https://github.com/DayBreak-u/darknet_face_with_landmark
加入关键点的darknet训练框架，轻量级的人脸检测，支持ncnn推理
https://github.com/DayBreak-u/darknet_face_with_landmark
darknet facedetect landmark lightmode ncnn yolov3
Last synced: 27 days ago
JSON representation
加入关键点的darknet训练框架，轻量级的人脸检测，支持ncnn推理
- Host: GitHub
- URL: https://github.com/DayBreak-u/darknet_face_with_landmark
- Owner: DayBreak-u
- License: other
- Created: 2020-05-17T13:09:16.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-07-29T07:16:23.000Z (over 4 years ago)
- Last Synced: 2024-10-23T00:36:39.581Z (6 months ago)
- Topics: darknet, facedetect, landmark, lightmode, ncnn, yolov3
- Language: C
- Homepage:
- Size: 27.7 MB
- Stars: 210
- Watchers: 15
- Forks: 71
- Open Issues: 27
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-ncnn - darknet_face_with_landmark
README
# darknet_face_with_landmark
## 更新 torch版本：https://github.com/ouyanghuiyu/yolo-face-with-landmark
### 借鉴AlexeyAB大神的 [darknet](https://github.com/AlexeyAB/darknet) 做适量修改，用于人脸检测以及关键点检测,支持ncnn推理
### 实现的功能
- 添加关键点检测分支，使用wing loss
- 添加 hswish,hsigmode 激活函数
## Installation
##### Clone and install
1. git clone https://github.com/ouyanghuiyu/darknet_face_with_landmark.git
2. 使用scripts/retinaface2yololandmark.py脚本将retinaface的标记文件转为yolo的格式使用
3. 其他编译训练都和原版darknet相同
4. 测试
```
./darknet detector test ./data/face.data ./cfg/mbv2_yolov3_face.cfg ./models/mbv2_yolov3_face_final.weights ./test_imgs/input/selfie.jpg -dont_show
```
或者使用yolo_landmark.py进行测试，更换里面的模型配置文件即可
## 精度
### Widerface测试
- 在wider face val精度（单尺度输入分辨率：**320*240**）
方法|Easy|Medium|Hard
------|--------|----------|--------
libfacedetection v1（caffe）|0.65 |0.5 |0.233
libfacedetection v2（caffe）|0.714 |0.585 |0.306
Retinaface-Mobilenet-0.25(Mxnet) |0.745|0.553|0.232
mbv2_yolov3_face(our) |**0.84**|**0.79**|**0.41**
- 在wider face val精度（单尺度输入分辨率：**640*480**）
方法|Easy|Medium|Hard
------|--------|----------|--------
libfacedetection v1（caffe）|0.741 |0.683 |0.421
libfacedetection v2（caffe）|0.773 |0.718 |0.485
Retinaface-Mobilenet-0.25(Mxnet) |**0.879**|0.807|0.481
mbv2_yolov3_face(our) |0.866|**0.848**|**0.718**
ps: 测试的时候,长边为320 或者 640 ,图像等比例缩放,yolo未作缩放.
## 测试
## References
- [darknet](https://github.com/AlexeyAB/darknet)