README.md

---

license: cc-by-4.0
---


# CREBench Reward Data

Pairwise creativity preference benchmarks used for CREward training and evaluation. Each benchmark contains rendered image pairs and human- or LVLM-derived preference labels along four creativity dimensions.

**Hugging Face:** [hanjy/CREBench](https://huggingface.co/datasets/hanjy/CREBench)

**Local root (after download):** `./CREBench/`

| Benchmark | Path | Label source |
|-----------|------|----------------|
| **CREBench_LVLM** | `CREBench_LVLM/` | Gemma-3-27B-IT pairwise judgments |
| **CREBench_Human** | `CREBench_Human/` | 5 human annotators per pair |

---

## Object categories

Both benchmarks use the same five object categories:

- `chair`
- `bowl`
- `car`
- `vase`
- `handbag`

---

## Directory layout

```
CREBench/
├── CREBench_LVLM/
│   ├── Label/
│   │   ├── gemma27b_preferences_chair.pkl
│   │   ├── gemma27b_preferences_bowl.pkl
│   │   ├── gemma27b_preferences_car.pkl
│   │   ├── gemma27b_preferences_vase.pkl
│   │   └── gemma27b_preferences_handbag.pkl
│   ├── chair/          # pair_0000_0.png, pair_0000_1.png, ...
│   ├── bowl/
│   ├── car/
│   ├── vase/
│   └── handbag/
└── CREBench_Human/
    ├── Label/
    │   ├── human_preferences_chair.pkl
    │   ├── human_preferences_bowl.pkl
    │   ├── human_preferences_car.pkl
    │   ├── human_preferences_vase.pkl
    │   └── human_preferences_handbag.pkl
    ├── chair/
    ├── bowl/
    ├── car/
    ├── vase/
    └── handbag/
```

---

## Image pairs

Each sample is a pair of images shown side-by-side during annotation.

| Field | Description |
|-------|-------------|
| **Naming** | `pair_{idx:04d}_0.png` and `pair_{idx:04d}_1.png` |
| **Index** | `idx` runs from `0` to `N-1` within each category folder |
| **Image `_0`** | First image in the pair (option A) |
| **Image `_1`** | Second image in the pair (option B) |

Pairs are aligned with label rows by sorted filename order: row `i` corresponds to `pair_{i:04d}_0.png` and `pair_{i:04d}_1.png`.

### Dataset sizes

| Benchmark | Pairs per category | Total pairs | Total images |
|-----------|-------------------|-------------|--------------|
| **CREBench_LVLM** | 1,000 | 5,000 | 10,000 |
| **CREBench_Human** | 100 | 500 | 1,000 |

---

## Labels (`.pkl`)

Labels are stored as NumPy arrays inside pickle files (`numpy.ndarray`, `dtype=int64`).

### Label dimensions (columns)

Each row has **4** integer labels in this order:

| Column | Dimension |
|--------|-----------|
| 0 | **Geometry** creativity |
| 1 | **Material** creativity |
| 2 | **Texture** creativity |
| 3 | **Overall** creativity |

### Label values

All entries are in **`{-1, 0, 1}`**:

| Value | Meaning |
|-------|---------|
| **`0`** | `pair_*_0.png` is more creative on that dimension |
| **`1`** | `pair_*_1.png` is more creative on that dimension |
| **`-1`** | Tie, no clear preference, or not decidable |

### Shapes

| Benchmark | File pattern | Shape | Description |
|-----------|--------------|-------|-------------|
| **CREBench_LVLM** | `Label/gemma27b_preferences_{category}.pkl` | **`(N, 4)`** | `N` = number of pairs in that category (1,000) |
| **CREBench_Human** | `Label/human_preferences_{category}.pkl` | **`(5, N, 4)`** | 5 annotators × `N` pairs (100) × 4 dimensions |

So for CREBench_LVLM, labels are **`num_samples × 4`**. For CREBench_Human, raw per-rater labels are **`5 × num_samples × 4`**; aggregate across raters (e.g. majority vote or mean) if a single preference vector per pair is needed.

