  Image Classification is a fundamental task in the field of computer vision
that frequently serves as a benchmark for gauging advancements in Computer
Vision. Over the past few years, significant progress has been made in image
classification due to the emergence of deep learning. However, challenges still
exist, such as modeling fine-grained visual information, high computation
costs, the parallelism of the model, and inconsistent evaluation protocols
across datasets. In this paper, we conduct a comprehensive survey of existing
papers on Vision Transformers for image classification. We first introduce the
popular image classification datasets that influenced the design of models.
Then, we present Vision Transformers models in chronological order, starting
with early attempts at adapting attention mechanism to vision tasks followed by
the adoption of vision transformers, as they have demonstrated success in
capturing intricate patterns and long-range dependencies within images.
Finally, we discuss open problems and shed light on opportunities for image
classification to facilitate new research ideas.
