and so so so various different improvement axes which you can actually refer to the paper like like for instance be using bigger patches of the lot and so on so so you just from 48 49 percent you able to now go close to 72 percent but by just focusing on the engineering details and and doing large-scale optimization really well and that that's really the success story of sub supervised learning just is just do the simple things right and you'll get really good numbers this is a table that shows the whatever graph you saw in