DINOv3
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.
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Oriane Siméoni
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Huy V. Vo
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Maximilian Seitzer
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Federico Baldassarre
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Maxime Oquab
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Cijo Jose
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Vasil Khalidov
689efc0ba4caabb4320e5e8d
Marc Szafraniec
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Seungeun Yi
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Michaël Ramamonjisoa
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Francisco Massa
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Daniel Haziza
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Luca Wehrstedt
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Jianyuan Wang
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Timothée Darcet
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Théo Moutakanni
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Leonel Sentana
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Claire Roberts
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Andrea Vedaldi
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Jamie Tolan
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John Brandt
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Camille Couprie
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Julien Mairal
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Hervé Jégou
689efc0ba4caabb4320e5e9e
Patrick Labatut
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Piotr Bojanowski
DINOv3
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.
DINOv3, a self-supervised learning model, achieves superior performance across various vision tasks by scaling datasets and models, addressing dense feature degradation, and enhancing flexibility with post-hoc strategies.
self-supervised learning DINOv3 data preparation design optimization Gram anchoring dense feature maps post-hoc strategies vision foundation model high-quality dense features vision tasks weakly-supervised foundation models scalable solutions deployment scenarios