README.md

metadata

license: apache-2.0
dataset_info:
  features:
    - name: image
      dtype: image
    - name: internal_id
      dtype: string
    - name: prompt
      dtype: string
    - name: url
      dtype: string
    - name: annotation
      struct:
        - name: symmetry
          dtype: int64
          range:
            - -1
            - 1
        - name: richness
          dtype: int64
          range:
            - -2
            - 2
        - name: color aesthetic
          dtype: int64
          range:
            - -1
            - 1
        - name: detail realism
          dtype: int64
          range:
            - -3
            - 1
        - name: safety
          dtype: int64
          range:
            - -3
            - 1
        - name: body
          dtype: int64
          range:
            - -4
            - 1
        - name: lighting aesthetic
          dtype: int64
          range:
            - -1
            - 2
        - name: lighting distinction
          dtype: int64
          range:
            - -1
            - 2
        - name: background
          dtype: int64
          range:
            - -1
            - 2
        - name: emotion
          dtype: int64
          range:
            - -2
            - 2
        - name: main object
          dtype: int64
          range:
            - -1
            - 1
        - name: color brightness
          dtype: int64
          range:
            - -1
            - 1
        - name: face
          dtype: int64
          range:
            - -3
            - 2
        - name: hands
          dtype: int64
          range:
            - -4
            - 1
        - name: clarity
          dtype: int64
          range:
            - -2
            - 2
        - name: detail refinement
          dtype: int64
          range:
            - -4
            - 2
        - name: unsafe type
          dtype: int64
          range:
            - 0
            - 3
        - name: object pairing
          dtype: int64
          range:
            - -1
            - 1
    - name: meta_result
      sequence:
        dtype: int64
    - name: meta_mask
      sequence:
        dtype: int64
  config_name: default
  splits:
    - name: train
      num_examples: 40743

VisionRewardDB-Image

Introduction

VisionRewardDB-Image is a comprehensive dataset designed to train VisionReward-Image models, providing detailed aesthetic annotations across 18 aspects. The dataset aims to enhance the assessment and understanding of visual aesthetics and quality. 🌟✨

For more detail, please refer to the Github Repository. 🔍📚

Annotation Detail

Each image in the dataset is annotated with the following attributes:

Dimension	Attributes
Composition	Symmetry; Object pairing; Main object; Richness; Background
Quality	Clarity; Color Brightness; Color Aesthetic; Lighting Distinction; Lighting Aesthetic
Fidelity	Detail realism; Detail refinement; Body; Face; Hands
Safety & Emotion	Emotion; Safety

Example: Scene Richness (richness)

• 2: Very rich
• 1: Rich
• 0: Normal
• -1: Monotonous
• -2: Empty

For more detailed annotation guidelines(such as the meanings of different scores and annotation rules), please refer to:

• annotation_deatil
• annotation_deatil_zh

Additional Feature Detail

The dataset includes two special features: annotation and meta_result.

Annotation

The annotation feature contains scores across 18 different dimensions of image assessment, with each dimension having its own scoring criteria as detailed above.

Meta Result

The meta_result feature transforms multi-choice questions into a series of binary judgments. For example, for the richness dimension:

Score	Is the image very rich?	Is the image rich?	Is the image not monotonous?	Is the image not empty?
2	1	1	1	1
1	0	1	1	1
0	0	0	1	1
-1	0	0	0	1
-2	0	0	0	0

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.

Meta Mask

The meta_mask feature is used for balanced sampling during model training:

• Elements with value 1 indicate that the corresponding binary judgment was used in training
• Elements with value 0 indicate that the corresponding binary judgment was ignored during training

Data Processing

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.

python extract.py [--save_imgs] [--process_qa]

Citation Information

@misc{xu2024visionrewardfinegrainedmultidimensionalhuman,
      title={VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation}, 
      author={Jiazheng Xu and Yu Huang and Jiale Cheng and Yuanming Yang and Jiajun Xu and Yuan Wang and Wenbo Duan and Shen Yang and Qunlin Jin and Shurun Li and Jiayan Teng and Zhuoyi Yang and Wendi Zheng and Xiao Liu and Ming Ding and Xiaohan Zhang and Xiaotao Gu and Shiyu Huang and Minlie Huang and Jie Tang and Yuxiao Dong},
      year={2024},
      eprint={2412.21059},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.21059}, 
}