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

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/


#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


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


#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


Model Backbone mAP config weight code
RetinaNet ResNet-50 40.7 config baidu wsfw
RetinaNet ResNet-101 41.7 config
Faster RCNN ResNet-50 42.0 config baidu dgpf
Faster RCNN ResNet-101 44.1 config
RepPoints ResNet-50 42.0 config baidu qx5d
RepPoints ResNet-101 43.8 config
FCOS ResNet-50 42.7 config baidu sedt
MaskRCNN ResNet-50 42.1 config baidu sv8m


Our code is based on the project MMDetection.

Thanks to the work GCNet and mmetection-distiller.


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