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pipeline_tag: image-classification
license: mit
---
# Breaking the Low-Rank Dilemma of Linear Attention: RAVLT Model Card
This model card describes the Rank-Augmented Vision Linear Transformer (RAVLT), introduced in the paper "[Breaking the Low-Rank Dilemma of Linear Attention](https://arxiv.org/abs/2411.07635)". RAVLT achieves state-of-the-art performance on ImageNet-1k classification while maintaining linear complexity.
**Key Features:**
* High accuracy: Achieves 84.4% Top-1 accuracy on ImageNet-1k (RAVLT-S).
* Parameter efficiency: Uses only 26M parameters (RAVLT-S).
* Computational efficiency: Achieves 4.6G FLOPs (RAVLT-S).
* Linear complexity.
RAVLT is based on Rank-Augmented Linear Attention (RALA), a novel attention mechanism that addresses the low-rank limitations of standard linear attention.
## Model Variants
Several RAVLT variants were trained, offering different tradeoffs between accuracy, parameters, and FLOPs:
| Model | Params (M) | FLOPs (G) | Checkpoint |
| -------- | ---------- | --------- | --------------- |
| RAVLT-T | 15 | 2.4 | [RAVLT-T](https://huggingface.co/aldjalkdf/RAVLT/blob/main/RAVLT_T.pth) |
| RAVLT-S | 26 | 4.6 | [RAVLT-S](https://huggingface.co/aldjalkdf/RAVLT/blob/main/RAVLT_S.pth) |
| RAVLT-B | 48 | 9.9 | [RAVLT-B](https://huggingface.co/aldjalkdf/RAVLT/blob/main/RAVLT_B.pth) |
| RAVLT-L | 95 | 16.0 | [RAVLT-L](https://huggingface.co/aldjalkdf/RAVLT/blob/main/RAVLT_L.pth) |
## How to use (Placeholder - Awaiting Code Release)
Instructions on how to use the model will be provided once the code repository is available. Code will be available at https://github.com/qhfan/RALA.
## Citation
```bibtex
@inproceedings{fan2024breakinglowrank,
title={Breaking the Low-Rank Dilemma of Linear Attention},
author={Qihang Fan and Huaibo Huang and Ran He },
year={2025},
booktitle={CVPR},
}
``` |