Papers › Visformer: The Vision-friendly Transformer
Visformer: The Vision-friendly Transformer
Zhengsu Chen, Lingxi Xie, Jianwei Niu, Xuefeng Liu, Longhui Wei, Qi Tian
The past year has witnessed the rapid development of applying the Transformer module to vision problems. While some researchers have demonstrated that Transformer-based models enjoy a favorable ability of fitting data, there are still growing number of evidences showing that these models suffer over-fitting especially when the training data is limited. This paper offers an empirical study by performing step-by-step operations to gradually transit a Transformer-based model to a convolution-based model. The results we obtain during the transition process deliver useful messages for improving visual recognition. Based on these observations, we propose a new architecture named Visformer, which is abbreviated from the `Vision-friendly Transformer'. With the same computational complexity, Visformer outperforms both the Transformer-based and convolution-based models in terms of ImageNet classification accuracy, and the advantage becomes more significant when the model complexity is lower or the training set is smaller. The code is available at https://github.com/danczs/Visformer.
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Code
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Code Syntology ran Syntology
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55120f2026b56aa2 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | Visformer-S | GFLOPs | 4.9 | #564 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Visformer-S | Number of params | 40.2M | #564 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Visformer-S | Top 1 Accuracy | 82.2% | #564 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Visformer-Ti | GFLOPs | 1.3 | #821 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Visformer-Ti | Number of params | 10.3M | #821 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Visformer-Ti | Top 1 Accuracy | 78.6% | #821 of 1060 | 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: Visformer
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