---
license: cc-by-4.0
pretty_name: "LAION-Natural"
task_categories:
- image-classification
language:
- en
tags:
- laion
- laion-natural
- laion-2b
- relaion
- relaion2b
- laion2b-en
- natural-images
- natural-scores
- image-quality
- image-filtering
- photograph-detection
- ccn2025
- visual-neuroscience
- clip
size_categories:
- 1B<n<10B
---

<p align="center">
  <img src="photorealistic_earth.jpg" alt="LAION-Natural" width="150"/>
</p>

# LAION-Natural: Naturalness Scores for ReLAION-2B (CCN 2025, Roth & Hebart)

**LAION-Natural** is a large-scale naturalness scoring dataset covering **2.1 billion images** from ReLAION-2B-en-research-safe. Each image receives a score predicting how "natural" or "photographic" it looks versus artificial/rendered content. At the recommended threshold of 0.7, the dataset identifies ~500 million natural photographs suitable for vision research, cognitive science, and model training.

> **Also known as:** LAION-Natural · LAION natural · ReLAION-Natural · ReLAION-2B-Natural · LAION-2B-Natural
>
> Introduced in: [*How to sample the world for understanding the visual system*](https://openreview.net/forum?id=T9k6KkZoca) (Roth & Hebart, CCN 2025)

**Looking for embeddings?** Pre-computed CLIP ViT-H/14 embeddings for the ~500M natural photographs are available at [**LAION-Natural Embeddings**](https://huggingface.co/datasets/andropar/relaion2b-natural-embeddings).

## Quick Start

```python
from datasets import load_dataset

# Load the dataset (streaming recommended due to size)
ds = load_dataset("andropar/relaion2b-natural", streaming=True)

# Filter to natural images only
for row in ds['train']:
    if row['natural_score'] and row['natural_score'] > 0.7:
        print(row['url'])  # Natural photograph URL
```

## Overview

| | |
|---|---|
| **Total rows** | ~2.1 billion |
| **Score range** | 0.0 (artificial) to 1.0 (natural) |
| **Recommended threshold** | > 0.7 for natural photographs |
| **Format** | Parquet (Snappy compressed) |
| **Source** | [ReLAION-2B-en-research-safe](https://huggingface.co/datasets/laion/relaion2B-en-research-safe) |

## Example Images

Examples of images at different natural score ranges:

**Non-natural (score < 0.3):** Graphics, logos, text overlays, screenshots
![Non-natural examples](examples/scores_non_natural.png)

**Low (score 0.3 - 0.5):** Mixed content, product images, some editing
![Low score examples](examples/scores_low.png)

**Medium (score 0.5 - 0.7):** Mostly natural with some artifacts
![Medium score examples](examples/scores_medium.png)

**High (score 0.7 - 0.85):** Natural photographs
![High score examples](examples/scores_high.png)

**Very high (score 0.85 - 1.0):** Clean natural photographs
![Very high score examples](examples/scores_very_high.png)

*Thumbnails shown solely to illustrate dataset characteristics. Source: ReLAION-2B-en-research-safe (Apache 2.0). Underlying images remain under the copyright of their original creators.*

## Dataset Structure

| Column | Type | Description |
|--------|------|-------------|
| `url` | string | Image URL from ReLAION-2B |
| `natural_score` | float32 | Naturalness prediction (0-1), null if no match found in original LAION-2B-en |

Files are named `relaion2b_natural_part-*.snappy.parquet`.

## How the Scores Were Created

1. **Manual labeling:** About 26k images were labeled via active learning over a pool of roughly 200k candidates from LAION-2B-en (21k usable after filtering broken URLs). Selection criteria for "natural" images:
   - No watermarks, logos, or banners
   - No heavy editing (B&W filters, high saturation, photoshopping)
   - Must be a real-world scene or object

2. **Classifier training:** A logistic regression classifier was trained on [CLIP ViT-L/14](https://huggingface.co/openai/clip-vit-large-patch14) image embeddings (768-dim). This simple linear model was chosen deliberately - we verified that nonlinear models (MLPs) do not improve over logistic regression on these features, indicating that the linear classifier efficiently captures the available signal.

3. **Scoring:** The classifier was applied to pre-computed CLIP ViT-L/14 embeddings for all of LAION-2B-en (~2.1B images).

4. **Matching:** Predicted scores were matched to ReLAION-2B-en-research-safe by URL. Some URLs have null scores where no match was found in the original dataset.

## Classifier

The trained classifier is included in the [`classifier/`](classifier/) directory and can be used to score new images. Since it operates on standard CLIP ViT-L/14 features, it can be applied to any image that can be embedded with CLIP.

### Performance

| Metric | Value |
|--------|-------|
| **ROC AUC** | 0.89 |
| **Average Precision** | 0.89 |
| **Precision @ threshold 0.7** | 0.89 |
| **Recall @ threshold 0.7** | 0.59 |
| **Accuracy @ threshold 0.5** | 0.80 |

The classifier was evaluated on a held-out test set of 4,200 labeled images. At the recommended threshold of 0.7, precision is high (89%). The tradeoff is lower recall (59%), meaning some natural images will be missed, but we decided that this trade-off was acceptable for our use case. 

