Keras implementation of Apple's Learning from Simulated and Unsupervised Images through Adversarial Training
Install dlutils from https://github.com/wayaai/deep-learning-utils:
$ pip install -U git+https://github.com/wayaai/deep-learning-utils.git
or
$ git clone https://github.com/wayaai/deep-learning-utils.git
$ python setup.py install develop
python3 sim-gan.py PATH_TO_SYNTHESEYES_DATASET PATH_TO_MPII_GAZE_DATASETIn apple's paper they use Unity Eyes to generate ~1.2 million synthetic images. I am on mac though so I just used the easily available SynthesEyes Dataset. This is small (only around ~11,000 images) so it would be much better if someone could generate a larger dataset w/ Unity Eyes and share it on s3.
The dataset of real image's used in apple's paper is the MPIIGaze Dataset. They use the normalized images provided in this dataset which are stored in matlab files. It was a bit of a pain to get these in an easily usable form so I'm sharing the ready to go datasets on s3.
- 50000 Unity Eyes: https://www.kaggle.com/4quant/eye-gaze
Implementation of 3.1 Appearance-based Gaze Estimation on UnityEyes and MPIIGaze datasets as described in paper.
- Currently only Python 3 support.
- Tensorflow support and maybe PyTorch support in future.
This is meant to be a light-weight and clean implementation that is easy to understand - no deep shit. It can also be used as a resource to understand GANs in general and how they can be implemented.
You can see a interactive Jupyter Notebook version of this script with training data on Kaggle or just the raw training set
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