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# 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](https://github.com/ShiqiYu/libfacedetection.train/blob/a61a428929148171b488f024b5d6774f93cdbc13/tasks/task1/onnx/yunet.onnx).
- 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 https://github.com/opencv/opencv_zoo/issues/44 for more information.
Results of accuracy evaluation with [tools/eval](../../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:
```shell
# 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:
```shell
# 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](./LICENSE).
## Reference
- https://github.com/ShiqiYu/libfacedetection
- https://github.com/ShiqiYu/libfacedetection.train
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