dylan-fan / mtcnn_tf

MTCNN implement by tensorflow. Easy to training and testing.

Description

MTCNN: Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Implement training and testing by tensorflow.

Dependencies

• Tensorflow v1.0.0 or higher
• TF-Slim
• Python 2.7
• Ubuntu 14.04 or CentOS 7.2 or higher
• Cuda 8.0

Prepare Data and Start Training

1. WIDER face dataset: Download WIDER_train.zip from here. You can only download Wider Face Training Images. Unzip it and move it to dataset folder.
2. Landmark dataset: Download train.zip from here. You can only download training set. Unzip it and move it to dataset folder.
3. Run ./clearAll.sh to clear all tmp file.
4. Run ./runAll.sh to finish all(Include prepare data and training). Please check this shell script to get more info.

Testing and predict

1. Copy your image file to testing/images
2. Run python testing/test_images.py --stage=onet. Anyway you can specify stage to pnet or rnet to check your model.
3. The result will output in testing/results_onet

Results

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-Tensorflow