This work is inspired by the fastai course 2019 edition "Practical deep learning for coders" v3. This is an extension of lesson 1 "Image classification" on the Stanford cars dataset:
[link] https://ai.stanford.edu/~jkrause/cars/car_dataset.html
3D Object Representations for Fine-Grained Categorization Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei 4th IEEE Workshop on 3D Representation and Recognition, at ICCV 2013 (3dRR-13). Sydney, Australia. Dec. 8, 2013.
[pdf] https://ai.stanford.edu/~jkrause/papers/3drr13.pdf
According to the dataset overview, it "contains 16,185 images of 196 classes of cars. The data is split into 8,144 training images and 8,041 testing images, where each class has been split roughly in a 50-50 split. Classes are typically at the level of Make, Model, Year"