The Vision Transformer (ViT) adapts the Transformer architecture for image classification tasks by treating image patches as sequences, similar to words in text. Instead of relying on convolutional layers, ViT uses self-attention mechanisms to capture global dependencies and features across the entire image. This approach has demonstrated significant success in handling large-scale image datasets and achieving state-of-the-art performance in image classification. Vision Transformers represent a shift from traditional convolutional neural networks (CNNs) to more flexible and scalable models for computer vision tasks.