  Hybrid images is a technique to generate images with two interpretations that
change as a function of viewing distance. It has been utilized to study
multiscale processing of images by the human visual system. Using 63,000 hybrid
images across 10 fruit categories, here we show that predictions of deep
learning vision models qualitatively matches with the human perception of these
images. Our results provide yet another evidence in support of the hypothesis
that Convolutional Neural Networks (CNNs) and Transformers are good at modeling
the feedforward sweep of information in the ventral stream of visual cortex.
Code and data is available at https://github.com/aliborji/hybrid_images.git.
