Why do you normalize each img's pixel value to -1.6~1.6 when training and testing? about deepglobe-road-extraction-challenge HOT 5 OPEN

In dlinknet, each conv-layer is followed by a batchnorm(conv-bn-relu), and, in fact, there's no difference between [-1.6,1.6] and [-1,1](or even [0,255]) in this case.
However, Unet(weight initialized by pytorch default initializer) without batchnorm do have better result using [-1.6,1.6] normalization. In this case, [-1.6,1.6](compare with [-1,1]) is equal to enlarge the initial learning rate and std of the initializer.
In dlinknet, channel-wise normalization equals adding two parameters weighting the RGB. It may have better result(I'm not sure), but I don't think this is so crucial~ 2333

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This operation is to norm the image from [0,255] to [-1.6,1.6], you can norm the image to other range(better zero centered), [-1,1] or [-1.2,1.2] are fine :)

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Thanks for your reply~ Is 1.6 a particular number that works the best in your case? And why don't you apply a std to each channel? Cause people always set mean and std at the same time. Haha~

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Alright, thanks again. Happy New Year btw~ Lol

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Happy New Year~ www

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