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Flower image classification based on visual transformer

Nowadays, the work of flower classification is relatively less. To solve the practical problems of a large amount of calculation, complex calculation and low classification accuracy in flower classification. This paper adopts the flower image classification method based on transfer learning and visual transformer. Its purpose is to use transfer learning to solve the shortcomings of the transformer model in small sample training, as well as a large amount of calculation of traditional flower image classification. Aiming at the transformer applied by deep learning in image classification, this paper uses two typical models, vision transformer (ViT) and swing transformer (Swin-T) for experimental research. The training accuracy obtained by retraining on small-scale flower image data can reach 96.2% of ViT and 97.6% of Swin-T respectively. The experimental results show that this method has high classification efficiency, good classification effect and strong robustness. Compared with the traditional classification methods, it can greatly improve the accuracy and has a good application prospect in flower image classification.