yzd-v/FGD

Focal and Global Knowledge Distillation for Detectors (CVPR 2022)

FGD

CVPR 2022 Paper: Focal and Global Knowledge Distillation for Detectors

Install MMDetection and MS COCO2017

• Our codes are based on MMDetection. Please follow the installation of MMDetection and make sure you can run it successfully.
• This repo uses mmdet==2.11.0 and mmcv-full==1.2.4
• If you want to use higher mmdet version, you may have to change the optimizer in apis/train.py and build_detector in tools/train.py.
• For mmdet>=2.12.0, if you want to use inheriting strategy, you have to initalize the student with teacher's parameters after model.init_weights().

Higher mmdet and mmcv-full version

• You can refer MGD to change model.init_weights() in train.py and self.student.init_weights() in distiller.py.

Add and Replace the codes

• Add the configs/. in our codes to the configs/ in mmdetectin's codes.
• Add the mmdet/distillation/. in our codes to the mmdet/ in mmdetectin's codes.
• Replace the mmdet/apis/train.py and tools/train.py in mmdetection's codes with mmdet/apis/train.py and tools/train.py in our codes.
• Add pth_transfer.py to mmdetection's codes.
• Unzip COCO dataset into data/coco/

Train

#single GPU
python tools/train.py configs/distillers/fgd/fgd_retina_rx101_64x4d_distill_retina_r50_fpn_2x_coco.py

#multi GPU
bash tools/dist_train.sh configs/distillers/fgd/fgd_retina_rx101_64x4d_distill_retina_r50_fpn_2x_coco.py 8

Transfer

# Tansfer the FGD model into mmdet model
python pth_transfer.py --fgd_path $fgd_ckpt --output_path $new_mmdet_ckpt

Test

#single GPU
python tools/test.py configs/retinanet/retinanet_r50_fpn_2x_coco.py $new_mmdet_ckpt --eval bbox

#multi GPU
bash tools/dist_test.sh configs/retinanet/retinanet_r50_fpn_2x_coco.py $new_mmdet_ckpt 8 --eval bbox

Results

Model	Backbone	Baseline(mAP)	+FGD(mAP)	config	weight	code
RetinaNet	ResNet-50	37.4	40.7	config	baidu	wsfw
RetinaNet	ResNet-101	38.9	41.7	config
Faster RCNN	ResNet-50	38.4	42.0	config	baidu	dgpf
Faster RCNN	ResNet-101	39.8	44.1	config
RepPoints	ResNet-50	38.6	42.0	config	baidu	qx5d
RepPoints	ResNet-101	40.5	43.8	config
FCOS	ResNet-50	38.5	42.7	config	baidu	sedt
MaskRCNN	ResNet-50	39.2	42.1	config	baidu	sv8m
GFL	ResNet-50	40.2	43.5	config

Model	Backbone	Baseline(Mask mAP)	+FGD(Mask mAP)	config	weight	code
SOLO	ResNet-50	33.1	36.0	config
MaskRCNN	ResNet-50	35.4	37.8	config	baidu	sv8m

Student	Teacher	Baseline(mAP)	+FGD(mAP)	config	weight	code
YOLOX-m	YOLOX-l	45.9	46.6	config	baidu	af9g

1. Please refer branch yolox

Citation

@inproceedings{yang2022focal,
  title={Focal and global knowledge distillation for detectors},
  author={Yang, Zhendong and Li, Zhe and Jiang, Xiaohu and Gong, Yuan and Yuan, Zehuan and Zhao, Danpei and Yuan, Chun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={4643--4652},
  year={2022}
}

Acknowledgement

Our code is based on the project MMDetection.

Thanks to the work GCNet and mmetection-distiller.