Datasets:
File size: 3,382 Bytes
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dataset_info:
features:
- name: source
dtype: image
- name: mask
dtype: image
- name: target
dtype: image
- name: caption
dtype: string
- name: category
dtype: string
splits:
- name: train
num_examples: 89927
- name: validation
num_examples: 4989
- name: test
num_examples: 5009
license: cc-by-nc-4.0
task_categories:
- image-to-image
tags:
- virtual-try-on
- fashion
- clothing
---
# OpenVTON
A large-scale virtual try-on dataset containing ~100K clothing image pairs with garment masks.
You
## Dataset Structure
Each sample contains:
- **source**: Garment image (clothing item)
- **mask**: Garment segmentation mask
- **target**: Person wearing the garment (ground truth)
- **caption**: Text description of the clothing
- **category**: Clothing category (e.g., pants, jeans, shirt)
## Splits
| Split | Samples |
|-------|---------|
| Train | 89,927 |
| Validation | 4,989 |
| Test | 5,009 |
| **Total** | **99,925** |
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("RenxingIntelligence/OpenVTON")
sample = dataset["train"][0]
sample["source"].show() # garment image
sample["mask"].show() # segmentation mask
sample["target"].show() # person wearing garment
print(sample["caption"])
print(sample["category"])
```
## Benchmark and Paper
This dataset is part of **OpenVTON-Bench**, a large-scale benchmark designed for the systematic evaluation of controllable virtual try-on (VTON) models.
**OpenVTON-Bench** is introduced in our paper:
> **OpenVTON-Bench: A Large-Scale Benchmark for Controllable Virtual Try-On**
> 📄 Paper: [https://arxiv.org/abs/2601.22725](https://arxiv.org/abs/2601.22725)
> 💻 Code: [https://github.com/RenxingIntelligence/OpenVTON-Bench](https://github.com/RenxingIntelligence/OpenVTON-Bench)
OpenVTON-Bench provides a standardized evaluation protocol for modern diffusion-based and transformer-based virtual try-on systems, enabling fair and reproducible comparison across different architectures.
---
## About OpenVTON-Bench
**OpenVTON-Bench** is a **large-scale, high-resolution benchmark** designed for the **systematic evaluation of controllable virtual try-on models**.
Unlike existing datasets and evaluation protocols that struggle with texture details and semantic consistency, OpenVTON-Bench provides:
* 🖼️ **~100K Image Pairs** with resolutions up to **1536×1536**, enabling evaluation of fine-grained texture generation.
* 🏷️ **Fine-Grained Taxonomy** covering **20 garment categories** for balanced semantic evaluation.
* 📐 **Multi-Level Automated Evaluation**, including:
* Pixel fidelity
* Garment consistency
* Semantic realism
This benchmark enables **fair, reproducible, and scalable comparison** across modern virtual try-on systems.
---
## Citation
If you use this dataset or the benchmark in your research, please cite:
```bibtex
@misc{li2026openvtonbenchlargescalehighresolutionbenchmark,
title={OpenVTON-Bench: A Large-Scale High-Resolution Benchmark for Controllable Virtual Try-On Evaluation},
author={Jin Li and Tao Chen and Shuai Jiang and Weijie Wang and Jingwen Luo and Chenhui Wu},
year={2026},
eprint={2601.22725},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.22725},
}
```
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