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@@ -65,24 +65,67 @@ This dataset contains aesthetic annotations for images. The annotations cover 18
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  Each image in the dataset is annotated with the following attributes:
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- 1. **Overall Symmetry (adjective)**
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- 2. **Object Composition (collocation)**
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- 3. **Main Object Position (place)**
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- 4. **Scene Richness (richness)**
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- 5. **Background Quality (background)**
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- 6. **Overall Clarity (sharpness)**
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- 7. **Brightness (color)**
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- 8. **Color Aesthetics (color_aes)**
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- 9. **Environmental Light and Shadow Prominence (shadow_degree)**
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- 10. **Light and Shadow Aesthetics (shadow_aes)**
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- 11. **Emotional Response (emotion)**
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- 12. **Detail Refinement (detail_fineness)**
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- 13. **Detail Authenticity (detail_facticity)**
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- 14. **Human Body Accuracy (body_correctness)**
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- 15. **Face Quality (face)**
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- 16. **Hand Quality (hand)**
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- 17. **Safety Rating (safe)**
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- 18. **Harm Type (harm)**
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Example: Scene Richness (richness)
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  - **2:** Very rich
@@ -105,11 +148,13 @@ The `annotation` feature contains scores across 18 different dimensions of image
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  ### Meta Result
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  The `meta_result` feature transforms multi-choice questions into a series of binary judgments. For example, for the `richness` dimension:
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- - Score 2 (Very rich) corresponds to [1,1,1,1]
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- - Score 1 (Rich) corresponds to [0,1,1,1]
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- - Score 0 (Normal) corresponds to [0,0,1,1]
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- - Score -1 (Monotonous) corresponds to [0,0,0,1]
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- - Score -2 (Empty) corresponds to [0,0,0,0]
 
 
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  Each element in the binary array represents a yes/no answer to a specific aspect of the assessment. For detailed questions corresponding to these binary judgments, please refer to the `meta_qa_en.txt` file.
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@@ -123,4 +168,5 @@ The `meta_mask` feature is used for balanced sampling during model training:
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  We provide `extract.py` for processing the dataset into JSONL format. The script can optionally extract the balanced positive/negative QA pairs used in VisionReward training by processing `meta_result` and `meta_mask` fields.
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  ```bash
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- python extract.py [--save_imgs] [--process_qa]
 
 
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  Each image in the dataset is annotated with the following attributes:
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+ <table border="1" style="border-collapse: collapse;">
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+ <tr>
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+ <th>Dimension</th>
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+ <th>Attribute</th>
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+ </tr>
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+ <tr>
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+ <td rowspan="5">Composition</td>
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+ <td>Symmetry</td>
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+ </tr>
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+ <tr>
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+ <td>Object pairing</td>
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+ </tr>
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+ <tr>
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+ <td>Main object</td>
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+ </tr>
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+ <tr>
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+ <td>Richness</td>
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+ </tr>
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+ <tr>
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+ <td>Background</td>
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+ </tr>
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+ <tr>
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+ <td rowspan="5">Quality</td>
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+ <td>Clarity</td>
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+ </tr>
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+ <tr>
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+ <td>Color Brightness</td>
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+ </tr>
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+ <tr>
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+ <td>Color Aesthetic</td>
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+ </tr>
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+ <tr>
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+ <td>Lighting Distinction</td>
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+ </tr>
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+ <tr>
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+ <td>Lighting Aesthetic</td>
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+ </tr>
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+ <tr>
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+ <td rowspan="5">Fidelity</td>
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+ <td>Detail realism</td>
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+ </tr>
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+ <tr>
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+ <td>Detail refinement</td>
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+ </tr>
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+ <tr>
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+ <td>Body</td>
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+ </tr>
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+ <tr>
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+ <td>Face</td>
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+ </tr>
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+ <tr>
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+ <td>Hands</td>
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+ </tr>
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+ <tr>
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+ <td rowspan="2">Safety & Emotion</td>
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+ <td>Emotion</td>
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+ </tr>
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+ <tr>
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+ <td>Safety</td>
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+ </tr>
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+ </table>
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  ### Example: Scene Richness (richness)
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  - **2:** Very rich
 
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  ### Meta Result
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  The `meta_result` feature transforms multi-choice questions into a series of binary judgments. For example, for the `richness` dimension:
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+ | Score | Is the image very rich? | Is the image rich? | Is the image not monotonous? | Is the image not empty? |
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+ |-------|------------------------|-------------------|---------------------------|----------------------|
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+ | 2 | 1 | 1 | 1 | 1 |
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+ | 1 | 0 | 1 | 1 | 1 |
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+ | 0 | 0 | 0 | 1 | 1 |
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+ | -1 | 0 | 0 | 0 | 1 |
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+ | -2 | 0 | 0 | 0 | 0 |
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  Each element in the binary array represents a yes/no answer to a specific aspect of the assessment. For detailed questions corresponding to these binary judgments, please refer to the `meta_qa_en.txt` file.
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  We provide `extract.py` for processing the dataset into JSONL format. The script can optionally extract the balanced positive/negative QA pairs used in VisionReward training by processing `meta_result` and `meta_mask` fields.
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  ```bash
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+ python extract.py [--save_imgs] [--process_qa]
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+ ```