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README.md

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-# 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 |
-| `应用场景` (Application Scenario) | The application context |
-| `表达形式` (Expression Form) | Form of expression annotations |
-| `要素组合` (Element Combination) | Element combination patterns |
-| `实体描述` (Entity Description) | Entity description types |
-| `是否有风格` (Has Style) | Whether the prompt includes style specifications |
-| `是否有美学知识` (Has Aesthetic Knowledge) | Whether aesthetic knowledge is required |
-| `是否有氛围` (Has Atmosphere) | Whether atmospheric elements are present |
-
-## 🏷️ Annotation Dimensions
-
-### 1. Application Scenario (应用场景)
-- **创意设计** (Aesthetic design): Logo design, character design, product design, etc.
-- **艺术** (art): Photography, artistic creation, etc.
-- **个性化娱乐** (entertainment): Entertainment and personalized content
-- **影视与故事** (film): Film and storytelling scenarios
-- **效率提升** (functional design): Efficiency and functional design
-
-### 2. Expression Form (表达形式)
-- **无** (None): No specific form requirements
-- **代词指代** (Pronoun Reference): Contains pronoun references
-- **否定** (Negation): Contains negative expressions
-- **统一性** (consistency): Requires consistent elements
-
-### 3. Element Combination (要素组合)
-- **无** (None): Single element
-- **反现实** (Anti-Realism): Anti-realistic combinations
-- **多实体多要素** (Multi-Entity Feature Matching): Complex multi-entity combinations
-- **布局与排版** (Layout & Typography): Specific layout requirements
-
-### 4. Entity Description (实体描述)
-- **无** (None): No specific entity descriptions
-- **属性** (attribute): Attribute descriptions
-- **关系** (relation): Relationship descriptions
-- **动作状态** (Action/State): Action or state descriptions
-- **数量** (quantity): Quantity specifications
-
-### 5. Style Specification (是否有风格)
-- **是** (Yes): Contains specific style requirements
-- **否** (No): No style specifications
-
-### 6. Aesthetic Knowledge (是否有美学知识)
-- **是** (Yes): Requires aesthetic understanding
-- **否** (No): No aesthetic knowledge required
-
-### 7. Atmospheric Elements (是否有氛围)
-- **是** (Yes): Contains atmospheric descriptions
-- **否** (No): No atmospheric elements
-
-## 📁 Files
-
-- `magic_bench_dataset.csv`: Complete dataset with all annotations
-- `magic_bench_chinese.csv`: Chinese prompts with annotations
-- `magic_bench_english.csv`: English prompts with annotations
-
-## 🚀 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')
-
-# Example: Filter prompts with style requirements
-stylized_prompts = df[df['是否有风格'] == '是']
-
-# Example: Get aesthetic design prompts
-aesthetic_prompts = df[df['应用场景'] == '创意设计']
-```
-
-## 📈 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-377}
-}
-```
-
-## 📜 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
-
----
-
+# 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 |
+| `应用场景` (Application Scenario) | The application context |
+| `表达形式` (Expression Form) | Form of expression annotations |
+| `要素组合` (Element Combination) | Element combination patterns |
+| `实体描述` (Entity Description) | Entity description types |
+| `是否有风格` (Has Style) | Whether the prompt includes style specifications |
+| `是否有美学知识` (Has Aesthetic Knowledge) | Whether aesthetic knowledge is required |
+| `是否有氛围` (Has Atmosphere) | Whether atmospheric elements are present |
+
+## 🏷️ Annotation Dimensions
+
+### 1. Application Scenario (应用场景)
+- **创意设计** (Aesthetic design): Logo design, character design, product design, etc.
+- **艺术** (art): Photography, artistic creation, etc.
+- **个性化娱乐** (entertainment): Entertainment and personalized content
+- **影视与故事** (film): Film and storytelling scenarios
+- **效率提升** (functional design): Efficiency and functional design
+
+### 2. Expression Form (表达形式)
+- **无** (None): No specific form requirements
+- **代词指代** (Pronoun Reference): Contains pronoun references
+- **否定** (Negation): Contains negative expressions
+- **统一性** (consistency): Requires consistent elements
+
+### 3. Element Combination (要素组合)
+- **无** (None): Single element
+- **反现实** (Anti-Realism): Anti-realistic combinations
+- **多实体多要素** (Multi-Entity Feature Matching): Complex multi-entity combinations
+- **布局与排版** (Layout & Typography): Specific layout requirements
+
+### 4. Entity Description (实体描述)
+- **无** (None): No specific entity descriptions
+- **属性** (attribute): Attribute descriptions
+- **关系** (relation): Relationship descriptions
+- **动作状态** (Action/State): Action or state descriptions
+- **数量** (quantity): Quantity specifications
+
+### 5. Style Specification (是否有风格)
+- **是** (Yes): Contains specific style requirements
+- **否** (No): No style specifications
+
+### 6. Aesthetic Knowledge (是否有美学知识)
+- **是** (Yes): Requires aesthetic understanding
+- **否** (No): No aesthetic knowledge required
+
+### 7. Atmospheric Elements (是否有氛围)
+- **是** (Yes): Contains atmospheric descriptions
+- **否** (No): No atmospheric elements
+
+## 📁 Files
+
+- `magic_bench_dataset.csv`: Complete dataset with all annotations
+- `magic_bench_chinese.csv`: Chinese prompts with annotations
+- `magic_bench_english.csv`: English prompts with annotations
+
+## 🚀 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')
+
+# Example: Filter prompts with style requirements
+stylized_prompts = df[df['是否有风格'] == '是']
+
+# Example: Get aesthetic design prompts
+aesthetic_prompts = df[df['应用场景'] == '创意设计']
+```
+
+## 📈 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