| --- |
| pretty_name: DesignTaste |
| language: |
| - en |
| - zh |
| license: other |
| license_name: designtaste-data-terms |
| license_link: LICENSE |
| task_categories: |
| - text-to-image |
| tags: |
| - benchmark |
| - visual-design |
| - image-generation |
| - design-evaluation |
| - aesthetics |
| configs: |
| - config_name: generated |
| default: true |
| data_files: |
| - split: test |
| path: data/generated/test-*.parquet |
| - config_name: tasks |
| data_files: |
| - split: test |
| path: data/tasks.parquet |
| - config_name: ratings |
| data_files: |
| - split: test |
| path: data/ratings.parquet |
| --- |
| |
| # DesignTaste |
|
|
| ### Benchmarking AI-Generated Visual Design Against Award-Winning Works |
|
|
| **403 design tasks | 2,418 generated images | 6 image models | 5 evaluation dimensions** |
|
|
| DesignTaste compares AI-generated visual designs with award-winning works from **100 Beste Plakate 2025** and the **ADC Annual Awards 2026**. Each model receives the same reconstructed design brief without seeing the original work. Generated images and award-winning works are then scored independently for their design idea and visual expression. |
|
|
| [GitHub](https://github.com/ArtArk-AI/DesignTaste) | [Evaluation protocol](https://github.com/ArtArk-AI/DesignTaste/blob/main/docs/EVALUATION.md) | [Submission guide](https://github.com/ArtArk-AI/DesignTaste/blob/main/docs/SUBMISSIONS.md) | [Reference links and attribution](metadata/reference_links.csv) | [Data terms](LICENSE) |
|
|
| ## What is included |
|
|
| | Track | Tasks | Generated images | Rating records | Design scope | |
| | --- | ---: | ---: | ---: | --- | |
| | `poster` | 100 | 600 | 700 | Poster design | |
| | `adc` | 303 | 1,818 | 2,121 | 20 disciplines, including branding, packaging, advertising, books, illustration, and spatial design | |
| | **Total** | **403** | **2,418** | **2,821** | | |
|
|
| Every task has one generated image from each of six models and seven rating records, including the award-winning reference. This release includes the original generated-image files, approximately **4.84 GB** of image bytes, embedded in Parquet shards. It also includes reviewed briefs, task metadata, source attribution, raw dimension scores, and the benchmark rubrics. |
|
|
| The **403 original award images are hosted externally**. The task metadata and [reference-links table](metadata/reference_links.csv) provide a `reference_url` for the selected source image and a `work_url` for its official work page. Original award-image bytes and comparison composites are not included in this repository. Source links do not grant permission to reuse the linked material. |
|
|
| ## Quick start |
|
|
| Stream generated examples without downloading the entire collection: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| generated = load_dataset( |
| "ArtArk/DesignTaste", "generated", split="test", streaming=True |
| ) |
| example = next(iter(generated)) |
| |
| image = example["image"] # Decoded PIL image |
| print(example["brief"]) |
| print(example["model_name"], example["overall_score"]) |
| ``` |
|
|
| Load the lightweight task and rating tables for generation or analysis: |
|
|
| ```python |
| tasks = load_dataset("ArtArk/DesignTaste", "tasks", split="test") |
| ratings = load_dataset("ArtArk/DesignTaste", "ratings", split="test") |
| |
| poster_tasks = tasks.filter(lambda row: row["track"] == "poster") |
| references = ratings.filter(lambda row: row["source"] == "reference") |
| |
| assert len(tasks) == 403 |
| assert len(ratings) == 2821 |
| assert len(references) == 403 |
| ``` |
|
|
| All three configurations have a single `test` split. `generated` is the default configuration. No training or validation split is supplied. |
|
|
| ## Data structure |
|
|
| | Configuration | Unit of observation | Main fields | |
| | --- | --- | --- | |
| | `generated` | One model output per task | `image`, `brief`, task identifiers, `source`, `model_name`, category context, image provenance, judge, five scores, `overall_score` | |
| | `tasks` | One design task | `brief`, task identifiers, reference URLs and hashes, title, creators, agency, credits, category and award metadata | |
| | `ratings` | One scored reference or model output | `rating_id`, task identifiers, `source`, `model_name`, image provenance, judge, five scores, `overall_score` | |
|
|
| Use `task_key`, such as `poster:1` or `adc:62199`, to join configurations. `task_id` preserves the original string identifier, and `track` is either `poster` or `adc`. A rating is identified by `(task_key, source)`. `source="reference"` denotes the award-winning work. Missing or inapplicable string metadata uses an empty string, and absent role credits use the string `{}` in `credits_json`. Numeric fields can be null where information is unavailable. The complete field definitions are in [metadata/schema.json](metadata/schema.json). |
|
|
| For Poster, `creators` retains the source creator attribution. For ADC, `creators` and `agency` identify the credited agency, while `credits_json` preserves the available role-specific credits from the official award page. These fields distinguish agency attribution from individual credits. `metadata_provenance`, `metadata_source_status`, and `metadata_limitations` describe the metadata source and its limits. ADC spans 20 `discipline` values and 135 more specific `category` values. |
|
|
| `image_sha256` identifies the original image file. `scored_image_sha256` identifies the actual judging input. ADC judging used image previews, so these hashes can differ. The release contains the original generated images, with preview dimensions and hashes retained as provenance. Preview files are not duplicated in the upload. `reference_image_count` describes the original ADC project's image count, even though each benchmark task evaluates one image. |
