Fine-grained Image Classification Based on Data Augmentation Vision Transformer

Recently, vision Transformer (ViT) has made a breakthrough in the field of image recognition. Its self-attention mechanism (MSA) can extract the discriminant token information of different pixel blocks to improve the accuracy of image classification, but the classification tokens in its deep layer are easy to ignore the local features between levels. Secondly, the embedded layer inputs fixed-size pixel patches into the network, which inevitably introduces additional image noise. Secondly, Hierarchical attention selection method (HAS) is proposed in this paper to improve the ability of network learning discriminative markers between levels by screening and fusing markers between levels. Therefore, a hierarchical attention selection method (HAS) is proposed, which improves the ability of discriminative tokens between levels of e-learning by screening and integrating tokens between levels. Experimental results show that the accuracy of this algorithm on the two general data sets of CUB-200-2011 and Stanford Dogs is better than that of the existing methods based on ViT framework, which is 1.4% and 1.6% higher than the original ViT, respectively.