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

• URL: http://arxiv.org/abs/2105.03404v1
• Date: Fri, 7 May 2021 17:31:44 GMT
• Title: ResMLP: Feedforward networks for image classification with data-efficient training
• Authors: Hugo Touvron, Piotr Bojanowski, Mathilde Caron, Matthieu Cord, Alaaeldin El-Nouby, Edouard Grave, Armand Joulin, Gabriel Synnaeve, Jakob Verbeek, Herv\'e J\'egou
• Abstract summary: We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. We will share our code based on the Timm library and pre-trained models.
• Score: 73.26364887378597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 will share our code based on the Timm library and pre-trained models.

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