proposed by Chen et al. (2020). This benchmark covers scenes, objects (food, cars, planes), and textures.
We replace the Birdsnap dataset with CUB because the former was not publicly available in its entirety. We
follow the experimental protocol as outlined by Chen et al. (2020), namely training a logistic regression on
precomputed features. Our model signiﬁcantly outperforms state-of-the-art SSL models, with most notable
diﬀerences on Stanford Cars (+14.8% versus DINO ViT-B/8) and FGVC Aircraft (+14.8% versus iBOT
DINOv2- Learning Robust Visual Features without Supervision