metadata

license: apache-2.0
dataset_info:
  features:
    - name: id
      dtype: int64
    - name: filename
      dtype: string
    - name: difficulty
      dtype: string
    - name: svg_code
      dtype: string
  splits:
    - name: train
      num_bytes: 1394385
      num_examples: 300
    - name: easy
      num_bytes: 105183
      num_examples: 100
    - name: medium
      num_bytes: 501313
      num_examples: 100
    - name: hard
      num_bytes: 787889
      num_examples: 100
  download_size: 1529634
  dataset_size: 2788770
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: easy
        path: data/easy-*
      - split: medium
        path: data/medium-*
      - split: hard
        path: data/hard-*
language:
  - en
pretty_name: SVGenius
size_categories:
  - n<1K
tags:
  - svg-understanding
  - svg-editing
  - svg-generation

SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation

We introduce SVGenius, the first large-scale complexity-stratified benchmark accessing (M)LLMs capabilities across three progressive dimensions: Understanding (perceptua and semantic QA), Editing (bug fixing, code optimization, style editing), and Generation (text-to-SVG, image-to-SVG, style transfer). Built on real-world data from 24 application domains with systematic complexity stratification, SVGenius evaluates models through 8 task categories and 18 metrics. We assess 22 mainstream models spanning different scales, architectures, training paradigms, and accessibility levels. SVGenius Dataset Overview

The dataset contains the following fields:

Field Name	Description
`id`	Unique identifier for each SVG icon sample
`filename`	Original filename of the SVG file, preserving the source naming convention with category and identifier information
`difficulty`	Complexity level of the SVG icon, includes 3 distinct categories: `easy`, `medium`, and `hard` for evaluating different levels of SVG processing capabilities
`svg_code`	Complete SVG markup code containing the vector graphics definition, including all paths,styles, and attributes

Language(s) (NLP): en, zh
License: mit

Uses

from datasets import load_dataset
ds = load_dataset("xiaoooobai/SVGenius")

Citation

@misc{chen2025svgeniusbenchmarkingllmssvg,
      title={SVGenius: Benchmarking LLMs in SVG Understanding, Editing and Generation}, 
      author={Siqi Chen and Xinyu Dong and Haolei Xu and Xingyu Wu and Fei Tang and Hang Zhang and Yuchen Yan and Linjuan Wu and Wenqi Zhang and Guiyang Hou and Yongliang Shen and          Weiming Lu and Yueting Zhuang},
      year={2025},
      eprint={2506.03139},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2506.03139}, 
}