---

## CREBench_LVLM

LVLM preferences from **Gemma-3-27B-IT** (`google/gemma-3-27b-it`) on pairwise image comparisons.

### Annotation protocol

For each pair, the model answers four questions (geometry, material, texture, overall), choosing **A**, **B**, or **Not decidable**. Responses are converted to the signed label format above (`A` → `0` for image `_0`, `B` → `1` for image `_1`, not decidable → `-1`).

### Example (chair)

- **Images:** `CREBench_LVLM/chair/pair_0000_0.png`, `pair_0000_1.png`, …, `pair_0999_1.png`
- **Labels:** `CREBench_LVLM/Label/gemma27b_preferences_chair.pkl` with shape `(1000, 4)`

---

## CREBench_Human

Human pairwise creativity judgments on a smaller subset of pairs (100 per category).

### Annotation protocol

Five raters judge each pair on the same four dimensions (geometry, material, texture, overall). Labels are stored per rater in the first dimension of the array (`shape[0] == 5`).

### Example (chair)

- **Images:** `CREBench_Human/chair/pair_0000_0.png`, …, `pair_0099_1.png`
- **Labels:** `CREBench_Human/Label/human_preferences_chair.pkl` with shape `(5, 100, 4)`

---

## Download and usage in Python

Install [huggingface_hub](https://huggingface.co/docs/huggingface_hub) if needed:

```bash
pip install huggingface_hub
```

### Download the full dataset

The Hub repo [`hanjy/CREBench`](https://huggingface.co/datasets/hanjy/CREBench) contains `CREBench_LVLM/` and `CREBench_Human/` at the top level:

```python
from huggingface_hub import snapshot_download

ROOT = snapshot_download(
    repo_id="hanjy/CREBench",
    repo_type="dataset",
    local_dir="./CREBench",
)
# ./CREBench/CREBench_LVLM/
# ./CREBench/CREBench_Human/
```

Equivalent CLI:

```bash
huggingface-cli download hanjy/CREBench \
  --repo-type dataset \
  --local-dir ./CREBench
```

For a private or gated repo, log in first (`huggingface-cli login` or set the `HF_TOKEN` environment variable).

### Download a single file

```python
from huggingface_hub import hf_hub_download

label_path = hf_hub_download(
    repo_id="hanjy/CREBench",
    repo_type="dataset",
    filename="CREBench_LVLM/Label/gemma27b_preferences_chair.pkl",
    local_dir="./CREBench",
)
```

### Load labels and images

```python
import pickle
from pathlib import Path

ROOT = Path("./CREBench")
category = "chair"

# CREBench_LVLM — (num_samples, 4)
with open(ROOT / "CREBench_LVLM/Label" / f"gemma27b_preferences_{category}.pkl", "rb") as f:
    lvlm_labels = pickle.load(f)  # e.g. shape (1000, 4)

# CREBench_Human — (5, num_samples, 4)
with open(ROOT / "CREBench_Human/Label" / f"human_preferences_{category}.pkl", "rb") as f:
    human_labels = pickle.load(f)  # e.g. shape (5, 100, 4)

geom, mat, tex, overall = 0, 1, 2, 3
pair_idx = 0
print("LVLM overall preference:", lvlm_labels[pair_idx, overall])
print("Human overall (5 raters):", human_labels[:, pair_idx, overall])
```

### Pairing images with labels

```python
def pair_paths(root: Path, category: str, idx: int):
    d = root / category
    return d / f"pair_{idx:04d}_0.png", d / f"pair_{idx:04d}_1.png"

img0, img1 = pair_paths(ROOT / "CREBench_LVLM", "chair", 0)
label_row = lvlm_labels[0]  # [geometry, material, texture, overall]
```


---

## Citation

If you use CREBench_LVLM or CREBench_Human, please cite the CREward paper:

```bibtex
@inproceedings{han2026creward,
  title={CREward: A Type-Specific Creativity Reward Model},
  author={Han, Jiyeon and Mahdavi-Amiri, Ali and Zhang, Hao and Jeong, Haedong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={21932--21941},
  year={2026}
}
```