MTCNN_TRAIN:MTCNN_使用PyTorch 0.4.0进行面部检测的训练脚本

# MTCNN_TRAIN MTCNN_Train Scripts with PyTorch 0.4.0 ## Declaration **The source code in this repository is mainly from [kuaikuaikim/DFace](https://github.com/kuaikuaikim/DFace).** **I reimplemented the part of MTCNN with PyTorch 0.4.0 and made some optimizations but most remains unchanged. If you want to know more details, please go to [kuaikuaikim/DFace](https://github.com/kuaikuaikim/DFace)** --- ## Introduction ~~This project is still in progess, I will finish it in my spare time as soon as possible !~~ This project is a reimplementation version of mtcnn face detection, most of the source code is from [kuaikuaikim/DFace](https://github.com/kuaikuaikim/DFace), I restructed the source code with Pytorch 0.4.0 and made some modifications and optimizations. All the contributions I have made is listed below. ## The Contributions 1. restruct the source code with PyTorch 0.4.0. 2. avoid some unnecessary image data copy operation in training data preparation, for example, ./prepare_data/gen_Pnet_data.py and so on. 3. remove some meaningless operation in traing process, and format the output information during training. 4. fix the bug that data_loader can't load the last mini_batch when the last minibatch'size is less than the batch_size in ./tools/image_reader.py. 5. to be continue. ## How to use For training PNet and RNet, I only use the [Widerface](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) for face classification and face bounding box regression. For training ONet, I use [Widerface](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) for face classification and face bounding box regression and use [Training Dataset](http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm) for face landmark regression. 1. Train PNet ``` python cd MTCNN_TRAIN python prepare_data/gen_Pnet_train_data.py python prepare_data/assemble_pnet_imglist.py python train_net/train_p_net.py ``` 2. Train RNet ``` python cd MTCNN_TRAIN python prepare_data/gen_Rnet_train_data.py python prepare_data/assemble_rnet_imglist.py python train_net/train_r_net.py ``` 3. Train ONet ``` python cd MTCNN_TRAIN python prepare_data/gen_landmark_48.py python prepare_data/gen_Onet_train_data.py python prepare_data/assemble_onet_imglist.py python train_net/train_o_net.py ``` 4. Test Image ``` python cd MTCNN_TRAIN python test_image.py ``` ## Results Because I didn't use much data to train, the detection results is not at the best.  ## Problems There still remains a problem to solve: When starting to train each stage network, the first batch will last for a long time about 30 minutes and I don't know why.