Comments (2)
Hi, @WangFeng18,
Good catch! In short, 0.08 is more aggressive data augmentation, and if I recall correctly, it brings about marginal improvement ( roughly < 0.3% ) on full ImageNet. Indeed, RandomGrayscale
is the key augmentation, and dropping it leads to a significant performance drop.
My code uses 0.2 because my initial baseline on ImageNet100 used this threshold. So I just want to be consistent. For full ImageNet, I want to closely match the standard data augmentation for supervised learning, and therefore I used 0.08. Does this make sense?
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Thanks for you reply!I thought you have resolved my confusion.
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