Training for libfacedetection in PyTorch

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].

Contents

• Installation
• Preparation
• Training
• Detection
• Evaluation on WIDER Face
• Export CPP source code
• Export to ONNX model
• Design your own model
• Citation

Installation

1. Install PyTorch >= v1.7.0 following official instruction. e.g.
   On GPU platforms (cu102):\

   conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch

2. Install MMCV >= v1.3.17 following official instruction. e.g.\

   pip install mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu102/torch1.7.0/index.html

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

   git clone https://github.com/Wwupup/wwfacedet
   cd wwfacedet
   python setup.py develop

4. Install dependencies.

   pip install -r requirements.txt

Note: Codes are based on Python 3+.

Preparation

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

Training

Following MMdetection training processing.

CUDA_VISIBLE_DEVICES=0,1 bash tools/dist_train.sh ./config/yunet/x.py 2

Detection

python tools/detect-image.py ./config/x.py ./work_dirs/x/latest.pth ./image.jpg

Evaluation on WIDER Face

python tools/test_widerface.py ./config/x.py ./work_dirs/x/latest.pth --mode 2

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

AP_easy=0.899, AP_medium=0.883, AP_hard=0.792

Export CPP source code

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

python tools/export2cpp.py ./config/x.py ./work_dirs/x/latest.pth

Export to onnx model

Export to onnx model for libfacedetection/example/opencv_dnn.

python tools/wwdet2onnx.py ./config/x.py ./work_dirs/x/latest.pth

Compare ONNX model with other works

Inference on exported ONNX models using ONNXRuntime:

python tools/compare_inference.py ./onnx/wwdet.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 = 0.3, nms_thresh = 0.45, some results are list as follow:

Model	AP_easy	AP_medium	AP_hard	#Params	Params Ratio	MFlops	Forward (ms)
SCRFD0.5(ICLR2022)	0.879	0.863	0.759	631410	7.43x	184	22.3
Retinaface0.5(CVPR2020)	0.899	0.866	0.660	426608	5.02X	245	13.9
YuNet(Ours)	0.885	0.877	0.762	85006	1.0x	136	10.6

The compared ONNX model is available in https://share.weiyun.com/nEsVgJ2v Password：gydjjs

Citation

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={Yuantao Feng and Shiqi Yu and Hanyang Peng and Yan-ran Li and Jianguo Zhang}
 title={Detect Faces Efficiently: A Survey and Evaluations},
 journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
 year={2021}
 }

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