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nicholsz t1_j6yniui wrote

Reply to comment by puppet_pals in [D] ImageNet normalization vs [-1, 1] normalization by netw0rkf10w

With data augmentation techniques (especially contrast or luminance randomization), normalizing would end up being a no-op in the end, right?

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netw0rkf10w OP t1_j6z15t0 wrote

I think normalization will be here to stay (maybe not the ImageNet one though), as it usually speeds up training.

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nicholsz t1_j6z1jgm wrote

Oh I meant fitting to the statistics of ImageNet / the training dataset. There's always got to be some kind of normalization

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puppet_pals t1_j701uqt wrote

>I think normalization will be here to stay (maybe not the ImageNet one though), as it usually speeds up training.

the reality is you are tied to the normalization scheme of whatever you are transfer learning from. (assuming you are transfer learning). Framework authors and people publishing weights should make normalization as easy as possible; typically via a 1/255.0 rescaling operation (or x/127.5 - 1, I'm indifferent though I opt for 1/255 personally)

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