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

CNNs and Transformers Perceive Hybrid Images Similar to Humans