  The recent breakthroughs in natural language processing for model pretraining
on large quantities of data have opened the way for similar foundation models
in computer vision. These models could greatly simplify the use of images in
any system by producing all-purpose visual features, i.e., features that work
across image distributions and tasks without finetuning. This work shows that
existing pretraining methods, especially self-supervised methods, can produce
such features if trained on enough curated data from diverse sources. We
revisit existing approaches and combine different techniques to scale our
pretraining in terms of data and model size. Most of the technical
contributions aim at accelerating and stabilizing the training at scale. In
terms of data, we propose an automatic pipeline to build a dedicated, diverse,
and curated image dataset instead of uncurated data, as typically done in the
self-supervised literature. In terms of models, we train a ViT model
(Dosovitskiy et al., 2020) with 1B parameters and distill it into a series of
smaller models that surpass the best available all-purpose features, OpenCLIP
(Ilharco et al., 2021) on most of the benchmarks at image and pixel levels.
