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.
@article{arxiv.2310.13198,
title = {A Car Model Identification System for Streamlining the Automobile Sales Process},
author = {Said Togru and Marco Moldovan},
journal= {arXiv preprint arXiv:2310.13198},
year = {2023}
}