Dear Scilabers, probably not the correct forum to ask this question, but i do not know better.
Dealing with neural networks and image segmentation I write some Scilab code for data augmentation. This is to increase my training data set. ( = more images) Data augmentation (for now) is done by: - image rotation - image horizontal shifting Now a basic question: - Does one apply the data augmentation only for the input images and keep the label images? Or - does one also rotate/shear the label images? A "label image" in this context is a binary image (black/white). white --> corresponds to pixels in the input image, which are of interest black --> corresponds to pixels in the input image, which are of no interest. Any help is appreciated. Thanks, Philipp
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