they did do other experiments that they didn't report here and I think I might have asked about that I just can't remember if if that was something they did but if they did it wasn't as as performant so for the vision benchmarks yeah so they wanted to use the the same model that was used for prior semi-supervised work and this was the wide residual networks with depth 28 and with 2 and and so yeah they use the same exact labeled samples that Auto augment use to find its optimal policy so for C far 10 which is a 10 10 Way image