Image classification refers to the problem of classifying images into categories according to their contents. Let's start with an example task of classifying, where a picture may be an image of a dog, or not. A naive approach that someone might take to accomplish this task is to take an input image, reshape it into a vector, and then train a linear classifier (or some other kind of classifier), like we did in Chapter 1, Setup and Introduction to TensorFlow. However, you would very quickly discover that this idea is bad for several reasons. Besides not scaling well to the size of your input image, your linear classifier will simply have a hard time being able to separate one image from another.

In contrast to humans, who can see meaningful patterns and content in an image, the computer only sees an array of numbers from 0 to 255. The wide fluctuation of these numbers at the same locations for different images of the same class prohibits using them directly as an input to the classifier. These 10 example dog images taken from Canadian Institute For Advanced Research (CIFAR) dataset illustrate this problem perfectly. Not only does the appearance of dogs differ, but their pose and position in front of the camera also does. For a machine, each image at a glance is completely different with no commonalities, whereas we as humans can clearly see that these are all dogs:

A better solution to our problem might be to tell the computer to extract some meaningful features from an input image, for example, common shapes, textures, or colors. We could then use these features, rather than the raw input image, as input to our classifier. Now, we are looking for the presence of these features in an image to tell us if it contains the object we want to identify or not.

These extracted features will look to us as simply a high-dimensional vector (but usually a much lower dimension than the original image space) that can be used as input for our classifier. Some well-known feature extraction methods that have been developed over the years are scale invariant features (SIFT), maximally stable extremal regions (MSER), local binary patterns (LBP), and histogram of oriented gradients (HOG).

The year 2012 saw one of the biggest turning points for Computer Vision (and subsequently, other machine learning areas) when the use of convolutional neural networks for image classification started a paradigm shift in how to solve this task (and many others). Rather than focusing on handcrafting better features to extract from our images, we use a data-driven approach that finds the optimal set of features to represent our problem dataset. A CNN will use large number of training images and learn for itself the best features to represent our data in order to solve the classification task.

In this chapter, we will cover the following topics:

• A look at the loss functions used for classification
• The Imagenet and CIFAR datasets
• Training a CNN to classify the CIFAR dataset
• Introduction to the data API
• How to initialize your weights
• How to regularize your models to get better results