Cat face detection using MTCNN

results:

It doesn't perform well, only for reference

Test an image

run > python mtcnn_test.py

Training data prepraring

download cat face dataset for landmark, then unzip it into ./data_set/original/
- run > python ./anno_store/tool/transform.py change train.csv into .txt(anno_train.txt)

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 mtcnn-pytorch