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«YOLO5Face» reproduced the paper "YOLO5Face: Why Reinventing a Face Detector"

ONLINE DEMO：FACE AND LANDMARKS DETECT

	ARCH	GFLOPs	Easy	Medium	Hard
zjykzj/YOLO5Face (This)	yolov5s-face	15.2	94.69	93.00	84.73
deepcam-cn/yolov5-face(Official)	yolov5s-face	/	94.33	92.61	83.15
zjykzj/YOLO5Face (This)	shufflenetv2-face	1.5	90.27	87.39	73.60
deepcam-cn/yolov5-face(Official)	shufflenetv2-face	/	90.76	88.12	73.82
zjykzj/YOLO5Face (This)	yolov5x-v7.0	204	95.79	94.53	87.63
zjykzj/YOLO5Face (This)	yolov5s-v7.0	15.8	94.84	93.28	84.67
zjykzj/YOLO5Face (This)	yolov5n-v7.0	4.2	93.25	91.11	80.33

Table of Contents

• Table of Contents
• News🚀🚀🚀
• Background
• Installation
• Usage
  • Train
  • Eval
  • Predict
• Maintainers
• Thanks
• Contributing
• License

News🚀🚀🚀

Version	Release Date	Major Updates
v1.1.1	2024/08/17	Update and add Gradio demo.
v1.1.0	2024/07/21	Supports additional models including shufflenetv2-face/yolov5x-v7.0/yolov5n-v7.0.
v1.0.0	2024/07/14	Adds keypoint detection, enabling face + keypoint detection.
v0.1.0	2024/06/29	Trains a face detector based on yolov5-v7.0 and the WIDERFACE dataset.

Background

YOLO5Face is a very interesting work that further abstracts the task of face detection. By directly using a universal object detection algorithm, good face detection results can be achieved. Of course, it also achieves 5-point facial keypoint regression. Based on ultralytics/yolov5, YOLO5Face can easily apply different models and training, such as using lightweight networks for real-time detection and using large networks for higher detection accuracy.

Note: the latest implementation of YOLO5Face in our warehouse is entirely based on ultralytics/yolov5 v7.0.

Installation

$ pip3 install -r requirements.txt

Or use docker container

$ docker run -it --runtime nvidia --gpus=all --shm-size=16g -v /etc/localtime:/etc/localtime -v $(pwd):/workdir --workdir=/workdir --name yolo5face ultralytics/yolov5:v7.0

Usage

• Download WIDERFACE dataset: Baidu Drive(08p0)

Train

# yolov5s_v7.0
$ python3 widerface_train.py --data widerface.yaml --weights "" --cfg models/yolo5face/cfgs/yolov5s_v7_0.yaml --hyp models/yolo5face/hyps/hyp.scratch-low.yaml --img 800 --epoch 300 --device 0
# yolov5s-face
$ python3 widerface_train.py --data widerface.yaml --weights "" --cfg models/yolo5face/cfgs/yolov5s_face.yaml --hyp models/yolo5face/hyps/hyp.scratch.yaml --img 800 --epoch 300 --device 0

Eval

$ python widerface_detect.py --weights ./runs/exp4-yolov5s_v7_0-i800-e300.pt --source ../datasets/widerface/images/val/ --folder_pict ../datasets/widerface/wider_face_split/wider_face_val_bbx_gt.txt --conf-thres 0.001 --iou-thres 0.6 --save-txt --save-conf --device 0
...
YOLOv5s_v7_0 summary: 157 layers, 7039792 parameters, 0 gradients, 15.8 GFLOPs
...
Speed: 0.4ms pre-process, 8.8ms inference, 0.8ms NMS per image at shape (1, 3, 640, 640)
Results saved to runs/detect/exp7
0 labels saved to runs/detect/exp7/labels
$ cd widerface_evaluate/
$ python3 evaluation.py -p ../runs/detect/exp7/labels/ -g ./ground_truth/
Reading Predictions : 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:00<00:00, 62.18it/s]
Processing easy: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00,  2.94it/s]
Processing medium: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00,  2.98it/s]
Processing hard: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████| 61/61 [00:20<00:00,  2.97it/s]
==================== Results ====================
Easy   Val AP: 0.9483604102331251
Medium Val AP: 0.9328484206418773
Hard   Val AP: 0.8467345083774318
=================================================

Predict

$ python detect_face_and_landmarks.py --weights ./runs/exp4-yolov5s_v7_0-i800-e300.pt --source assets/selfie.jpg --imgsz 2048 --conf-thres 0.25 --iou-thres 0.45 --hide-labels --hide-conf

Maintainers

• zhujian - Initial work - zjykzj

Thanks

• zjykzj/YOLOv3
• ultralytics/yolov5
• deepcam-cn/yolov5-face
• WIDER FACE: A Face Detection Benchmark

Contributing

Anyone's participation is welcome! Open an issue or submit PRs.

Small note:

• Git submission specifications should be complied with Conventional Commits
• If versioned, please conform to the Semantic Versioning 2.0.0 specification
• If editing the README, please conform to the standard-readme specification.

License

Apache License 2.0 © 2024 zjykzj