sort of your training your training curve with with pre trained weights and this blue line is what you see with random initialization if you sort of extend these far enough out they basically converge to the same value but there's this sort of huge difference in in how quickly they converge and you know when you when you first see this plot you might think oh well this means that transfer learning is doing its job like you know you've learned some useful features and you're sort of reusing it and that's why you're converging foster