Research on the performance of hybrid vision models based on ViT

ViT is a model proposed by the Google team in 2020 to apply a transformer in image classification, although it is not the first paper to apply a transformer in visual tasks, because of its model is simple and effective, and scalable, it has become a milestone work in the application of transformer in CV field, and has also triggered the subsequent related research. The core conclusion of the original ViT paper is that when there is enough data for pre-training, ViT outperforms CNN, breaks through the limitation of the transformer's lack of inductive bias, and can achieve better migration results in downstream tasks. However, when the training dataset is not large enough, ViT usually performs worse than ResNets of the same size, because Transformer lacks inductive bias, a kind of a priori knowledge, assumptions made in advance, compared with CNN. Through its innovative architecture and powerful performance, the visual representation transform (ViT) model continues to advance the field of computer vision, while facing some challenges and room for improvement. With the deepening of research and the continuous development of technology, ViT is expected to play a greater role in more practical applications. The article aims to explore the advantages and applicability of the ViT model and tries to construct a hybrid visual model to improve its generalization ability for different types of datasets, demonstrating the hybrid model's significance in improving the performance of the ViT model.