Awesome
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
cd YOLOF/ python setup.py develop
- Install
mish-cuda
to speed up the training and inference when usingCSPDarkNet-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}
}