https://github.com/trigeorgis/mdm

A TensorFlow implementation of the Mnemonic Descent Method.
https://github.com/trigeorgis/mdm

deep-learning face face-alignment mdm menpo pretrained-models snapchat tensorflow

Last synced: about 2 months ago
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A TensorFlow implementation of the Mnemonic Descent Method.

• Host: GitHub
• URL: https://github.com/trigeorgis/mdm
• Owner: trigeorgis
• License: bsd-3-clause
• Created: 2016-06-30T22:20:13.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2022-05-21T17:31:33.000Z (over 2 years ago)
• Last Synced: 2024-08-01T08:11:54.209Z (4 months ago)
• Topics: deep-learning, face, face-alignment, mdm, menpo, pretrained-models, snapchat, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 1.68 MB
• Stars: 124
• Watchers: 12
• Forks: 48
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-tensorflow - Mnemonic Descent Method - Tensorflow implementation of ["Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment"](http://ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf) (Models/Projects)
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README

        
# MDM

A Tensorflow implementation of the Mnemonic Descent Method.

    Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment

    G. 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.

# Installation Instructions

## Menpo

We are an avid supporter of the Menpo project (http://www.menpo.org/) which we use

in various ways throughout the implementation.

Please look at the installation instructions at:

    http://www.menpo.org/installation/

## TensorFlow

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-sources

but use 

    git clone https://github.com/trigeorgis/tensorflow.git

as the TensorFlow repo. This is a fork of Tensorflow (#ff75787c) but it includes some

extra C++ ops, such as for the extraction of patches around the landmarks.

# Pretrained models

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

# Training a model

Currently the TensorFlow implementation does not contain the same data augmnetation steps

as 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

```