SimGAN

Implementation of SimGAN using Tensorflow 2.

• Notes available here

Getting Started

1. Install required python packages

pip install -r requirements.txt

2. Dataset

You can find the dataset from Kaggle here. Both real (MPIIGaze) and Simulated (UnityEyes) can be found there in ready to go h5 format. ( real_gaze.h5 and gaze.h5 )

3. Training

To start training, run the following script:

python simgan.py <real_h5_file> <simulated_h5_file> [--refiner_model <REF_MODEL_PATH>] [--discriminator_model <DISC_MODEL_PATH>]

Args:

• <real_h5_file> - Path to the real dataset in h5 format (real_gaze.h5).
• <synthetic_h5_file> - Path to the synthetic dataset in h5 format (gaze.h5)
• <REF_MODEL_PATH> - (Optional) Refiner model to start training from.
• <DISC_MODEL_PATH> - (Optional) Discriminator model to start training from.

Output: Every DEBUG_INTERVAL steps, Intermediate model checkpoints (refiner and discriminator) are saved in the cache directory.