So far, we have seen the various ways of building an image classification model. In this section, we will learn about some of the practical considerations that need to be taken care of when building models. The ones we will discuss in this chapter are as follows:
- Dealing with imbalanced data
- The size of an object within an image when performing classification
- The difference between training and validation images
- The number of convolutional and pooling layers in a network
- Image sizes to train on GPUs
- Leveraging OpenCV utilities
Dealing with imbalanced data
Imagine a scenario where you are trying to predict an object that occurs very rarely within our dataset – let's say in 1% of the total images. For example, this can be the task of predicting whether an X-ray image suggests a rare lung infection.
How do we measure the accuracy of the model that is trained to predict the rare lung infection? If we simply predict...
