《基于Python实习的带有关键点检测的超轻量级人脸检测器（1M人脸检测模型）》+源代码+文档说明

# 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](https://github.com/biubug6/Pytorch_Retinaface)修改anchor尺寸,使其更适合边缘计算; 重新实现了[Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-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. <p align="center"><img src="img/1.jpg"\></p> ## 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](#installation) - [Training](#training) - [Evaluation](#evaluation) - [C++_inference _ncnn](#c++_inference_ncnn) - [References](#references) ## 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: ```Shell ./data/widerface/ train/ images/ label.txt val/ images/ wider_val.txt ``` ps: wider_val.txt only include val file names but not label information. 2. We provide the organized dataset we used as in the above directory structure. Link: from [google cloud](https://drive.google.com/open?id=11UGV3nbVv1x9IC--_tK3Uxf7hA6rlbsS) or [baidu cloud](https://pan.baidu.com/s/1jIp9t30oYivrAvrgUgIoLQ) 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: ```Shell 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 ```Shell mobilenet0.25_Final.pth RBF_Final.pth slim_Final.pth ``` ## Evaluation ### Evaluation widerface val 1. Generate txt file ```Shell python test_widerface.py --trained_model weight_file --network mobile0.25 or slim or RFB ``` 2. Evaluate txt results. Demo come from [Here](https://github.com/wondervictor/WiderFace-Evaluation) ```Shell cd ./widerface_evaluate python setup.py build_ext --inplace python evaluation.py ``` 3. You can also use widerface official Matlab evaluate demo in [Here](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/WiderFace_Results.html) ## C++_inference _ncnn 1. Generate onnx file ```Shell python convert_to_onnx.py --trained_model weight_file --network mobile0.25 or slim or RFB ``` 2. Onnx file change to ncnn(*.param and *.param) ```Shell cp *.onnx ./Face_Detector_ncnn/tools cd ./Face_Detector_ncnn/tools ./onnx2ncnn face.param face.bin ``` 3. Move *.param and *.bin to model ```Shell cp face.param ../model cp face.bin ../model ``` 4. Build Project(set opencv path in CmakeList.txt) ```Shell mkdir build cd build cmake .. make -j4 ``` 5. run ```Shell ./FaceDetector *.jpg ``` We also provide the converted file in "./model". ```Shell face.param face.bin ``` ## References - [FaceBoxes](https://github.com/zisianw/FaceBoxes.PyTorch) - [Retinaface (mxnet)](https://github.com/deepinsight/insightface/tree/master/RetinaFace) - [Retinaface (pytorch)](https://github.com/biubug6/Pytorch_Retinaface) - [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) ``` @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} ```