Research on automatic classification of image resources based on deep learning algorithms

This paper explores how to enhance the performance of deep learning image classification models by introducing attention mechanisms and optimizing feature extraction methods. Techniques such as CBAM (Convolutional Block Attention Module) and depthwise separable convolutions were employed, with experiments showing that the CBAM model achieved an accuracy of 89.2%, while the optimized model reduced computational cost to 1.20 GFLOPs. These results validate the effectiveness of the proposed methods in improving classification accuracy and reducing computational costs, offering new directions for future research.