  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.
