KFrank:

If you have multi-label, three-class classification problem – e.g., this
image does or does not contain a “person” and also does or does
not contain a “dog” and also does or does not contain a “shrubbery”
(that is, your image can be “true” for any number of your three classes,
including for none of them or all of them) – then you don’t have to do
anything additional to account for training images containing no
objects (other than to make sure that they are labelled correctly)


Thanks a lot for your delicate reply~~ One more question about the implement detail, that should I annotate the “background-only” images? Or in other words, how does the “ground-truth” look like? An empty bbox list for object detection task? An “all-zeros” mask for segmentation task？Or whatsoever?