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DINOv2 icon

DINOv2

A family of foundation models producing universal features suitable for image-level visual tasks (image classification, instance retrieval, video understanding) as well as pixel-level visual tasks (depth estimation, semantic segmentation).

DINOv2 models demonstrate strong out-of-distribution performance and the produced features are usable without requiring any fine-tuning.

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Platforms

  • Self-Hosted
Depth Estimation: State-of-the-art results and strong generalization on estimating depth from a single image.
Semantic Segmentation: Competitive results without any fine-tuning on clustering an images into object classes.
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Instance Retrieval: Directly use frozen features to find art pieces similar to a given image from a large art collection.
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GitHub repository

  •  13,338 Stars
  •  1,264 Forks
  •  297 Open Issues
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DINOv2 was added to AlternativeTo by Mauricio B. Holguin on and this page was last updated .
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What is DINOv2?

A family of foundation models producing universal features suitable for image-level visual tasks (image classification, instance retrieval, video understanding) as well as pixel-level visual tasks (depth estimation, semantic segmentation).

DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any requirement for fine-tuning. The models were pretrained on a dataset of 142 M images without using any labels or annotations.