Papers › MetaFormer Is Actually What You Need for Vision

MetaFormer Is Actually What You Need for Vision

22 Nov 2021CVPR 2022 1arXiv:2111.11418archive 2025-07-28

Weihao Yu, Mi Luo, Pan Zhou, Chenyang Si, Yichen Zhou, Xinchao Wang, Jiashi Feng, Shuicheng Yan

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.

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rwightman/pytorch-image-models officialmentioned in papermentioned on GitHubpytorch report
sail-sg/poolformer officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
DarshanDeshpande/jax-models mentioned on GitHubjax report
IMvision12/keras-vision-models mentioned on GitHubpytorch report
Westlake-AI/openmixup mentioned on GitHubpytorch report
facebookresearch/xformers mentioned on GitHubpytorch report
huggingface/transformers mentioned on GitHubpytorch report
sithu31296/image-classification mentioned on GitHubpytorchMIT report
sithu31296/semantic-segmentation mentioned on GitHubpytorch report
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Tasks

Image ClassificationObject DetectionRecommendation SystemsSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification ImageNet MetaFormer PoolFormer-M48 GFLOPs 23.2 #533 of 1060 Archive leaderboard report
Image Classification ImageNet MetaFormer PoolFormer-M48 Number of params 73M #533 of 1060 Archive leaderboard report
Image Classification ImageNet MetaFormer PoolFormer-M48 Top 1 Accuracy 82.5% #533 of 1060 Archive leaderboard report
Object Detection COCO minival PoolFormer-S36 (Mask R-CNN) AP50 63.1 #169 of 220 Archive leaderboard report
Object Detection COCO minival PoolFormer-S36 (Mask R-CNN) AP75 44.8 #169 of 220 Archive leaderboard report
Object Detection COCO minival PoolFormer-S36 (Mask R-CNN) box AP 41.0 #169 of 220 Archive leaderboard report
Semantic Segmentation ADE20K PoolFormer-M48 Validation mIoU 42.7 #214 of 235 Archive leaderboard report
Semantic Segmentation DensePASS PoolFormer (MiT-B1) mIoU 43.18% #9 of 36 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: MetaFormer, PoolFormer

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMetaFormerMulti-Head AttentionPoolFormerPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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