<details>
<summary>Detailed diagnostics (click to expand)</summary>

**ROC and Precision-Recall curves:**

| | |
|---|---|
| ![ROC curve](classifier/roc_curve.png) | ![PR curve](classifier/precision_recall_curve.png) |

**Score distributions by true label:**

![Score distributions](classifier/score_distributions.png)

**Confusion matrices:**

| Threshold = 0.5 | Threshold = 0.7 |
|---|---|
| ![CM 0.5](classifier/confusion_matrix_t05.png) | ![CM 0.7](classifier/confusion_matrix_t07.png) |

**Typical errors** - most misclassifications occur on genuinely ambiguous images:

| False positives (predicted natural, actually non-natural) | False negatives (predicted non-natural, actually natural) |
|---|---|
| ![FP](classifier/false_positives.png) | ![FN](classifier/false_negatives.png) |

False positives tend to be product or studio photography with subtle watermarks/overlays. False negatives tend to be natural scenes with text, heavy cropping, or unusual framing.

</details>

### Files

| File | Description |
|------|-------------|
| `classifier/classifier_weights.json` | Portable weights (JSON) - use this for framework-agnostic inference |
| `classifier/classifier_weights.npz` | Weights as numpy arrays (coef + intercept) |
| `classifier/laion_natural_img_clf_vitl14.pkl` | Original scikit-learn pickle |
| `classifier/diagnostics.json` | Full evaluation metrics |

### Usage

**Option 1: Framework-agnostic (recommended)**

```python
import json
import numpy as np

# Load weights
with open("classifier/classifier_weights.json") as f:
    weights = json.load(f)

coef = np.array(weights["coef"], dtype=np.float32)
intercept = weights["intercept"]

def predict_natural_score(clip_embedding):
    """Score a CLIP ViT-L/14 embedding (768-dim, L2-normalized)."""
    logit = np.dot(clip_embedding, coef) + intercept
    return 1.0 / (1.0 + np.exp(-logit))

# Example: extract features with CLIP and score
import clip, torch
from PIL import Image

model, preprocess = clip.load("ViT-L/14")
image = preprocess(Image.open("photo.jpg")).unsqueeze(0)
with torch.no_grad():
    embedding = model.encode_image(image)
    embedding /= embedding.norm(dim=-1, keepdim=True)
    embedding = embedding.cpu().numpy().squeeze()

score = predict_natural_score(embedding)
print(f"Natural score: {score:.3f}")  # > 0.7 = likely a natural photograph
```

**Option 2: scikit-learn**

```python
import pickle

with open("classifier/laion_natural_img_clf_vitl14.pkl", "rb") as f:
    clf = pickle.load(f)

# clf.predict_proba(embeddings)[:, 1] gives natural scores
```

## Usage Examples

**Filter a subset with pandas:**
```python
import pandas as pd

df = pd.read_parquet("relaion2b_natural_part-000.snappy.parquet")

# High-quality natural images
natural = df[df['natural_score'] > 0.7]
print(f"Found {len(natural):,} natural images")

# Very high confidence
very_natural = df[df['natural_score'] > 0.9]
```

**Load all files:**
```python
from datasets import load_dataset

# Full dataset (streaming)
ds = load_dataset("andropar/relaion2b-natural", streaming=True)

# Or load specific files
import glob
files = glob.glob("relaion2b_natural_part-*.snappy.parquet")
df_all = pd.concat([pd.read_parquet(f) for f in files])
```

**Combine with image downloading:**
```python
import requests
from PIL import Image
from io import BytesIO

def download_image(url):
    resp = requests.get(url, timeout=10)
    return Image.open(BytesIO(resp.content))

# Get natural image URLs and download
natural_urls = df[df['natural_score'] > 0.8]['url'].tolist()
images = [download_image(url) for url in natural_urls[:100]]
```

## Use Cases

- **Dataset filtering:** Remove non-photographic content from web-scraped image datasets
- **Quality assessment:** Score images for naturalness before model training
- **Research:** Study distribution of natural vs. artificial images on the web
- **Preprocessing:** Filter training data for vision models that need natural photographs

## Related Datasets

- **[LAION-Natural Embeddings](https://huggingface.co/datasets/andropar/relaion2b-natural-embeddings)** — CLIP ViT-H/14 embeddings for the ~500M images with natural_score > 0.7
- **[LAION-Natural (alias)](https://huggingface.co/datasets/andropar/laion-natural)** — Alias repository for discoverability

## Licensing / Content

This repository contains only **metadata** (URLs and natural scores). No images are distributed.

- The underlying images are hosted by third-party websites and remain under their original copyrights and terms of use.
- Our additions (naturalness scores, classifier, documentation) are released under **CC-BY 4.0**.
- This dataset is based on [ReLAION-2B-en-research-safe](https://huggingface.co/datasets/laion/relaion2B-en-research-safe), which is licensed under **Apache 2.0**.
- Please check license compatibility for any commercial usage.

## Limitations

- "Naturalness" reflects our specific labeling criteria - may not match your definition
- These are ML predictions, not ground truth labels
- The classifier was trained on CLIP ViT-L/14 features; performance may differ with other embedding models
- Some URLs may be broken or point to different/removed images
- Null scores indicate URLs not found in original LAION-2B-en dataset

## Citation

```bibtex
@inproceedings{
  roth2025how,
  title={How to sample the world for understanding the visual system},
  author={Johannes Roth and Martin N Hebart},
  booktitle={8th Annual Conference on Cognitive Computational Neuroscience},
  year={2025},
  url={https://openreview.net/forum?id=T9k6KkZoca}
}
```

---

Questions or issues? Open a discussion!

This dataset is intended for **research purposes**. Verify license compatibility before commercial use.