RealVLG-11B

<p align="center">
<h1 align="center">
RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation
<br>
[CVPR 2026]
</h1>
<p align="center">
<a href="https://lif314.github.io/"><strong>Linfei Li</strong></a>
·
<a href="https://scholar.google.com/citations?user=8VOk_S4AAAAJ&hl=en"><strong>Lin Zhang*</strong></a>
·
<a href="https://scholar.google.com/citations?user=A0N_mS0AAAAJ&hl=en"><strong>Ying Shen</strong></a>
</p>

<h3 align="center"><a href="https://lif314.github.io/projects/realvlg_r1/">🌐Project page</a>
| <a href="https://arxiv.org/abs/2603.14880">📝Paper(arXiv)</a> | <a href="https://github.com/lif314/RealVLG-R1">💻Code </a>
</h3>
<div align="center"></div>
</p>

## Sample

Each data sample is annotated as follows:
```json
[
{
"image_name": "",
"image_path": "",
"object_id": "",
"mask_path": "",
"description": "",
"label": "", # short description
"bbox": [x1, y1, x2, y2],
"grasps": [
[x0,y0,x1,y1,x2,y2,x3,y3],
...
],
"contact_points": [
[x1,y1, x2, y2],
...
]
}
]
```

The definition diagrams of bbox and grasp are shown in the figure below:

## Usage

Download the dataset and extract `xxx_VLG.zip`. In each `xxx_VLG` folder, run `python metadata_viewer.py` to view the data formatting. The left/right keys switch between different objects in the same image, and the up/down keys switch between images. The visualization of different data subsets is shown below:

| Subdata | Cornell_VLG | VMRD_VLG | OCID_VLG | GraspNet_VLG | Jacquard_VLG |
|---------|-------------|----------|----------|--------------|---------------|
| Demo |  |  |  |  |  |

For more detailed data loading, please refer to `metadata_viewer.py`.

> Note: ``Jacquard_VLG`` is a simulated dataset not discussed in the paper. Its language annotations are derived from ShapeNetSem category labels.

## License

We thank all previous work. If you use this dataset, please cite the relevant work and comply with their licenses.

- [Cornell](https://www.kaggle.com/datasets/oneoneliu/cornell-grasp)
- [VMRD](https://opendatalab.com/OpenDataLab/VMRD)
- [OCID-Grasp](https://github.com/stefan-ainetter/grasp_det_seg_cnn)
- [GraspNet](https://graspnet.net/)
- [Jacquard](https://jacquard.liris.cnrs.fr/)