ltrottier / mtcnn-tensorflow Goto Github PK

This work is used for reproduce MTCNN, a Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks.

1. You need CUDA-compatible GPUs to train the model.

• Tensorflow 1.2.1
• Python 2.7
• Ubuntu 16.04
• Cuda 8.0

1. Download the WIDER Face Dataset. Unzip it and put the WIDER_train folder in the prepare_data folder.
2. Download the Landmark Dataset. Unzip it and put both the lfw_5590 and net_7876 into the prepare_data folder.
3. Run gen_12net_data.py to generate training data(Face Detection Part) for PNet.
4. Run gen_landmark_data.py lfwnet PNet to generate training data(Face Landmark Detection Part) for PNet.
5. Run gen_imglist.py PNet to merge two parts of training data.
6. Run gen_tfrecords.py PNet to generate tfrecord for PNet.
7. In train_models/, run train_net.py PNet to train PNet.
8. After training PNet, run gen_hard_example.py --test_mode PNet to generate training data(Face Detection Part) for RNet.
9. Run gen_landmark_data.py lfwnet RNet to generate training data(Face Landmark Detection Part) for RNet.
10. Run gen_imglist.py PNet to merge two parts of training data.
11. Run gen_tfrecords.py RNet to generate tfrecords for RNet.
12. In train_models/, run train_net.py RNet to train RNet.
13. After training RNet, run gen_hard_example.py --test_mode RNet to generate training data(Face Detection Part) for ONet.
14. Run gen_landmark_data.py lfwnet ONet to generate training data(Face Landmark Detection Part) for ONet.
15. Run gen_imglist.py ONet to merge two parts of training data.
16. Run gen_tfrecords.py ONet to generate tfrecords for ONet.
17. In train_models/, run train_net.py ONet to train ONet.

• 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 on FDDB

MIT LICENSE

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
3. MTCNN-CAFFE
4. deep-landmark