/yolov5-face

YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)

Primary LanguagePythonGNU General Public License v3.0GPL-3.0

What's New

2022.10 yolov7-face

https://github.com/derronqi/yolov7-face

2022.8: yolov7

Method Easy Medium Hard #Flops(G) Link
yolov7-tiny 94.0 92.3 83.2 4.960 -
yolov7 - - - 36.946 -

2021.12: Yolov5-face to TensorRT.

Backbone Pytorch(ms) TensorRT_FP16(ms)
yolov5n-0.5 7.7 2.1
yolov5n-face 7.7 2.4
yolov5s-face 5.6 2.2
yolov5m-face 9.9 3.3
yolov5l-face 15.9 4.5

Pytorch=1.10.0+cu102 TensorRT=8.2.0.6 Hardware=rtx2080ti

2021.11: BlazeFace

Method multi scale Easy Medium Hard Model Size(MB) Link
BlazeFace Ture 88.5 85.5 73.1 0.472 https://github.com/PaddlePaddle/PaddleDetection
BlazeFace-FPN-SSH Ture 90.7 88.3 79.3 0.479 https://github.com/PaddlePaddle/PaddleDetection
yolov5-blazeface True 90.4 88.7 78.0 0.493 https://pan.baidu.com/s/1RHp8wa615OuDVhsO-qrMpQ pwd:r3v3 https://drive.google.com/file/d/1adi6ke2vCLQFcpbvFqWo_J4wZIfPqSMG
yolov5-blazeface-fpn True 90.8 89.4 79.1 0.493 -

2021.08: Add new training dataset Multi-Task-Facial,improve large face detection.

Method Easy Medium Hard
YOLOv5s 94.56 92.92 83.84
YOLOv5m 95.46 93.87 85.54

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

Method Backbone Easy Medium Hard #Params(M) #Flops(G)
DSFD (CVPR19) ResNet152 94.29 91.47 71.39 120.06 259.55
RetinaFace (CVPR20) ResNet50 94.92 91.90 64.17 29.50 37.59
HAMBox (CVPR20) ResNet50 95.27 93.76 76.75 30.24 43.28
TinaFace (Arxiv20) ResNet50 95.61 94.25 81.43 37.98 172.95
SCRFD-34GF(Arxiv21) Bottleneck Res 96.06 94.92 85.29 9.80 34.13
SCRFD-10GF(Arxiv21) Basic Res 95.16 93.87 83.05 3.86 9.98
- - - - - - -
YOLOv5s CSPNet 94.67 92.75 83.03 7.075 5.751
YOLOv5s6 CSPNet 95.48 93.66 82.8 12.386 6.280
YOLOv5m CSPNet 95.30 93.76 85.28 21.063 18.146
YOLOv5m6 CSPNet 95.66 94.1 85.2 35.485 19.773
YOLOv5l CSPNet 95.78 94.30 86.13 46.627 41.607
YOLOv5l6 CSPNet 96.38 94.90 85.88 76.674 45.279

Small family

Method Backbone Easy Medium Hard #Params(M) #Flops(G)
RetinaFace (CVPR20 MobileNet0.25 87.78 81.16 47.32 0.44 0.802
FaceBoxes (IJCB17) 76.17 57.17 24.18 1.01 0.275
SCRFD-0.5GF(Arxiv21) Depth-wise Conv 90.57 88.12 68.51 0.57 0.508
SCRFD-2.5GF(Arxiv21) Basic Res 93.78 92.16 77.87 0.67 2.53
- - - - - - -
YOLOv5n ShuffleNetv2 93.74 91.54 80.32 1.726 2.111
YOLOv5n-0.5 ShuffleNetv2 90.76 88.12 73.82 0.447 0.571

Pretrained-Models

Name Easy Medium Hard FLOPs(G) Params(M) Link
yolov5n-0.5 90.76 88.12 73.82 0.571 0.447 Link: https://pan.baidu.com/s/1UgiKwzFq5NXI2y-Zui1kiA pwd: s5ow, https://drive.google.com/file/d/1XJ8w55Y9Po7Y5WP4X1Kg1a77ok2tL_KY/view?usp=sharing
yolov5n 93.61 91.52 80.53 2.111 1.726 Link: https://pan.baidu.com/s/1xsYns6cyB84aPDgXB7sNDQ pwd: lw9j,https://drive.google.com/file/d/18oenL6tjFkdR1f5IgpYeQfDFqU4w3jEr/view?usp=sharing
yolov5s 94.33 92.61 83.15 5.751 7.075 Link: https://pan.baidu.com/s/1fyzLxZYx7Ja1_PCIWRhxbw Link: eq0q,https://drive.google.com/file/d/1zxaHeLDyID9YU4-hqK7KNepXIwbTkRIO/view?usp=sharing
yolov5m 95.30 93.76 85.28 18.146 21.063 Link: https://pan.baidu.com/s/1oePvd2K6R4-gT0g7EERmdQ pwd: jmtk, https://drive.google.com/file/d/1Sx-KEGXSxvPMS35JhzQKeRBiqC98VDDI
yolov5l 95.78 94.30 86.13 41.607 46.627 Link: https://pan.baidu.com/s/11l4qSEgA2-c7e8lpRt8iFw pwd: 0mq7, https://drive.google.com/file/d/16F-3AjdQBn9p3nMhStUxfDNAE_1bOF_r

Data preparation

  1. Download WIDERFace datasets.
  2. Download annotation files from google drive.
cd data
python3 train2yolo.py /path/to/original/widerface/train [/path/to/save/widerface/train]
python3 val2yolo.py  /path/to/original/widerface [/path/to/save/widerface/val]

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

Landmark Visulization

First row: RetinaFace, 2nd row: YOLOv5m-Face YOLO5Face was used in the 3rd place standard face recogntion track of the ICCV2021 Masked Face Recognition Challenge.

AXera demo

https://github.com/AXERA-TECH/ax-samples/blob/main/examples/ax_yolov5s_face_steps.cc

Android demo

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

OpenCV DNN demo

https://github.com/hpc203/yolov5-face-landmarks-opencv-v2

ONNXRuntime/MNN/TNN/NCNN C++ demo

https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ort/cv/yolo5face.cpp

https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/mnn/cv/mnn_yolo5face.cpp

https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/tnn/cv/tnn_yolo5face.cpp

https://github.com/DefTruth/lite.ai.toolkit/blob/main/lite/ncnn/cv/ncnn_yolo5face.cpp

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}
}