What are augmentations?

Augmentations are an artifical way of expanding the existing datasets by performing some transformations, color shifts or many other things on the data. It helps in diversifying the data and even increasing the data when there is scarcity of data for a model to train on. There are many kinds of augmentations which can be used according to the type of data you are working on some of which are geometric and numerical transformation, PCA, cropping, padding, shifting, noise injection etc.

Augmentations really depend on the type of output classes and the features you want your model to learn. For eg. if you have mostly properly illuminated images in your dataset and want your model to predict poorly illuminated images too, you can apply channel shifting on your data and include the resultant images in your dataset for better results.

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