In this work, we present DINOv2, a new series of image encoders pretrained on large curated data with no
supervision. This is the ﬁrst SSL work on image data that leads to visual features that close the performance
gap with (weakly) supervised alternatives across a wide range of benchmarks and without the need for
ﬁnetuning. A few properties emerge from these models, such as an understanding of object parts and scene
geometry regardless of the image domains. We expect that more of these properties will emerge at larger
scales of models and data, akin to instruction emergence in large language models, and plan to continue
scaling along these axes. This paper also demonstrates that these visual features are compatible with
classiﬁers as simple as linear layers - meaning the underlying information is readily available. In future work,
we plan to leverage this ability to train a a language-enabled AI system that can process visual features as
DINOv2- Learning Robust Visual Features without Supervision