DRIIS: Research on Image Classification of Art Education System Based on Deep Learning

Abstract

With the fast advancement of computer technology, using it as an auxiliary teaching technique in art classes may not only improve the content of art classes but also give students with advice. This research develops an art education picture categorization technique and a system based on an upgraded deep learning model. To categorize artistic photos, this technique suggests using the dual-kernel squeeze-and-excitation (DKSE) module with deep separable convolutional operations to form a convolutional neural network. The suggested model has a classification accuracy of 86.49%, which is 26.29% better than the standard classification models. The classification accuracy of the DKSE module branch is 87.54% when the convolutional kernels are [Formula: see text] and [Formula: see text]. The suggested DKSE network model extracts both the overall and partial information of artistic images efficiently.