UVA Building Classifier

This project was completed for Dr. Stephen Baek's Deep Learning Class as part of the UVA MSDS residentia program. I implemented a range of convolutional neural networks with the ultimate goal of correctly classifying images of UVA buildings into one of 18 categories. I created and trained two models from scratch, both inspired by the VGG architecture, and then capitalized on transfer learning of a RESNET architecture. While all three models had test accuracies of between 80 and 90%, my final model, an ensemble of all three, achieved >99% accuracy on the training set and >95% accuracy on the test set.

Notebook