| --- |
| license: other |
| license_name: per-file |
| license_link: https://huggingface.co/datasets/Gemus-AI/DesignAnything-Bench/blob/main/LICENSES.md |
| pretty_name: Design Anything Bench Inputs |
| language: |
| - en |
| tags: |
| - design |
| - benchmark |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: images |
| default: true |
| data_files: |
| - split: inputs |
| path: metadata/images.jsonl |
| --- |
| |
| # Design Anything Bench Inputs |
|
|
| The input images that the 228 tasks of the Design Anything benchmark (corpus v0.6) provide to the system under test: photographs, drawings, maps, references and generated stand-ins that the briefs ask the designer to work from. |
|
|
| Related material: |
|
|
| - Task briefs, scoring specifications, the evaluation protocol and the evaluator source: https://github.com/Gemus-AI/DesignAnything |
| - Design Anything's outputs on these tasks: https://huggingface.co/datasets/Gemus-AI/DesignAnything-Results |
|
|
| ## Contents |
|
|
| | Content | Files | Size | |
| | --- | ---: | ---: | |
| | Input images (`blobs/`) | 682 | 1.16 GB | |
|
|
| ```text |
| metadata/images.jsonl one row per image (the dataset viewer shows these) |
| blobs/sha256/<first two hex digits>/<sha256>.<ext> the images, byte-identical |
| LICENSES.md licenses and third-party attribution |
| CHECKSUMS.sha256 SHA-256 of every other file |
| ``` |
|
|
| Each task's brief (`benchmark/tasks/<taskId>/scenario.json` in the GitHub repository) lists the images it provides in `referenceAssets`, with a `path` and a `sha256`. The image with that digest is `blobs/sha256/<first two hex digits>/<sha256>.<ext>` here. The images are byte-identical to what the systems received, so metadata embedded in them (for example EXIF) is kept as well. |
|
|
| ### Row fields (`metadata/images.jsonl`) |
|
|
| - `sha256`, `path`, `bytes`, `contentType`. |
| - `tasks`: the tasks that provide the image; `corpusPaths`: the `referenceAssets[].path` values that name it. |
| - `origin`: `authored` (made by the benchmark authors), `generated` (generated by the benchmark authors with an image model), or `third-party`. |
| - `license`, `source`, `licenseUrl`, `attribution`: see below. |
| - `redistributionReview`: `reviewed` for every image in this release (see below). |
| - `flags`: `share-alike`, `url-source` (the record points to a source web page), `label-conflict` (two records give different license labels), `exif` / `xmp` (the file carries embedded metadata). |
|
|
| ## Licenses |
|
|
| All 682 inputs were reviewed for redistribution on 2026-10-17. The authors confirmed the `authored` and `generated` images. Each of the 55 third-party images was checked against its source page: the license on the page matches the record, and the CC BY-SA version and author match the attribution. The aerial photograph `site-overhead.png` for the architecture base-A tasks is USDA NAIP imagery, a US federal government work; it was labeled CC0 in the task corpus and is listed here as public domain. |
|
|
| Seven inputs carry CC BY-SA and two carry OGL-Toronto-1.0; their original attributions are listed separately in `LICENSES.md`. The third-party images that carry EXIF or XMP metadata are byte-identical to the files published at their sources. |
|
|
| This dataset is **not** released under a single license. Every image keeps its own license, recorded per file: |
|
|
| - `authored` and `generated` images: CC BY 4.0, attribution "The Design Anything Authors". |
| - `third-party` images: the license of their source (public domain, CC0, CC BY-SA, Open Government Licence – Toronto), with the attribution in `LICENSES.md`. CC BY-SA images remain under CC BY-SA. |
|
|
| The CC BY 4.0 grant for `authored` and `generated` images applies to the extent the authors hold rights in them. |
|
|
| To report a problem with any file, or to request its removal, open a discussion on this dataset page. |
|
|
| ## Citation |
|
|
| Please cite the Design Anything paper: https://arxiv.org/abs/2610.24771. Citation details are on the project page: https://designanything.gemus.ai |
|
|
