A Vision Transformer (ViT) splits an image into patches, treats each like a token, and uses attention to relate them. It has largely overtaken older convolutional designs on accuracy at scale and underlies modern detectors, segmenters, and multimodal models.
The book · Volume 6 of 7
AI for Video: A Complete Guide to Intelligence in Video Products: From Models and Inference to Computer Vision, Speech, Search, and Generative Video
A Vision Transformer often wins on accuracy but costs more compute per frame than a convolutional model, which matters on live video. Volume 6 covers the Vision Transformer in detail: ViT and video variants, detection and segmentation backbones, model artifacts and the ML minimum for choosing between architectures. Written by Nikolay Sapunov, CEO at Fora Soft.
Choosing between a Vision Transformer and a lighter CNN for your video model?
Tell us the task, frame rate and hardware, and we will compare Vision Transformer and CNN options with latency and cost estimates. 250+ video products shipped since 2005.
