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We present ResMLP, an architecture built entirely upon multi-layerperceptrons for image classification. It is a simple residual network thatalternates (i) a linear layer in which image patches interact, independentlyand identically across channels, and (ii) a two-layer feed-forward network inwhich channels interact independently per patch. When trained with a moderntraining strategy using heavy data-augmentation and optionally distillation, itattains surprisingly good accuracy/complexity trade-offs on ImageNet. We alsotrain ResMLP models in a self-supervised setup, to further remove priors fromemploying a labelled dataset. Finally, by adapting our model to machinetranslation we achieve surprisingly good results.  We share pre-trained models and our code based on the Timm library.

ResMLP: Feedforward networks for image classification with data-efficient training