images improves the quality of the features in domains that are not covered by this dataset. Overall, the
conclusion of this ablation is that our dataset provides a good balance of diﬀerent types of images that leads
to the best performance overall.

6.3 Model Size and Data

We quantify the importance of scaling data with the model size in Fig. 4. As the size of models grow, training
on LVD-142M becomes more beneﬁcial than training on ImageNet-22k. For instance, a ViT-g trained on
LVD-142M matches the performance on ImageNet-1k of a model trained on ImageNet-22k while signiﬁcantly
outperforming it on the other benchmarks.

6.4 Loss Components
DINOv2- Learning Robust Visual Features without Supervision