ViT (Vision Transformer) is a groundbreaking computer vision model that applies the Transformer architecture—originally designed for NLP—to image data. It splits images into patches, treats them as sequences, and uses self-attention mechanisms to capture global visual relationships. ViT achieves state-of-the-art results in image classification with less dependency on convolutional layers, revolutionizing tasks like image recognition, segmentation, and transfer learning.