|
|
| The two brief hashes use different serializations of the same text. `brief_sha256` hashes the UTF-8 brief directly. `annotation_brief_sha256` hashes `json.dumps(brief, ensure_ascii=False, sort_keys=True, separators=(",", ":"))` encoded as UTF-8. All 403 brief texts and all 2,821 annotation hashes have been checked against their respective conventions. |
|
|
| ## Evaluation and scoring |
|
|
| The judge receives one anonymous image, its brief, and the track-specific rubric. The main judge is `gpt-6.1-sol` for 2,820 records. The reference for ADC task `62340` has the source judge label `codex_visual_review`. The released rating tables contain scores, not the judge's textual rationales. |
|
|
| The five reporting names are shared across tracks, while the detailed criteria reflect their different design tasks. Raw scores were assigned as integers from 0 to 10. The Parquet score columns store numeric values on that same **0-10 scale**. |
|
|
| | Dimension / score field | Poster rubric ID | ADC rubric ID | Weight | |
| | --- | --- | --- | ---: | |
| | Thematic Expression / `thematic_expression` | `brief_communication` | `brief_fit` | 2 (20%) | |
| | Creative Originality / `creative_originality` | `conceptual_originality` | `concept_strength` | 3 (30%) | |
| | Visual Organization / `visual_organization` | `visual_organization` | `category_fit` | 1.5 (15%) | |
| | Aesthetic Expression / `aesthetic_expression` | `aesthetic_expression` | `visual_craft` | 2 (20%) | |
| | Formal Completion / `formal_completion` | `formal_resolution` | `visible_communication` | 1.5 (15%) | |
|
|
| `overall_score` uses a **0-100 scale** and the default weights **2:3:1.5:2:1.5**: |
|
|
| ```text |
| overall_score = 10 * sum(weight_i * raw_score_i) / sum(weight_i) |
| ``` |
|
|
| Multiply an individual raw dimension by 10 to display it on a 0-100 scale. The overall score is already on that scale. Models are compared with the reference for the same task, and each task contributes equally to its track's mean. Higher/tied/lower counts compare independently assigned scores rather than direct preference votes. `project_group` identifies related tasks. There are 99 groups in Poster and 229 in ADC. |
|
|
| ## Released results |
|
|
| The following overall means are reproduced from the frozen scores with the default weights. Each model contributes **403 images**, comprising 100 Poster and 303 ADC outputs. These are benchmark judgments, not the original award juries' ratings. |
|
|
| | Model / source | `source` identifier | Poster | ADC | |
| | --- | --- | ---: | ---: | |
| | Award-winning works | `reference` | 78.275 | 80.343 | |
| | Seedream 5.0 Pro | `doubao-seedream-5-0-pro-260628` | 72.440 | 72.566 | |
| | Nano Banana Pro | `nano-banana-pro` | 75.180 | 77.028 | |
| | GPT-Image-2.5 Flare | `gpt-image-2.5-flare` | 74.710 | 78.094 | |
| | GPT-Image-2 | `gpt-image-2` | 75.060 | 79.477 | |
| | GPT-Image-2.5 Sunburst | `gpt-image-2.5-sunburst` | 75.840 | 79.705 | |
| | ArtArk | `artark` | 79.635 | 80.736 | |
|
|
| For new evaluations, generate one image per task from its brief and applicable category context, then use the matching rubric and scoring prompt in the [submission guide](https://github.com/ArtArk-AI/DesignTaste/blob/main/docs/SUBMISSIONS.md). Reweighting the released dimensions requires no new image generation or judging. |
|
|
| ## Scope and limitations |
|
|
| The English briefs were reconstructed from public award materials and reviewed for factual grounding and room for alternative design solutions. They are not verified original client specifications. The ADC cohort retains 303 of 333 initial tasks. Thirty were excluded because one or more models declined generation under their content moderation policies. |
|
|
| Scores reflect one primary model judge and its rubric. A single still image cannot establish the quality of an entire campaign, design system, physical product, motion sequence, or audience experience. Award collections and the retained tasks also represent a limited range of design practice. Use the source work pages and metadata to understand each task's context, and interpret score differences within these limits. |
|
|
| ## Rights and attribution |
|
|
| The dataset is accompanied by the [DesignTaste Data Terms and Rights Notice](LICENSE). The generated images are supplied for benchmark inspection and reproducibility. Applicable provider terms and rights in depicted third-party material remain relevant. Original award images remain on their external hosts, under their respective rights and source terms. |
|
|
| The [benchmark code](https://github.com/ArtArk-AI/DesignTaste) is MIT-licensed. That code license does not extend a blanket MIT or Creative Commons grant to this dataset's images, award metadata, or other third-party materials. Attribution is provided through the task metadata and the citation below. Report corrections through the [project issue tracker](https://github.com/ArtArk-AI/DesignTaste/issues). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{chen2026designtaste, |
| title = {DesignTaste: Benchmarking AI-Generated Visual Design Against Award-Winning Works}, |
| author = {Chen, Zhou and Li, JiaXiang and Pan, DanYing and Li, Ye |
| and Yang, TianRui and Zhang, EnBao and Xiao, YaNan and Sun, LiXin |
| and Liu, Anqi and Zhao, Dong and Li, JianYuan and Ding, YuanYuan |
| and Kong, Kai and Xu, Xinwen and Du, HuiTang and Zhou, Min and Li, Zhiqiang}, |
| year = {2026}, |
| url = {https://github.com/ArtArk-AI/DesignTaste}, |
| note = {Benchmark code and data, version 0.1.0} |
| } |
| ``` |
|
|
