Team Members: Sujay Chebbi, Lai Jiang, Immanuel Ponminissery, Jennifer Robinson, Patricia Schutter
Abstract
Image recognition is an exciting new field and has many important applications ranging from delivery service, payment processing, and public safety. We wanted to take image recognition and apply it to cars. Have you ever wondered what the car model is when you received an Amber Alert? What kind of car is the suspect driving? We believe by applying cameras with high speed image recognition capability at key highway intersections, we are able to match the car to the model and help police find the criminal faster. We used the Stanford Car Dataset and a variety of transfer learning models. We found RestNet50 to be our best model.
Files:
- making_folders.ipynb: Creates folders for different models
- moving_pics.ipynb: Copies pictures from source folder to corresponding folders
- calling_best_saved_models.ipynb: Predicting using trained models
- cnn.ipynb: Basic CNN implementation
- Vgg16.ipynb: Transfer learning technique VGG16
- Resnet50.ipynb: Transfer learning technique ResNet50
- Resnet101.ipynb: Transfer learning technique ResNet101
- inception_v3.ipynb: Transfer learning technique InceptionV3
- Xception.ipynb: Transfer learning technique Xception
