Improve model card: update paper link and summarize abstract

Hi! I'm Niels from the Hugging Face community science team.

I've opened this PR to improve your model card:
- Summarized the paper abstract into a more readable format.
- Updated the paper reference to link to the Hugging Face paper page.
- Added relevant tags for discoverability (e.g., fine-grained visual recognition, chain-of-thought).
- Maintained existing metadata for library compatibility.

Please feel free to review and merge!

README.md

@@ -1,25 +1,47 @@
 ---
+library_name: transformers
 license: mit
 pipeline_tag: image-text-to-text
-library_name: transformers
+tags:
+- fine-grained-visual-recognition
+- chain-of-thought
+- vision-reasoning
 ---
 
 # Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning
 
-This is the official model released for the paper **[Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning](https://openreview.net/pdf?id=kyzHM557gE)**.
+This is the official model repository for the paper **[Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning](https://huggingface.co/papers/2602.07605)**.
 
-## Abstract
+## Introduction
 
-Any entity in the visual world can be hierarchically grouped based on shared characteristics and mapped to fine-grained sub-categories. While Multi-modal Large Language Models (MLLMs) achieve strong performance on coarse-grained visual tasks, they often struggle with Fine-Grained Visual Recognition (FGVR). Adapting general-purpose MLLMs to FGVR typically requires large amounts of annotated data, which is costly to obtain, leaving a substantial performance gap compared to contrastive CLIP models dedicated for discriminative tasks. Moreover, MLLMs tend to overfit to seen sub-categories and generalize poorly to unseen ones. To address these challenges, we propose Fine-R1, an MLLM tailored for FGVR through an R1-style training framework: (1) Chain-of-Thought Supervised Fine-tuning, where we construct a high-quality FGVR CoT dataset with rationales of "visual analysis, candidate sub-categories, comparison, and prediction", transition the model into a strong open-world classifier; and (2) Triplet Augmented Policy Optimization, where Intra-class Augmentation mixes trajectories from anchor and positive images within the same category to improve robustness to intra-class variance, while Inter-class Augmentation maximizes the response distinction conditioned on images across sub-categories to enhance discriminative ability. With only 4-shot training, Fine-R1 outperforms existing general MLLMs, reasoning MLLMs, and even contrastive CLIP models in identifying both seen and unseen sub-categories, showing promise in working in knowledge-intensive domains where gathering expert annotations for all sub-categories is arduous.
+**Fine-R1** is a Multi-modal Large Language Model (MLLM) specifically designed for **Fine-Grained Visual Recognition (FGVR)**. While general MLLMs often struggle with distinguishing between highly similar sub-categories, Fine-R1 bridges the gap between generative models and specialized discriminative models (like CLIP) through an R1-style training framework.
 
+### Key Innovations:
+- **Chain-of-Thought Supervised Fine-tuning (CoT-SFT)**: The model is trained on high-quality FGVR CoT datasets, teaching it to perform visual analysis, consider candidate sub-categories, and compare them before predicting.
+- **Triplet Augmented Policy Optimization (TAPO)**: This includes Intra-class Augmentation to handle visual variance and Inter-class Augmentation to maximize distinction between similar sub-categories.
 
-## GitHub Repository
+With only 4-shot training, Fine-R1 excels in identifying both seen and unseen sub-categories, outperforming many general reasoning MLLMs and contrastive models.
 
-[https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026)
-
-## Model Version
-Fine-R1-7B
+## Resources
+- **Paper:** [Hugging Face Papers](https://huggingface.co/papers/2602.07605)
+- **GitHub:** [PKU-ICST-MIPL/FineR1_ICLR2026](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026)
 
 ## Usage
 
-This model can be used with the Hugging Face `transformers` library. For detailed usage examples and how to integrate it into your projects, please refer to the official [GitHub Repository](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026).
+This model is compatible with the Hugging Face `transformers` library. For detailed instructions on environment setup, training scripts, and evaluation pipelines (closed-world and open-world), please refer to the official [GitHub Repository](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026).
+
+## Citation
+
+If you find Fine-R1 helpful in your research, please cite the following paper:
+
+```bibtex
+@article{he2026finer1,
+  title={Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning},
+  author={He, Hulingxiao and Geng, Zijun and Peng, Yuxin},
+  journal={arXiv preprint arXiv:2602.07605},
+  year={2026}
+}
+```
+
+## License
+This project is licensed under the MIT License.