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README.md

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-license: cc-by-nc-4.0
-dataset_info:
-  features:
-  - name: index
-    dtype: int64
-  - name: image
-    dtype: image
-  - name: size
-    dtype: int64
-  - name: category
-    dtype: string
-  - name: class_id
-    dtype: string
-  - name: model
-    dtype: string
-  - name: gen_type
-    dtype: string
-  - name: reference
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 5879033389.852
-    num_examples: 540258
-  download_size: 46404626455
-  dataset_size: 5879033389.852
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
----
+---
+license: cc-by-nc-4.0
+---
+
+# DANI: Discrepancy Accessing for Natural and AI Images
+
+**A Large-Scale Dataset for Visual Research on AI-Synthesized and Natural Images**
+
+## Overview
+
+DANI (Discrepancy Accessing for Natural and AI Images) is a large-scale, multimodal dataset for benchmarking and broad visual research on both AI-generated images (AIGIs) and natural images.  
+The dataset is designed to support a wide range of computer vision and multimodal research tasks, including but not limited to:
+- AI-generated vs. real image discrimination
+- Representation learning
+- Image quality assessment
+- Style transfer
+- Image reconstruction
+- Domain adaptation
+- Multimodal understanding and beyond
+
+DANI accompanies the paper:
+
+> Liu, Renyang; Lyu, Ziyu; Zhou, Wei; Ng, See-Kiong.  
+> *D-Judge: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance.*  
+> ACM International Conference on Multimedia (MM), 2025.
+
+## Dataset Summary
+
+DANI contains over **445,000 images**, including 5,000 natural images (from COCO, with resolutions 224, 256, 512, 1024) and more than 440,000 AI-generated images produced by diverse state-of-the-art generative models.  
+Each sample is annotated with detailed metadata, enabling comprehensive evaluation and flexible use for a broad range of visual and multimodal research.
+Images are generated using a wide range of generative models and protocols:
+
+- **Models:** GALIP, DFGAN, SD_V14, SD_V15, Versatile Diffusion (VD), SD_V21, SD_XL, Dalle2, Dalle3, and COCO (real images)
+- **Image Sizes:** 224, 256, 512, 768, 1024
+- **Generation Types:** Text-to-Image (T2I), Image-to-Image (I2I), Text and Image-to-Image (TI2I)
+- **Categories:** indoor, outdoor, etc.
+
+## Data Fields
+
+Each sample in the dataset contains the following fields:
+
+| Field      | Description                                                                  |
+|------------|------------------------------------------------------------------------------|
+| index      | Unique index for each image                                                  |
+| image      | The image itself (as a file, not just path)                                  |
+| size       | Image resolution (e.g., 224, 256, 512, 768, 1024)                            |
+| category   | Scene category (e.g., `indoor`, `outdoor`, etc.)                             |
+| class_id   | COCO class or semantic category ID/name                                      |
+| model      | Generative model used (`GALIP`, `DFGAN`, `SD_V14`, `SD_V15`, `VD`, etc.)     |
+| gen_type   | Generation method (`T2I`, `I2I`, `TI2I`)                                     |
+| reference  | Whether it is a real/natural image (`True` for real, `False` for generated)  |
+
+> *Note:*  
+> - **COCO** images have `reference=True`, and may appear at multiple resolutions.  
+> - For AI-generated images, the `model` and `gen_type` fields indicate the specific generative model and generation protocol (T2I, I2I, or TI2I) used for each sample.
+
+## Model/Generation Configurations
+
+The dataset covers the following models and settings:
+
+| Model    | Image Size | Generation Types Supported            |
+|----------|------------|---------------------------------------|
+| GALIP    | 224        | T2I                                   |
+| DFGAN    | 256        | T2I                                   |
+| SD_V14   | 512        | T2I, I2I, TI2I                        |
+| SD_V15   | 512        | T2I, I2I, TI2I                        |
+| VD       | 512        | T2I, I2I, TI2I                        |
+| SD_V21   | 768        | T2I, I2I, TI2I                        |
+| SD_XL    | 1024       | T2I, I2I, TI2I                        |
+| Dalle2   | 512        | T2I, I2I                              |
+| Dalle3   | 1024       | T2I                                   |
+| COCO     | 224,256,512,1024 | Reference/Real Images           |
+
+For each generation type (`T2I`, `I2I`, `TI2I`), a diverse set of models are covered.
+
+## Usage
+
+You can load DANI directly using the 🤗 datasets library:
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("Renyang/DANI")
+print(ds)
+# Output: DatasetDict({
+#     train: Dataset({
+#         features: ['index', 'image', 'size', 'category', 'class_id','model', 'gen_type','reference'],
+#         num_rows: 540257
+#     })
+# })
+# Access images and metadata
+img = ds["train"][0]["image"]
+meta = {k: ds["train"][0][k] for k in ds["train"].column_names if k != "image"}
+
+```
+
+*Note:* Images are loaded as PIL Images. Use `.convert("RGB")` if needed.
+
+## Citation
+
+If you use this dataset or the associated benchmark, please cite:
+
+```bibtex
+@inproceedings{liu2024djudge,
+  title = {D-Judge: How Far Are We? Evaluating the Discrepancies Between AI-synthesized Images and Natural Images through Multimodal Guidance},
+  author = {Liu, Renyang and Lyu, Ziyu and Zhou, Wei and Ng, See-Kiong},
+  booktitle = {ACM International Conference on Multimedia (MM)},
+  organization = {ACM},
+  year = {2025},
+}
+```
+
+## License
+
+This dataset is released under the [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) license (for non-commercial research use).
+
+## Contact
+
+For questions or collaborations, please visit [Renyang Liu's homepage](https://ryliu68.github.io/).