Abstract
This paper describes an end-to-end training methodology for CNN-based fine-grained vehicle model classification. The method relies exclusively on images, without using complicated architectures. No extra annotations, pose normalization or part localization are needed. Different full CNN-based models are trained and validated using CompCars [31] dataset, for a total of 431 different car models. We obtained a top-1 validation accuracy of 97.62% which substantially outperforms previous works.
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Acknowledgments
This work was supported in part by the Spanish Ministry of Science, Innovation and Universities under Research Grant DPI2017-90035-R, in part by the Community Region of Madrid (Spain) under Research Grant S2018/EMT-4362 and in part by the Electronic Component Systems for European Leadership Joint Undertaking through the European Union’s H2020 Research and Innovation Program and Germany, Austria, Spain, Italy, Latvia, Belgium, The Netherlands, Sweden, Finland, Lithuania, Czech Republic, Romania and Norway, under Grant 73746.
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Corrales, H. et al. (2020). CNNs for Fine-Grained Car Model Classification. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science(), vol 12014. Springer, Cham. https://doi.org/10.1007/978-3-030-45096-0_13
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