  This paper presents SimMIM, a simple framework for masked image modeling. We
simplify recently proposed related approaches without special designs such as
block-wise masking and tokenization via discrete VAE or clustering. To study
what let the masked image modeling task learn good representations, we
systematically study the major components in our framework, and find that
simple designs of each component have revealed very strong representation
learning performance: 1) random masking of the input image with a moderately
large masked patch size (e.g., 32) makes a strong pre-text task; 2) predicting
raw pixels of RGB values by direct regression performs no worse than the patch
classification approaches with complex designs; 3) the prediction head can be
as light as a linear layer, with no worse performance than heavier ones. Using
ViT-B, our approach achieves 83.8% top-1 fine-tuning accuracy on ImageNet-1K by
pre-training also on this dataset, surpassing previous best approach by +0.6%.
When applied on a larger model of about 650 million parameters, SwinV2-H, it
achieves 87.1% top-1 accuracy on ImageNet-1K using only ImageNet-1K data. We
also leverage this approach to facilitate the training of a 3B model
(SwinV2-G), that by $40\times$ less data than that in previous practice, we
achieve the state-of-the-art on four representative vision benchmarks. The code
and models will be publicly available at https://github.com/microsoft/SimMIM.
