| --- |
| dataset_info: |
| features: |
| - name: image |
| dtype: image |
| - name: completion |
| dtype: string |
| splits: |
| - name: test |
| num_bytes: 269520572 |
| num_examples: 2000 |
| download_size: 269520572 |
| dataset_size: 269520572 |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/test* |
| --- |
| |
| # SVG Basic Benchmark Dataset (svg_basic_benchmark_v0) |
| |
| Presto Design is proud to announce this public benchmark to help advance the field of machine-powered graphic design. |
| |
| ## Why This Matters |
| |
| Professional graphic design is increasingly reliant on Large Language Models (LLMs) due to their unique capabilities that traditional image generation models like Stable Diffusion cannot match. LLMs excel at: |
| - Working with branded assets and specific stock photos |
| - Utilizing brand-specific fonts and typography |
| - Creating scalable, resolution-independent designs |
| - Maintaining precise control over design elements |
| - Generating semantic, editable markup (SVG) |
| |
| As a leader in developing cutting-edge LLM-powered graphic design models, Presto Design recognizes that LLMs have historically struggled with graphic design tasks. This benchmark represents a "fifth grader test" for LLMs - given an image, can they perfectly replicate it? |
| |
| This fundamental capability is a crucial stepping stone toward developing truly creative and professional design systems. |
| |
| ## Dataset Contents |
| |
| - 2000 test samples, each containing: |
| - image: A rendered PNG version of the SVG poster (280x280 pixels for speed and efficiency) |
| - completion: The full SVG markup for the poster |
| |
| ## Design Features |
| |
| The benchmark tests comprehensive understanding of SVG features including: |
| - Backgrounds: Plain colors, gradients, or background images with controlled opacity |
| - Layouts: Text wrapping and random layouts with weighted distribution |
| - Text: Various font styles, sizes, and colors using a consistent color scheme |
| - Shapes: Geometric shapes and masked images |
| - Images: Integration of stock photos from the Unsplash dataset with proper attribution |
| - Advanced Features: masks, gradients, strokes, icons |
| - Color Replication |
| |
| ## Technical Details |
| |
| - SVG files are fully valid and renderable |
| - Images are resized to maintain performance |
| - Color schemes are programmatically generated for consistency |
| - All external resources (images, fonts) are properly embedded |
| |
| ## Usage |
| |
| This dataset is primarily used for: |
| 1. Benchmarking SVG generation models |
| 2. Testing SVG manipulation and rendering capabilities |
| 3. Evaluating layout algorithm performance |
| 4. Validating text placement and wrapping functionality |
| |
| ## Resources |
| |
| - Dataset: https://huggingface.co/datasets/Presto-Design/svg_basic_benchmark_v0 |
| - Benchmark Runner: https://github.com/Presto-design/svg-benchmark |
|
|
| ## Dataset Structure |
|
|
| The dataset is organized with the following structure: |
| - `test/`: Contains 2000 examples across 1 chunks |
|
|
| ## Loading the Dataset |
|
|
| You can load this dataset using the Hugging Face `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the entire dataset |
| dataset = load_dataset("Presto-Design/svg_basic_benchmark_v0") |
| |
| # Or load specific splits |
| dataset = load_dataset("Presto-Design/svg_basic_benchmark_v0", split="test") |
| ``` |
|
|
