Image Classification Method of Improved ResNet by Integrating Self-Attention Mechanism
To solve the problem of limited recognition accuracy in image classification tasks on large datasets due to the lack of global informa-tion in convolutional neural networks,it is proposed to introduce self attention mechanism into convolutional neural networks.Firstly,image features are extracted through convolutional neural networks and the self attention module is improved;Secondly,the CA module based on convolution operation calculates attention to reconstruct feature maps,highlighting important features and suppressing general features,add-ing global information to the network;Finally,a Dropout layer is introduced after the Avgpool output layer to reduce overfitting and improve the robustness and generalization performance of the model.Experiments on publicly available datasets ImageNet-1K,Oxford 102 Flowers,and CIFAR-100 have shown that the proposed method improves recognition accuracy by 1.8%,0.72%,and 13.7%compared to ResNet50,re-spectively;Compared to the ResNet50 model,it has better recognition performance.
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