I just tried the library in a Kaggle Quickdraw comp, basically @radek 's version of lesson1 achieving a remarkable top 17%. I say remarkable cause this was with a single, not postprocessed model (top solutions postprocessed + emsembled heavily many models).
So, I’d like to share what I have learnt, it’s just my intuitions so feel free to add/discuss any of them:
-1) “Vanilla” Fast.ai handles without problem the huge amount of images converging nicely in a few epochs.
-2) If you have zillions of varied data don’t use augmentation, it will not beat real images. (What zillions means will actually depend on the number of classes but 50 million images is enough to forget about augmentation)
-3) Be careful with momentum if you have noisy data (5% of images where “swap” noise). I benefited a lot from reducing momentum sustancially. My guess is that momentum hurts normal “noise balancing” observed in NNets with swap noise.
-4) related to 3), Im quite curious about Noise handling. Especifically, why with so much data noise could not be completely filtered by my -single- model. Made me wonder about how well adaptative learning rates + cycles filter noise, considering that optimal filtering should be random.
So that’s it, maybe obvious hints but I found them very usefull in this case. Any aditional insight, especially about points 3 and 4) will be welcome, I think noise was the key to that classification and no single model that I know of was able to completely filter it without being ensembled with others, not even the top ones.