- 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
- run in web browser! 👉 https://github.com/zineos/ncnn-webassembly-blazeface
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.
- Ubuntu20.04
- Python3.8.5
- Pytorch1.7
- CUDA11.2 + CUDNN8
- 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.
| 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
| 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
- 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 |
-
git clone https://github.com/zineos/Blazeface.git
-
Pytorch version 1.7.0+ and torchvision 0.6.0+ are needed.
-
Codes are based on Python 3
- The dataset directory as follows:
./data/widerface/
train/
images/
label.txt
val/
images/
wider_val.txtps: wider_val.txt only include val file names but not label information.
- We provide the organized dataset we used as in the above directory structure.
Link: from google cloud or baidu cloud Password: ruck
-
Before training, you can check network configuration (e.g. batch_size, min_sizes and steps etc..) in
config/config.py and tool/train.py. -
Train the model using WIDER FACE:
CUDA_VISIBLE_DEVICES=0 python train.py --network BlazeIf you don't want to train, we also provide a trained model on ./weights
Blaze_Final_640.pth- Generate txt file
cd blazeface/evaluator
python test_widerface.py --trained_model weight_file --network Blaze- Evaluate txt results. Demo come from Here
cd ./widerface_evaluate
python setup.py build_ext --inplace
python evaluation.py- You can also use widerface official Matlab evaluate demo in Here
- Generate onnx file
cd tools
python export.py --trained_model weight_file --network Blaze- 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- Build Project(set opencv path in CmakeList.txt)
mkdir build
cd build
cmake ..
make -j4- run
./FaceDetector *.jpgWe also provide the converted file in ".demo_ncnn/model".
blazeface.param
blazeface.bin- FaceBoxes
- Retinaface (mxnet)
- Retinaface (pytorch)
- Ultra-Light-Fast-Generic-Face-Detector-1MB
- Face-Detector-1MB
@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}
}