Visualizing Attention in Vision Transformer

In 2022, the Vision Transformer (ViT) emerged as a viable competitor to convolutional neural networks (CNNs), which are now state-of-the-art in computer vision and widely employed in many image recognition applications. In terms of computational efficiency and accuracy, ViT models exceed the present state-of-the-art (CNN) by almost a factor of four. A vision transformer model's performance is determined by decisions such as the optimizer, network depth, and dataset-specific hyperparameters. CNNs are more straightforward to optimize than ViT. The difference between a pure transformer and a CNN front end is to marry a transformer to a CNN front end.