yolo-face-with-landmark
实现的功能
- 添加关键点检测分支，使用wing loss
Installation
Clone and install
- git clone https://github.com/ouyanghuiyu/yolo-face-with-landmark
- 使用src/retinaface2yololandmark.py脚本将retinaface的标记文件转为yolo的格式使用,
- 使用src/create_train.py 创建训练样本
训练
python train.py --net mbv3_large_75 --backbone_weights \
./pretrained/mobilenetv3-large-0.75-9632d2a8.pth --batch-size 16
测试
python evaluation_on_widerface.py
cd widerface_evaluate
python evaluation.py
demo
python demo.py
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 | Flops |
|---|---|---|---|---|
| Retinaface-Mobilenet-0.25(Mxnet) | 0.745 | 0.553 | 0.232 | |
| mbv3large_1.0_yolov3(our) | 0.861 | 0.781 | 0.387 | 405M |
| mbv3large_1.0_yolov3_light(our) | 0.856 | 0.770 | 0.370 | 311M |
| mbv3large_0.75_yolov3(our) | 0.853 | 0.778 | 0.382 | 334M |
| mbv3large_0.75_yolov3_light(our) | 0.845 | 0.766 | 0.365 | 240M |
| mbv3samll_1.0_yolov3(our) | 0.798 | 0.696 | 0.3 | 185M |
| mbv3small_1.0_yolov3_light(our) | 0.759 | 0.662 | 0.300 | 91M |
| mbv3samll_0.75_yolov3(our) | 0.768 | 0.673 | 0.305 | 174M |
| mbv3small_0.75_yolov3_light(our) | 0.754 | 0.647 | 0.291 | 80M |
- 在wider face val精度（单尺度输入分辨率：640*480）
| 方法 | Easy | Medium | Hard |
|---|---|---|---|
| Retinaface-Mobilenet-0.25(mxnet) | 0.879 | 0.807 | 0.481 |
| mbv3large_1.0_yolov3(our) | 0.900 | 0.882 | 0.707 |
| mbv3large_1.0_yolov3_light(our) | 0.900 | 0.874 | 0.683 |
| mbv3large_0.75_yolov3(our) | 0.886 | 0.871 | 0.694 |
| mbv3large_0.75_yolov3_light(our) | 0.881 | 0.862 | 0.678 |
| mbv3samll_1.0_yolov3(our) | 0.856 | 0.827 | 0.602 |
| mbv3small_1.0_yolov3_light(our) | 0.847 | 0.807 | 0.578 |
| mbv3samll_0.75_yolov3(our) | 0.841 | 0.815 | 0.584 |
| mbv3small_0.75_yolov3_light(our) | 0.832 | 0.796 | 0.553 |
ps: 测试的时候,长边为320 或者 640 ,图像等比例缩放
测试
