In previous chapters, we learned about leveraging convolutional neural networks (CNNs) along with pre-trained models to perform image classification. This chapter will further solidify our understanding of CNNs and the various practical aspects to be considered when leveraging them in real-world applications. We will start by understanding the reasons why CNNs predict the classes that they do by using class activation maps (CAMs). Following this, we will understand the various data augmentations that can be done to improve the accuracy of a model. Finally, we will learn about the various instances where models could go wrong in the real world and highlight the aspects that should be taken care of in such scenarios to avoid pitfalls.
The following topics will be covered in this chapter:
- Generating CAMs
- Understanding the impact of batch...
