What is DINOv2?

DINOv2 is a self-supervised method for training computer vision models developed by Meta Research and released in April 2023.

About the model

Here is an overview of the

DINOv2

model:

Date of Release
Model Type Object Detection
Architecture
Framework Used
Annotation Format
Stars on GitHub +

DINOv2 is a self-supervised method for training computer vision models developed by Meta Research and released in April 2023. Because DINOv2 is self-supervised, the input data does not require labels, which means models based on this architecture can learn richer information about the contents of an image.

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Model Performance

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Deploy DINOv2 to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

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DINOv2 Annotation Format

DINOv2

uses the

uses the

annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.

Convert data between formats

Label data automatically with DINOv2

You can automatically label a dataset using

DINOv2

with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use

DINOv2

to train a computer vision model.

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