Papers › ResT: An Efficient Transformer for Visual Recognition
ResT: An Efficient Transformer for Visual Recognition
Qinglong Zhang, YuBin Yang
This paper presents an efficient multi-scale vision Transformer, called ResT, that capably served as a general-purpose backbone for image recognition. Unlike existing Transformer methods, which employ standard Transformer blocks to tackle raw images with a fixed resolution, our ResT have several advantages: (1) A memory-efficient multi-head self-attention is built, which compresses the memory by a simple depth-wise convolution, and projects the interaction across the attention-heads dimension while keeping the diversity ability of multi-heads; (2) Position encoding is constructed as spatial attention, which is more flexible and can tackle with input images of arbitrary size without interpolation or fine-tune; (3) Instead of the straightforward tokenization at the beginning of each stage, we design the patch embedding as a stack of overlapping convolution operation with stride on the 2D-reshaped token map. We comprehensively validate ResT on image classification and downstream tasks. Experimental results show that the proposed ResT can outperform the recently state-of-the-art backbones by a large margin, demonstrating the potential of ResT as strong backbones. The code and models will be made publicly available at https://github.com/wofmanaf/ResT.
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Code
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Code Syntology ran Syntology
9 samples harvested; 1 ran; 0 honoured the contract we drafted; 8 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 9 of the 9 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from wofmanaf/ResT. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the 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 | ResT-Large | GFLOPs | 7.9 | #415 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ResT-Large | Number of params | 51.63M | #415 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ResT-Large | Top 1 Accuracy | 83.6% | #415 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ResT-Small | GFLOPs | 1.9 | #749 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ResT-Small | Number of params | 13.66M | #749 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | ResT-Small | Top 1 Accuracy | 79.6% | #749 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
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