DyFPN

Introduction

Dynamic Feature Pyramid Networks for Object Detection. arXiv

By Mingjian Zhu, Kai Han, Changbin Yu, Yunhe Wang

This is the implementation of DyFPN. Basically, we follow the setting of testing a model in MMDetection. Please refer to MMDetection for installation and dataset preparation.

Test

1. Download the pre-trained model in Onedrive.

2. Create a new folder named checkpoint and put the pre-trained model in it.

3. Test the model with the following command.

python tools/test.py configs/dyfpn/faster_rcnn_r50_dyfpn_1x_coco.py checkpoints/DyFPN_B_CNNGate.pth --eval bbox

Citation

@article{zhu2020dynamic,
  title={Dynamic Feature Pyramid Networks for Object Detection},
  author={Zhu, Mingjian and Han, Kai and Yu, Changbin and Wang, Yunhe},
  journal={arXiv preprint arXiv:2012.00779},
  year={2020}