# vision transformer

> machine learning algorithm for vision processing

**Wikidata**: [Q107675654](https://www.wikidata.org/wiki/Q107675654)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Vision_transformer)  
**Source**: https://4ort.xyz/entity/vision-transformer

## Summary
A vision transformer (ViT) is a machine learning algorithm specifically designed for vision processing and computer vision tasks. It is a subclass of the transformer model architecture, which was originally developed by Google Brain for other machine learning applications.

## Key Facts
- **Classification:** Subclass of the transformer model architecture.
- **Primary Use:** Computer vision and vision processing.
- **Parent Architecture:** Transformer (developed by Google Brain).
- **Academic Origin:** Described in the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale."
- **Common Alias:** ViT.
- **Related Models:** Swin Transformer (a shifted window based variant).
- **International Recognition:** Documented in 10 Wikipedia language editions, including English, Korean, and Spanish.

## FAQs
### Q: What is a vision transformer?
A: A vision transformer (ViT) is a machine learning algorithm used for vision processing. It adapts the transformer architecture, originally created by Google Brain, for use in computer vision.

### Q: What is the primary source describing the vision transformer?
A: The vision transformer is described in the academic work titled "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale."

### Q: How does the Swin Transformer relate to the vision transformer?
A: The Swin Transformer is a related entity that functions as a shifted window based vision transformer. It is a specific variation of the broader vision transformer category.

## Why It Matters
The vision transformer represents a significant application of the transformer architecture to the field of computer vision. Originally developed by Google Brain as a general machine-learning model class, the transformer was adapted to process visual data, as detailed in the foundational paper "An Image is Worth 16x16 Words." This shift is important because it provides a structured method for image recognition at scale, moving beyond traditional algorithms to leverage transformer-based processing. Its relevance is further highlighted by its global academic and digital footprint, appearing in multiple languages and serving as the basis for further innovations like the Swin Transformer.

## Notable For
- **Architecture Adaptation:** Successfully applies the transformer class, originally a Google Brain development, to the domain of computer vision.
- **Foundational Research:** Defined by the specific methodology of treating images as a series of "16x16 words" for recognition at scale.
- **Architectural Variants:** Serves as the foundation for related technologies such as the shifted window based Swin Transformer.

## Body

### Classification and Architecture
The vision transformer (ViT) is a machine learning algorithm categorized as a subclass of the transformer architecture. The transformer class was first developed by Google Brain. While the parent architecture is a broad machine-learning model, the ViT is specifically optimized for computer vision.

### Technical Description and Origins
The vision transformer is formally described by the academic source "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale." It is utilized globally and is known by several aliases across different languages, including:
- ViT
- Transformateur pour la vision (French)
- 视觉变换器 (Chinese)
- 비전 트랜스포머 (Korean)

### Related Entities
The vision transformer is closely related to the Swin Transformer. The Swin Transformer is a specific type of vision transformer that utilizes a shifted window based approach for processing visual information.

### Global Presence
The entity is documented across 10 different Wikipedia language editions, including:
- English (en)
- Korean (ko)
- Spanish (es)
- Polish (pl)
- Persian (fa)
- Hebrew (he)