If you don't you might not realize they are comparing ResNet (designed for ultimate performance) vs EffcientNet to show how close it gets in accuracy in the FLOPs budget.

Note that the best accuracy for EffcientNet is roughly 1-2% better than ResNet/ResNeXT/SENet/etc but does have a much better FLOPs budget.

But these other architectures were never optimised for FLOPs. It wasn't event a consideration when designing them.

And EffcientNet is about a (manually designed) technique for scaling neural networks up in accuracy. Only EffcientNet-B0 is designed by AutoML, the others are scaled up. See the paper[1] for complete details.

Like-for-like should be against MobileNet etc. EfficentNet is better, but the comparison is more reasonable in general.

https://machinethink.net/blog/mobile-architectures/ is a really good overview.

[1] https://arxiv.org/pdf/1905.11946.pdf