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https://github.com/Krasjet-Yu/YOLO-FaceV2
YOLO-FaceV2: A Scale and Occlusion Aware Face Detector
https://github.com/Krasjet-Yu/YOLO-FaceV2
Last synced: 3 months ago
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YOLO-FaceV2: A Scale and Occlusion Aware Face Detector
- Host: GitHub
- URL: https://github.com/Krasjet-Yu/YOLO-FaceV2
- Owner: Krasjet-Yu
- Created: 2022-08-02T18:26:09.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2024-02-20T14:03:15.000Z (9 months ago)
- Last Synced: 2024-02-20T15:28:10.213Z (9 months ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 2.26 MB
- Stars: 145
- Watchers: 2
- Forks: 24
- Open Issues: 32
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-face-detection-and-recognition - YOLO-FaceV2 - Yu/YOLO-FaceV2?style=social"/> : "YOLO-FaceV2: A Scale and Occlusion Aware Face Detector ". (**[arXiv 2022](https://arxiv.org/abs/2208.02019)**). "微信公众号「江大白」《[超越Yolo5-Face，Yolo-Facev2开源，各类Trick优化，值得学习！](https://mp.weixin.qq.com/s?__biz=Mzg5NzgyNTU2Mg==&mid=2247498561&idx=1&sn=b7ff0592644ab6bc5b716e07294e1c0a&source=41#wechat_redirect)》" (Face Detection)
- awesome-yolo-object-detection - YOLO-FaceV2 - Yu/YOLO-FaceV2?style=social"/> : "YOLO-FaceV2: A Scale and Occlusion Aware Face Detector ". (**[arXiv 2022](https://arxiv.org/abs/2208.02019)**). "微信公众号「江大白」《[超越Yolo5-Face，Yolo-Facev2开源，各类Trick优化，值得学习！](https://mp.weixin.qq.com/s?__biz=Mzg5NzgyNTU2Mg==&mid=2247498561&idx=1&sn=b7ff0592644ab6bc5b716e07294e1c0a&source=41#wechat_redirect)》" (Applications)
- awesome-yolo-object-detection - YOLO-FaceV2 - Yu/YOLO-FaceV2?style=social"/> : "YOLO-FaceV2: A Scale and Occlusion Aware Face Detector ". (**[arXiv 2022](https://arxiv.org/abs/2208.02019)**). "微信公众号「江大白」《[超越Yolo5-Face，Yolo-Facev2开源，各类Trick优化，值得学习！](https://mp.weixin.qq.com/s?__biz=Mzg5NzgyNTU2Mg==&mid=2247498561&idx=1&sn=b7ff0592644ab6bc5b716e07294e1c0a&source=41#wechat_redirect)》" (Applications)
README
# YOLO-FaceV2
## Introduction
YOLO-FaceV2: A Scale and Occlusion Aware Face Detector
*[https://arxiv.org/abs/2208.02019](https://arxiv.org/abs/2208.02019)*
## Framework Structure
## Environment Requirments
Create a Python Virtual Environment.
```shell
conda create -n {name} python=x.x
```
Enter Python Virtual Environment.
```shell
conda activate {name}
```
Install pytorch in *[this](https://pytorch.org/get-started/previous-versions/)*.
```shell
pip install torch==1.10.0+cu111 torchvision==0.11.0+cu111 torchaudio==0.10.0 -f https://download.pytorch.org/whl/torch_stable.html
```
Install other python package.
```shell
pip install -r requirements.txt
```
## Step-Through Example
### Installation
Get the code.
```shell
git clone https://github.com/Krasjet-Yu/YOLO-FaceV2.git
```
### Dataset
Download the [WIDER FACE](http://shuoyang1213.me/WIDERFACE/) dataset. Then convert it to YOLO format.
```shell
# You can modify convert.py and voc_label.py if needed.
python3 data/convert.py
python3 data/voc_label.py
```
## Preweight
The link is [yolo-facev2s.pt](https://github.com/Krasjet-Yu/YOLO-FaceV2/releases/download/v1.0/preweight.pt)
### Training
Train your model on WIDER FACE.
```shell
python train.py --weights preweight.pt
--data data/WIDER_FACE.yaml
--cfg models/yolov5s_v2_RFEM_MultiSEAM.yaml
--batch-size 32
--epochs 250
```
### Test
```shell
python detect.py --weights ./preweight/best.pt --source ./data/images/test.jpg --plot-label --view-img
```
### Evaluate
Evaluate the trained model via next code on WIDER FACE
If you don't want to train, you can also directly use our trained model to evaluate.
The link is [yolo-facev2_last.pt](https://github.com/Krasjet-Yu/YOLO-FaceV2/releases/download/v1.0/best.pt)
```shell
python widerface_pred.py --weights runs/train/x/weights/best.pt
--save_folder ./widerface_evaluate/widerface_txt_x
cd widerface_evaluate/
python evaluation.py --pred ./widerface_txt_x
```
Download the *[eval_tool](http://shuoyang1213.me/WIDERFACE/support/eval_script/eval_tools.zip)* to show the performance.
The result is shown below:
## Finetune
see in *[https://github.com/ultralytics/yolov5/issues/607](https://github.com/ultralytics/yolov5/issues/607)*
```shell
# Single-GPU
python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --evolve
# Multi-GPU
for i in 0 1 2 3 4 5 6 7; do
sleep $(expr 30 \* $i) && # 30-second delay (optional)
echo 'Starting GPU '$i'...' &&
nohup python train.py --epochs 10 --data coco128.yaml --weights yolov5s.pt --cache --device $i --evolve > evolve_gpu_$i.log &
done
# Multi-GPU bash-while (not recommended)
for i in 0 1 2 3 4 5 6 7; do
sleep $(expr 30 \* $i) && # 30-second delay (optional)
echo 'Starting GPU '$i'...' &&
"$(while true; do nohup python train.py... --device $i --evolve 1 > evolve_gpu_$i.log; done)" &
done
```
## Reference
*[https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)*
*[https://github.com/deepcam-cn/yolov5-face](https://github.com/deepcam-cn/yolov5-face)*
*[https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection)*
*[https://github.com/dongdonghy/repulsion_loss_pytorch](https://github.com/dongdonghy/repulsion_loss_pytorch)*
## Cite
If you think this work is helpful for you, please cite
```shell
@ARTICLE{2022arXiv220802019Y,
author = {{Yu}, Ziping and {Huang}, Hongbo and {Chen}, Weijun and {Su}, Yongxin and {Liu}, Yahui and {Wang}, Xiuying},
title = "{YOLO-FaceV2: A Scale and Occlusion Aware Face Detector}",
journal = {arXiv e-prints},
keywords = {Computer Science - Computer Vision and Pattern Recognition},
year = 2022,
month = aug,
eid = {arXiv:2208.02019},
pages = {arXiv:2208.02019},
archivePrefix = {arXiv},
eprint = {2208.02019},
primaryClass = {cs.CV},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220802019Y},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
```
## Contact
We use code's license is MIT License. The code can be used for business inquiries or professional support requests.