Update README.md

README.md

@@ -1,3 +1,115 @@
 ---
 license: mit
+task_categories:
+- image-to-text
+- text-to-image
+language:
+- en
 ---
+# UniCTokens Dataset
+
+*Version · 2025-10-24*
+
+
+## 1 Data Overview
+
+| Item                   | Description                                                                            |
+| ---------------------- | -------------------------------------------------------------------------------------- |
+| **Total concepts**     | 20 (Human × 10 · Animal × 5 · Object × 5)                                              |
+| **Images per concept** | **N ≈ 10 – 15** (already split into *train* / *test*)                                  |
+| **Negative samples**   | `random_images/` (100 random irrelevant images) + `negative_example/` (hard negatives) |
+
+
+## 2 Benchmark Tasks
+
+### 2.1 MMU (Multi-Modal Understanding)
+
+| Sub-task         | Source files                      | Evaluation focus                                                 |
+| ---------------- | --------------------------------- | ---------------------------------------------------------------- |
+| **Text-Only QA** | `test/<concept>/text_only.json`   | Check whether the model remembers concept knowledge (no image) |
+| **VQA**          | `test/<concept>/vqa.json` + image | Visual question answering about the concept image                |
+| **Rec**          | `test/*.png`            | Pure visual recognition capability                               |
+
+### 2.2 T2I (Text-to-Image Generation)
+
+| Mode                              | Input                                                           | Metrics                                                           |
+| --------------------------------- | --------------------------------------------------------------- | ----------------------------------------------------------------- |
+| **Vanilla generation**            | Prompts from the DreamBooth Dataset → target-concept images | CLIP-I / CLIP-T · ArcFace similarity                              |
+| **Personalized knowledge-driven** | `t2i_conditions.json`                                           | Combined T2I-Score: must satisfy both visual & textual attributes |
+
+
+## 3 Directory Structure
+
+```text
+UniCTokens/
+├── black_512x512.png          # Pure black placeholder
+├── concepts_list.json         # List of 20 concept names
+├── template.json              # Template for generating training data
+├── random_images/             # 100 simple negative samples for training
+│   ├── 0.png
+│   └── … 99.png
+├── concept/                   # 🔑 Concept data (train / test)
+│   ├── train/
+│   │   └── <concept_name>/    # 20 folders
+│   │       ├── 0.png … N.png          # Original training images
+│   │       ├── cropped/               # Cropped regions
+│   │       ├── info.json              # Concept profile & extra info
+│   │       ├── conversations.json     # Training dialogues
+│   │       ├── positive_recognitions.json  # Positive QA pairs
+│   │       ├── random_recognitions.json    # Negative QA pairs
+│   │       └── negative_example/      # Hard negatives + score.json
+│   └── test/
+│       └── <concept_name>/
+│           ├── 0.png … 4.png
+│           ├── text_only.json         # Text-only QA
+│           ├── vqa.json               # VQA pairs
+│           └── t2i_conditions.json    # Conditions for knowledge-driven T2I
+├── gen_showo_training_data.py   # Script to create Stage-1/2/3 training files
+├── gen_test_data.py             # Script to create all evaluation files
+└── README.md
+```
+
+
+## 4 Quick Start
+
+1. **Set the dataset root**
+   Open `gen_showo_training_data.py` and `gen_test_data.py`, change
+
+   ```python
+   DATA_ROOT = "/path/to/UniCTokens_Dataset"
+   ```
+
+   to the actual dataset path.
+
+2. **Generate data**
+
+   ```bash
+   # Create Stage-1/2/3 training samples
+   python gen_showo_training_data.py
+
+   # Create MMU & T2I evaluation samples
+   python gen_test_data.py
+   ```
+
+
+## 5 License
+
+The dataset is released under **CC-BY-NC 4.0** and is intended for academic research **only**. Commercial use is not permitted.
+
+
+## 6 Citation
+
+```bibtex
+@article{an2025unictokens,
+  title={UniCTokens: Boosting Personalized Understanding and Generation via Unified Concept Tokens},
+  author={An, Ruichuan and Yang, Sihan and Zhang, Renrui and Shen, Zijun and Lu, Ming and Dai, Gaole and Liang, Hao and Guo, Ziyu and Yan, Shilin and Luo, Yulin and others},
+  journal={arXiv preprint arXiv:2505.14671},
+  year={2025}
+}
+```
+
+
+## 7 Contact
+
+* GitHub Issues: [https://github.com/arctanxarc/UniCTokens/issues](https://github.com/arctanxarc/UniCTokens/issues)
+* Email: [[email protected]](mailto:[email protected])