Understanding the impact of data augmentation and batch normalization

One clever way of improving the accuracy of models is by leveraging data augmentation. As already mentioned in Chapter 4, we have provided a great deal of extra detail about data augmentation in the GitHub repository. In the real world, you would encounter images that have different properties: for example, some images might be much brighter, some might contain objects of interest near the edges, and some images might be more jittery than others. In this section, we will learn about how the usage of data augmentation can help in improving the accuracy of a model. Furthermore, we will learn about how data augmentation can practically be a pseudo-regularizer for our models.

To understand the impact of data augmentation and batch normalization, we will go through a dataset of recognizing traffic signs. We will evaluate three scenarios:

• No batch normalization/data augmentation
• Only batch normalization...