Papers › Three things everyone should know about Vision Transformers
Three things everyone should know about Vision Transformers
Hugo Touvron, Matthieu Cord, Alaaeldin El-Nouby, Jakob Verbeek, Hervé Jégou
After their initial success in natural language processing, transformer architectures have rapidly gained traction in computer vision, providing state-of-the-art results for tasks such as image classification, detection, segmentation, and video analysis. We offer three insights based on simple and easy to implement variants of vision transformers. (1) The residual layers of vision transformers, which are usually processed sequentially, can to some extent be processed efficiently in parallel without noticeably affecting the accuracy. (2) Fine-tuning the weights of the attention layers is sufficient to adapt vision transformers to a higher resolution and to other classification tasks. This saves compute, reduces the peak memory consumption at fine-tuning time, and allows sharing the majority of weights across tasks. (3) Adding MLP-based patch pre-processing layers improves Bert-like self-supervised training based on patch masking. We evaluate the impact of these design choices using the ImageNet-1k dataset, and confirm our findings on the ImageNet-v2 test set. Transfer performance is measured across six smaller datasets.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Fine-Grained Image Classification | Stanford Cars | ViT-L (attn finetune) | Accuracy | 93.8% | #62 of 83 | Archive leaderboard | report |
| Image Classification | CIFAR-10 | ViT-B (attn fine-tune) | Percentage correct | 99.3 | #10 of 265 | Archive leaderboard | report |
| Image Classification | CIFAR-100 | ViT-L (attn fine-tune) | Percentage correct | 93.0 | #11 of 211 | Archive leaderboard | report |
| Image Classification | Flowers-102 | ViT-B (attn finetune) | Accuracy | 98.5 | #24 of 52 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-L@384 (attn finetune) | Top 1 Accuracy | 85.5% | #218 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-B@384 (attn finetune) | Top 1 Accuracy | 84.3% | #324 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-B-36x1 | Top 1 Accuracy | 84.1% | #347 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-B-18x2 | Top 1 Accuracy | 84.1% | #348 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-B (hMLP + BeiT) | Top 1 Accuracy | 83.4% | #425 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-S-24x2 | Top 1 Accuracy | 82.6% | #520 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-S-48x1 | Top 1 Accuracy | 82.3% | #549 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet V2 | ViT-B-36x1 | Top 1 Accuracy | 73.9 | #19 of 33 | Archive leaderboard | report |
| Image Classification | iNaturalist 2018 | ViT-L (attn finetune) | Top-1 Accuracy | 75.3% | #27 of 60 | 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
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