mtcnn-pytorch:人脸检测算法

# mtcnn-pytorch # Descriptions in chinese https://blog.csdn.net/Sierkinhane/article/details/83308658 # results:     # Test an image * run > python mtcnn_test.py # Training data prepraring * download [WIDER FACE](https://pan.baidu.com/s/1sJTO7TcQ2576RUqR_IIhbQ) (passcode:lsl3) face detection data then store it into ./data_set/face_detection * run > python ./anno_store/tool/format/transform.py change .mat(wider_face_train.mat) into .txt(anno_train.txt) * download [CNN_FacePoint](http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm) face detection and landmark data then store it into ./data_set/face_landmark # Training * preparing data for P-Net * run > python mtcnn/data_preprocessing/gen_Pnet_train_data.py * run > python mtcnn/data_preprocessing/assemble_pnet_imglist.py * train P-Net * run > python mtcnn/train_net/train_p_net.py * preparing data for R-Net * run > python mtcnn/data_preprocessing/gen_Rnet_train_data.py (maybe you should change the pnet model path) * run > python mtcnn/data_preprocessing/assemble_rnet_imglist.py * train R-Net * run > python mtcnn/train_net/train_r_net.py * preparing data for O-Net * run > python mtcnn/data_preprocessing/gen_Onet_train_data.py * run > python mtcnn/data_preprocessing/gen_landmark_48.py * run > python mtcnn/data_preprocessing/assemble_onet_imglist.py * train O-Net * run > python mtcnn/train_net/train_o_net.py # Citation [DFace](https://github.com/kuaikuaikim/DFace)