---
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.