A simple, fast, and efficient object detector without FPN.

• This repo provides an implementation for YOLOF based on cvpods. A neat and re-organized Detectron2 version of YOLOF is available at https://github. com/chensnathan/YOLOF.

You Only Look One-level Feature,
Qiang Chen, Yingming Wang, Tong Yang, Xiangyu Zhang, Jian Cheng, Jian Sun

• Install cvpods

  cd YOLOF/
  python setup.py develop
• Install mish-cuda to speed up the training and inference when using CSPDarkNet-53 as the backbone (optional)

  git clone https://github.com/thomasbrandon/mish-cuda
  cd mish-cuda
  python setup.py build install
  cd ..
• Download the pretrained model in OneDrive or in the Baidu Cloud with code qr6o to train with the CSPDarkNet-53 backbone (optional)

  mkdir pretrained_models
  # download the `cspdarknet53.pth` to the `pretrained_models` directory
• Train

  cd playground/detection/coco/yolof/yolof.res50.C5.1x
  pods_train --num-gpus 8
• Test

  cd playground/detection/coco/yolof/yolof.res50.C5.1x
  pods_test --num-gpus 8 MODEL.WEIGHTS /path/to/checkpoint_file

The models listed below can be found in this onedrive link or in the BaiduCloud link with code qr6o. The FPS is tested on a 2080Ti GPU. More models will be available in the near future.

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{chen2021you,
  title={You Only Look One-level Feature},
  author={Chen, Qiang and Wang, Yingming and Yang, Tong and Zhang, Xiangyu and Cheng, Jian and Sun, Jian},
  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
  year={2021}
}