Duplicate from bethgelab/PAMELA

Co-authored-by: Juliane Verwiebe <juliane-v@users.noreply.huggingface.co>

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+# Audio files - uncompressed
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PAMELA.zip

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- text-to-image
+language:
+- en
+tags:
+- aesthetics
+- rlhf
+- personalization
+- human-preferences
+---
+
+---
+license: cc-by-4.0
+task_categories:
+- text-to-image
+language:
+- en
+tags:
+- aesthetics
+- rlhf
+- personalization
+- human-preferences
+---
+
+<div align="center">
+  <h1 style="font-family: serif; font-size: 3rem; margin-bottom: 0;">PAM∃LA</h1>
+  <p style="font-family: serif; font-size: 1.5rem; color: gray;">Personalizing Text-to-Image Generation to Individual Taste</p>
+
+  <div style="font-family: serif; font-size: 1.1rem; margin-top: 10px;">
+    <strong>Anne-Sofie Maerten<sup>1,2</sup></strong>, 
+    <strong>Juliane Verwiebe<sup>2</sup></strong>, 
+    <strong>Shyamgopal Karthik<sup>2</sup></strong>,
+    <strong>Ameya Prabhu<sup>2</sup></strong>,
+    <strong>Johan Wagemans<sup>1</sup></strong>,
+    <strong>Matthias Bethge<sup>2</sup></strong>
+  </div>
+
+  <div style="font-family: serif; font-size: 0.95rem; color: #555; margin-top: 5px; margin-bottom: 20px; line-height: 1.5;">
+    <sup>1</sup>Tübingen AI Center, University of Tübingen <br>
+    <sup>2</sup>Department of Brain and Cognition, KU Leuven
+  </div>
+
+  <div style="display: flex; justify-content: center; gap: 10px;">
+    <a href="https://arxiv.org/abs/2604.07427"><img src="https://img.shields.io/badge/arXiv-paper-b31b1b.svg" alt="arXiv"></a>
+    <a href="https://pamela-bench.github.io"><img src="https://img.shields.io/badge/Project-Page-blue" alt="Project Page"></a>
+    <a href="https://github.com/PAMELA-bench/PAMELA_Predictor"><img src="https://img.shields.io/badge/Code-GitHub-black" alt="GitHub"></a>
+  </div>
+</div>
+
+---
+
+
+![group_examples_figure](https://cdn-uploads.huggingface.co/production/uploads/69736f9c416757acafd97a83/zn-RleThp589Ss5gbJ3Cd.png)
+
+PAM∃LA is a dataset of **AI-generated images** rated by **human participants** for aesthetic quality. It pairs each rating with rich participant demographics and image metadata, enabling research on **personalized aesthetic prediction**, demographic biases in visual preference, and aesthetic modeling for generative content.
+
+
+## Dataset Statistics
+
+| Split              | # ratings | Description                                |
+|--------------------|----------:|--------------------------------------------|
+| `pamela_train`     |    50,222 | Training set                               |
+| `pamela_val_unseen`|       926 | Val — users **not** seen in training       |
+| `pamela_val_seen`  |     6,551 | Val — seen users, unseen images            |
+| `pamela_test_unseen`|    2,470 | Test — users **not** seen in training      |
+| `pamela_test_seen` |     9,735 | Test — seen users, unseen images           |
+| **Total**          | **69,904**|                                            |
+
+**Splits ending in `_seen`** evaluate model performance on **known users rating new images**.
+**Splits ending in `_unseen`** evaluate generalization to **completely new users** — the harder, more realistic setting for personalization research.
+
+### Image content
+
+- **5,077** unique images
+- **21** visual groups: *abstract, animal, architecture, automotive, cinematic, cityscape, fashion, flower painting, food, landscape (art and photorealism), lifestyle, portrait (art and photorealism), product, scene, special, sports, still life, street, travel*
+- **26** art-historical styles applied to the **Art** subset (e.g., *Impressionism*, *Synthetic_Cubism*, *Art_Nouveau_Modern*, *Romanticism*, *Baroque*, *Cubism*, ...). **Photorealism** images carry no style label (`null`).
+- **2** image types: `Art` (26,237 ratings) and `Photorealism` (43,667 ratings)
+
+### Participants
+
+- **199** unique participants
+- Age range: **19–60**
+- Gender: male (40,525), female (29,086), prefer not to say (293)
+- **23** nationalities represented
+
+## Dataset Structure
+
+Each entry in the annotation JSON files has the following schema:
+
+```json
+{
+  "image_path": "./images/2f8e75f4-da55-4074-9328-052199f7cfb0.png",
+  "image_id": 1049071,
+  "participant_id": "p_5d2aeb40",
+  "image_metadata": {
+    "group": "landscape",
+    "style": "Art_Nouveau_Modern",
+    "category": "AI image",
+    "prompt": "A grand palace, rendered in muted greens and greys ...",
+    "type": "Art"
+  },
+  "user_demographics": {
+    "age": 28,
+    "age_binned": "twenties",
+    "gender": "male",
+    "nationality": "United States"
+  },
+  "quality_label": "good",
+  "original_score": 3.497
+}
+```
+
+### Field reference
+
+| Field | Type | Description |
+|---|---|---|
+| `image_path` | string | Relative path to the PNG file under `images/` |
+| `image_id` | int | Unique numeric image ID |
+| `participant_id` | string | Pseudonymous user ID (`p_*`) |
+| `image_metadata.group` | string | Visual subject category (one of 19) |
+| `image_metadata.style` | string | Art style label (one of 26) |
+| `image_metadata.category` | string | Always `"AI image"` |
+| `image_metadata.prompt` | string | Text prompt describing the image|
+| `image_metadata.type` | string | `"Art"` or `"Photorealism"` |
+| `user_demographics.age` | int | Participant age (19–60) |
+| `user_demographics.age_binned` | string | Decade bucket (e.g., `"twenties"`) |
+| `user_demographics.gender` | string | `"male"`, `"female"`, or `"prefer not to say"` |
+| `user_demographics.nationality` | string | Country of origin |
+| `quality_label` | string | One of: `poor`, `fair`, `good`, `great`, `excellent` |
+| `original_score` | float | Continuous aesthetic rating (1.0–5.0) |
+
+
+
+## Notes
+
+- The participant pool (199 users, 23 nationalities) is **not** demographically representative of the global population. Aggregated scores should not be treated as universal aesthetic judgments.
+- Subgroups defined by combinations of demographics (e.g., women aged 50+ from a specific country) may be very small. **Avoid claims about narrow subpopulations** without checking sample sizes.