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Blazeface

实现功能

  • Blazeface的训练/测试/评估/ncnn C++推理
  • Face-Detector-1MB slim和RFB版本的训练/测试/评估/ncnn C++推理
  • 人脸5个关键点检测
  • 支持onnx导出
  • 网络parameter和flop计算

网页版demo

带有关键点检测的超轻量级人脸检测器（小于1MB）

提供了一系列适合移动端部署包含关键的人脸检测器: 在Face-Detector-1MB基础上实现了Blazeface 并添加了关键点检测和ncnn C++部署功能.

测试的运行环境

  • Ubuntu20.04
  • Python3.8.5
  • Pytorch1.7
  • CUDA11.2 + CUDNN8

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
libfacedetection v1（caffe） 0.65 0.5 0.233
libfacedetection v2（caffe） 0.714 0.585 0.306
version-slim(原版) 0.765 0.662 0.385
version-RFB(原版) 0.784 0.688 0.418
version-slim(our) 0.795 0.683 0.34.5
version-RFB(our) 0.814 0.710 0.363
Retinaface-Mobilenet-0.25(our) 0.811 0.697 0.376
Blazeface 0.782 0.715 0.394
  • 在wider face val精度（单尺度输入分辨率：640*480）
方法 Easy Medium Hard
libfacedetection v1（caffe） 0.741 0.683 0.421
libfacedetection v2（caffe） 0.773 0.718 0.485
version-slim(原版) 0.757 0.721 0.511
version-RFB(原版) 0.851 0.81 0.541
version-slim(our) 0.850 0.808 0.595
version-RFB(our) 0.865 0.828 0.622
Retinaface-Mobilenet-0.25(our) 0.873 0.836 0.638
Blazeface 0.877 0.816 0.639

ps: 测试的时候,长边为320 或者 640 ,图像等比例缩放.

Parameter and flop

方法 parameter(M) flop(M)
version-slim(our) 0.343 98.793
version-RFB(our) 0.359 118.435
Retinaface-Mobilenet-0.25(our) 0.426 193.921
Blazeface 0.175 228.134

ps: 320*240作为输入

inference speed benmark

方法 inference(ms) nms(ms)
version-slim(our) 3.3 20
version-RFB(our) 4.3 21
Retinaface-Mobilenet-0.25(our) 4.6 20
Blazeface 4.3 22

ps: 长边为 640 作为输入, 图像等比例缩放, 使用gpu为3090, 使用test.py测试

Model size comparison

  • Comparison of several open source lightweight face detection models:
Model model file size（MB）
libfacedetection v1（caffe） 2.58
libfacedetection v2（caffe） 3.34
Official Retinaface-Mobilenet-0.25 (Mxnet) 1.68
version-slim 1.04
version-RFB 1.11
Blazeface 0.78

Contents

Installation

Clone and install

  1. git clone https://github.com/zineos/Blazeface.git

  2. Pytorch version 1.7.0+ and torchvision 0.6.0+ are needed.

  3. Codes are based on Python 3

Data
  1. The dataset directory as follows:
  ./data/widerface/
    train/
      images/
      label.txt
    val/
      images/
      wider_val.txt

ps: wider_val.txt only include val file names but not label information.

  1. We provide the organized dataset we used as in the above directory structure.

Link: from google cloud or baidu cloud

Training

  1. Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) in config/config.py and tool/train.py.

  2. Train the model using WIDER FACE:

CUDA_VISIBLE_DEVICES=0 python train.py --network Blaze

If you don't want to train, we also provide a trained model on ./weights

Blaze_Final_640.pth

Evaluation

Evaluation widerface val

  1. Generate txt file
cd blazeface/evaluator
python test_widerface.py --trained_model weight_file --network Blaze
  1. Evaluate txt results. Demo come from Here
cd ./widerface_evaluate
python setup.py build_ext --inplace
python evaluation.py
  1. You can also use widerface official Matlab evaluate demo in Here

C++_inference _ncnn

  1. Generate onnx file
cd tools
python export.py --trained_model weight_file --network Blaze
  1. Onnx file change to ncnn(*.param and *.param)
cd ./demo_ncnn/model
python3 -m onnxsim blazeface.onnx blazeface_sim.onnx
./demo_ncnn/tools/onnx2ncnn blazeface_sim.onn blazeface.param blazeface.bin
  1. Build Project(set opencv path in CmakeList.txt)
mkdir build
cd build
cmake ..
make -j4
  1. run
./FaceDetector *.jpg

We also provide the converted file in ".demo_ncnn/model".

blazeface.param
blazeface.bin

References

@article{bazarevsky2019blazeface,
  title={Blazeface: Sub-millisecond neural face detection on mobile gpus},
  author={Bazarevsky, Valentin and Kartynnik, Yury and Vakunov, Andrey and Raveendran, Karthik and Grundmann, Matthias},
  journal={arXiv preprint arXiv:1907.05047},
  year={2019}
}