  Vision foundation models are renowned for their generalization ability due to
massive training data. Nevertheless, they demand tremendous training resources,
and the training data is often inaccessible, e.g., CLIP, DINOv2, posing great
challenges to developing derivatives that could advance research in this field.
In this work, we offer a very simple and general solution, named Proteus, to
distill foundation models into smaller equivalents on ImageNet-1K without
access to the original training data. Specifically, we remove the designs from
conventional knowledge distillation settings that result in dataset bias and
present three levels of training objectives, i.e., token, patch, and feature,
to maximize the efficacy of knowledge transfer. In this manner, Proteus is
trained at ImageNet-level costs with surprising ability, facilitating the
accessibility of training foundation models for the broader research community.
Leveraging DINOv2-g/14 as the teacher, Proteus-L/14 matches the performance of
the Oracle method DINOv2-L/14 (142M training data) across 15 benchmarks and
outperforms other vision foundation models including CLIP-L/14 (400M),
OpenCLIP-L/14 (400M/2B) and SynCLR-L/14 (600M).
