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This project presents an automated solution for the efficient identificationof car models and makes from images, aimed at streamlining the vehicle listingprocess on online car-selling platforms. Through a thorough explorationencompassing various efficient network architectures including ConvolutionalNeural Networks (CNNs), Vision Transformers (ViTs), and hybrid models, weachieved a notable accuracy of 81.97% employing the EfficientNet (V2 b2)architecture. To refine performance, a combination of strategies, includingdata augmentation, fine-tuning pretrained models, and extensive hyperparametertuning, were applied. The trained model offers the potential for automatinginformation extraction, promising enhanced user experiences across car-sellingwebsites.

A Car Model Identification System for Streamlining the Automobile Sales Process