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A TensorFlow implementation of the Mnemonic Descent Method.
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trigeorgis/mdm
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A Tensorflow implementation of the Mnemonic Descent Method.
Mnemonic Descent Method: A recurrent process applied for end-to-end face alignmentG. Trigeorgis, P. Snape, M. A. Nicolaou, E. Antonakos, S. Zafeiriou.Proceedings of IEEE International Conference on Computer Vision & Pattern Recognition (CVPR'16).Las Vegas, NV, USA, June 2016.We are an avid supporter of the Menpo project (http://www.menpo.org/) which we usein various ways throughout the implementation.
Please look at the installation instructions at:
http://www.menpo.org/installation/Follow the installation instructions of Tensorflow at and install it inside the conda enviroment you have created
https://www.tensorflow.org/versions/r0.9/get_started/os_setup.html#installing-from-sourcesbut use
git clone https://github.com/trigeorgis/tensorflow.gitas the TensorFlow repo. This is a fork of Tensorflow (#ff75787c) but it includes someextra C++ ops, such as for the extraction of patches around the landmarks.
Disclaimer:The pretrained models can only be used for non-commercial academic purposes.
A pretrained model on 300W train set can be found at:https://www.doc.ic.ac.uk/~gt108/theano_mdm.pb
Currently the TensorFlow implementation does not contain the same data augmnetation stepsas we did in the paper, but this will be updated shortly.
# Activate the conda environment where tf/menpo resides. source activate menpo # Start training python mdm_train.py --datasets='databases/lfpw/trainset/*.png:databases/afw/*.jpg:databases/helen/trainset/*.jpg' # Track the train process and evaluate the current checkpoint against the validation set python mdm_eval.py --dataset_path="./databases/ibug/*.jpg" --num_examples=135 --eval_dir=ckpt/eval_ibug --device='/cpu:0' --checkpoint_dir=$PWD/ckpt/train python mdm_eval.py --dataset_path="./databases/lfpw/testset/*.png" --num_examples=300 --eval_dir=ckpt/eval_lfpw --device='/cpu:0' --checkpoint_dir=$PWD/ckpt/train python mdm_eval.py --dataset_path="./databases/helen/testset/*.jpg" --num_examples=330 --eval_dir=ckpt/eval_helen --device='/cpu:0' --checkpoint_dir=$PWD/ckpt/train # Run tensorboard to visualise the results tensorboard --logdir==$PWD/ckpt