README.md

# Magic Bench: A Comprehensive Text-to-Image Generation Evaluation Dataset

## 📖 Overview

Magic Bench is a comprehensive evaluation dataset designed for text-to-image generation models. It contains 377 carefully curated prompts with detailed annotations across multiple dimensions, providing both Chinese and English versions for cross-lingual evaluation.

## 🎯 Dataset Features

- **377 evaluation prompts** covering diverse scenarios
- **Bilingual support**: Both Chinese and English prompts
- **Multi-dimensional annotations**: 9 different evaluation dimensions
- **Comprehensive coverage**: Aesthetic design and artistic photography scenarios

## 📊 Dataset Structure

The dataset includes the following fields:

| Field | Description |
|-------|-------------|
| `prompt_text_cn`| Chinese version of the prompt |
| `prompt_text_en`| English version of the prompt |
| `Application Scenario`| The application context |
| `Expression Form`| Form of expression annotations |
| `Element Composition`| Element combination patterns |
| `Entity Description`| Entity description types |

## 🏷️ Annotation Dimensions

### 1. Application Scenario 
- **Aesthetic design**: Logo design, character design, product design, etc.
- **Art** : Photography, artistic creation, etc.
- **Entertainment** : Entertainment and personalized content
- **Film** : Film and storytelling scenarios
- **Functional design** : Efficiency and functional design

### 2. Expression Form 
- **Pronoun Reference**: Contains pronoun references
- **Negation**: Contains negative expressions
- **Consistency**: Requires consistent elements

### 3. Element Combination 
- **Anti-Realism**: Anti-realistic combinations
- **Multi-Entity Feature Matching**: Complex multi-entity combinations
- **Layout & Typography**: Specific layout requirements

### 4. Entity Description 
- **Attribute** : Attribute descriptions
- **Relation** : Relationship descriptions
- **Action/State** : Action or state descriptions
- **Quantity** : Quantity specifications

## 📁 Files

- `magic_bench_dataset.csv`: Complete dataset
- `magic_bench_chinese.csv`: Chinese prompts with labels
- `magic_bench_english.csv`: English prompts with labels

## 🚀 Usage

```python
import pandas as pd

# Load the complete dataset
df = pd.read_csv('magic_bench_dataset.csv')

# Load Chinese version
df_cn = pd.read_csv('magic_bench_chinese.csv')

# Load English version
df_en = pd.read_csv('magic_bench_english.csv')


```

## 📈 Statistics

- **Total prompts**: 377
- **Aesthetic design prompts**: 95 (25.2%)
- **Art prompts**: 80 (21.2%)
- **Prompts with style specifications**: 241 (63.9%)
- **Prompts requiring aesthetic knowledge**: 131 (34.7%)
- **Prompts with atmospheric elements**: 22 (5.8%)

## 🎯 Use Cases

1. **Model Evaluation**: Comprehensive evaluation of text-to-image models
2. **Benchmark Comparison**: Compare different models across various dimensions
3. **Research**: Study model capabilities in different scenarios
4. **Fine-tuning**: Use as training or validation data for model improvement

## 📄 Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{magic_bench_377,
  title={Magic Bench: A Comprehensive Text-to-Image Generation Evaluation Dataset},
  author={outongtong},
  year={2025},
  email={outongtong.ott@bytedance.com},
  url={https://huggingface.co/datasets/ByteDance-Seed/MagicBench}
}
```

## 📜 License

This dataset is released under the [cc-by-nc-4.0](LICENSE).

## 🤝 Contributing

We welcome contributions to improve the dataset. Please feel free to:
- Report issues or suggest improvements
- Submit pull requests with enhancements
- Share your evaluation results using this dataset

## 📞 Contact

For questions or collaborations, please contact: outongtong.ott@bytedance.com

---

**Keywords**: text-to-image, evaluation, benchmark, dataset, computer vision, AI, machine learning