AFAIK first application of fastai v2 for Kaggle Prize

Fastai v2 has successfully been applied by others to win a Kaggle competition. Read their winning solution over here

Couple points that they mentioned I found interesting:

  • “Fastai v2 head is different than v1 for vision models, the v1 head works better.”
  • “We used a pretty high eps for Adam optimizer in v2, defaults (in v1) work better.”

Awesome! Which one? (The link doesn’t go anywhere) :slight_smile:

Sorry about that. It is updated now! :slight_smile:

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@jeremy @sgugger I assume you guys have done experiments selecting these parameters for v2. Do you think these results could be specific to this dataset?

The problem with the head was a mistake by me. I removed the initial batchnorm in the head, without testing that change properly. I thought it wouldn’t have any negative impact, but I was wrong.

I’m interested in the eps issue myself, and also noticed it in the rsna comp. I’m not sure still when it should be high and when low. Perhaps the recent Sadam is the better approach


Thanks for your response.

I also noticed something similar with eps, although I just players around with a couple values on a quick baseline I had for RSNA.

Thanks for sharing the SAdam paper. This looks like an interesting approach where they replace eps, and introduce an activation function (soft plus) with its own hyperparameter that needs to be tuned. In addition, it seems it is possible to play around with the activation function, leading to another parameter to tune. So there is probably some additional work that needs to be done finding some generic parameters choices that would work on a wide variety of problems.

Nevertheless, this is very interesting work! Thanks again for sharing!