A Car Model Identification System for Streamlining the Automobile Sales Process
This work addresses a domain-specific problem for online car-selling platforms, but it is incremental as it applies existing methods to a new dataset.
The paper tackled the problem of automating car model identification from images to streamline online vehicle listings, achieving 81.97% accuracy using an EfficientNet (V2 b2) architecture.
This project presents an automated solution for the efficient identification of car models and makes from images, aimed at streamlining the vehicle listing process on online car-selling platforms. Through a thorough exploration encompassing various efficient network architectures including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models, we achieved a notable accuracy of 81.97% employing the EfficientNet (V2 b2) architecture. To refine performance, a combination of strategies, including data augmentation, fine-tuning pretrained models, and extensive hyperparameter tuning, were applied. The trained model offers the potential for automating information extraction, promising enhanced user experiences across car-selling websites.