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https://github.com/deepcam-cn/yolov5-face

YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)
https://github.com/deepcam-cn/yolov5-face

arcface blazeface pytorch retinaface scrfd shufflenet tensorrt tinaface yolo yolov5 yolov7

Last synced: 29 days ago
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YOLO5Face: Why Reinventing a Face Detector (https://arxiv.org/abs/2105.12931) ECCV Workshops 2022)

Host: GitHub
URL: https://github.com/deepcam-cn/yolov5-face
Owner: deepcam-cn
License: gpl-3.0
Created: 2021-04-26T06:55:59.000Z (over 3 years ago)
Default Branch: master
Last Pushed: 2024-05-07T06:37:51.000Z (6 months ago)
Last Synced: 2024-05-07T07:36:09.835Z (6 months ago)
Topics: arcface, blazeface, pytorch, retinaface, scrfd, shufflenet, tensorrt, tinaface, yolo, yolov5, yolov7
Language: Python
Homepage:
Size: 9.76 MB
Stars: 1,953
Watchers: 31
Forks: 479
Open Issues: 151
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

awesome-face-detection-and-recognition - YOLO5Face - cn/yolov5-face?style=social"/> : "YOLO5Face: Why Reinventing a Face Detector". (**[arXiv 2021](https://arxiv.org/abs/2105.12931)**) (Face Detection)
awesome-yolo-object-detection - YOLO5Face - cn/yolov5-face?style=social"/> : "YOLO5Face: Why Reinventing a Face Detector". (**[arXiv 2021](https://arxiv.org/abs/2105.12931)**) (Applications)
awesome-yolo-object-detection - YOLO5Face - cn/yolov5-face?style=social"/> : "YOLO5Face: Why Reinventing a Face Detector". (**[arXiv 2021](https://arxiv.org/abs/2105.12931)**) (Applications)

README

        ## What's New

**2024.04** [ncnn-android-yolov8-face](https://github.com/derronqi/ncnn-android-yolov8-face)

**2023.04** [yolov8-face](https://github.com/derronqi/yolov8-face) (🔥🔥🔥↑) 

**2022.10** [yolov7-face](https://github.com/derronqi/yolov7-face) (🔥🔥🔥↑)

**2022.8**: yolov7

| Method           |  Test Size | Easy  | Medium | Hard  | FLOPs (B) @640 | 

| -----------------| ---------- | ----- | ------ | ----- | -------------- | 

| yolov7-lite-t    | 640        | 88.7  | 85.2   | 71.5  |  0.8           |

| yolov7-lite-s    | 640        | 92.7  | 89.9   | 78.5  |  3.0           | 

| yolov7-tiny      | 640        | 94.7  | 92.6   | 82.1  |  13.2          | 

| yolov7s          | 640        | 94.8  | 93.1   | 85.2  |  16.8          | 

| yolov7           | 640        | 96.9  | 95.5   | 88.0  |  103.4         | 

| yolov7+TTA       | 640        | 97.2  | 95.8   | 87.7  |  103.4         | 

**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](https://drive.google.com/file/d/1Pwd6ga06cDjeOX20RSC1KWiT888Q9IpM/view?usp=sharing),improve large face detection.

| Method               | Easy  | Medium | Hard  | 

| -------------------- | ----- | ------ | ----- |

| ***YOLOv5s***        | 94.56 | 92.92  | 83.84 |

| ***YOLOv5m***        | 95.46 | 93.87  | 85.54 |

**QQ Group**: 1164802745

## Introduction

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

![](data/images/yolov5-face-p6.png)

## 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](https://drive.google.com/file/d/1tU_IjyOwGQfGNUvZGwWWM4SwxKp2PUQ8/view?usp=sharing).

```shell

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

```shell

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

```

## WIDERFace Evaluation

```shell

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

cd widerface_evaluate

python3 evaluation.py

```

#### Test

![](data/images/result.jpg)

#### Landmark Visulization 

![](data/images/landmark.png)

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](https://www.face-benchmark.org/challenge.html).** 

#### 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}

      }