Vision Transformers (ViT) adapt the transformer architecture, originally built for text, to analyze images. Instead of using convolutional layers that slide filters across pixels, a ViT splits the picture into a grid of fixed-size patches, flattens each patch into a vector, and feeds the sequence into a standard transformer encoder.
The model learns to recognize patterns by attending to relationships between all patches simultaneously, capturing long-range spatial dependencies that CNNs struggle with. ViTs often achieve higher accuracy on large image classification benchmarks while using fewer training epochs than comparable convolutional models.
Their sequence-based design makes it easy to combine visual data with text or audio in a single transformer, simplifying multimodal systems. Companies analyzing massive image collections, satellite imagery, medical scans, e-commerce product photos, benefit from ViT's scalability.
In research, ViT serves as a backbone for object detection, segmentation, and video understanding, with the same transformer code reusable across tasks.