Taken from a Kaggle competition for my UCSC CSE 144 Applied Machine Learning Course. This was my final project for the course. I worked with two other students Hugo Lin and Tyler Knowlton.
We are given data with labels. This data comes from four different datasets: the Stanford Cars dataset, Food101 dataset, Flowers dataset from Nilsback and Zisserman, and the FGVC Aircraft data set. The goal of this project was to train an already constructed model to be able to classify 100 classes from each of these datasets. Our team first attempted this with VGG16 and later RESNET50.
The data was connected to the Google Colabs through a shared drive. If you wish to run this locally the data will be included.
Zeyu-Wang. (2023). UCSC CSE 144 Fall 2023 Final Project. Kaggle. https://kaggle.com/competitions/ucsc-cse-144-fall-2023-final-project