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.

该研究提出了一种自动化解决方案，通过图像识别车辆型号和制造商，旨在简化在线汽车销售平台的车辆清单流程。经过对包括卷积神经网络（CNNs）、视觉Transformer（ViTs）和混合模型在内的各种高效网络架构的彻底探索，采用EfficientNet（V2 b2）架构达到了81.97%的令人瞩目的准确率。通过数据增强、微调预训练模型和广泛的超参数调整等一系列策略，进一步提升了性能。训练好的模型为信息提取的自动化提供了潜力，有望提高汽车销售网站的用户体验。

用于优化汽车销售流程的汽车型号识别系统