face_detection_yunet

YuNet

YuNet is a light-weight, fast and accurate face detection model, which achieves 0.834(AP_easy), 0.824(AP_medium), 0.708(AP_hard) on the WIDER Face validation set.

Notes:

• Model source: here.
• This model can detect faces of pixels between around 10x10 to 300x300 due to the training scheme.
• For details on training this model, please visit https://github.com/ShiqiYu/libfacedetection.train.
• This ONNX model has fixed input shape, but OpenCV DNN infers on the exact shape of input image. See opencv#44 for more information.

Results of accuracy evaluation with tools/eval.

Models	Easy AP	Medium AP	Hard AP
YuNet	0.8498	0.8384	0.7357
YuNet quant	0.7751	0.8145	0.7312

*: 'quant' stands for 'quantized'.

Demo

Python

Run the following command to try the demo:

# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image

# get help regarding various parameters
python demo.py --help

C++

Install latest OpenCV and CMake >= 3.24.0 to get started with:

# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH /path/to/opencv/installation .
cmake --build build

# detect on camera input
./build/demo
# detect on an image
./build/demo -i=/path/to/image
# get help messages
./build/demo -h

Example outputs

License

All files in this directory are licensed under MIT License.

Reference

• https://github.com/ShiqiYu/libfacedetection
• https://github.com/ShiqiYu/libfacedetection.train