Research on Art Classification Based on the Improved Resnet50 Network

In the field of computer vision, deep learning technology has made breakthroughs in image classification. ResNet50, an efficient deep residual network architecture, has demonstrated excellent performance in many image classification tasks. However, In the task of classifying artworks, ResNet50, still shows some limitations. It has difficulty identifying different artistic styles and is lacking in capturing the delicate details of the artwork. This article proposes an improved resnet50 model. By introducing SE blocks and extended convolution parameters, this article have made a deep revamp to ResNet50 to enhance its performance in art classification tasks. SE blocks increase the sensitivity of the network to differences in art styles by dynamically adjusting the dependencies between channels. Concurrently, the enhanced convolutional operation expands the model's sensory scope, so that the network can capture more delicate artistic details. The experimental results show that the improved model has achieved significant performance improvement on wikiart artwork classification datasets. In upcoming research, the paper plan to enhance art classification by broadening the dataset to encompass diverse art movements and forms, and by optimizing convolutional neural networks for large-scale data. This aims to improve the model's generalization and applicability across various visual arts, ensuring practical effectiveness and reliability.