Add pipeline tag and improve model card

README.md

@@ -1,10 +1,39 @@
 ---
 license: mit
+pipeline_tag: image-classification
 ---
 
 # CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction
 
-📄 Paper: https://arxiv.org/abs/2411.16170        
+This repository contains the model weights for the paper [CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction](https://huggingface.co/papers/2411.16170).
 
+CARE (de**C**oupled du**A**l-interactive linea**R** att**E**ntion) is a novel linear attention mechanism designed to unleash the power of linear attention for resource-constrained mobile devices. It utilizes an asymmetrical feature decoupling strategy to manage local inductive bias and long-range dependencies, alongside a dual interaction module to facilitate communication across features and layers.
 
-💻 Official GitHub Code: https://github.com/zhouyuan888888/CARE-Transformer
+## Performance
+
+The CARE Transformer achieves high efficiency and accuracy on the ImageNet-1K dataset:
+
+| Method | Type | GMACs | Params (M) | Top-1 Acc (%) |
+| :---: | :---: | :---: | :--------: | :-----------: |
+| CARE-S0 | LA+CONV | 0.7 | 7.3 | 78.4 |
+| CARE-S1 | LA+CONV | 1.0 | 9.6 | 80.1 |
+| CARE-S2 | LA+CONV | 1.9 | 19.5 | 82.1 |
+
+## Resources
+
+- **Paper:** [https://arxiv.org/abs/2411.16170](https://arxiv.org/abs/2411.16170)
+- **Official GitHub Repository:** [https://github.com/zhouyuan888888/CARE-Transformer](https://github.com/zhouyuan888888/CARE-Transformer)
+
+## Citation
+
+If you find this work useful, please cite:
+
+```bibtex
+@inproceedings{zhou2025care,
+  title={CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction},
+  author={Zhou, Yuan and Xu, Qingshan and Cui, Jiequan and Zhou, Junbao and Zhang, Jing and Hong, Richang and Zhang, Hanwang},
+  booktitle={Proceedings of the Computer Vision and Pattern Recognition Conference},
+  pages={20135--20145},
+  year={2025}
+}
+```