Data Augmentation

DL models require a lot of data to learn patterns from input data. The performance of a DL model usually increases with the quantity of relevant data. When we have access to a small amount of training data, and we’re unable to collect more data, we utilize a famous approach called data augmentation, eliminating the need for manual data collection. This technique increases the diversity of an existing training set by generating additional examples from the existing data points. Let’s explore TF’s data augmentation techniques for image data.

Common transformations for augmentation

Image augmentation applies random transformations to the images in the original dataset. We can apply a host of transformations to digital images. Image transformations fall into two broad categories:

• Position-based transformations: These include:

  • Resize: This changes the height and width of the image.

  • Translate: This moves all image pixels horizontally or vertically.

  • Rotate: This revolves an image, to a certain number of degrees, around the center (or some other point) in a clockwise or anticlockwise direction.

  • Zoom in/zoom out: This ...