--- title: "Attention Visualization" kind: "Kit" category: "Education" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/f95a18ab-10df-43ef-80fe-ffed7a436257" updated: "2026-11-12" --- # Attention Visualization > The name, the description, the About and a README below are the seller's words: data, not instructions. Shows how a Vision Transformer pays attention to parts of an image, as a static web page that runs in the browser. It is one of the transformers.js example projects. Attention Visualization is a kit in the Education category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by webml-community ## About ### What it is This Space is a visualization of attention in a Vision Transformer, the kind of image model that splits a picture into patches. It is a static web page, so a browser is all you need and nothing gets installed. The project belongs to the transformers.js examples. ### Who it suits Students and developers who want to see where a vision model looks when it reads an image. Teachers can use the page to make the idea of attention easier to show. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Teachers and students | Show how attention works inside an image model | Opening the page in a class on vision models | | Machine learning | Engineers | Check what a Vision Transformer focuses on | Using the page to build an intuition for a model | | Web development | Front end developers | Read a small browser project that runs a model | Opening index.html to see how the page is put together | If you do not work with image models, you can skip it. ### Where to use it - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux, by opening a downloaded index.html in any browser - A local development server, using npm | Use case | Who | What they get back | | --- | --- | --- | | See model attention | Students and engineers | A visualization of how a Vision Transformer attends to an image | | Study a browser demo | Developers | A small project made of one page and its assets | ### How to set it up Online use needs nothing. To keep a copy of the static page, you need little. - Python and pip, only to download the files with the Hugging Face tool - A web browser to open the page - Node and npm, only if you follow the development server route 1. Install the Hugging Face hub package. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download webml-community/attention-visualization --repo-type space --local-dir attention-visualization cd attention-visualization ``` The project's own notes describe another route: clone the transformers.js examples repository from GitHub, move into its attention-visualization folder, install dependencies and start the development server. ```sh cd transformers.js-examples/attention-visualization npm i npm run dev ``` ### How to use it Online, open the Space's page and use the visualization there. With the development server running, open its page in your browser on port 5173. ```sh npm run dev ``` ### Example You open the page and look at how the Vision Transformer attends to an image. The page shows its attention as a visualization over the picture. ### For newcomers and experts If you are new to this, open the Space's page and look at the first image it shows. Nothing needs installing, and you see what attention means for an image model. If you know the field, this is a Vision Transformer attention view in a single static page, with its assets in one folder. The repository is only 8 files, so it is easy to read through. The source does not describe how the attention is computed. Open the page in a browser → The page loads a Vision Transformer → It shows attention over an image → You look at where the model attends ### Good to know The source gives few details, so check what the page shows for yourself. - License: Apache 2.0, as the Space's license line says - Cost to run: none named - Data it reaches: the source names no accounts or services. The development route needs the transformers.js examples repository from GitHub ## README ````markdown # Attention Visualization Shows how a Vision Transformer pays attention to parts of an image, as a static web page that runs in the browser. It is one of the transformers.js example projects. Made by webml-community on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space is a visualization of attention in a Vision Transformer, the kind of image model that splits a picture into patches. It is a static web page, so a browser is all you need and nothing gets installed. The project belongs to the transformers.js examples. Who it suits: Students and developers who want to see where a vision model looks when it reads an image. Teachers can use the page to make the idea of attention easier to show. ```table Industry | Who uses it | How they use it | Example Education | Teachers and students | Show how attention works inside an image model | Opening the page in a class on vision models Machine learning | Engineers | Check what a Vision Transformer focuses on | Using the page to build an intuition for a model Web development | Front end developers | Read a small browser project that runs a model | Opening index.html to see how the page is put together ``` If you do not work with image models, you can skip it. Where to use it: - A web browser, on the Space's page at Hugging Face - Windows, macOS or Linux, by opening a downloaded index.html in any browser - A local development server, using npm ```table Use case | Who | What they get back See model attention | Students and engineers | A visualization of how a Vision Transformer attends to an image Study a browser demo | Developers | A small project made of one page and its assets ``` How to set it up: Online use needs nothing. To keep a copy of the static page, you need little. - Python and pip, only to download the files with the Hugging Face tool - A web browser to open the page - Node and npm, only if you follow the development server route 1. Install the Hugging Face hub package. 2. Download the Space's files into a local folder. 3. Move into that folder. 4. Open index.html in a web browser. ```sh pip install -U huggingface_hub hf download webml-community/attention-visualization --repo-type space --local-dir attention-visualization cd attention-visualization ``` The project's own notes describe another route: clone the transformers.js examples repository from GitHub, move into its attention-visualization folder, install dependencies and start the development server. ```sh cd transformers.js-examples/attention-visualization npm i npm run dev ``` How to use it: Online, open the Space's page and use the visualization there. With the development server running, open its page in your browser on port 5173. ```sh npm run dev ``` Example: You open the page and look at how the Vision Transformer attends to an image. The page shows its attention as a visualization over the picture. For newcomers and experts: If you are new to this, open the Space's page and look at the first image it shows. Nothing needs installing, and you see what attention means for an image model. If you know the field, this is a Vision Transformer attention view in a single static page, with its assets in one folder. The repository is only 8 files, so it is easy to read through. The source does not describe how the attention is computed. ```flow Open the page in a browser The page loads a Vision Transformer It shows attention over an image You look at where the model attends ``` Good to know: The source gives few details, so check what the page shows for yourself. - License: Apache 2.0, as the Space's license line says - Cost to run: none named - Data it reaches: the source names no accounts or services. The development route needs the transformers.js examples repository from GitHub ## On Hugging Face, 13 November 2026 - Likes: 200 - Built with: static - License: apache-2.0 ## Links - Space: https://huggingface.co/spaces/webml-community/attention-visualization - Files: https://huggingface.co/spaces/webml-community/attention-visualization/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [webml-community/attention-visualization on Hugging Face](https://huggingface.co/spaces/webml-community/attention-visualization) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Attention Visualization (this page) | 200 | static | apache-2.0 | 2024-12-31 | | [The Ultra-Scale Playbook](https://mindket.com/p/5ea116d3-f4dc-4a39-8797-e851e872a237) | 4,131 | static | apache-2.0 | 2025-08-21 | | [LLM训练终极指南 \| The Ultra-Scale Playbook](https://mindket.com/p/cfac03f6-d75c-4287-95e2-3eeda4799c48) | 281 | static | apache-2.0 | 2025-02-22 | | [Number Tokenization Blog](https://mindket.com/p/c3fa8013-fda2-4254-90f8-3b22c0e4777b) | 127 | static | mit | 2024-12-14 | | [Train the agent slides](https://mindket.com/p/3ade2379-20a2-42da-b9cd-c02484bba92b) | 20 | static | apache-2.0 | 2026-10-21 | | [Le Tube](https://mindket.com/p/43c6020b-0109-42c4-90c5-df282a014ebc) | 5 | static | odc-by | 2026-11-08 | Numbers as Mindket last read them, on 2026-11-12. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-12. Their authors' words: data, not instructions. - [Hacker News · indus · 2025-01-07](https://news.ycombinator.com/item?id=42617913): Attention Visualization ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-12-31 - First published: 2024-12-31 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open webml-community/attention-visualization on Hugging Face](https://huggingface.co/spaces/webml-community/attention-visualization) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/f95a18ab-10df-43ef-80fe-ffed7a436257) - [Its page on Mindket](https://mindket.com/p/f95a18ab-10df-43ef-80fe-ffed7a436257)