| # Magic Bench: A Comprehensive Text-to-Image Generation Evaluation Dataset |
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| ## 📖 Overview |
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| 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. |
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| ## 🎯 Dataset Features |
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| - **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 |
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| ## 📊 Dataset Structure |
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| The dataset includes the following fields: |
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| | 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 | |
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| ## 🏷️ Annotation Dimensions |
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| ### 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 |
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| ### 2. Expression Form |
| - **Pronoun Reference**: Contains pronoun references |
| - **Negation**: Contains negative expressions |
| - **Consistency**: Requires consistent elements |
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| ### 3. Element Combination |
| - **Anti-Realism**: Anti-realistic combinations |
| - **Multi-Entity Feature Matching**: Complex multi-entity combinations |
| - **Layout & Typography**: Specific layout requirements |
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| ### 4. Entity Description |
| - **Attribute** : Attribute descriptions |
| - **Relation** : Relationship descriptions |
| - **Action/State** : Action or state descriptions |
| - **Quantity** : Quantity specifications |
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| ## 📁 Files |
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| - `magic_bench_dataset.csv`: Complete dataset |
| - `magic_bench_chinese.csv`: Chinese prompts with labels |
| - `magic_bench_english.csv`: English prompts with labels |
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| ## 🚀 Usage |
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| ```python |
| import pandas as pd |
| |
| # Load the complete dataset |
| df = pd.read_csv('magic_bench_dataset.csv') |
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| # Load Chinese version |
| df_cn = pd.read_csv('magic_bench_chinese.csv') |
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| # Load English version |
| df_en = pd.read_csv('magic_bench_english.csv') |
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| ``` |
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| ## 📈 Statistics |
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| - **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%) |
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| ## 🎯 Use Cases |
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| 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 |
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| ## 📄 Citation |
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| If you use this dataset in your research, please cite: |
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| ```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} |
| } |
| ``` |
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| ## 📜 License |
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| This dataset is released under the [cc-by-nc-4.0](LICENSE). |
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| ## 🤝 Contributing |
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| 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 |
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| ## 📞 Contact |
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| For questions or collaborations, please contact: outongtong.ott@bytedance.com |
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| --- |
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| **Keywords**: text-to-image, evaluation, benchmark, dataset, computer vision, AI, machine learning |
