---
title: UniVG-R1-data
canonical_url: "https://www.modelscope.cn/datasets/GD-ML/UniVG-R1-data"
md_url: "https://www.modelscope.cn/datasets/GD-ML/UniVG-R1-data.md"
repository: GD-ML/UniVG-R1-data
last_updated: 2025-07-17
license: mit
storage_size: "116 MB"
downloads: 508
stars: 1
---

# UniVG-R1-data

> UniVG-R1-data - GD-ML 在 ModelScope 开源的数据集。Model details We propose UniVG-R1, a reasoning-guided MLLM for universal visual grounding, which leverages reinforcement learning to enhance reasoning across complex multi-image and multi-modal scenarios.

GD-ML/UniVG-R1-data 是 ModelScope 魔搭社区上的数据集，存储大小 116 MB，采用 mit 许可。

- **Repository**: GD-ML/UniVG-R1-data
- **License**: mit
- **Storage size**: 116 MB
- **Downloads**: 508
- **Stars**: 1
- **Last updated**: 2025-07-17

Source: https://www.modelscope.cn/datasets/GD-ML/UniVG-R1-data

---

# UniVG-R1 Model Card
<a href='https://amap-ml.github.io/UniVG-R1-page/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> 
<a href='https://arxiv.org/abs/2505.14231'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
<a href='https://github.com/AMAP-ML/UniVG-R1'><img src='https://img.shields.io/badge/Code-GitHub-blue'></a>

## Model details
We propose UniVG-R1, a reasoning-guided MLLM for universal visual grounding, which leverages reinforcement learning to enhance reasoning across complex multi-image and multi-modal scenarios.

## Dataset details
We provide three JSON files as follows:
1. revised_MIG_bench.json: which contains our revised version of the MIG_bench.
2. stage1_cotsft.json: which contains the CoT-SFT data required for stage 1.
3. stage2_rl.json: which contains the RL data required for stage 2.
4. zero_shot_evaluation: which contains the bounding boxes annotations for zero-shot evaluation.