Papers › DeiT III: Revenge of the ViT
DeiT III: Revenge of the ViT
Hugo Touvron, Matthieu Cord, Hervé Jégou
A Vision Transformer (ViT) is a simple neural architecture amenable to serve several computer vision tasks. It has limited built-in architectural priors, in contrast to more recent architectures that incorporate priors either about the input data or of specific tasks. Recent works show that ViTs benefit from self-supervised pre-training, in particular BerT-like pre-training like BeiT. In this paper, we revisit the supervised training of ViTs. Our procedure builds upon and simplifies a recipe introduced for training ResNet-50. It includes a new simple data-augmentation procedure with only 3 augmentations, closer to the practice in self-supervised learning. Our evaluations on Image classification (ImageNet-1k with and without pre-training on ImageNet-21k), transfer learning and semantic segmentation show that our procedure outperforms by a large margin previous fully supervised training recipes for ViT. It also reveals that the performance of our ViT trained with supervision is comparable to that of more recent architectures. Our results could serve as better baselines for recent self-supervised approaches demonstrated on ViT.
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Code
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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 | ViT-B @384 (DeiT III, 21k) | Top 1 Accuracy | 86.7% | #126 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-L | GFLOPs | 191.2 | #198 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-L | Number of params | 304.8M | #198 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-L | Top 1 Accuracy | 85.8% | #198 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-B @224 (DeiT III, 21k) | Top 1 Accuracy | 85.7% | #207 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-H @224 (DeiT III) | Top 1 Accuracy | 85.2% | #245 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-B @384 (DeiT III) | Number of params | 87M | #270 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-B @384 (DeiT III) | Top 1 Accuracy | 85.0% | #270 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-L @224 (DeiT III) | Top 1 Accuracy | 84.9% | #274 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-B @224 (DeiT III) | Top 1 Accuracy | 83.8% | #386 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-S @384 (DeiT III) | GFLOPs | 15.5 | #428 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-S @384 (DeiT III) | Number of params | 22M | #428 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-S @384 (DeiT III) | Top 1 Accuracy | 83.4% | #428 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-S @224 (DeiT III, 21k) | Top 1 Accuracy | 83.1% | #464 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ViT-S @224 (DeiT III) | Top 1 Accuracy | 81.4% | #643 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet ReaL | ViT-L @384 (DeiT III, 21k) | Number of params | 304M | #54 of 57 | Archive leaderboard | report |
| Image Classification | ImageNet ReaL | ViT-L @384 (DeiT III, 21k) | Top 1 Accuracy | 87.7% | #54 of 57 | Archive leaderboard | report |
| Image Classification | ImageNet ReaL | ViT-H @224 (DeiT III, 21k) | Number of params | 632M | #55 of 57 | Archive leaderboard | report |
| Image Classification | ImageNet ReaL | ViT-H @224 (DeiT III, 21k) | Top 1 Accuracy | 87.2% | #55 of 57 | Archive leaderboard | report |
| Image Classification | ImageNet ReaL | ViT-L @224 (DeiT III, 21k) | Top 1 Accuracy | 87.0% | #56 of 57 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | DeiT-L | mIoU | 55.6 | #29 of 95 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | DeiT-B | mIoU | 54.1 | #36 of 95 | 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: 3-Augment
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