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.

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

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

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.
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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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