We were talking about models trained on ImageNet, specifically about the trade-off between accuracy and FLOPs. But the higher-accuracy models listed in your link use extra data. So it's not quite the same benchmark we were talking about.
The deepmind paper NFNet-F4+ you were talking about also has external training data.
The number one in accuracy (Meta pseudo labels) is also faster for inference (390M vs 570M parameters) vs the deepmind one.
So what are you disagreeing with?
