Skip to content
Dog breed classification with logistic regression and a convoluted neural network
Python
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
Images
confusion_matrices
plots
JTD_final_project.pdf
README.md
dog_picker.py
sample.log

README.md

dog-picker

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.

You can’t perform that action at this time.