Abstract: There is a deficiency of information available on waste classification, and many municipalities and educational institutions struggle with this issue. We address this challenge by utilizing the efficiency and accuracy of the neural networks to classify items and implement waste image classification with a deep residual shrinkage network built on the ResNet network and SENet network. By filtering the Garbage dataset to obtain the data set necessary for the experiment, and by enhancing ResNet, SENet and soft threshold processes are incorporated into the ResNet structure. And by training the network and optimizing its hyperparameters, a greater recognition rate and recognition effect are achieved for the classification of campus waste. The experimental findings indicate that the proposed approach is feasible to a certain extent.

Key words: deep learning, residual network, attention mechanism, image classification