155 lines
6.5 KiB
Markdown
Executable File
155 lines
6.5 KiB
Markdown
Executable File
## What's New
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**2021.11**: BlazeFace
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| Method | multi scale | Easy | Medium | Hard | Model Size(MB) | Link |
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| -------------------- | ----------- | ----- | ------ | ----- | -------------- | ----- |
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| BlazeFace | Ture | 88.5 | 85.5 | 73.1 | 0.472 | https://github.com/PaddlePaddle/PaddleDetection |
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| BlazeFace-FPN-SSH | Ture | 90.7 | 88.3 | 79.3 | 0.479 | https://github.com/PaddlePaddle/PaddleDetection |
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| yolov5-blazeface | True | 90.4 | 88.7 | 78.0 | 0.493 | https://pan.baidu.com/s/1RHp8wa615OuDVhsO-qrMpQ pwd:r3v3 |
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| yolov5-blazeface-fpn | True | 90.8 | 89.4 | 79.1 | 0.493 | - |
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**2021.08**: Yolov5-face to TensorRT.
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Inference time on rtx2080ti.
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|Backbone|Pytorch |TensorRT_FP16 |
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|:---:|:----:|:----:|
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|yolov5n-0.5|11.9ms|2.9ms|
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|yolov5n-face|20.7ms|2.5ms|
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|yolov5s-face|25.2ms|3.0ms|
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|yolov5m-face|61.2ms|3.0ms|
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|yolov5l-face|109.6ms|3.6ms|
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> Note: (1) Model inference (2) Resolution 640x640
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**2021.08**: Add new training dataset [Multi-Task-Facial](https://drive.google.com/file/d/1Pwd6ga06cDjeOX20RSC1KWiT888Q9IpM/view?usp=sharing),improve large face detection.
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| Method | Easy | Medium | Hard |
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| -------------------- | ----- | ------ | ----- |
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| ***YOLOv5s*** | 94.56 | 92.92 | 83.84 |
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| ***YOLOv5m*** | 95.46 | 93.87 | 85.54 |
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## Introduction
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Yolov5-face is a real-time,high accuracy face detection.
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## Performance
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Single Scale Inference on VGA resolution（max side is equal to 640 and scale).
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***Large family***
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| Method | Backbone | Easy | Medium | Hard | \#Params(M) | \#Flops(G) |
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| :------------------ | -------------- | ----- | ------ | ----- | ----------- | ---------- |
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| DSFD (CVPR19) | ResNet152 | 94.29 | 91.47 | 71.39 | 120.06 | 259.55 |
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| RetinaFace (CVPR20) | ResNet50 | 94.92 | 91.90 | 64.17 | 29.50 | 37.59 |
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| HAMBox (CVPR20) | ResNet50 | 95.27 | 93.76 | 76.75 | 30.24 | 43.28 |
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| TinaFace (Arxiv20) | ResNet50 | 95.61 | 94.25 | 81.43 | 37.98 | 172.95 |
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| SCRFD-34GF(Arxiv21) | Bottleneck Res | 96.06 | 94.92 | 85.29 | 9.80 | 34.13 |
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| SCRFD-10GF(Arxiv21) | Basic Res | 95.16 | 93.87 | 83.05 | 3.86 | 9.98 |
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| - | - | - | - | - | - | - |
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| ***YOLOv5s*** | CSPNet | 94.67 | 92.75 | 83.03 | 7.075 | 5.751 |
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| **YOLOv5s6** | CSPNet | 95.48 | 93.66 | 82.8 | 12.386 | 6.280 |
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| ***YOLOv5m*** | CSPNet | 95.30 | 93.76 | 85.28 | 21.063 | 18.146 |
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| **YOLOv5m6** | CSPNet | 95.66 | 94.1 | 85.2 | 35.485 | 19.773 |
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| ***YOLOv5l*** | CSPNet | 95.78 | 94.30 | 86.13 | 46.627 | 41.607 |
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| ***YOLOv5l6*** | CSPNet | 96.38 | 94.90 | 85.88 | 76.674 | 45.279 |
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***Small family***
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| Method | Backbone | Easy | Medium | Hard | \#Params(M) | \#Flops(G) |
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| -------------------- | --------------- | ----- | ------ | ----- | ----------- | ---------- |
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| RetinaFace (CVPR20 | MobileNet0.25 | 87.78 | 81.16 | 47.32 | 0.44 | 0.802 |
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| FaceBoxes (IJCB17) | | 76.17 | 57.17 | 24.18 | 1.01 | 0.275 |
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| SCRFD-0.5GF(Arxiv21) | Depth-wise Conv | 90.57 | 88.12 | 68.51 | 0.57 | 0.508 |
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| SCRFD-2.5GF(Arxiv21) | Basic Res | 93.78 | 92.16 | 77.87 | 0.67 | 2.53 |
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| - | - | - | - | - | - | - |
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| ***YOLOv5n*** | ShuffleNetv2 | 93.74 | 91.54 | 80.32 | 1.726 | 2.111 |
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| ***YOLOv5n-0.5*** | ShuffleNetv2 | 90.76 | 88.12 | 73.82 | 0.447 | 0.571 |
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## Pretrained-Models
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| Name | Easy | Medium | Hard | FLOPs(G) | Params(M) | Link |
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| ----------- | ----- | ------ | ----- | -------- | --------- | ------------------------------------------------------------ |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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## Data preparation
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1. Download WIDERFace datasets.
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2. Download annotation files from [google drive](https://drive.google.com/file/d/1tU_IjyOwGQfGNUvZGwWWM4SwxKp2PUQ8/view?usp=sharing).
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```shell
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python3 train2yolo.py
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python3 val2yolo.py
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```
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## Training
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```shell
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CUDA_VISIBLE_DEVICES="0,1,2,3" python3 train.py --data data/widerface.yaml --cfg models/yolov5s.yaml --weights 'pretrained models'
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```
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## WIDERFace Evaluation
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```shell
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python3 test_widerface.py --weights 'your test model' --img-size 640
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cd widerface_evaluate
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python3 evaluation.py
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```
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#### Test
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#### Android demo
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https://github.com/FeiGeChuanShu/ncnn_Android_face/tree/main/ncnn-android-yolov5_face
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#### opencv dnn demo
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https://github.com/hpc203/yolov5-face-landmarks-opencv-v2
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#### References
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https://github.com/ultralytics/yolov5
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https://github.com/DayBreak-u/yolo-face-with-landmark
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https://github.com/xialuxi/yolov5_face_landmark
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https://github.com/biubug6/Pytorch_Retinaface
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https://github.com/deepinsight/insightface
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#### Citation
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- If you think this work is useful for you, please cite
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@article{YOLO5Face,
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title = {YOLO5Face: Why Reinventing a Face Detector},
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author = {Delong Qi and Weijun Tan and Qi Yao and Jingfeng Liu},
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booktitle = {ArXiv preprint ArXiv:2105.12931},
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year = {2021}
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}
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#### Main Contributors
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https://github.com/derronqi
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https://github.com/changhy666
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https://github.com/bobo0810
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