@article{,
title= {GANGogh training data set},
keywords= {GANGogh training data, Generative Adversarial Networks (GANS), Machine Learning, Art},
journal= {},
author= {},
year= {},
url= {https://github.com/rkjones4/GANGogh},
license= {},
abstract= {This is a training data set that can be used for the GANGogh machine learning model.

Once downloaded, modify the styles variable in tflib/wikiartGenre.py as follows:
styles = {'abstract': 14999,
          'animal-painting': 1798,
          'cityscape': 6598,
          'figurative': 4500,
          'flower-painting': 1800,
          'genre-painting': 14997,
          'landscape': 15000,
          'marina': 1800,
          'mythological-painting': 2099,
          'nude-painting-nu': 3000,
          'portrait': 14999,
          'religious-painting': 8400,
          'still-life': 2996,
          'symbolic-painting': 2999}},
superseded= {},
terms= {}
}