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Model: StevenHH2000/Fine-R1-7B
Source: Original Platform

README.md

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+---
+library_name: transformers
+license: mit
+pipeline_tag: image-text-to-text
+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 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)**.
+
+## Introduction
+
+**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.
+
+With only 4-shot training, Fine-R1 excels in identifying both seen and unseen sub-categories, outperforming many general reasoning MLLMs and contrastive models.
+
+## 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 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.