facebookresearch/dinov3

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11249 stars · 939 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Reference PyTorch implementation and pretrained models for DINOv3, Meta's self-supervised vision transformer backbone family. It includes training, evaluation, and probing recipes for tasks like segmentation, depth estimation, and image classification.

Use cases

• extract visual features from images with a pretrained vision transformer
• fine-tune a self-supervised vision backbone for classification
• run linear probing for semantic segmentation on ADE20K
• estimate monocular depth from images
• train vision models on satellite or microscopy imagery
• download DINOv3 weights for use with Hugging Face Transformers or timm

When to choose

• you need a strong general-purpose vision backbone for downstream tasks
• you want reference PyTorch code for self-supervised vision training or distillation
• you work on remote sensing or scientific imagery and want metadata-guided adaptation recipes

When to avoid

• you need a lightweight model for edge or mobile deployment
• you want a non-PyTorch framework like TensorFlow or JAX
• you need a permissively licensed model for commercial use without checking the custom license

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision machine-learning computer-vision deep-learning image-processing python cross-platform vision-transformer self-supervised-learning pytorch pretrained-models feature-extraction foundation-model meta-ai model-weights gpu

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