README.md

---
license: apache-2.0
task_categories:
- image-segmentation
pipeline_tag: any-to-any
library_name: bagel-mot
tags:
- sgt
- semantic-generative-tuning
- unified-multimodal
- visual-understanding
- visual-generation
---

# SAM-SGT: Segmentation Training Data for Semantic Generative Tuning

This repository contains the **SAM-SGT** dataset, a collection of approximately 190k segmentation samples derived from SAM. It was introduced in the paper [Semantic Generative Tuning for Unified Multimodal Models](https://huggingface.co/papers/2605.18714).

[**Project Page**](https://song2yu.github.io/SGT/) | [**Paper**](https://huggingface.co/papers/2605.18714) | [**Code**](https://github.com/song2yu/SGT)

## Dataset Description

SGT (Semantic Generative Tuning) is a training paradigm that couples visual understanding and generation in Unified Multimodal Models (UMMs) by using image segmentation as a generative proxy. This dataset provides high-level semantic supervision used to align multimodal representation spaces.

- **Content**: ~190,000 segmentation samples sourced from the Segment Anything (SAM) dataset.
- **Format**: Tar-sharded.
- **Role**: Serves as a high-level semantic proxy task to enhance vision-centric perception and generative layout fidelity.

### Training Data Distribution
| Data Source | Samples |
|-------------|---------|
| **SGT — Segmentation (SAM)** | **190k** |
| General VQA | 180k |
| Doc / Chart / Screen | 103k |
| Math / Reasoning | 101k |
| Language | 72k |
| General OCR | 45k |
| **Total** | **~691k** |

## Usage

You can download the dataset using the provided script from the official repository:

```bash
# download sam subset || Chinese users can use --use-mirror
python download_sam.py --target-dir ./data/SAM-SGT --use-mirror
```

## Citation

If you find this work useful, please cite:

```bibtex
@article{yu2026sgt,
  title     = {Semantic Generative Tuning for Unified Multimodal Models},
  author    = {Yu, Songsong and Chen, Yuxin and Shan, Ying and Li, Yanwei},
  journal   = {arXiv preprint arXiv:2605.18714},
  year      = {2026},
}
```