Vision Transformers Insights

What is Vision Transformers?

Vision Transformers (ViTs) apply the Transformer architecture, originally designed for natural language processing, to image recognition tasks. Instead of processing sequential words, an image is split into patches, treated as tokens, and fed into a standard Transformer encoder. ViTs have shown competitive performance compared to convolutional neural networks (CNNs) on image classification benchmarks and are commonly used in tasks like image recognition, object detection, and image segmentation.

Find organizations using Vision Transformers on Sumble →

What other technologies are related to Vision Transformers?

Vision Transformers Competitor Technologies

DeiT

A competing Vision Transformer architecture.

mentioned alongside Vision Transformers in 88% (64) of relevant job posts

DINO

DINO is a self-supervised learning method for Vision Transformers, making it a competitor in the broader sense of approaches to training Vision Transformers.

mentioned alongside Vision Transformers in 43% (68) of relevant job posts

U-Net

U-Net is a convolutional neural network architecture, often used for image segmentation, representing a competing approach to Vision Transformers in computer vision tasks.

mentioned alongside Vision Transformers in 26% (75) of relevant job posts

ResNet

ResNet is a CNN architecture and a competing approach to Vision Transformers in computer vision tasks.

mentioned alongside Vision Transformers in 9% (88) of relevant job posts

CNNs

CNNs are a general class of neural networks frequently used in computer vision, representing a competing approach to Vision Transformers.

mentioned alongside Vision Transformers in 3% (85) of relevant job posts

YOLO

YOLO is a real-time object detection system, usually based on CNNs, and a competing approach to Vision Transformers for object detection tasks.

mentioned alongside Vision Transformers in 3% (75) of relevant job posts