The focus of this post is Image Data Augmentation. When we work with image classification projects, the input which a user will give can vary in many aspects like angles, zoom and stability while clicking the picture. So we should train our model to accept and make sense of almost all types of inputs.
This can be done by training the model for all possibilities. But we can’t go around clicking the same training picture in every possible angles and imagine that when the training set is as big as 10000 pictures!
This can be easily be solved by a technique called Image Data Augmentation, which takes an image, converts it and save it all the possible forms we specify. We will be using Keras for this, which is a deep learning library for Theano and Tensorflow.