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
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preparing data for P-Net
- run > python mtcnn/data_preprocessing/gen_Pnet_train_data.py
- run > python mtcnn/data_preprocessing/assemble_pnet_imglist.py
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train P-Net
- run > python mtcnn/train_net/train_p_net.py
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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
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train R-Net
- run > python mtcnn/train_net/train_r_net.py
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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
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train O-Net
- run > python mtcnn/train_net/train_o_net.py