Papers › Token Merging: Your ViT But Faster
Token Merging: Your ViT But Faster
Daniel Bolya, Cheng-Yang Fu, Xiaoliang Dai, Peizhao Zhang, Christoph Feichtenhofer, Judy Hoffman
We introduce Token Merging (ToMe), a simple method to increase the throughput of existing ViT models without needing to train. ToMe gradually combines similar tokens in a transformer using a general and light-weight matching algorithm that is as fast as pruning while being more accurate. Off-the-shelf, ToMe can 2x the throughput of state-of-the-art ViT-L @ 512 and ViT-H @ 518 models on images and 2.2x the throughput of ViT-L on video with only a 0.2-0.3% accuracy drop in each case. ToMe can also easily be applied during training, improving in practice training speed up to 2x for MAE fine-tuning on video. Training with ToMe further minimizes accuracy drop, leading to 2x the throughput of ViT-B on audio for only a 0.4% mAP drop. Qualitatively, we find that ToMe merges object parts into one token, even over multiple frames of video. Overall, ToMe's accuracy and speed are competitive with state-of-the-art on images, video, and audio.
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Code
Syntology Ran 4 of 5 code samples harvested from 2 repositories linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong; 2 ran · fixture could not drive it.
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Code Syntology ran Syntology
5 samples harvested; 4 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Efficient ViTs | ImageNet-1K (with DeiT-S) | ToMe ($r=8$) | GFLOPs | 3.4 | #15 of 41 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-S) | ToMe ($r=8$) | Top 1 Accuracy | 79.7 | #15 of 41 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-S) | ToMe ($r=13$) | GFLOPs | 2.7 | #23 of 41 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-S) | ToMe ($r=13$) | Top 1 Accuracy | 79.4 | #23 of 41 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-S) | ToMe ($r=16$) | GFLOPs | 2.3 | #29 of 41 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-S) | ToMe ($r=16$) | Top 1 Accuracy | 79.1 | #29 of 41 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-T) | ToMe ($r=8$) | GFLOPs | 0.9 | #14 of 22 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-T) | ToMe ($r=8$) | Top 1 Accuracy | 71.7 | #14 of 22 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-T) | ToMe ($r=12$) | GFLOPs | 0.8 | #17 of 22 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-T) | ToMe ($r=12$) | Top 1 Accuracy | 71.4 | #17 of 22 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-T) | ToMe ($r=16$) | GFLOPs | 0.6 | #18 of 22 | Archive leaderboard | report |
| Efficient ViTs | ImageNet-1K (with DeiT-T) | ToMe ($r=16$) | Top 1 Accuracy | 70.7 | #18 of 22 | 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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