Could not use YOLOV5 P6 configs

Prerequisite

[X] I have searched the existing and past issues but cannot get the expected help.
[X] I have read the FAQ documentation but cannot get the expected help.
[X] The bug has not been fixed in the latest version.

🐞 Describe the bug

Hi there,

I have been trying to make YOLOV5 P6 custom training pipeline working for a while. Unfortunately, I could not make it working. I used the same setting for P5 models, it worked well. Here are the settings I put:

_base_ = '../yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco.py'
# _base_ = '../yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py'
data_root = '/mnt/ssd/datasets-ml/COTS_GoPro_1080_v3/'
train_batch_size_per_gpu = 2
train_num_workers = 6
num_classes = 1
metainfo = {
    'CLASSES': ('COTS', ),
    'PALETTE': [
        (220, 20, 60),
    ]
}
train_dataloader = dict(
    batch_size=train_batch_size_per_gpu,
    num_workers=train_num_workers,
    dataset=dict(
        data_root=data_root,
        metainfo=metainfo,
        data_prefix=dict(img=''),
        ann_file='mmdet_split_train.json'))
val_dataloader = dict(
    dataset=dict(
        data_root=data_root,
        metainfo=metainfo,
        data_prefix=dict(img=''),
        ann_file='mmdet_split_val.json',))
val_dataloader = dict(
    dataset=dict(
        data_root=data_root,
        metainfo=metainfo,
        data_prefix=dict(img=''),
        ann_file='mmdet_split_val.json'))
val_evaluator = dict(ann_file=data_root + 'mmdet_split_val.json')
test_evaluator = dict(ann_file=data_root + 'mmdet_split_test.json')
model = dict(bbox_head=dict(head_module=dict(num_classes=1)))
default_hooks = dict(logger=dict(interval=1))

With the base file:yolov5_l-v61_syncbn_fast_8xb16-300e_coco.py, the model can be trained. But with the base file: yolov5_l-p6-v62_syncbn_fast_8xb16-300e_coco.py, I got the following error messages:

11/09 19:05:34 - mmengine - WARNING - Failed to search registry with scope "mmyolo" in the "log_processor" registry tree. As a workaround, the current "log_processor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmyolo" is a correct scope, or whether the registry is initialized. 11/09 19:05:34 - mmengine - INFO - System environment: sys.platform: linux Python: 3.8.13 (default, Oct 21 2022, 23:50:54) [GCC 11.2.0] CUDA available: True numpy_random_seed: 1934098022 GPU 0: NVIDIA GeForce RTX 3090 GPU 1: NVIDIA GeForce RTX 2080 Ti CUDA_HOME: /usr NVCC: Cuda compilation tools, release 10.1, V10.1.24 GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 PyTorch: 1.10.1 PyTorch compiling details: PyTorch built with:

GCC 7.3
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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: 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 8.2
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=1.10.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.2 OpenCV: 4.6.0 MMEngine: 0.3.0

Runtime environment: cudnn_benchmark: True mp_cfg: {'mp_start_method': 'fork', 'opencv_num_threads': 0} dist_cfg: {'backend': 'nccl'} seed: None Distributed launcher: none Distributed training: False GPU number: 1

