Dog breed classification with logistic regression and a convoluted neural network
Humans are excellent at image classification. Show us a packed grocery store produce aisle and we can instantly pick out lettuce and bananas from hundreds of other options. Even though apples and pomegranates are both red, spherical, and contain a stem we can easily distinguish them visually. In fact, image classification is so simple for our brains that we take it for granted. Computers, on the other hand, have had more difficult time with image classification.
A picture is simply a multi-dimensional array of numbers to a classification algorithm, and extracting features and information from millions (or billions) of pixels is a formidable challenge. Advances in machine learning and computing power are enabling computers to approach human image classification accuracies. These technologies will have a powerful impact on the medical imaging field, however here I turn to a much more important problem – dog breed classification. For this project I built logistic regression and convoluted neural network (CNN) models to classify dog breeds based on an image.