Add Readme.md

README.md

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 ---
+pretty_name: Pick-a-Pic v2 · FiFA Filtered
+license: mit
 dataset_info:
 - config_name: FiFA-100k
   features:
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     path: FiFA-5k/train-*
   default: true
 ---
+
+# Pick-a-Pic v2 · **FiFA** Filtered Subsets
+
+These subsets were produced by filtering the original [Pick-a-Pic v2 dataset](https://arxiv.org/abs/2305.01569) using **FiFA**, a data filtering algorithm proposed in the paper [*Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models*](https://arxiv.org/abs/2410.10166).
+
+## Overview
+
+The filtering process is based on three key metrics:
+
+1. **Preference Margin**: Estimated using [PickScore](https://huggingface.co/yuvalkirstain/PickScore_v1)
+2. **Text Quality**: Estimated through LLM scoring
+3. **Text Diversity**: Estimated using K-NN distance
+
+## Dataset Configurations
+
+| Configuration | Size | Description |
+|---------------|------|-------------|
+| `FiFA-500` | 500 triplets | Small subset for quick experiments |
+| `FiFA-1k` | 1,000 triplets | Lightweight training set |
+| `FiFA-5k` | 5,000 triplets | Medium-sized training set |
+| `FiFA-10k` | 10,000 triplets | Standard training set |
+| `FiFA-20k` | 20,000 triplets | Large training set |
+| `FiFA-50k` | 50,000 triplets | Extended training set |
+| `FiFA-100k` | 100,000 triplets | Full-scale training set |
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+# Load a specific configuration
+dataset = load_dataset("Dragonjinny/FiFA-pickapic-v2", "FiFA-5k", split="train")
+
+# Access the data
+import io
+from PIL import Image
+
+for example in dataset:
+    caption = example["caption"]  # The prompt text
+    jpg_0 = example["jpg_0"]      # First image (bytes)
+    jpg_1 = example["jpg_1"]      # Second image (bytes)
+    label_0 = example["label_0"]  # Binary label (0 or 1) indicating which image is preferred
+    
+    # Convert bytes to PIL Images
+    image1 = Image.open(io.BytesIO(jpg_0)).convert("RGB")
+    image2 = Image.open(io.BytesIO(jpg_1)).convert("RGB")
+    
+    # Now you can work with the images
+    print(f"Caption: {caption}")
+    print(f"Preferred image: {'jpg_0' if label_0 == 1 else 'jpg_1'}")
+    # image1.show()  # Display the first image
+    # image2.show()  # Display the second image
+```
+
+## Data Format
+
+Each example contains:
+- `caption`: The prompt text
+- `jpg_0`: First image (bytes)
+- `jpg_1`: Second image (bytes)
+- `label_0`: Binary label (0 or 1) indicating which image is preferred
+
+## Citation
+
+If you use this dataset in your research, please cite our paper:
+
+```bibtex
+@inproceedings{
+yang2025automated,
+title={Automated Filtering of Human Feedback Data for Aligning Text-to-Image Diffusion Models},
+author={Yongjin Yang and Sihyeon Kim and Hojung Jung and Sangmin Bae and SangMook Kim and Se-Young Yun and Kimin Lee},
+booktitle={The Thirteenth International Conference on Learning Representations},
+year={2025},
+url={https://openreview.net/forum?id=8jvVNPHtVJ}
+}
+```
+
+## License
+
+This dataset is licensed under [MIT License](https://opensource.org/licenses/MIT), following the license of the original Pick-a-Pic v2 dataset.