11/09 19:05:35 - mmengine - INFO - Config: default_scope = 'mmyolo' default_hooks = dict( timer=dict(type='IterTimerHook'), logger=dict(type='LoggerHook', interval=50), param_scheduler=dict( type='YOLOv5ParamSchedulerHook', scheduler_type='linear', lr_factor=0.01, max_epochs=200), checkpoint=dict( type='CheckpointHook', interval=5, save_best='auto', max_keep_ckpts=3), sampler_seed=dict(type='DistSamplerSeedHook'), visualization=dict(type='mmdet.DetVisualizationHook')) env_cfg = dict( cudnn_benchmark=True, mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), dist_cfg=dict(backend='nccl')) vis_backends = [dict(type='LocalVisBackend')] visualizer = dict( type='mmdet.DetLocalVisualizer', vis_backends=[dict(type='LocalVisBackend')], name='visualizer') log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True) log_level = 'INFO' load_from = None resume = False file_client_args = dict(backend='disk') data_root = 'data/coco/' dataset_type = 'YOLOv5CocoDataset' metainfo = dict(CLASSES=('COTS', ), PALETTE=[(220, 20, 60)]) num_classes = 1 img_scale = (1280, 1280) deepen_factor = 1.0 widen_factor = 1.0 max_epochs = 200 save_epoch_intervals = 5 train_batch_size_per_gpu = 8 train_num_workers = 8 val_batch_size_per_gpu = 1 val_num_workers = 2 persistent_workers = True batch_shapes_cfg = dict( type='BatchShapePolicy', batch_size=1, img_size=1280, size_divisor=32, extra_pad_ratio=0.5) anchors = [[(19, 27), (44, 40), (38, 94)], [(96, 68), (86, 152), (180, 137)], [(140, 301), (303, 264), (238, 542)], [(436, 615), (739, 380), (925, 792)]] strides = [8, 16, 32, 64] num_det_layers = 4 model = dict( type='YOLODetector', data_preprocessor=dict( type='mmdet.DetDataPreprocessor', mean=[0.0, 0.0, 0.0], std=[255.0, 255.0, 255.0], bgr_to_rgb=True), backbone=dict( type='YOLOv5CSPDarknet', deepen_factor=1.0, widen_factor=1.0, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True)), neck=dict( type='YOLOv5PAFPN', deepen_factor=1.0, widen_factor=1.0, in_channels=[256, 512, 768, 1024], out_channels=[256, 512, 768, 1024], num_csp_blocks=3, norm_cfg=dict(type='BN', momentum=0.03, eps=0.001), act_cfg=dict(type='SiLU', inplace=True)), bbox_head=dict( type='YOLOv5Head', head_module=dict( type='YOLOv5HeadModule', num_classes=1, in_channels=[256, 512, 768, 1024], widen_factor=1.0, featmap_strides=[8, 16, 32, 64], num_base_priors=3), prior_generator=dict( type='mmdet.YOLOAnchorGenerator', base_sizes=[[(19, 27), (44, 40), (38, 94)], [(96, 68), (86, 152), (180, 137)], [(140, 301), (303, 264), (238, 542)], [(436, 615), (739, 380), (925, 792)]], strides=[8, 16, 32, 64]), loss_cls=dict( type='mmdet.CrossEntropyLoss', use_sigmoid=True, reduction='mean', loss_weight=0.004687500000000001), loss_bbox=dict( type='IoULoss', iou_mode='ciou', bbox_format='xywh', eps=1e-07, reduction='mean', loss_weight=0.037500000000000006, return_iou=True), loss_obj=dict( type='mmdet.CrossEntropyLoss', use_sigmoid=True, reduction='mean', loss_weight=3.0), prior_match_thr=4.0, obj_level_weights=[4.0, 1.0, 0.4]), test_cfg=dict( multi_label=True, nms_pre=30000, score_thr=0.001, nms=dict(type='nms', iou_threshold=0.65), max_per_img=300)) albu_train_transforms = [ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ] pre_transform = [ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='LoadAnnotations', with_bbox=True) ] train_pipeline = [ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='LoadAnnotations', with_bbox=True), dict( type='Mosaic', img_scale=(1280, 1280), pad_val=114.0, pre_transform=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, scaling_ratio_range=(0.5, 1.5), border=(-640, -640), border_val=(114, 114, 114)), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction')) ] train_dataloader = dict( batch_size=8, num_workers=8, persistent_workers=True, pin_memory=True, sampler=dict(type='DefaultSampler', shuffle=True), dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco/', metainfo=dict(CLASSES=('COTS', ), PALETTE=[(220, 20, 60)]), ann_file='mmdet_split_train.json', data_prefix=dict(img=''), filter_cfg=dict(filter_empty_gt=False, min_size=32), pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='LoadAnnotations', with_bbox=True), dict( type='Mosaic', img_scale=(1280, 1280), pad_val=114.0, pre_transform=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='LoadAnnotations', with_bbox=True) ]), dict( type='YOLOv5RandomAffine', max_rotate_degree=0.0, max_shear_degree=0.0, scaling_ratio_range=(0.5, 1.5), border=(-640, -640), border_val=(114, 114, 114)), dict( type='mmdet.Albu', transforms=[ dict(type='Blur', p=0.01), dict(type='MedianBlur', p=0.01), dict(type='ToGray', p=0.01), dict(type='CLAHE', p=0.01) ], bbox_params=dict( type='BboxParams', format='pascal_voc', label_fields=['gt_bboxes_labels', 'gt_ignore_flags']), keymap=dict(img='image', gt_bboxes='bboxes')), dict(type='YOLOv5HSVRandomAug'), dict(type='mmdet.RandomFlip', prob=0.5), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', 'flip_direction')) ])) test_pipeline = [ dict(type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='YOLOv5KeepRatioResize', scale=(1280, 1280)), dict( type='LetterResize', scale=(1280, 1280), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, scope='mmdet'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param')) ] val_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, pin_memory=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco/', test_mode=True, data_prefix=dict(img=''), ann_file='mmdet_split_val.json', pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='YOLOv5KeepRatioResize', scale=(1280, 1280)), dict( type='LetterResize', scale=(1280, 1280), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, scope='mmdet'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param')) ], batch_shapes_cfg=dict( type='BatchShapePolicy', batch_size=1, img_size=1280, size_divisor=32, extra_pad_ratio=0.5))) test_dataloader = dict( batch_size=1, num_workers=2, persistent_workers=True, pin_memory=True, drop_last=False, sampler=dict(type='DefaultSampler', shuffle=False), dataset=dict( type='YOLOv5CocoDataset', data_root='data/coco/', test_mode=True, data_prefix=dict(img=''), ann_file='mmdet_split_test.json', pipeline=[ dict( type='LoadImageFromFile', file_client_args=dict(backend='disk')), dict(type='YOLOv5KeepRatioResize', scale=(1280, 1280)), dict( type='LetterResize', scale=(1280, 1280), allow_scale_up=False, pad_val=dict(img=114)), dict(type='LoadAnnotations', with_bbox=True, scope='mmdet'), dict( type='mmdet.PackDetInputs', meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'scale_factor', 'pad_param')) ], batch_shapes_cfg=dict( type='BatchShapePolicy', batch_size=1, img_size=1280, size_divisor=32, extra_pad_ratio=0.5))) param_scheduler = None optim_wrapper = dict( type='OptimWrapper', optimizer=dict( type='SGD', lr=0.01, momentum=0.937, weight_decay=0.0005, nesterov=True, batch_size_per_gpu=8), constructor='YOLOv5OptimizerConstructor') custom_hooks = [ dict( type='EMAHook', ema_type='ExpMomentumEMA', momentum=0.0001, update_buffers=True, strict_load=False, priority=49) ] val_evaluator = dict( type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), ann_file='data/coco/mmdet_split_val.json', metric='bbox') test_evaluator = dict( type='mmdet.CocoMetric', proposal_nums=(100, 1, 10), ann_file='data/coco/mmdet_split_test.json', metric='bbox') train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=200, val_interval=5) val_cfg = dict(type='ValLoop') test_cfg = dict(type='TestLoop') launcher = 'none' work_dir = './work_dirs/yolov5_l_cots'

