no, it won't directly. conv nets handle translation invariance but not scale invariance. having said that there's no reason you can't use aggressive data augmentation for this (resizing before patch sampling). i wonder how much the semi supervised approach might help too; if you've labelled _only_ small bees in a subset of the data, trained a model, applied to a larger dataset & retrained there will be a small amount of detections (that are true positives) to bees that are slightly larger (and smaller) than the ones you labelled.... (maybe?)
Ok. Why isn't there a kind of network function that works with scaling, like convolution works with translation?
I think a CNN can handle scale invariance e.g. With max pooling
Maybe, but you shouldn't count on it. Perhaps a big bee is supposed to be counted as two small bees, you didn't specify when you trained it.