In this paper, we present several approaches to configuration of deep convolutional neural networks for image classification. A common problem when creating deep structures is their proper designing and configuration. This paper shows the learning of the baseline model for image classification and its variations with different structures based on the baseline model. Each of them has different configurations related to downsampling, pooling and filters dilatation. The paper is intended as a guideline for proper designing of deep structures based on experiences resulting from the modifications of deep models configurations.
International Conference on Artificial Intelligence and Soft Computing / 2019 / Springer, Cham / p. ( 223-235)