ViTs (Vision Transformers)

Vision Transformers (ViTs) are a type of artificial intelligence model designed to interpret images, similar to how they process text. They divide an image into smaller parts called patches, then analyze these patches collectively to understand the overall content. Using a technique called "self-attention," ViTs can weigh the importance of each patch relative to others, allowing for detailed and flexible recognition. This approach enables ViTs to excel in tasks like image classification, often matching or surpassing traditional methods, by effectively capturing complex patterns and relationships within visual data.