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Showing content from https://github.com/ShiqiYu/libfacedetection.train below:

ShiqiYu/libfacedetection.train: The training program for libfacedetection for face detection and 5-landmark detection.

It is the training program for libfacedetection. The source code is based on MMDetection. Some data processing functions from SCRFD modifications.

Visualization of our network architecture: [netron].

• Training for libfacedetection in PyTorch
  • Contents
  • Installation
  • Preparation
  • Training
  • Detection
  • Evaluation on WIDER Face
  • Export CPP source code
  • Export to onnx model
  • Compare ONNX model with other works
  • Citation

1. Create conda environment. e.g.

   conda create -n yunet python=3.8
   conda activate yunet

2. Install PyTorch == v1.8.2 (LTS) following official instruction. e.g.
   On GPU platforms (cu11.1):

   # LINUX:
   conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c nvidia
   # WINDOWS:
   conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch-lts -c conda-forge

   On GPU platforms (cu10.2):

   conda install pytorch torchvision torchaudio cudatoolkit=10.2 -c pytorch-lts

3. Install MMCV >= v1.3.17 but <=1.6.0 following official instruction. e.g.

   # cu11.1
   pip install mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.8.0/index.html
   # cu10.2
   pip install mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.8.0/index.html

4. Clone this repository. We will call the cloned directory as $TRAIN_ROOT.

   git clone https://github.com/ShiqiYu/libfacedetection.train.git
   cd libfacedetection.train

5. Install dependencies.

   python setup.py develop
   pip install -r requirements.txt

Note:

1. Codes are based on Python 3+.
2. If meet error "ModuleNotFoundError: No module named 'torch.ao'", you can Ctrl + click to origin line and replace torch.ao to torch

1. Download the WIDER Face dataset and its evaluation tools.
2. Extract zip files under $TRAIN_ROOT/data/widerface as follows:

   $ tree data/widerface
   data/widerface
   ├── wider_face_split
   ├── WIDER_test
   ├── WIDER_train
   ├── WIDER_val
   └── labelv2
         ├── train
         │   └── labelv2.txt
         └── val
             ├── gt
             └── labelv2.txt

NOTE:
The labelv2 comes from SCRFD.

Following MMdetection training processing.

CUDA_VISIBLE_DEVICES=0,1 bash tools/dist_train.sh ./configs/yunet_n.py 2 12345

python tools/detect_image.py ./configs/yunet_n.py ./weights/yunet_n.pth ./image.jpg

python tools/test_widerface.py ./configs/yunet_n.py ./weights/yunet_n.pth --mode 2

Performance on WIDER Face (Val): confidence_threshold=0.02, nms_threshold=0.45, in origin size:

AP_easy=0.892, AP_medium=0.883, AP_hard=0.811

The following bash code can export a CPP file for project libfacedetection

python tools/yunet2cpp.py ./configs/yunet_n.py ./weights/yunet_n.pth

Export to onnx model for libfacedetection/example/opencv_dnn.

python tools/yunet2onnx.py ./configs/yunet_n.py ./weights/yunet_n.pth

Inference on exported ONNX models using ONNXRuntime:

python tools/compare_inference.py ./onnx/yunet_n.onnx --mode AUTO --eval --score_thresh 0.02 --nms_thresh 0.45

Some similar approaches(e.g. SCRFD, Yolo5face, retinaface) to inference are also supported.

With Intel i7-12700K and input_size = origin size, score_thresh = 0.02, nms_thresh = 0.45, some results are list as follow:

The compared models can be downloaded from Google Drive.

We published a paper for the main idea of this repository:

@article{yunet,
  title={YuNet: A Tiny Millisecond-level Face Detector},
  author={Wu, Wei and Peng, Hanyang and Yu, Shiqi},
  journal={Machine Intelligence Research},
  pages={1--10},
  year={2023},
  doi={10.1007/s11633-023-1423-y},
  publisher={Springer}
}

The paper can be open accessed at https://link.springer.com/article/10.1007/s11633-023-1423-y.

The loss used in training is EIoU, a novel extended IoU. More details can be found in:

@article{eiou,
 author={Peng, Hanyang and Yu, Shiqi},
 journal={IEEE Transactions on Image Processing},
 title={A Systematic IoU-Related Method: Beyond Simplified Regression for Better Localization},
 year={2021},
 volume={30},
 pages={5032-5044},
 doi={10.1109/TIP.2021.3077144}
}

The paper can be open accessed at https://ieeexplore.ieee.org/document/9429909.

We also published a paper on face detection to evaluate different methods.

@article{facedetect-yu,
  author={Feng, Yuantao and Yu, Shiqi and Peng, Hanyang and Li, Yan-Ran and Zhang, Jianguo},
  journal={IEEE Transactions on Biometrics, Behavior, and Identity Science}, 
  title={Detect Faces Efficiently: A Survey and Evaluations}, 
  year={2022},
  volume={4},
  number={1},
  pages={1-18},
  doi={10.1109/TBIOM.2021.3120412}
}

The paper can be open accessed at https://ieeexplore.ieee.org/document/9580485

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