DINOv3 Web

DINOv3 Web

Visualizes the dense features a DINOv3 model extracts from an image, running locally in your browser. For developers and vision researchers who want to inspect those features.

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 is a single web page that shows the dense features a DINOv3 model finds in an image. The model runs on your own machine inside the browser, so you need no server and no install. It uses an ONNX build of the model published by onnx-community.

Who it suits: It suits people who work with images and want a quick look at what DINOv3 features look like, without setting up a Python environment.

Industry | Who uses it | How they use it | Example
Computer vision research | Vision researchers | Look at the dense features DINOv3 produces for an image | A researcher checks the features of a sample image
Software | Web developers | See how an ONNX model runs inside a browser tab | A developer studies how in-browser inference is wired up
Machine learning | ML engineers | Preview DINOv3 features before using the model elsewhere | An engineer compares features across a few pictures
Education | Students | Learn what image features look like on real pictures | A student tries one photo and reads the feature view

You can skip it if you do not work with images or models.

Where to use it: You use it in these places.

• A web browser, on the Space's page on Hugging Face
• A local copy opened in a browser on Windows, macOS or Linux

Use case | Who | What they get back
Inspect image features | Researchers | A view of the dense features for one image
Try a model with no setup | Developers | DINOv3 running in a browser tab
Teach the idea | Teachers and students | A visible example of what image features are

How to set it up: The online page needs only a web browser. A local copy needs Python for the download step.

• A web browser
• Python with pip, only to fetch a local copy

1. Open the Space's page on Hugging Face to use it online, with nothing to install.
2. For a local copy, install the Hugging Face Hub package.
3. Download the Space's files into a folder and move into it.
4. Open index.html in a web browser.

pip install -U huggingface_hub
hf download webml-community/dinov3-web --repo-type space --local-dir dinov3-web
cd dinov3-web

How to use it: Open the page in your browser and give it an image, using the controls the page shows. The page then draws the image features on screen. Nothing needs a command beyond the download steps above.

Example: You open the Space's page, pick a photo and wait for the page to process it. Your browser runs the model on your own machine, and the page shows the dense features it found in that photo.

For newcomers and experts: DINOv3 is a vision model that turns an image into many small numeric descriptions, one per patch of the picture. This page draws them so you can see them. Start by opening the Space's page and trying one image.

Under the hood, the Space is a static HTML page with no server code. The model is an ONNX file from the onnx-community group, run on your machine in the browser. Speed depends on your own hardware, and the source does not say more about requirements.

Open the page in your browser
Give it an image
The ONNX model runs on your machine
Dense features are drawn on screen

Good to know: The page does its work in your browser, and the source gives few other details.

• License: apache-2.
• Cost to run: none named.
• Data it reaches: your own image, processed locally in the browser. It names one ONNX model from onnx-community.
• Not for: server-side processing, since the page runs the model locally in your browser.

On Hugging Face, 13 November 2026

• Likes: 162
• Built with: static
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/webml-community/dinov3-web
• Files: https://huggingface.co/spaces/webml-community/dinov3-web/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.