  The purpose of the research is to determine if currently available
self-supervised learning techniques can accomplish human level comprehension of
visual images using the same degree and amount of sensory input that people
acquire from. Initial research on this topic solely considered data volume
scaling. Here, we scale both the volume of data and the quality of the image.
This scaling experiment is a self-supervised learning method that may be done
without any outside financing. We find that scaling up data volume and picture
resolution at the same time enables human-level item detection performance at
sub-human sizes.We run a scaling experiment with vision transformers trained on
up to 200000 images up to 256 ppi.
