DINOv3
Fuente:
arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908489562980352 |
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| author | Siméoni, Oriane Vo, Huy V. Seitzer, Maximilian Baldassarre, Federico Oquab, Maxime Jose, Cijo Khalidov, Vasil Szafraniec, Marc Yi, Seungeun Ramamonjisoa, Michaël Massa, Francisco Haziza, Daniel Wehrstedt, Luca Wang, Jianyuan Darcet, Timothée Moutakanni, Théo Sentana, Leonel Roberts, Claire Vedaldi, Andrea Tolan, Jamie Brandt, John Couprie, Camille Mairal, Julien Jégou, Hervé Labatut, Patrick Bojanowski, Piotr |
| author_facet | Siméoni, Oriane Vo, Huy V. Seitzer, Maximilian Baldassarre, Federico Oquab, Maxime Jose, Cijo Khalidov, Vasil Szafraniec, Marc Yi, Seungeun Ramamonjisoa, Michaël Massa, Francisco Haziza, Daniel Wehrstedt, Luca Wang, Jianyuan Darcet, Timothée Moutakanni, Théo Sentana, Leonel Roberts, Claire Vedaldi, Andrea Tolan, Jamie Brandt, John Couprie, Camille Mairal, Julien Jégou, Hervé Labatut, Patrick Bojanowski, Piotr |
| contents | Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm. This technical report introduces DINOv3, a major milestone toward realizing this vision by leveraging simple yet effective strategies. First, we leverage the benefit of scaling both dataset and model size by careful data preparation, design, and optimization. Second, we introduce a new method called Gram anchoring, which effectively addresses the known yet unsolved issue of dense feature maps degrading during long training schedules. Finally, we apply post-hoc strategies that further enhance our models' flexibility with respect to resolution, model size, and alignment with text. As a result, we present a versatile vision foundation model that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. We also share the DINOv3 suite of vision models, designed to advance the state of the art on a wide spectrum of tasks and data by providing scalable solutions for diverse resource constraints and deployment scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10104 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DINOv3 Siméoni, Oriane Vo, Huy V. Seitzer, Maximilian Baldassarre, Federico Oquab, Maxime Jose, Cijo Khalidov, Vasil Szafraniec, Marc Yi, Seungeun Ramamonjisoa, Michaël Massa, Francisco Haziza, Daniel Wehrstedt, Luca Wang, Jianyuan Darcet, Timothée Moutakanni, Théo Sentana, Leonel Roberts, Claire Vedaldi, Andrea Tolan, Jamie Brandt, John Couprie, Camille Mairal, Julien Jégou, Hervé Labatut, Patrick Bojanowski, Piotr Computer Vision and Pattern Recognition Machine Learning Self-supervised learning holds the promise of eliminating the need for manual data annotation, enabling models to scale effortlessly to massive datasets and larger architectures. By not being tailored to specific tasks or domains, this training paradigm has the potential to learn visual representations from diverse sources, ranging from natural to aerial images -- using a single algorithm. This technical report introduces DINOv3, a major milestone toward realizing this vision by leveraging simple yet effective strategies. First, we leverage the benefit of scaling both dataset and model size by careful data preparation, design, and optimization. Second, we introduce a new method called Gram anchoring, which effectively addresses the known yet unsolved issue of dense feature maps degrading during long training schedules. Finally, we apply post-hoc strategies that further enhance our models' flexibility with respect to resolution, model size, and alignment with text. As a result, we present a versatile vision foundation model that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. We also share the DINOv3 suite of vision models, designed to advance the state of the art on a wide spectrum of tasks and data by providing scalable solutions for diverse resource constraints and deployment scenarios. |
| title | DINOv3 |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2508.10104 |