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> Does anybody actually use this network anymore? Why not? It's still good for simple classification tasks. We use it as an encoder for a segmentation model in
by plonk 4y ago
> Does anybody actually use this network anymore?
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.
- jph00 4y agoI don't think that's true - have a look at this analysis here: https://www.kaggle.com/code/jhoward/which-image-models-are-best 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.
- YetAnotherNick 4y ago> 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%20%3E%20VGG%3A%20ResNet%2D50,16%20(6.21%20vs%209.0) https://github.com/jcjohnson/cnn-benchmarks#:~:text=ResNet%2....