Vehicle Classification

To build such a model, we will use The Stanford Cars Dataset, an extensive collection of car images. It consists of 16,185 total images labeled with 196 classes based on the car's Make/Model/Year. An example of one of these classes is shown below. The dataset is split into training images and testing images (roughly 50–50% train-test split), and each car class has around 40 images in the training set and the same amount in the testing set as well. The dataset contained no missing values, so no data removal or imputations was required. A challenge was to automate the extraction cars' Make/Model/Year since strings varied in length and character type.