Papers › MetaFormer Is Actually What You Need for Vision
MetaFormer Is Actually What You Need for Vision
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 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
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