The generation of artificial data based on existing observations, known as data augmentation, is a technique used in machine learning to improve model accuracy, generalisation, and to control overfitting. Augmentor is a software package, available in both Python and Julia versions, that provides a high level API for the expansion of image data using a stochastic, pipeline-based approach which effectively allows for images to be sampled from a distribution of augmented images at runtime. Augmentor provides methods for most standard augmentation practices as well as several advanced features such as label-preserving, randomised elastic distortions, and provides many helper functions for typical augmentation tasks used in machine learning.
@article{arxiv.1708.04680,
title = {Augmentor: An Image Augmentation Library for Machine Learning},
author = {Marcus D. Bloice and Christof Stocker and Andreas Holzinger},
journal= {arXiv preprint arXiv:1708.04680},
year = {2017}
}