Result has been saved to /mnt/ssd/code/CCIP/mmyolo/work_dirs/yolov5_l_cots/modules_statistic_results.json Traceback (most recent call last): File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg obj = obj_cls(**args) # type: ignore File "/mnt/ssd/code/CCIP/mmyolo/mmyolo/models/dense_heads/yolov5_head.py", line 197, in init self.special_init() File "/mnt/ssd/code/CCIP/mmyolo/mmyolo/models/dense_heads/yolov5_head.py", line 205, in special_init assert len(self.obj_level_weights) == len( AssertionError

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 121, in build_from_cfg obj = obj_cls(*args) # type: ignore File "/mnt/ssd/code/CCIP/mmyolo/mmyolo/models/detectors/yolo_detector.py", line 41, in init super().init( File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmdet/models/detectors/single_stage.py", line 35, in init self.bbox_head = MODELS.build(bbox_head) File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/registry/registry.py", line 454, in build return self.build_func(cfg, args, **kwargs, registry=self) File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 240, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 135, in build_from_cfg raise type(e)( AssertionError: class YOLOv5Head in mmyolo/models/dense_heads/yolov5_head.py:

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/aslab/code/CCIP/mmyolo/tools/train.py", line 106, in main() File "/home/aslab/code/CCIP/mmyolo/tools/train.py", line 95, in main runner = Runner.from_cfg(cfg) File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/runner/runner.py", line 434, in from_cfg runner = cls( File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/runner/runner.py", line 404, in init self.model = self.build_model(model) File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/runner/runner.py", line 803, in build_model model = MODELS.build(model) File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/registry/registry.py", line 454, in build return self.build_func(cfg, *args, **kwargs, registry=self) File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 240, in build_model_from_cfg return build_from_cfg(cfg, registry, default_args) File "/home/aslab/anaconda3/envs/mmyolo/lib/python3.8/site-packages/mmengine/registry/build_functions.py", line 135, in build_from_cfg raise type(e)( AssertionError: class YOLODetector in mmyolo/models/detectors/yolo_detector.py: class YOLOv5Head in mmyolo/models/dense_heads/yolov5_head.py:

Process finished with exit code 1

I set $PYTHONPATH to be the root path of mmyolo. The error message is not clear to me for debugging... Also, I would like to know how I can solve this warning from mmegine: Failed to search registry with scope "mmyolo" in the "log_processor" registry tree. As a workaround, the current "log_processor" registry in "mmengine" is used to build instance. This may cause unexpected failure when running the built modules. Please check whether "mmyolo" is a correct scope, or whether the registry is initialized. Any help would be greatly appreciated!

Best regards, Yang

Environment

sys.platform: linux Python: 3.8.13 (default, Oct 21 2022, 23:50:54) [GCC 11.2.0] CUDA available: True numpy_random_seed: 2147483648 GPU 0: NVIDIA GeForce RTX 3090 GPU 1: NVIDIA GeForce RTX 2080 Ti CUDA_HOME: /usr/local/cuda-11.2 NVCC: Cuda compilation tools, release 11.2, V11.2.152 GCC: gcc (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0 PyTorch: 1.10.1 PyTorch compiling details: PyTorch built with:

GCC 7.3
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 v2.2.3 (Git Hash 7336ca9f055cf1bfa13efb658fe15dc9b41f0740)
OpenMP 201511 (a.k.a. OpenMP 4.5)
LAPACK is enabled (usually provided by MKL)
NNPACK is enabled
CPU capability usage: 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 8.2
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=1.10.1, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON,

TorchVision: 0.11.2 OpenCV: 4.6.0 MMEngine: 0.3.0 MMCV: 2.0.0rc2 MMDetection: 3.0.0rc3 MMYOLO: 0.1.2+0b48313

Additional information

No response

open-mmlab / mmyolo