xrosliang / yolof

You Only Look One-level Feature (YOLOF), CVPR2021

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

Getting Started

• Install cvpods by following the installation guidance.
• 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

Main results

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.

Model	COCO val mAP	FPS
YOLOF_R_50_C5_1x	37.7	32
YOLOF_R_50_DC5_1x	39.2	20
YOLOF_R_101_C5_1x	39.8	21
YOLOF_R_101_DC5_1x	40.5	15
YOLOF_X_101_64x4d_C5_1x	42.2	10
YOLOF_CSP_D_53_DC5_3x	41.2	39
YOLOF_CSP_D_53_DC5_9x	42.8	39
YOLOF_CSP_D_53_DC5_9x_stage2_3x	43.2	39

Citation

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}
}