---
title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2010-11929-vision-transformer.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Alexey Dosovitskiy and 11 coauthors (listed on the arXiv abs page), An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: multimodal'
tags: arxiv, vision-transformer, vision, multimodal
arxiv_id: 2010.11929
alias: 2010.11929-vision-transformer
type: pointer
format: pointer
sources:
  - id: S1
    title: 'An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale'
    publisher: arXiv
    authors: 'Alexey Dosovitskiy and 11 coauthors (listed on the arXiv abs page)'
    url: https://arxiv.org/abs/2010.11929
    version_read: 'v2 (3 Jun 2021)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

## Summary
[An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) (2020) is the Vision Transformer (ViT) paper. It showed that a standard Transformer, applied to image patches, can outperform convolutional networks on image recognition when it is pretrained at scale.

What it introduced: the recipe of splitting an image into patches, treating those patches as tokens, and training a Transformer encoder for classification, with a study of how that approach scales.

What it led to: the vision backbone used in CLIP and in many later multimodal models, including the LLaVA recipe in batch B. Live notes: [ao-evals-multimodal-agents](https://iinventedlight.com/library/notes/ao-evals-multimodal-agents/). See also the CLIP pointer in batch B.

If you want the vision-side reason Transformers left language and entered image models, start here. Where to read it: https://arxiv.org/abs/2010.11929 (v2, last revised 3 Jun 2021).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).