Comparison of Convolutional Neural Network Models for Mobile Devices - Isuyama, Vivian Kimie; Albertini, Bruno de Carvalho (Anais do Workshop em Desempenho de Sistemas Computacionais e de Comunicação (WPerformance); 2021: Anais do XX Workshop em Desempenho de Sistemas Computacionais e de ComunicaçãoDO - 10.5753/wperformance.2021.15724, 2021)
In recent years mobile devices have become an important part of our daily lives and Deep Convolutional Neural Networks have been performing well in the task of image classification. Some considerations have to be made when running a Neural Network inside a mobile device such as computational complexity and storage size. In this paper, common architectures for image classification were analyzed to retrieve the values of accuracy rate, model complexity, memory usage, and inference time. Those values were compared and it was possible to show which architecture to choose from considering mobile restrictions.