End-to-End Car Make and Model Classification using Compound Scaling and Transfer Learning

Recently, Morocco has started to invest in IoT systems to transform our cities into smart cities that will promote economic growth and make life easier for citizens. One of the most vital addition is intelligent transportation systems which represent the foundation of a smart city. However, the problem often faced in such systems is the recognition of entities, in our case, car and model makes. This paper proposes an approach that identifies makes and models for cars using transfer learning and a workflow that first enhances image quality and quantity by data augmentation and then feeds the newly generated data into a deep learning model with a scaling feature-that is, compound scaling. In addition, we developed a web interface using the FLASK API to make real-time predictions. The results obtained were 80% accuracy, fine-tuning it to an accuracy rate of 90% on unseen data. Our framework is trained on the commonly used Stanford Cars dataset.