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I tried it on a non-image problem and it performed worse. Didn't play with the learning rate though, maybe i will try to increase it and test again.
Yeah you may be able to increase the learning rate quite a bit. And in fact I had to because otherwise the loss was a big strangely stagnant. Not sure exactly why.
It seems worth playing with LR scheduling other than OneCycle to unleash the full potential of these new optimizers.https://forums.fast.ai/t/imagenette-woof-leaderboards-guidelines-for-proving-new-high-scores/52714/19 …
Yeah I set div_factor to 1 to get rid of the warmup stage and just have it learn at constant lr then decay. Not rigorously tested at all yet but that seems to make sense.
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