Intro to PyTorch 2: Convolutional Neural Networks

The model we developed for classifying images in the CIFAR-10 dataset was only able to achieve a 53% accuracy on the validation set, and really struggled to correctly classify images of some classes, like birds and cats ( 33–35%). This was expected, since we would normally use Convolutional Neural Networks for image classification. In this part of the tutorial series, we will focus on CNN's and improving the performance of image classification on CIFAR-10. Before we dive into the code, let's discuss the basics of convolutional neural networks so we can have a better understanding of what our code is doing. If you're comfortable with how CNN's work, feel free to skip this section. In comparison to feed-forward networks, like the one we developed in the previous part of the series, CNN's have different architecture, and are composed of different types of layers. In the figure below, we can see the general architecture of a typical CNN, including the different types of layers it can contain.