Garbage Image Classification Algorithm Based on Swin Transformer*

By comparing the accuracy of VGG16, ResNetl01 and Swin Transformer on the garbage classification dataset, it is proved that Swin Transformer has an advantage in garbage classification tasks. Experimental results show that, with the self-attention mechanism and multi-scale feature modeling capabilities, Swin Transformer exhibits higher accuracy than traditional convolutional neural network algorithms. The accuracy of the Swi Transformer algorithm reaches 97%, which is 2.9 % higher than VGG16 and 1.1 % higher than ResNetl01.