3 ms·
> 8.7x faster to train This is an achievement but it would be helpful to have put "to train" in the title as this is quite different from efficiency at inferen
by dheera 6y ago
> 8.7x faster to train
This is an achievement but it would be helpful to have put "to train" in the title as this is quite different from efficiency at inference time, which is what often actually matters in deployed applications.
From Table 3 on Page 7 it appears to me that NFNet is significantly heavier in the number of parameters than EfficientNet for similar accuracies. For example EffNet-B5 achieves 83.7% with 30M params and 9.9B FLOPs on Top-1 while NFNet-F0 achieves 83.6% with 71.5M params and 12.38B FLOPs on Top-1.
It appears to me at first glance that NFNet has not achieved SOTA at inference.
- trott 6y ago> It appears to me at first glance that NFNet has not achieved SOTA at inference. It has, for larger models (F1 vs B7). See Fig 4 in the Appendix.
- The_rationalist 6y agoNo it hasn't https://paperswithcode.com/sota/image-classification-on-imagenet https://paperswithcode.com/sota/image-classification-on-imag...
- trott 6y ago> No it hasn't https://paperswithcode.com/sota/image-classification-on-imag https://paperswithcode.com/sota/image-classification-on-imag... 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_rationalist 6y agoThe 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?
- trott 6y ago> The deepmind paper NFNet-F4+ you were talking about also has external training data. @dheera and I did not mention NFNet-F4+. All models, tables, figures and numbers that we did mention resulted from training on ImageNet alone.