The Vision Transformer (ViT) is a novel architecture in deep learning specifically designed for image classification tasks. Unlike traditional convolutional neural networks (CNNs), ViT employs a transformer architecture that uses self-attention mechanisms to process and understand image data. The model divides an image into smaller patches, which are then treated as sequences similar to tokens in NLP tasks. By leveraging self-attention, ViT can capture long-range dependencies and contextual information within the image, resulting in improved performance on various benchmark datasets. Vision Transformers have gained popularity due to their ability to achieve state-of-the-art results in image recognition while offering scalability and flexibility in training.