YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931)

Introduction

Yolov5-face is a real-time,high accuracy face detection.

Performance

Single Scale Inference on VGA resolution（max side is equal to 640 and scale).

Large family

Small family

Pretrained-Models

Data preparation

1. Download WIDERFace datasets.
2. Download annotation files from google drive.

python3 train2yolo.py
python3 val2yolo.py

Training

CUDA_VISIBLE_DEVICES="0,1,2,3" python3 train.py --data data/widerface.yaml --cfg models/yolov5s.yaml --weights 'pretrained models'

WIDERFace Evaluation

python3 test_widerface.py --weights 'your test model' --img-size 640

cd widerface_evaluate
python3 evaluation.py

Test

Android demo

https://github.com/FeiGeChuanShu/ncnn_Android_face/tree/main/ncnn-android-yolov5_face

References

https://github.com/ultralytics/yolov5

https://github.com/DayBreak-u/yolo-face-with-landmark

https://github.com/xialuxi/yolov5_face_landmark

https://github.com/biubug6/Pytorch_Retinaface

https://github.com/deepinsight/insightface

Citation

• If you think this work is useful for you, please cite

  @article{YOLO5Face,
  title = {YOLO5Face: Why Reinventing a Face Detector},
  author = {Delong Qi and Weijun Tan and Qi Yao and Jingfeng Liu},
  booktitle = {ArXiv preprint ArXiv:2105.12931},
  year = {2021}
  }