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.
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.
https://github.com/jcjohnson/cnn-benchmarks#:~:text=ResNet%2....
Probably because it makes the hardware look good.
Think about changing the model every other year: - 2015: ResNet trained in Nvidia k80 - 2017: Inception trained in Nvidia 1080 ti - 2019: Transformer trained in Nvidia V100 - 2021: GTP-3 trained in a cluster
Now you have your new fancy algorithm X and an Nvidia 4090. How much better is your algorithm compared to the state of the art, and how much have you improved compared to the algorithms 5 years ago? Now you are in a nightmare and you have to run all the past algorithms in order to compare it. Or how fast is the new Nvidia card? which noone still have and nvidia has decided to give numbers based on a their own model?
It makes me feel like i’m missing something! Is is still used as a backbone in the same way as legacy code is everywhere, or is it something else entirely??
Better would be mobilenets or efficientNets or NFNets or vision transformers or almost anything that's come out in the 8 years since VGG was published (great work it was at the time!).