Awesome

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 (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 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