BRCars: a Dataset for Fine-Grained Classification of Car Images

  • Daniel M. Kuhn UFRGS
  • Viviane P. Moreira UFRGS


Fine-grained computer vision tasks refer to the ability of distinguishing objects that belong to the same parent class, differentiating themselves by subtle visual elements. Image classification in car models is considered a fine-grained classification task. In this work, we introduce BRCars, a dataset that seeks to replicate the main challenges inherent to the task of classifying car images in many practical applications. BRCars contains around 300K images collected from a Brazilian car advertising website. The images correspond to 52K car instances and are distributed among 427 different models. The images are both from the exterior and the interior of the cars and present an unbalanced distribution across the different models. In addition, they are characterized by a lack of standardization in terms of perspective. We adopted a semi-automated annotation pipeline with the help of the new CLIP neural network, which enabled distinguishing thousands of images among different perspectives using textual queries. Experiments with standard deep learning classifiers were performed to serve as baseline results for future work on this topic. BRCars dataset is available at

Palavras-chave: Deep learning, Visualization, Computer vision, Computational modeling, Pipelines, Neural networks, Automobiles, fine grained computer vision, car model classification
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KUHN, Daniel M.; MOREIRA, Viviane P.. BRCars: a Dataset for Fine-Grained Classification of Car Images. In: CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 34. , 2021, Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 .