| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-to-image |
| language: |
| - en |
| tags: |
| - benchmark |
| - evaluation |
| - text-to-image |
| - vlm-eval |
| - image-generation |
| - diffusion-models |
| - leaderboard |
| pretty_name: ImageBench |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: verdicts |
| data_files: verdicts.csv |
| - config_name: scores |
| data_files: scores.csv |
| - config_name: prompts |
| data_files: prompts.csv |
| --- |
| |
| # ImageBench — 50 Text-to-Image Models Judged by VLMs on 192 Prompts |
|
|
| Reproducibility dataset for [imagebench.ai](https://imagebench.ai): the 192-prompt V1.2 benchmark, per-(model, prompt) VLM verdicts, and per-model aggregate scores for 50 text-to-image models. |
|
|
| **Live leaderboard + every generated image**: <https://imagebench.ai> |
| **Methodology**: <https://imagebench.ai/methodology-v1> |
| **Reproducibility repo**: <https://github.com/dh7/image-bench-ai> |
|
|
| ## What's in this dataset |
|
|
| | File | Rows | Description | |
| |---|---|---| |
| | `prompts.csv` | 64 tests × 3 variants | The V1.2 benchmark: prompt variants, category/subcategory/difficulty, VLM judge questions, evaluation criteria, VLM-routing assignment. | |
| | `verdicts.csv` | **9,600** (50 models × 192 prompts) | Per-image pass/fail verdict, VLM judge reasoning, HPS aesthetic score, VLM model used. | |
| | `scores.csv` | 50 | Per-model aggregate: Overall (0-100), pass rate, aesthetic Estimated Preference Score, per-category pass rates. | |
|
|
| ## Column reference |
|
|
| ### `verdicts.csv` |
| - `model_slug` — canonical model identifier (matches URLs on imagebench.ai) |
| - `model_name` — human-readable name |
| - `category` / `subcategory` / `difficulty` — benchmark taxonomy |
| - `prompt_id` — matches image filename on imagebench.ai/gallery |
| - `prompt` — the actual prompt sent to the model |
| - `verdict` — `PASS` or `FAIL` from the VLM judge |
| - `vlm` — which VLM produced the verdict (Qwen 3.5 122B / Gemini 3.1 Pro / etc.) |
| - `judge_response` — the VLM's textual reasoning for the verdict |
| - `hps_mu` — HPSv3 aesthetic score (higher = judged more aesthetically pleasing by human-preference model) |
| - `hps_pref` — pairwise HPS preference metadata |
|
|
| ### `scores.csv` |
| - `model_slug`, `model_name` |
| - `overall` — 0-100 blended score (capability + aesthetic preference) |
| - `pass_rate` — % of prompts passing the VLM verdict |
| - `eps` — Estimated Preference Score (aesthetic, based on HPSv3) |
| - `pass_<category>` — pass rate per category (text rendering, spatial reasoning, human realism, truthfulness, studio quality, graphical design) |
|
|
| ## How this was produced |
|
|
| 1. **Prompts**: 64 tests × 3 variants, hand-curated across 6 capability categories with binary judge questions per test. |
| 2. **Generation**: each model produces 192 images (one per prompt) via its native API — no cherry-picking, no re-runs. |
| 3. **Judging**: VLM-routed — Qwen 3.5 122B handles most categories, with per-category routing to specialists (Gemini 3.1 Pro for hands, etc.) based on calibration. |
| 4. **Aesthetic score**: HPSv3 (Human Preference Score v3) applied to every generated image. |
| 5. **Overall**: 0.5 × normalized pass rate + 0.5 × normalized EPS. |
|
|
| Full methodology + calibration study: <https://imagebench.ai/methodology-v1> |
|
|
| ## Known limitations |
|
|
| - **Text-to-image only** — no editing, inpainting, video, long-form coherence. |
| - **Prompt bias** — the 192-prompt set is weighted toward tests that *separate* models. "Every model passes" mundane prompts are under-represented, so pass rates are lower than typical real-world use. |
| - **VLM judge bias** — VLMs have their own systematic errors. We validate on a hands-specific calibration blog: <https://imagebench.ai/blog/hands-benchmark-qwen35-122b> |
| - **Category weighting** — Overall gives each category equal weight, but "text rendering" is inherently harder to pass than "professional studio quality". Interpret cross-category comparisons carefully. |
|
|
| ## Citation |
|
|
| If you use this dataset in research or writing, please cite: |
|
|
| ``` |
| ImageBench: 50 Text-to-Image Models Judged by VLMs on 192 Prompts |
| https://imagebench.ai |
| ``` |
|
|
| ## License |
|
|
| **CC-BY 4.0** — free to use with attribution. |
|
|
| ## Feedback |
|
|
| Issues, corrections, or methodology critique: open an issue at <https://github.com/dh7/image-bench-ai> or leave a comment on this dataset. |
|
|
