A Novel Image Classification Model Jointing Attention and ResNet for Scratch

In recent years, Scratch has been widely used in helping teenagers learn to program. Before programming with Scratch, users usually need to select a background image with a proper style to set the environment of role activities and emotional tone of works. In order to make the image styles rich and diverse, users can use the fast neural style transfer method to generate the image. However, when users use this method, they are usually puzzled with the optional styles, which makes it a common phenomenon that the contents of transformed images do not match the chosen styles. To resolve this problem, we design a novel image classification model to help recognize the scene of the image, which can guide users to select optional styles for the image. First, we improve the residual network with two attention modules and reconstruct the residual module structure, which improves the accuracy of model classification. Then, we propose a self-adjusting learning rate module, which can accelerate the convergence of the model and reduce fluctuations of the loss function. The experiment results show that our model outperforms other classic image classification methods in classifying the background image of Scratch. The classification accuracy on the testset reaches 98%.