> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/QwenLM/Qwen3-VL/llms.txt
> Use this file to discover all available pages before exploring further.

# 2D Object Grounding

> Precise object grounding with bounding boxes and points

# 2D Object Grounding

Qwen3-VL provides precise 2D object grounding capabilities, allowing you to locate and label objects within images using relative position coordinates. The model supports both bounding boxes and point-based grounding for diverse positioning and labeling tasks.

## Capabilities

Qwen3-VL's 2D grounding uses relative position coordinates to:

* **Bounding Boxes**: Draw rectangular boxes around objects
* **Point Grounding**: Mark specific locations with coordinate points
* **Flexible Combinations**: Mix boxes and points for complex annotation tasks
* **Multi-object Detection**: Ground multiple objects simultaneously
* **Relative Coordinates**: Position-independent coordinate system (0-1 range)

## How It Works

### Coordinate System

Positions are expressed as relative coordinates:

* X-axis: 0 (left edge) to 1 (right edge)
* Y-axis: 0 (top edge) to 1 (bottom edge)

This makes grounding resolution-independent and portable across different image sizes.

### Grounding Formats

1. **Bounding Boxes**: `[x_min, y_min, x_max, y_max]`
2. **Points**: `[x, y]`
3. **Combined**: Mix both formats in a single response

## Use Cases

* **Object Detection**: Locate and label objects in images
* **Annotation**: Generate training data for computer vision models
* **Visual Search**: Find specific items within images
* **Quality Control**: Identify defects or anomalies in products
* **Spatial Analysis**: Analyze object positions and distributions
* **Interactive Applications**: Enable click-to-identify features

## Try It Out

Explore 2D object grounding with our interactive cookbook:

> **[2D Grounding Cookbook](https://github.com/QwenLM/Qwen3-VL/blob/main/cookbooks/2d_grounding.ipynb)**
>
> Using relative position coordinates, it supports both boxes and points, allowing for diverse combinations of positioning and labeling tasks.

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/QwenLM/Qwen3-VL/blob/main/cookbooks/2d_grounding.ipynb)

## Key Features

* **High Precision**: Accurate object localization
* **Format Flexibility**: Support for boxes, points, and combinations
* **Multi-object Support**: Ground multiple items in one pass
* **Resolution Independent**: Works across different image sizes
* **Natural Language Queries**: Describe what to ground in plain text

## Advanced Capabilities

### Referring Expression Comprehension

Ground objects based on natural language descriptions:

* "The red car on the left"
* "The person wearing glasses"
* "The largest apple in the bowl"

### Dense Captioning

Generate descriptions for multiple grounded regions in an image.

### Visual Question Answering with Grounding

Answer questions while providing visual evidence through grounding.

## Related Capabilities

* [3D Grounding](https://mintlify.com/QwenLM/Qwen3-VL/capabilities/grounding-3d.md) - 3D bounding boxes for spatial scenes
* [Omni Recognition](https://mintlify.com/QwenLM/Qwen3-VL/capabilities/omni-recognition.md) - Identify what objects to ground
* [Spatial Understanding](https://mintlify.com/QwenLM/Qwen3-VL/capabilities/spatial-understanding.md) - Understand spatial relationships
* [Video Understanding](https://mintlify.com/QwenLM/Qwen3-VL/capabilities/video-understanding.md) - Grounding in video frames


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.