Summary

In this chapter, you have seen the building blocks of computer vision models. We've learned about convolutional layers, and both the ReLU activation and regularization methods. You have also seen a number of ways to use neural networks creatively, such as with Siamese networks and bounding box predictors.

You have also successfully implemented and tested all these approaches on a simple benchmark task, the MNIST dataset. We scaled up our training and used a pretrained VGG model to classify thousands of plant images, before then using a Keras generator to load images from disk on the fly and customizing the VGG model to fit our new task.

We also learned about the importance of image augmentation and the modularity tradeoff in building computer vision models. Many of these building blocks, such as convolutions, batchnorm, and dropout, are used in other areas beyond computer vision. They are fundamental tools that you will see outside of computer vision applications as well. By learning...