Datasets:
File size: 1,780 Bytes
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license: cc-by-nc-4.0
task_categories:
- image-to-image
language:
- en
tags:
- benchmark
- reasoning-driven-image-generation
pretty_name: RIG-Bench
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: test
path: samples.jsonl
---
# RIG-bench
Anonymous submission to the **NeurIPS 2026 Evaluations & Datasets (E&D) Track**.
A benchmark for **reasoning-driven image generation**: given visual context (images + instruction + optional demonstration pairs), the model must produce the answer as a single image.
- 2,000 samples
- 4 task families × 11 subtasks
- ~1.4 GB
## Files
```
RIG-bench/
├── README.md
├── samples.jsonl # 2,000 records
└── images/<sample_id>/
├── input_<order>.<ext>
├── demo_<j>_input_<k>.<ext>, demo_<j>_output_<k>.<ext> (some samples)
└── target.<ext>
```
## Data Format
Each line in `samples.jsonl` corresponds to one benchmark sample.
A sample includes the input context, the expected visual answer, task labels.
| Field | Meaning |
|---|---|
| `input` | input images, and optional example images. |
| `output` | Target ground truth answer image. |
| `main_family` | Four cognitively demanding domains. |
| `subtask` | Eleven fine-grained subtasks |
## Loading
```python
import json
from pathlib import Path
from PIL import Image
from huggingface_hub import snapshot_download
ROOT = Path(snapshot_download(repo_id="anonymous-submission-RIG-bench/RIG-Bench",repo_type="dataset"))
samples = [json.loads(l) for l in (ROOT / "samples.jsonl").open(encoding="utf-8")]
s = samples[0]
input_images = [Image.open(ROOT / img["path"]) for img in s["input"]["images"]]
GT_target_image = Image.open(ROOT / s["output"]["target_image"])
``` |