This repository contains Bradley Reardon, Divya Parmar and Haruna Salim's final project for George Washington University's DATS 6203:Machine Learning II course. Our project objective was to apply a deep learning framework and network to a real world problem. For our project we applied preprocessing techniques and a convolutinal neural network to classify cars by make and model.

Data Source 1

• Code contains all of our code and data used in the project.
• Proposal contains the proposal for the project.
• Presentation contains a PowerPoint presentation of our project.
• Report contains a report of our findings from this project.

1. To successfully execute the code, make sure you have following libraries installed on your python interpreter enviroment:

• sklearn
• opencv-python
• opencv-contrib-python
• tensorflow
• keras
• seaborn
• pandas
• matplotlib

2. After downloading the training images from here and the test images from here, copy the images from the car_test into the Dataset/Test folder and the images from the car_train into the Dataset/Train folder.

3. To cut data preprocessing time, you can access the already preprocessed numpy files here. Simply copy the train_data.npy and test_data.npy into the DataStorage folder in the Code folder. If you also want to reprocess the data, just execute the DataGenerator.py file again.

4. When you run the main.py file. Set either pretrained or custom to true or false or both.

5. After the preprocessing and trainingoccurs, the model will be saved to the SavedModel folder and will be used for prediction on the test set.