Mushroom Image Classification and Recognition Based on Improved Swin Transformer

Abstract

Classification of mushrooms are essential for preventing even life-threatening consequences of accidentally eating wild mushrooms. In this study, a dataset including 114 varieties of mushrooms is collected and built. Next, the Swin Transformer model with robust classification characteristics is improved to classif mushroom images. Also, the classification accuracy of the improved Swin Transformer with different parameters was compared. In addition, ResNet50 is compared with the improved Swin Transformer under the optimal parameters. The model's training speed is further enhanced by integrating the improved Swin Transformer with ResNet50, thereby proposing the Swin_ResNet classification algorithm. Experimental results show that the classification accuracy of the optimal improved Swin Transformer algorithm is 87.66%, which is 14.86% and 7.64% higher than the ResNet50 and Swin Transformer models, respectively. In addition, the classification accuracy of Swin_ResNet is 85.31%, and the training time is 86.57% shorter than that of the improved Swin Transformer. This greatly improves the training efficiency.