Enhance dataset card for MagicData340K with comprehensive details, usage, and citation

This PR significantly enhances the dataset card for `MagicData340K`.

It includes:
- Added `task_categories` (`image-text-to-text`, `text-to-image`) and relevant `tags` to the metadata for better discoverability.
- A link to the project page: [https://wj-inf.github.io/MagicMirror-page/](https://wj-inf.github.io/MagicMirror-page/)
- An introductory description of the dataset and the MagicMirror framework, based on the paper abstract.
- An illustrative image from the GitHub repository.
- A clear "Sample Usage" section with a code snippet from the GitHub README for assessing T2I models.
- The BibTeX citation for the paper.

README.md

@@ -1,18 +1,48 @@
 ---
 license: mit
+task_categories:
+- image-text-to-text
+- text-to-image
+tags:
+- text-to-image
+- evaluation
+- artifacts
 ---
 
-Paper:
-https://arxiv.org/abs/2509.10260
+# MagicData340K: A Large-Scale Dataset for Fine-Grained Artifacts Assessment in Text-to-Image Generation
 
-MagicMirror: A Large-Scale Dataset and Benchmark for Fine-Grained Artifacts Assessment in Text-to-Image Generation
+This repository hosts **MagicData340K**, a large-scale human-annotated dataset central to the [MagicMirror framework](https://wj-inf.github.io/MagicMirror-page/). The MagicMirror framework introduces a comprehensive approach for the systematic and fine-grained evaluation of physical artifacts (such as anatomical and structural flaws) in Text-to-Image (T2I) generation.
 
+`MagicData340K` is the first human-annotated large-scale dataset, comprising 340,000 generated images, each with fine-grained artifact labels. These annotations are guided by a detailed taxonomy of generated image artifacts, making the dataset crucial for understanding and improving the perceptual quality of T2I models.
 
-Dataset:
-https://huggingface.co/datasets/wj-inf/MagicData340k
+**Paper**: [MagicMirror: A Large-Scale Dataset and Benchmark for Fine-Grained Artifacts Assessment in Text-to-Image Generation](https://arxiv.org/abs/2509.10260)
+**Project Page**: [https://wj-inf.github.io/MagicMirror-page/](https://wj-inf.github.io/MagicMirror-page/)
+**Code (MagicMirror Benchmark)**: [https://github.com/wj-inf/MagicMirror](https://github.com/wj-inf/MagicMirror)
 
-Model:
-https://huggingface.co/datasets/wj-inf/MagicAssessor-7B
+<p align="center"><img src="https://github.com/wj-inf/MagicMirror/blob/main/assets/output_example.png?raw=true" width="95%"></p>
 
-Benchmark:
-https://github.com/wj-inf/MagicMirror
+## Related Hugging Face Assets
+
+*   **Dataset (Self-reference)**: [wj-inf/MagicData340k](https://huggingface.co/datasets/wj-inf/MagicData340k)
+*   **Model (MagicAssessor VLM)**: [wj-inf/MagicAssessor-7B](https://huggingface.co/datasets/wj-inf/MagicAssessor-7B)
+
+## Sample Usage
+
+The MagicMirror framework, which utilizes this dataset, allows for the assessment of Text-to-Image (T2I) models. After setting up the environment as detailed in the [MagicMirror GitHub repository](https://github.com/wj-inf/MagicMirror), you can organize your image data (e.g., as `./output/sdxl/merged_result_sdxl.jsonl`) and run the assessment script:
+
+```bash
+bash run.sh flux-schnell sdxl
+```
+
+## Citation
+
+If you find MagicData340K or the MagicMirror framework useful for your research, please cite the paper:
+
+```bibtex
+@article{wang2025magicmirror,
+  title   = {MagicMirror: A Large-Scale Dataset and Benchmark for Fine-Grained Artifacts Assessment in Text-to-Image Generation},
+  author  = {Wang, Jia and Hu, Jie and Ma, Xiaoqi and Ma, Hanghang and Zeng, Yanbing and Wei, Xiaoming},
+  journal = {arXiv preprint arXiv:2509.10260},
+  year    = {2025}
+}
+```