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While training ResNet18 and ResNet50 on an extremely tiny subset of ImageNet, I noticed a curious phenomena where the shallower model performed better. Obviously, the original ResNet paper and other benchmarks as well as conventional wisdom would indicate that deeper models perform better on the entire ImageNet than shallower models, but the key assumption is that it's a gigantic, complex dataset with large amounts of classes.

Is significant reduction in dataset complexity resulting in ResNet50 to overfit and therefore perform slightly worse than ResNet18? Or is there something else that's happening?

And to ask a more general question, what is the relationship between dataset complexity and model complexity?

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  • $\begingroup$ How did the difference between the training and validation loss compare for the two models? The bigger the gap between training and val loss, the more overfitting is probably going on. $\endgroup$ Aug 6 '19 at 6:35
  • $\begingroup$ Does this or this help? $\endgroup$ Aug 6 '19 at 6:45

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