What Is Vision Transformer (ViT)?
A Vision Transformer (ViT) is a model architecture that applies the Transformer's self-attention mechanism to image recognition by dividing an image into fixed-size patches, treating each patch as a token, and processing them through standard Transformer layers.
How Vision Transformer (ViT) Works
Traditional image recognition relied on CNNs, but ViT demonstrated that Transformers can match or exceed CNN performance on vision tasks. A ViT splits an image into a grid of patches (e.g., 16x16 pixels each), flattens each patch into a vector, adds positional embeddings, and processes them through Transformer encoder layers. The self-attention mechanism allows every patch to attend to every other patch, capturing global relationships that CNNs build up only through many layers. ViTs have become the foundation of modern computer vision, powering image generation models, multimodal AI systems like GPT-4 Vision, and advanced object detection systems.
Real-World Examples
Google's ViT model achieving state-of-the-art accuracy on ImageNet by processing images as sequences of patches
GPT-4 Vision using a ViT-based encoder to understand and analyze images uploaded by users
A medical imaging system using ViT to detect subtle patterns across an entire X-ray simultaneously through global attention