Awesome

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

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

This repo provides a neat implementation for YOLOF based on Detectron2. A cvpods version can be found in https://github.com/megvii-model/YOLOF.

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

Getting Started

Our project is developed on detectron2. Please follow the official detectron2 installation.
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 ..
```
Install YOLOF by:
```
python setup.py develop
```
Then link your dataset path to datasets
```
cd datasets/
ln -s /path/to/coco coco
```
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 with yolof

python ./tools/train_net.py --num-gpus 8 --config-file ./configs/yolof_R_50_C5_1x.yaml

Test with yolof

python ./tools/train_net.py --num-gpus 8 --config-file ./configs/yolof_R_50_C5_1x.yaml --eval-only MODEL.WEIGHTS /path/to/checkpoint_file

Note that there might be API changes in future detectron2 releases that make the code incompatible.

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	36
YOLOF_R_50_DC5_1x	39.2	23
YOLOF_R_101_C5_1x	39.8	23
YOLOF_R_101_DC5_1x	40.5	17
YOLOF_X_101_64x4d_C5_1x	42.2	11
YOLOF_CSP_D_53_DC5_3x	41.2	41
YOLOF_CSP_D_53_DC5_9x	42.8	41
YOLOF_CSP_D_53_DC5_9x_stage2_3x	43.2	41

Note that, the speed reported in this repo is 2~3 FPS faster than the one reported in the cvpods version.

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}
}