State-of-the-art machine learning models use millions, sometimes billions, of parameters. When training our models, the number of distinct examples we need is proportional to the number of parameters our model has.

Invariance is a trait of a neural network model (for instance, an image classification model) that can robustly classify objects even when the objects are placed in different orientations, have different sizes, and illumination differences. Many of these potential differences in our dataset can be created artificially, instead of collecting more images. Methods could be to alter brightness or contrast of the image, stretch or skew operations, or a variety of translation methods.

Create Python script that will take any image dataset and augment it in some way to expand the dataset. Submission must include a script that will augment any image files within its folder. Please include a small sample un-augmented dataset with your submission.