UniSVG is a comprehensive dataset designed for unified SVG generation (from textual prompts and images) and SVG understanding (color, category, usage, etc.). It comprises 525k data items tailored for Multi-modal Large Language Models (MLLM) training and evaluation.
For more information, please visit the project homepage or Arxiv paper.
Unlike bitmap images, scalable vector graphics (SVG) maintain quality when scaled, frequently employed in computer vision and artistic design in the representation of SVG code. In this era of proliferating AI-powered systems, enabling AI to understand and generate SVG has become increasingly urgent. However, AI-driven SVG understanding and generation (U&G) remain significant challenges. SVG code, equivalent to a set of curves and lines controlled by floating-point parameters, demands high precision in SVG U&G. Besides, SVG generation operates under diverse conditional constraints, including textual prompts and visual references, which requires powerful multi-modal processing for condition-to-SVG transformation. Recently, the rapid growth of Multi-modal Large Language Models (MLLMs) have demonstrated capabilities to process multi-modal inputs and generate complex vector controlling parameters, suggesting the potential to address SVG U&G tasks within a unified model. To unlock MLLM's capabilities in the SVG area, we propose an SVG-centric dataset called UniSVG, comprising 525k data items, tailored for MLLM training and evaluation. To our best knowledge, it is the first comprehensive dataset designed for unified SVG generation (from textual prompts and images) and SVG understanding (color, category, usage, etc.).
To install the dataset, you can use the datasets library from Hugging Face:
pip install datasets
Here is an example of how to load and use the dataset:
from datasets import load_dataset
# Load the dataset
UniSVG_dataset = load_dataset("lili24/UniSVG")
# Print the first example
print(UniSVG_dataset[0])
The UniSVG dataset consists of two parts with distinct licensing:
Kaggle: SVG Icons
Hugging Face: SVGEN-500k-Instruct
The icons in these sources are distributed under various permissive licenses that generally allow both non-commercial and commercial use. As these platforms act as aggregators, all included content is licensed permissively enough to be redistributed as part of this collection.
If you use this dataset in your research, please cite the following paper:
@inproceedings{li2025unisvg,
title={UniSVG: A Unified Dataset for Vector Graphic Understanding and Generation with Multimodal Large Language Models},
author={Li, Jinke and Yu, Jiarui and Wei, Chenxing and Dong, Hande and Lin, Qiang and Yang, Liangjing and Wang, Zhicai and Hao, Yanbin},
booktitle={Proceedings of the 33rd ACM international conference on multimedia},
year={2025}
}
19 commits
UniSVG is a comprehensive dataset designed for unified SVG generation (from textual prompts and images) and SVG understanding (color, category, usage, etc.). It comprises 525k data items tailored for Multi-modal Large Language Models (MLLM) training and evaluation.
For more information, please visit the project homepage or Arxiv paper.
Unlike bitmap images, scalable vector graphics (SVG) maintain quality when scaled, frequently employed in computer vision and artistic design in the representation of SVG code. In this era of proliferating AI-powered systems, enabling AI to understand and generate SVG has become increasingly urgent. However, AI-driven SVG understanding and generation (U&G) remain significant challenges. SVG code, equivalent to a set of curves and lines controlled by floating-point parameters, demands high precision in SVG U&G. Besides, SVG generation operates under diverse conditional constraints, including textual prompts and visual references, which requires powerful multi-modal processing for condition-to-SVG transformation. Recently, the rapid growth of Multi-modal Large Language Models (MLLMs) have demonstrated capabilities to process multi-modal inputs and generate complex vector controlling parameters, suggesting the potential to address SVG U&G tasks within a unified model. To unlock MLLM's capabilities in the SVG area, we propose an SVG-centric dataset called UniSVG, comprising 525k data items, tailored for MLLM training and evaluation. To our best knowledge, it is the first comprehensive dataset designed for unified SVG generation (from textual prompts and images) and SVG understanding (color, category, usage, etc.).
To install the dataset, you can use the datasets library from Hugging Face:
pip install datasets
Here is an example of how to load and use the dataset:
from datasets import load_dataset
# Load the dataset
UniSVG_dataset = load_dataset("lili24/UniSVG")
# Print the first example
print(UniSVG_dataset[0])
The UniSVG dataset consists of two parts with distinct licensing:
Kaggle: SVG Icons
Hugging Face: SVGEN-500k-Instruct
The icons in these sources are distributed under various permissive licenses that generally allow both non-commercial and commercial use. As these platforms act as aggregators, all included content is licensed permissively enough to be redistributed as part of this collection.
If you use this dataset in your research, please cite the following paper:
@inproceedings{li2025unisvg,
title={UniSVG: A Unified Dataset for Vector Graphic Understanding and Generation with Multimodal Large Language Models},
author={Li, Jinke and Yu, Jiarui and Wei, Chenxing and Dong, Hande and Lin, Qiang and Yang, Liangjing and Wang, Zhicai and Hao, Yanbin},
booktitle={Proceedings of the 33rd ACM international conference on multimedia},
year={2025}
}
19 commits