I don't think that's true - have a look at this analysis here:
https://www.kaggle.com/code/jhoward/which-image-models-are-b...
Those slow and inaccurate models at the bottom of the graph are the VGG models. A resnet34 is faster and more accurate than any VGG model. And there are better options now -- for example resnet34d is as fast as resnet34, and more accurate. And then convnext is dramatically better still.
Re:Accelerated PyTorch Training on M1 Mac
by YetAnotherNick on Wednesday May 18, 2022 @04:18PM
(#31427133)
> ResNet > VGG: ResNet-50 is faster than VGG-16 and more accurate than VGG-19 (7.02 vs 9.0); ResNet-101 is about the same speed as VGG-19 but much more accurate than VGG-16 (6.21 vs 9.0).
https://github.com/jcjohnson/cnn-benchmarks#:~:text=ResNet%2....
Why not? It's still good for simple classification tasks. We use it as an encoder for a segmentation model in some cases. Most ResNet variants are much heavier.