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

Code

• github.com/facebookresearch/deit
  Officialpytorch★ 4,327
• github.com/rwightman/pytorch-image-models
  In paperpytorch★ 36,538
• github.com/xmu-xiaoma666/External-Attention-pytorch
  pytorch★ 12,169
• github.com/martinsbruveris/tensorflow-image-models
  tf★ 291
• github.com/Mayurji/Image-Classification-PyTorch
  pytorch★ 219
• github.com/lucidrains/res-mlp-pytorch
  pytorch★ 201
• github.com/liuruiyang98/Jittor-MLP
  jax★ 170
• github.com/lalithjets/surgical_vqa
  pytorch★ 63
• github.com/rishikksh20/ResMLP-pytorch
  pytorch★ 45
• github.com/leaderj1001/Bag-of-MLP
  pytorch★ 20

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.

Tasks

Benchmark Results

Reproductions