Recent image generation models such as Stable Diffusion have exhibited an
impressive ability to generate fairly realistic images starting from a simple
text prompt. Could such models render real images obsolete for training image
prediction models? In this paper, we answer part of this provocative question
by investigating the need for real images when training models for ImageNet
classification.  Provided only with the class names that have been used to
build the dataset, we explore the ability of Stable Diffusion to generate
synthetic clones of ImageNet and measure how useful these are for training
classification models from scratch. We show that with minimal and
class-agnostic prompt engineering, ImageNet clones are able to close a large
part of the gap between models produced by synthetic images and models trained
with real images, for the several standard classification benchmarks that we
consider in this study. More importantly, we show that models trained on
synthetic images exhibit strong generalization properties and perform on par
with models trained on real data for transfer.
Project page: https://europe.naverlabs.com/imagenet-sd