  Deep neural networks (DNNs) are machine learning algorithms that have
revolutionised computer vision due to their remarkable successes in tasks like
object classification and segmentation. The success of DNNs as computer vision
algorithms has led to the suggestion that DNNs may also be good models of human
visual perception. We here review evidence regarding current DNNs as adequate
behavioural models of human core object recognition. To this end, we argue that
it is important to distinguish between statistical tools and computational
models, and to understand model quality as a multidimensional concept where
clarity about modelling goals is key. Reviewing a large number of
psychophysical and computational explorations of core object recognition
performance in humans and DNNs, we argue that DNNs are highly valuable
scientific tools but that as of today DNNs should only be regarded as promising
-- but not yet adequate -- computational models of human core object
recognition behaviour. On the way we dispel a number of myths surrounding DNNs
in vision science.
