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yolo-face-with-landmark's Introduction

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
  1. git clone https://github.com/ouyanghuiyu/yolo-face-with-landmark
  2. 使用src/retinaface2yololandmark.py脚本将retinaface的标记文件转为yolo的格式使用,
  3. 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

References

yolo-face-with-landmark's People

Contributors

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