README.md

---
pretty_name: VisionValueBench
tags:
- text-to-image
- cultural-values
- evaluation
configs:
- config_name: all
  default: true
  data_files:
  - split: test
    path: data/*/*.parquet
- config_name: flux2
  data_files:
  - split: test
    path: data/flux2/*.parquet
- config_name: flux2_upsampler
  data_files:
  - split: test
    path: data/flux2_upsampler/*.parquet
- config_name: gptimage2
  data_files:
  - split: test
    path: data/gptimage2/*.parquet
- config_name: hidream
  data_files:
  - split: test
    path: data/hidream/*.parquet
- config_name: hidream_refiner
  data_files:
  - split: test
    path: data/hidream_refiner/*.parquet
- config_name: ideogram4_magic
  data_files:
  - split: test
    path: data/ideogram4_magic/*.parquet
- config_name: mai26flash
  data_files:
  - split: test
    path: data/mai26flash/*.parquet
- config_name: nanobanana2
  data_files:
  - split: test
    path: data/nanobanana2/*.parquet
- config_name: scenarios_2773
  data_files:
  - split: corpus
    path: scenarios/scenarios_2773/*.parquet
- config_name: evaluation_scenarios_960
  data_files:
  - split: test
    path: scenarios/evaluation_scenarios_960/*.parquet
---

# VisionValueBench

VisionValueBench studies visual value leakage in multilingual text-to-image generation.
[Project homepage](https://zucanlv.github.io/VisionValueBench/) · [Code and execution prompts](https://github.com/zucanlv/VisionValueBench)

## Scope

This package contains the 2,773 retained scenarios, their 960-scenario evaluation subset, and 68,764 evaluation-image records. The frozen benchmark contains 68,764 completed IDs against a design target of 69,120 across six base generators and two extensions.

Evaluation JPEG bytes are preserved exactly as submitted to the automatic judges and checked against recorded review-image hashes. Scenario text and frozen numerical labels accompany each image. Final annotation files contain binary votes and decisions; this package does not include rich evidence responses or original generated images.

| Configuration | Records |
| --- | ---: |
| `flux2` | 5,760 |
| `flux2_upsampler` | 5,760 |
| `gptimage2` | 13,243 |
| `hidream` | 5,760 |
| `hidream_refiner` | 5,760 |
| `ideogram4_magic` | 5,753 |
| `mai26flash` | 13,302 |
| `nanobanana2` | 13,426 |

## Scenario collections

| Configuration | Split | Scenarios | Scope |
| --- | --- | ---: | --- |
| `scenarios_2773` | `corpus` | 2,773 | Full retained scenario collection after removing 180 of the 2,953 constructed scenarios |
| `evaluation_scenarios_960` | `test` | 960 | Final evaluation subset, with 160 scenarios per value dimension |

Both tables contain the canonical English `scenario` text, `scenario_id`, `value_dimension`, opposing `value_endpoints`, `concept`, `construction_scenario_id`, and `is_evaluation`. The same `scenario_id` joins these tables to the image records and numerical labels. Every evaluation scenario belongs to the retained collection. The final evaluation set retains 897 initially selected scenarios and adds 63 replacements after screening. The 63 replacements were sampled using refill seed 20260921.

```python
from datasets import load_dataset
scenarios = load_dataset("ZucanLyu/VisionValueBench", "scenarios_2773", split="corpus")
evaluation_scenarios = load_dataset("ZucanLyu/VisionValueBench", "evaluation_scenarios_960", split="test")
```

The `corpus` and `test` names describe these collections; they do not define a disjoint training/test partition. These tables can be loaded without downloading images. JSONL downloads are also available: [2,773 scenarios](scenarios/scenarios_2773.jsonl) and [960 evaluation scenarios](scenarios/evaluation_scenarios_960.jsonl). `SCENARIO_SPEC.json`, `SCENARIO_PROVENANCE.json`, and `SCENARIO_ARTIFACT_MANIFEST.json` document the fields, frozen source hashes, screening, and verification. The canonical English text does not include the translated generation prompts.

## Fields and judgments

`sample_id`, `model_configuration`, `scenario_id`, `prompt_language`, `culture_cue`, and `six_shared_conditions` identify each scientific condition. `scenario` holds the English scenario context supplied to the evaluators; it is not the translated generation prompt. `image` contains embedded image bytes with a relative sample filename; `evaluation_sha256`, `image_width`, and `image_height` describe those bytes.

SAE (Scenario-Anticipated Evaluation) and IAE (Image-Adaptive Evaluation) remain separate. Each method has three judges (`luna`, `gemini`, `muse`) and 12 binary endpoint votes. `sae_votes` and `iae_votes` retain each judge's vector; `sae_supported` and `iae_supported` are per-endpoint majorities (at least two supporting judges). Opposing orientations are judged independently. See `dataset_spec.json` for endpoint order and judge model names.

## Load

```python
from datasets import load_dataset
dataset = load_dataset("ZucanLyu/VisionValueBench", "all", split="test", streaming=True)
row = next(iter(dataset))
image = row["image"]
```

The `test` split names the evaluation collection; it does not imply an independent training/test partition. Each named configuration selects its own shards; `all` references the same files without duplicating records.

The same numerical labels are available separately in `labels.jsonl.gz` for analyses that do not need image downloads.

## Verification and availability

`ARTIFACT_MANIFEST.json` records shard hashes, sizes, row counts, and image-byte totals. `BUILD_STATUS.json` records local build/readback checks. Repository access and viewer availability depend on the current Hub settings. Licensing has not been specified.