1424, acc: 97.6946, loss_bbox: 0.0714, loss: 0.2643
2023-02-20 11:18:40,385 - mmdet - INFO - Epoch [2][50/999] lr: 1.000e-02, eta: 1:24:23, time: 0.521, data_time: 0.049, memory: 4898, loss_rpn_cls: 0.0413, loss_rpn_bbox: 0.0078, loss_cls: 0.1352, acc: 97.7832, loss_bbox: 0.0701, loss: 0.2544
2023-02-20 11:19:04,296 - mmdet - INFO - Epoch [2][100/999] lr: 1.000e-02, eta: 1:24:07, time: 0.478, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0341, loss_rpn_bbox: 0.0076, loss_cls: 0.1161, acc: 97.8613, loss_bbox: 0.0647, loss: 0.2226
2023-02-20 11:19:28,319 - mmdet - INFO - Epoch [2][150/999] lr: 1.000e-02, eta: 1:23:52, time: 0.480, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0335, loss_rpn_bbox: 0.0083, loss_cls: 0.1099, acc: 98.0410, loss_bbox: 0.0617, loss: 0.2134
2023-02-20 11:19:52,421 - mmdet - INFO - Epoch [2][200/999] lr: 1.000e-02, eta: 1:23:37, time: 0.482, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0307, loss_rpn_bbox: 0.0084, loss_cls: 0.1146, acc: 98.0293, loss_bbox: 0.0590, loss: 0.2128
2023-02-20 11:20:16,578 - mmdet - INFO - Epoch [2][250/999] lr: 1.000e-02, eta: 1:23:21, time: 0.483, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0358, loss_rpn_bbox: 0.0092, loss_cls: 0.1361, acc: 97.4609, loss_bbox: 0.0804, loss: 0.2614
2023-02-20 11:20:40,682 - mmdet - INFO - Epoch [2][300/999] lr: 1.000e-02, eta: 1:23:05, time: 0.482, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0348, loss_rpn_bbox: 0.0074, loss_cls: 0.0972, acc: 98.2617, loss_bbox: 0.0549, loss: 0.1943
2023-02-20 11:21:05,017 - mmdet - INFO - Epoch [2][350/999] lr: 1.000e-02, eta: 1:22:49, time: 0.487, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0303, loss_rpn_bbox: 0.0075, loss_cls: 0.1079, acc: 98.1777, loss_bbox: 0.0554, loss: 0.2012
2023-02-20 11:21:29,163 - mmdet - INFO - Epoch [2][400/999] lr: 1.000e-02, eta: 1:22:32, time: 0.483, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0369, loss_rpn_bbox: 0.0081, loss_cls: 0.1183, acc: 98.2090, loss_bbox: 0.0576, loss: 0.2210
2023-02-20 11:21:53,252 - mmdet - INFO - Epoch [2][450/999] lr: 1.000e-02, eta: 1:22:14, time: 0.482, data_time: 0.008, memory: 4898, loss_rpn_cls: 0.0346, loss_rpn_bbox: 0.0084, loss_cls: 0.1305, acc: 97.9531, loss_bbox: 0.0634, loss: 0.2370
2023-02-20 11:22:17,328 - mmdet - INFO - Epoch [2][500/999] lr: 1.000e-02, eta: 1:21:55, time: 0.482, data_time: 0.007, memory: 4898, loss_rpn_cls: 0.0312, loss_rpn_bbox: 0.0062, loss_cls: 0.0898, acc: 98.2910, loss_bbox: 0.0472, loss: 0.1744

Thank you.
config.txt