  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.
