text: There are also various methods that combine both masked image modeling and invariance criteria to learn visual 
  representations from static images, such as iBOT (Zhou et al., 2021) and DINOv2 (Zhou et al., 2021; Oquab et al., 
  2023), the latter is currently the most competitive instantiation of self-supervised learning with static images, scaled 
  to a model with over 1.1B parameters trained on a curated dataset of 142M images. 