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

05/07/2021
by   Hugo Touvron, et al.
43

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset