GranD

[](https://grounding-anything.com)

# 🚀 GranD - Grounding Anything Dataset
The [Grounding-anything](https://grounding-anything.com/) Dataset (GranD) dataset offers densely annotated data, acquired through an automated annotation pipeline that leverages state-of-the-art (SOTA) vision and V-L models. This documentation covers how to download the GranD dataset and a guide to the automated annotation pipeline used to create GranD.

## 💻 Download GranD 📂
```
git lfs install
git clone https://huggingface.co/datasets/MBZUAI/GranD
```
- Images -> [Download](https://ai.meta.com/datasets/segment-anything-downloads/). GranD utilizes images from the SAM dataset.

## 📚 Additional Resources
- **Paper:** [ArXiv](https://arxiv.org/abs/2311.03356).
- **GitHub Repository:** [GitHub - GLaMM](https://github.com/mbzuai-oryx/groundingLMM).
- **Project Page:** For a detailed overview and insights into the project, visit our [Project Page - GLaMM](https://mbzuai-oryx.github.io/groundingLMM/).

## 📜 Citations and Acknowledgments

```bibtex
@article{hanoona2023GLaMM,
title={GLaMM: Pixel Grounding Large Multimodal Model},
author={Rasheed, Hanoona and Maaz, Muhammad and Shaji, Sahal and Shaker, Abdelrahman and Khan, Salman and Cholakkal, Hisham and Anwer, Rao M. and Xing, Eric and Yang, Ming-Hsuan and Khan, Fahad S.},
journal={The IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2024}
}
```

# 🚀 GranD——接地万物数据集（Grounding Anything Dataset）
[](https://grounding-anything.com)

该[接地万物（Grounding-anything）](https://grounding-anything.com/)数据集（简称GranD）提供稠密标注数据，其采集自一套依托当前最优（SOTA）视觉与视觉-语言（Vision-Language, V-L）模型构建的自动化标注流水线。本文档将涵盖GranD数据集的下载方法，以及用于构建该数据集的自动化标注流水线的使用指南。

## 💻 下载GranD数据集 📂

git lfs install
git clone https://huggingface.co/datasets/MBZUAI/GranD

- 图像资源：[下载链接](https://ai.meta.com/datasets/segment-anything-downloads/). GranD数据集使用了Segment Anything Model（SAM）数据集的图像。

## 📚 附加资源
- **学术论文：** [ArXiv预印本](https://arxiv.org/abs/2311.03356)。
- **GitHub仓库：** [GitHub - GLaMM](https://github.com/mbzuai-oryx/groundingLMM)。
- **项目主页：** 如需了解该项目的详细概述与核心见解，请访问我们的[GLaMM项目主页](https://mbzuai-oryx.github.io/groundingLMM/)。

## 📜 引用与致谢
bibtex
@article{hanoona2023GLaMM,
title={GLaMM: Pixel Grounding Large Multimodal Model},
author={Rasheed, Hanoona and Maaz, Muhammad and Shaji, Sahal and Shaker, Abdelrahman and Khan, Salman and Cholakkal, Hisham and Anwer, Rao M. and Xing, Eric and Yang, Ming-Hsuan and Khan, Fahad S.},
journal={The IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2024}
}