Every modification in machine learning depends on your data. Let’s say if you are using data obtained from a sensor, then you may want to add some noise to the training data so that you can increase your dataset. There may be noise from the sensor also.
Python’s libraries can help you with augmenting images for your machine learning projects. It can convert a set of input images into a new, much larger set of slightly altered images.
It provides most standard augmentation techniques available.
Some techniques can be applied to both images and landmarks on images.
It defines flexible stochastic ranges for each augmentation, e.g. "rotate each image by a value between -45 and 45 degrees" or "rotate each image by a value sampled from the normal distribution N(0, 5.0)".
Easily convert all stochastic ranges to deterministic values to augment different batches of images in the exactly identical way.
Hope this answer helps.