---
library_name: transformers
license: mit
pipeline_tag: image-text-to-text
tags:
- fine-grained-recognition
- chain-of-thought
- vision-language
- reasoning
- qwen2-vl
- arxiv:2602.07605
---

# Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning

This is the official 3B model released 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)**.

**Authors**: Hulingxiao He, Zijun Geng, and Yuxin Peng.

## Introduction

Fine-R1 is a Multi-modal Large Language Model (MLLM) specifically designed to excel in Fine-Grained Visual Recognition (FGVR). While traditional MLLMs often struggle with FGVR compared to contrastive models like CLIP, Fine-R1 bridges this performance gap by incorporating Chain-of-Thought (CoT) reasoning. It achieves state-of-the-art performance, even surpassing strong CLIP-like models, in identifying both seen and unseen fine-grained sub-categories with only 4-shot training.

## Methodology

Fine-R1 employs an R1-style training framework consisting of two key stages:

1.  **Chain-of-Thought Supervised Fine-tuning (SFT)**: This stage involves constructing a high-quality FGVR CoT dataset with rationales covering "visual analysis, candidate sub-categories, comparison, and prediction." This process trains the model to act as a strong open-world classifier.
2.  **Triplet Augmented Policy Optimization (TAPO)**: This stage enhances the model's robustness and discriminative ability. It uses Intra-class Augmentation to improve robustness to intra-class variance and Inter-class Augmentation to maximize response distinction across sub-categories.

## GitHub Repository

For code, data, and detailed training/evaluation instructions, please refer to the official repository:
[https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026)

## Model Version
Fine-R1-3B

## 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).

## Citation

If you find this model or the research helpful, please consider citing:

```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}
}
```