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A Car Model Identification System for Streamlining the Automobile Sales Process

arXiv 2310.13198 ↗First posted 19 Oct 2023cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 29 references that are in the map. First posted on arXiv 19 Oct 2023.

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21 of 29 references.

References · 21

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 12,049
  4. 2016Densely Connected Convolutional Networkscited by 6,720
  5. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  7. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  8. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  9. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  10. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
  11. 2021Barlow Twins: Self-Supervised Learning via Redundancy Reductioncited by 1,838
  12. 2016SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizecited by 1,060
9 more
  1. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,028
  2. 2018AutoAugment: Learning Augmentation Policies from Datacited by 730
  3. 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 727
  4. 2021CoAtNet: Marrying Convolution and Attention for All Data Sizescited by 569
  5. 2017The Effectiveness of Data Augmentation in Image Classification using Deep Learningcited by 370
  6. 2023Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performancecited by 147
  7. 2020Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learningcited by 126
  8. 2022Image Data Augmentation for Deep Learning: A Surveycited by 96
  9. 2018Benchmark Analysis of Representative Deep Neural Network Architecturescited by 77

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