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

Hugo Touvron; Piotr Bojanowski; Mathilde Caron; Matthieu Cord; Alaaeldin El-Nouby; Edouard Grave; Gautier Izacard; Armand Joulin; Gabriel Synnaeve; Jakob Verbeek; Hervé Jégou

arXiv · 2105.03404

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

Hugo Touvron·Piotr Bojanowski·Mathilde Caron·Matthieu Cord·Alaaeldin El-Nouby·Edouard Grave·Gautier Izacard·Armand Joulin·Gabriel Synnaeve·Jakob Verbeek·Hervé Jégou

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Abstract

We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which image patches interact, independently and identically across channels, and (ii) a two-layer feed-forward network in which channels interact independently per patch. When trained with a modern training strategy using heavy data-augmentation and optionally distillation, it attains surprisingly good accuracy/complexity trade-offs on ImageNet. We also train ResMLP models in a self-supervised setup, to further remove priors from employing a labelled dataset. Finally, by adapting our model to machine translation we achieve surprisingly good results. We share pre-trained models and our code based on the Timm library.

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Hugo Touvron, Piotr Bojanowski, Mathilde Caron, Matthieu Cord, Alaaeldin El-Nouby, Edouard Grave, Gautier Izacard, Armand Joulin, Gabriel Synnaeve, Jakob Verbeek, Hervé Jégou. 2021-05-07. ResMLP: Feedforward networks for image classification with data-efficient training. https://arxiv.org/abs/2105.03404

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