that's when you see like a little bit more of a gap between transfer learning versus random initialization maybe it's like two percent but then by the time you've gotten to fifty thousand examples that gap is like almost gone and and like there's really no reason why you'd a priori you want to use like kind of imagenet sized architecture on like 5000 examples like I think that's sort of like also like kind of yeah merits sort of further study we've been talking about over parametrizations I think that's like an interesting