We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain, thus exposing their hidden inside. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images. We report two findings. First, explicit mapping between the brain and deep-network features across dimensions of space, layers, scales, and channels is crucial. This mapping method, Fac-torTopy, is plug-and-play for any deep-network; with it, one can paint a picture of the network onto the brain (liter-ally!). Second, our visualization shows how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior, growing with more data or network capacity. It also provides in-sight into fine-tuning: how pre-trained models change when adapting to small datasets. We found brain-like hierarchi-cally organized network suffer less from catastrophic for-getting after fine-tuned.