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I use Albumentations augmentations in my computer vision tasks. However, I don't fully understand when to use normalization on my images (I use min-max normalization). Do I need to use normalization before augmentation functions, but values would not be between 0-1, or do I use normalization just after augmentations, so that values are between 0-1, or I use normalization in both cases - before and after augmentations?

For example, when I use Sharpen, values are not in 0-1 range (they vary in -0.5-1.5 range). Does that affect model performance? If yes, how?

Thanks in advance.

ztsv-av
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The basic idea is that you should have the input of your neural network around 0 and with a variance of 1. There is a mathematical reason why it helps the learning process of neural network. This is not the case for other algorithms like tree boosting.

If you train from scratch the type of normalization (min max or other) should not impact the model performance (except if, for exemple your max/min value is really extrem compare to your other data point).

Adrien Forbu
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  • So, as long as inputs are around 0 (-0.5 - 0.5 for example) with a variance of 1, it does not really matter when to apply augmentation functions? I guess it is better to use them before normalization function, so that all inputs have same range of values...(I apply several augmentation functions with a specific probability for each of them). – ztsv-av Feb 15 '22 at 05:46
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    I would say apply normalization as the last operation of your augmentation pipeline. – Adrien Forbu Feb 16 '22 at 19:59