Abstract
Image classification is a computer vision task that has several applications in diverse fields like security, biology or medicine; and, currently, deep learning techniques have become the state-of-the-art to create image classification models. This growing use of deep learning techniques is due to the large amount of data, the fast increase of the computer processing capacity, and the openness of deep learning tools. However, whenever deep learning techniques are used to solve a classification problem, we can find several deep learning frameworks with their own peculiarities, and different models in each framework; hence, it is natural to wonder which option fits better our problem. In this paper, we present DeepCompareJ, an open-source tool that has been designed to compare, with respect to a given dataset, the quality of deep models created using different frameworks.
Supported by Ministerio de Industria, Economía y Competitividad, project MTM2017-88804-P; Agencia de Desarrollo Econmico de La Rioja, project 2017-I-IDD-00018; and FPU Grant 16/06903 of the Spanish MEC.
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Inés, A., Domínguez, C., Heras, J., Mata, E., Pascual, V. (2020). DeepCompareJ: Comparing Image Classification Models. 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_32
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