Datasets:
metadata
license: apache-2.0
task_categories:
- visual-question-answering
- image-to-text
- text-to-image
language:
- en
tags:
- benchmark
- design
- multimodal
- graphic-design
- svg
- typography
- layout
- animation
- lottie
pretty_name: 'GDB: GraphicDesignBench'
size_categories:
- 1K<n<10K
configs:
- config_name: category-1
data_files:
- split: train
path: category-1/train-*
- config_name: category-2
data_files:
- split: train
path: category-2/train-*
- config_name: layout-1
data_files:
- split: train
path: layout-1/train-*
- config_name: layout-2
data_files:
- split: train
path: layout-2/train-*
- config_name: layout-3
data_files:
- split: train
path: layout-3/train-*
- config_name: layout-4
data_files:
- split: train
path: layout-4/train-*
- config_name: layout-5
data_files:
- split: train
path: layout-5/train-*
- config_name: layout-6
data_files:
- split: train
path: layout-6/train-*
- config_name: layout-7
data_files:
- split: train
path: layout-7/train-*
- config_name: layout-8
data_files:
- split: train
path: layout-8/train-*
- config_name: lottie-1
data_files:
- split: train
path: lottie-1/train-*
- config_name: lottie-2
data_files:
- split: train
path: lottie-2/train-*
- config_name: svg-1
data_files:
- split: train
path: svg-1/train-*
- config_name: svg-2
data_files:
- split: train
path: svg-2/train-*
- config_name: svg-3
data_files:
- split: train
path: svg-3/train-*
- config_name: svg-4
data_files:
- split: train
path: svg-4/train-*
- config_name: svg-5
data_files:
- split: train
path: svg-5/train-*
- config_name: svg-6
data_files:
- split: train
path: svg-6/train-*
- config_name: svg-7
data_files:
- split: train
path: svg-7/train-*
- config_name: svg-8
data_files:
- split: train
path: svg-8/train-*
- config_name: template-1
data_files:
- split: train
path: template-1/train-*
- config_name: template-2
data_files:
- split: train
path: template-2/train-*
- config_name: template-3
data_files:
- split: train
path: template-3/train-*
- config_name: template-4
data_files:
- split: train
path: template-4/train-*
- config_name: template-5
data_files:
- split: train
path: template-5/train-*
- config_name: temporal-1
data_files:
- split: train
path: temporal-1/train-*
- config_name: temporal-2
data_files:
- split: train
path: temporal-2/train-*
- config_name: temporal-3
data_files:
- split: train
path: temporal-3/train-*
- config_name: temporal-4
data_files:
- split: train
path: temporal-4/train-*
- config_name: temporal-5
data_files:
- split: train
path: temporal-5/train-*
- config_name: temporal-6
data_files:
- split: train
path: temporal-6/train-*
- config_name: typography-1
data_files:
- split: train
path: typography-1/train-*
- config_name: typography-2
data_files:
- split: train
path: typography-2/train-*
- config_name: typography-3
data_files:
- split: train
path: typography-3/train-*
- config_name: typography-4
data_files:
- split: train
path: typography-4/train-*
- config_name: typography-5
data_files:
- split: train
path: typography-5/train-*
- config_name: typography-6
data_files:
- split: train
path: typography-6/train-*
- config_name: typography-7
data_files:
- split: train
path: typography-7/train-*
- config_name: typography-8
data_files:
- split: train
path: typography-8/train-*
GDB: GraphicDesignBench
39 benchmarks for evaluating vision-language models on graphic design tasks — layout, typography, SVG, template matching, animation. Built on 1,148 real design layouts from the Lica dataset.
Paper: arXiv:2604.04192 | Code: github.com/lica-world/GDB | Blog: lica.world
Usage
from datasets import load_dataset
ds = load_dataset("lica-world/GDB", "svg-1")
Schema
| Field | Type | Description |
|---|---|---|
sample_id |
string | Sample identifier |
benchmark_id |
string | e.g. svg-1, typography-3 |
domain |
string | layout, typography, svg, template, temporal, category, lottie |
task_type |
string | understanding or generation |
prompt |
string | Evaluation prompt |
ground_truth |
string | Expected answer (JSON for complex types) |
image |
Image | Input image (when applicable) |
metadata |
string | Task-specific fields as JSON |
Evaluation
pip install git+https://github.com/lica-world/GDB.git
from gdb.registry import BenchmarkRegistry
registry = BenchmarkRegistry()
registry.discover()
bench = registry.get("svg-1")
scores = bench.evaluate(predictions, ground_truth)
Citation
@article{gdb2026,
title={GDB: A Real-World Benchmark for Graphic Design},
author={Deganutti, Adrienne and Hirsch, Elad and Zhu, Haonan and Seol, Jaejung and Mehta, Purvanshi},
journal={arXiv preprint arXiv:2604.04192},
year={2026}
}
Apache 2.0