OmniSVG/MMSVG-Illustration

Dataset

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linked in READMEs

Dec 29, 2025

updated

Browse cluster: Vision-Language Models and Multimodal AI →

README

OmniSVG: A Unified Scalable Vector Graphics Generation Model

![Project Page]

Dataset Card for MMSVG-Illustration

Dataset Description

This dataset contains SVG illustration examples for training and evaluating SVG models for text-to-SVG and image-to-SVG task.

Dataset Structure

Features

The dataset contains the following fields:

Field Name	Description
`id`	Unique ID for each SVG
`svg`	SVG code (resized to 200×200, simplified with picosvg)
`description`	Description of the SVG
`keywords`	Keywords associated with the SVG
`detail`	Detailed description of the SVG
`image`	PNG image preview (resized to 448×448)
`token len`	Token length (OmniSVG tokenizer)

Changelog

v2.0 (2025-12-22)

Data Volume: Increased from 65,751 to 255,412 samples
Deduplication: Removed duplicate SVGs based on MD5 hash (keeping only one instance per unique SVG)
Added PNG Previews: Each SVG now includes a corresponding PNG image
Enhanced Captions: Increased caption diversity with description, keywords, and detail fields
SVG Normalization: All SVGs resized to 200×200 and simplified using picosvg
PNG Standardization: All PNG images resized to 448×448 for uniform input size

Citation

@article{yang2025omnisvg,
  title={OmniSVG: A Unified Scalable Vector Graphics Generation Model}, 
  author={Yiying Yang and Wei Cheng and Sijin Chen and Xianfang Zeng and Jiaxu Zhang and Liao Wang and Gang Yu and Xinjun Ma and Yu-Gang Jiang},
  journal={arXiv preprint arxiv:2504.06263},
  year={2025}
}

Contributors

OmniSVG

94 commits

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OmniSVG/MMSVG-Illustration

Dataset

stars

commits

linked in READMEs

Dec 29, 2025

updated

Browse cluster: Vision-Language Models and Multimodal AI →

README

OmniSVG: A Unified Scalable Vector Graphics Generation Model

![Project Page]

Dataset Card for MMSVG-Illustration

Dataset Description

This dataset contains SVG illustration examples for training and evaluating SVG models for text-to-SVG and image-to-SVG task.

Dataset Structure

Features

The dataset contains the following fields:

Field Name	Description
`id`	Unique ID for each SVG
`svg`	SVG code (resized to 200×200, simplified with picosvg)
`description`	Description of the SVG
`keywords`	Keywords associated with the SVG
`detail`	Detailed description of the SVG
`image`	PNG image preview (resized to 448×448)
`token len`	Token length (OmniSVG tokenizer)

Changelog

v2.0 (2025-12-22)

Data Volume: Increased from 65,751 to 255,412 samples
Deduplication: Removed duplicate SVGs based on MD5 hash (keeping only one instance per unique SVG)
Added PNG Previews: Each SVG now includes a corresponding PNG image
Enhanced Captions: Increased caption diversity with description, keywords, and detail fields
SVG Normalization: All SVGs resized to 200×200 and simplified using picosvg
PNG Standardization: All PNG images resized to 448×448 for uniform input size

Citation

@article{yang2025omnisvg,
  title={OmniSVG: A Unified Scalable Vector Graphics Generation Model}, 
  author={Yiying Yang and Wei Cheng and Sijin Chen and Xianfang Zeng and Jiaxu Zhang and Liao Wang and Gang Yu and Xinjun Ma and Yu-Gang Jiang},
  journal={arXiv preprint arxiv:2504.06263},
  year={2025}
}