facebookresearch/dino

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7611 stars · 1049 forks observed · 2026-08-28

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

PyTorch implementation of DINO, a self-supervised learning method for training Vision Transformers, with pretrained model weights. It is the official research code from Facebook AI Research accompanying the paper 'Emerging Properties in Self-Supervised Vision Transformers'.

Use cases

• train vision transformers with self-supervised learning
• download pretrained DINO ViT backbones for downstream tasks
• extract image features without labels
• reproduce DINO research results
• evaluate self-supervised models with k-NN or linear probing
• convert vision transformer backbones to ONNX

When to choose

• you need self-supervised pretraining of ViTs on your own images
• you want strong frozen visual features for classification, retrieval, or segmentation
• you are reproducing or building on the DINO paper

When to avoid

• you need the newest features and models - use DINOv2 instead
• you want a general-purpose computer vision toolkit rather than a research codebase
• you lack GPU resources for training

Facets

library · maturity maintenance

machine-learning deep-learning image-processing machine-learning computer-vision deep-learning python self-supervised-learning vision-transformer pytorch pretrained-models research gpu

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