Style classification of media painting images by integrating ResNet and attention mechanism

The progress of deep learning technology has made image classification an important application field. Image style classification is a complex task involving the recognition of the whole picture, including the recognition of salient features and detailed features. This study is based on the ResNet algorithm and has improved its Resnet 50 version with excellent performance. In the model architecture, we introduce blur pool operation and replace the traditional Relu function with Celu activation function. In addition, the triplet attention mechanism was integrated to further enhance the model performance. Through a series of experiments, it is found that the improved ResNet50 model has the highest classification accuracy of 80.6% on large-scale image data sets, which is 11.7% higher than the traditional ResNet50 model. In terms of recognition of similar style images, the model incorporating triplet attention demonstrated higher average accuracy (74%) and recall (82%). This improvement has achieved certain results and has certain technical reference value for various styles of image classification fields.