DINOv3

Summary

🆕 [2025-08-14] 🔥 DINOv3 backbones are now available in Hugging Face Hub and supported by the Hugging Face Transformers library DINOv3 🦖🦖🦖 Meta AI Research, FAIR Oriane Siméoni, Huy V. Vo, Maximilian Seitzer, Federico Baldassarre, Maxime Oquab, Cijo Jose, Vasil Khalidov, Marc Szafraniec, Seungeun Yi, Michaël Ramamonjisoa, Francisco Massa, Daniel Haziza, Luca Wehrstedt, Jianyuan Wang, Timothée Darcet, Théo Moutakanni, Leonel Sentana, Claire Roberts, Andrea Vedaldi, Jamie Tolan, John Brandt, Camille Couprie, Julien Mairal, Hervé Jégou, Patrick Labatut, Piotr Bojanowski [ 📜 Paper ] [ 📰 Blog ] [ 🌐 Website ] [ 📖 BibTeX ] Reference PyTorch implementation and models for DINOv3. For details, see the DINOv3 paper. Overview High-resolution dense features. We visualize the cosine similarity maps obtained with DINOv3 output features between the patches marked with a red cross and all other patches. An extended family of versatile vision foundation models producing high-quality dense features and achieving outstanding performance on various vision tasks including outperforming the specialized state of the art across a broad range of settings, without fine-tuning Pretrained models ℹ️ Please follow the link provided below to get access to all the model weights: once accepted, an e-mail will be sent with the complete list of URLs pointing to all the available model weights (both backbones and adapters). These URLs can then be used to either: download the model or adapter weights to a local filesystem and point torch.hub.load() to these local weights via the weights or backbone_weights parameters, or to these local weights via the or parameters, or directly invoke torch.hub.load() to download and load a backbone or an adapter from its URL via also the weights or backbone_weights parameters. See the example code snippets below. ⚠️ Please use wget instead of a web browser to download the weights. ViT models pretrained on web dataset (LVD-1689M): Model Parameters Pretraining Dataset Dow...