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Data augmentation #20

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egistific opened this issue Feb 3, 2020 · 1 comment
Open

Data augmentation #20

egistific opened this issue Feb 3, 2020 · 1 comment

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@egistific
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Thanks for sharing the testing code. I'm trying to reproduce the training code would like to know the implementation details for data augmentation.

. Random in-plain rotation: what are the parameters used?
. Random scaling for both in-plain and depth dimension: is each dimension scaled independently and what are the parameters used?
. Random gaussian noise is also randomly added with the probability of 0.5: which dimensions is noise added to and what are the parameters used?
. For each image in the training set, is data augmentation performed 3 times (rotation, scaling, adding noise)?
. Does data augmentation increase the number of samples in the training set (i.e. both original images and augmented images are used)?

@zhangboshen
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Thanks for sharing the testing code. I'm trying to reproduce the training code would like to know the implementation details for data augmentation.

. Random in-plain rotation: what are the parameters used?
. Random scaling for both in-plain and depth dimension: is each dimension scaled independently and what are the parameters used?
. Random gaussian noise is also randomly added with the probability of 0.5: which dimensions is noise added to and what are the parameters used?
. For each image in the training set, is data augmentation performed 3 times (rotation, scaling, adding noise)?
. Does data augmentation increase the number of samples in the training set (i.e. both original images and augmented images are used)?

Hi, please check our training code for details: https://github.com/zhangboshen/A2J/tree/master/src_train

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