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---
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license: apache-2.0
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dataset_info:
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features:
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- name: image
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dtype: binary
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- name: internal_id
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dtype: string
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- name: url
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dtype: string
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- name: annotation
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struct:
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- name: symmetry
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dtype: int64
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- name: richness
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dtype: int64
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- name: color aesthetic
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dtype: int64
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- name: detail realism
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dtype: int64
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- name: safety
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dtype: int64
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- name: body
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dtype: int64
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- name: lighting aesthetic
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dtype: int64
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- name: lighting distinction
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dtype: int64
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- name: background
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dtype: int64
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- name: emotion
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dtype: int64
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- name: main object
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dtype: int64
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- name: color brightness
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dtype: int64
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- name: face
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dtype: int64
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- name: hands
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dtype: int64
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- name: clarity
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dtype: int64
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- name: detail refinement
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dtype: int64
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- name: unsafe type
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dtype: int64
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- name: object pairing
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dtype: int64
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- name: meta_result
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dtype: sequence[int64]
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- name: meta_mask
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dtype: sequence[int64]
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splits:
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- name: train
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num_examples: 40743
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---
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# VRDB-Image
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This dataset contains aesthetic annotations for images. The annotations cover 18 aspects of visual aesthetics and quality assessment.
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## Annotation Details
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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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- **1:** Rich
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- **0:** Normal
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- **-1:** Monotonous
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- **-2:** Empty
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For more detailed annotation guidelines, please refer to:
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- [annotation_deatils](https://flame-spaghetti-eb9.notion.site/VisionReward-Image-Annotation-Details-196a0162280e80ef8359c38e9e41247e)
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- [annotation_deatils_ch](https://flame-spaghetti-eb9.notion.site/VisionReward-Image-195a0162280e8044bcb4ec48d000409c)
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## Additional Feature Details
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The dataset includes two special features: `annotation` and `meta_result`.
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### Annotation
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The `annotation` feature contains scores across 18 different dimensions of image assessment, with each dimension having its own scoring criteria as detailed above.
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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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### Meta Mask
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The `meta_mask` feature is used for balanced sampling during model training:
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- Elements with value 1 indicate that the corresponding binary judgment was used in training
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- Elements with value 0 indicate that the corresponding binary judgment was ignored during training
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## Data Processing
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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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``` |
