If you want human like recognition (eg rotation and orientation invarience) of objects it seems like you would want to at least train them on image sequence dat

If you want human like recognition (eg rotation and orientation invarience) of objects it seems like you would want to at least train them on image sequence data with multiple views of the same object (like video). It's not like humans learn image matching based on a sequence of disjointed 2D images. We train on binocular moving images of changing distance and orientation. Maybe the training sets are not well chosen for this sort if issue. Certainly expanding the set with rotations translations and scalings isn't difficult, but different orientation views would require a bit more effort.