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Face-Detector-1MB-with-landmark

实现功能

  • Retinaface-mobile0.25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

带有关键点检测的超轻量级人脸检测器

提供了一系列适合移动端部署包含关键的人脸检测器: 对Retinaface-mobile0.25修改anchor尺寸,使其更适合边缘计算; 重新实现了Face-Detector-1MB 并添加了关键点检测和ncnn C++部署功能, 在绝大部分情况下精度均好于原始版本.

测试的运行环境

  • Ubuntu18.04
  • Python3.7
  • Pytorch1.2
  • CUDA10.0 + CUDNN7.5

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
  • 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

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

ps: 320*240作为输入

Contents

Installation

Clone and install
  1. git clone https://github.com/biubug6/Face-Detector-1MB-with-landmark.git

  2. Pytorch version 1.1.0+ and torchvision 0.3.0+ 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 Password: ruck

Training

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

  2. Train the model using WIDER FACE:

CUDA_VISIBLE_DEVICES=0 python train.py --network mobile0.25 or 
CUDA_VISIBLE_DEVICES=0 python train.py --network slim or
CUDA_VISIBLE_DEVICES=0 python train.py --network RFB

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

mobilenet0.25_Final.pth 
RBF_Final.pth
slim_Final.pth

Evaluation

Evaluation widerface val

  1. Generate txt file
python test_widerface.py --trained_model weight_file --network mobile0.25 or slim or RFB
  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
python convert_to_onnx.py --trained_model weight_file --network mobile0.25 or slim or RFB
  1. Onnx file change to ncnn(*.param and *.param)
cp *.onnx ./Face_Detector_ncnn/tools
cd ./Face_Detector_ncnn/tools
./onnx2ncnn face.param face.bin
  1. Move *.param and *.bin to model
cp face.param ../model
cp face.bin ../model
  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 "./model".

face.param
face.bin

References

@inproceedings{deng2019retinaface,
title={RetinaFace: Single-stage Dense Face Localisation in the Wild},
author={Deng, Jiankang and Guo, Jia and Yuxiang, Zhou and Jinke Yu and Irene Kotsia and Zafeiriou, Stefanos},
booktitle={arxiv},
year={2019}

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1M人脸检测模型(含关键点)

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