tensorfolw训练mtcnn模型

## Description This work is used for reproduce MTCNN,a Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks. ## Prerequisites 1. You need CUDA-compatible GPUs to train the model. 2. You should first download [WIDER Face](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and [Celeba](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html).**WIDER Face** for face detection and **Celeba** for landmark detection(This is required by original paper.But I found some labels were wrong in Celeba. So I use [this dataset](http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm) for landmark detection). ## Dependencies * Tensorflow 1.2.1 * TF-Slim * Python 2.7 * Ubuntu 16.04 * Cuda 8.0 ## Prepare For Training Data 1. Download Wider Face Training part only from Official Website , unzip to replace `WIDER_train` and put it into `prepare_data` folder. 2. Download landmark training data from [here]((http://mmlab.ie.cuhk.edu.hk/archive/CNN_FacePoint.htm)),unzip and put them into `prepare_data` folder. 3. Run `prepare_data/gen_12net_data.py` to generate training data(Face Detection Part) for **PNet**. 4. Run `gen_landmark_aug_12.py` to generate training data(Face Landmark Detection Part) for **PNet**. 5. Run `gen_imglist_pnet.py` to merge two parts of training data. 6. Run `gen_PNet_tfrecords.py` to generate tfrecord for **PNet**. 7. After training **PNet**, run `gen_hard_example` to generate training data(Face Detection Part) for **RNet**. 8. Run `gen_landmark_aug_24.py` to generate training data(Face Landmark Detection Part) for **RNet**. 9. Run `gen_imglist_rnet.py` to merge two parts of training data. 10. Run `gen_RNet_tfrecords.py` to generate tfrecords for **RNet**.(**you should run this script four times to generate tfrecords of neg,pos,part and landmark respectively**) 11. After training **RNet**, run `gen_hard_example` to generate training data(Face Detection Part) for **ONet**. 12. Run `gen_landmark_aug_48.py` to generate training data(Face Landmark Detection Part) for **ONet**. 13. Run `gen_imglist_onet.py` to merge two parts of training data. 14. Run `gen_ONet_tfrecords.py` to generate tfrecords for **ONet**.(**you should run this script four times to generate tfrecords of neg,pos,part and landmark respectively**) ## Some Details * When training **PNet**,I merge four parts of data(pos,part,landmark,neg) into one tfrecord,since their total number radio is almost 1:1:1:3.But when training **RNet** and **ONet**,I generate four tfrecords,since their total number is not balanced.During training,I read 64 samples from pos,part and landmark tfrecord and read 192 samples from neg tfrecord to construct mini-batch. * It's important for **PNet** and **RNet** to keep high recall radio.When using well-trained **PNet** to generate training data for **RNet**,I can get 14w+ pos samples.When using well-trained **RNet** to generate training data for **ONet**,I can get 19w+ pos samples. * Since **MTCNN** is a Multi-task Network,we should pay attention to the format of training data.The format is: [path to image][cls_label][bbox_label][landmark_label] For pos sample,cls_label=1,bbox_label(calculate),landmark_label=[0,0,0,0,0,0,0,0,0,0]. For part sample,cls_label=-1,bbox_label(calculate),landmark_label=[0,0,0,0,0,0,0,0,0,0]. For landmark sample,cls_label=-2,bbox_label=[0,0,0,0],landmark_label(calculate). For neg sample,cls_label=0,bbox_label=[0,0,0,0],landmark_label=[0,0,0,0,0,0,0,0,0,0]. * Since the training data for landmark is less.I use transform,random rotate and random flip to conduct data augment(the result of landmark detection is not that good). ## Result          **Result on FDDB**  ## License MIT LICENSE ## References 1. Kaipeng Zhang, Zhanpeng Zhang, Zhifeng Li, Yu Qiao , " Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks," IEEE Signal Processing Letter 2. [MTCNN-MXNET](https://github.com/Seanlinx/mtcnn) 3. [MTCNN-CAFFE](https://github.com/CongWeilin/mtcnn-caffe) 4. [deep-landmark](https://github.com/luoyetx/deep-landmark)