Is it feasible to do random cropping as a data augmentation technique in the context of multi-label image classification?

Question

I have read 2 top-ranking solutions in kaggle concerning multi-label image classification. In both of the competitions I read, random cropping was performed. To me, this seems like a bad move to make because we could have a mismatch between the labels and the cropped images. Here are the two links:

1.human-protein-atlas-image-classification

2.iMet Collection 2019 - FGVC6

If the reason for cropping is an input size image constraint for the used model architecture, then isn't it better to resize the image instead of cropping it?

Answer 1

I haven't visited the links yet, but random crop helps as long as you can keep the presence of classes, even only a small part of the actual object.