  Transformers have shown great potential in computer vision tasks. A common
belief is their attention-based token mixer module contributes most to their
competence. However, recent works show the attention-based module in
Transformers can be replaced by spatial MLPs and the resulted models still
perform quite well. Based on this observation, we hypothesize that the general
architecture of the Transformers, instead of the specific token mixer module,
is more essential to the model's performance. To verify this, we deliberately
replace the attention module in Transformers with an embarrassingly simple
spatial pooling operator to conduct only basic token mixing. Surprisingly, we
observe that the derived model, termed as PoolFormer, achieves competitive
performance on multiple computer vision tasks. For example, on ImageNet-1K,
PoolFormer achieves 82.1% top-1 accuracy, surpassing well-tuned Vision
Transformer/MLP-like baselines DeiT-B/ResMLP-B24 by 0.3%/1.1% accuracy with
35%/52% fewer parameters and 50%/62% fewer MACs. The effectiveness of
PoolFormer verifies our hypothesis and urges us to initiate the concept of
"MetaFormer", a general architecture abstracted from Transformers without
specifying the token mixer. Based on the extensive experiments, we argue that
MetaFormer is the key player in achieving superior results for recent
Transformer and MLP-like models on vision tasks. This work calls for more
future research dedicated to improving MetaFormer instead of focusing on the
token mixer modules. Additionally, our proposed PoolFormer could serve as a
starting baseline for future MetaFormer architecture design. Code is available
at https://github.com/sail-sg/poolformer.
