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license: cc-by-nc-4.0
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# 📢 SRefCOCO: A Large-Scale Multi-Modal Visual Grounding Dataset

## 📖 Introduction
Visual grounding aims to locate target regions in an image based on natural language instructions. However, mainstream paradigms are largely restricted to "text-image" bimodal interactions, severely limiting their application flexibility in highly dynamic real-world physical scenarios. 

**SRefCOCO** breaks this traditional text-only constraint by introducing a novel **Speech-Text-Image** triplet dataset. It is designed to endow embodied AI models with end-to-end "listen, read, and look" perceptual capabilities. 
The dataset is built upon the classic visual grounding datasets (e.g., RefCOCO, RefCOCO+, RefCOCOg) and extends them with robust acoustic instructions.

* **Images:** Standard RGB images from the COCO dataset.
* **Texts:** Natural language referring expressions.
* **Speech (Audio):** 16kHz `.wav` audio files generated via Edge-TTS and heavily augmented to simulate complex real-world physical environments.

## 🚀 How to Use
You can easily load the dataset using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("xutao2025/SRefcoco")

# Print the first training sample
print(dataset['train'][0])