YOLO-FaceV2

Introduction

YOLO-FaceV2: A Scale and Occlusion Aware Face Detector
https://arxiv.org/abs/2208.02019

Framework Structure

Environment Requirments

Create a Python Virtual Environment.

conda create -n {name} python=x.x

Enter Python Virtual Environment.

conda activate {name}

Install pytorch in this.

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.

pip install -r requirements.txt

Step-Through Example

Installation

Get the code.

git clone https://github.com/Krasjet-Yu/YOLO-FaceV2.git

Dataset

Download the WIDER FACE dataset. Then convert it to YOLO format.

# 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

Training

Train your model on WIDER FACE.

python train.py --weights preweight.pt    
                --data data/WIDER_FACE.yaml    
                --cfg models/yolov5s_v2_RFEM_MultiSEAM.yaml     
                --batch-size 32   
                --epochs 250

Test

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

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 to show the performance.

The result is shown below:

Finetune

see in ultralytics/yolov5#607

# 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/deepcam-cn/yolov5-face

https://github.com/open-mmlab/mmdetection

https://github.com/dongdonghy/repulsion_loss_pytorch

Cite

If you think this work is helpful for you, please cite

@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.