upload data
Browse files- .gitattributes +3 -0
- README.md +207 -0
- annotation.xlsx +3 -0
- annotation_ch.xlsx +3 -0
- data/train-00000-of-00010.parquet +3 -0
- data/train-00001-of-00010.parquet +3 -0
- data/train-00002-of-00010.parquet +3 -0
- data/train-00003-of-00010.parquet +3 -0
- data/train-00004-of-00010.parquet +3 -0
- data/train-00005-of-00010.parquet +3 -0
- data/train-00006-of-00010.parquet +3 -0
- data/train-00007-of-00010.parquet +3 -0
- data/train-00008-of-00010.parquet +3 -0
- data/train-00009-of-00010.parquet +3 -0
- meta_qa_en.txt +59 -0
.gitattributes
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# Video files - compressed
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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*.webm filter=lfs diff=lfs merge=lfs -text
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# Video files - compressed
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*.mp4 filter=lfs diff=lfs merge=lfs -text
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*.webm filter=lfs diff=lfs merge=lfs -text
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*.xlsx filter=lfs diff=lfs merge=lfs -text
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annotation.xlsx filter=lfs diff=lfs merge=lfs -text
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annotation_ch.xlsx filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: apache-2.0
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---
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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 # Binary image data
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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: adjective
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dtype: int64
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- name: richness
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dtype: int64
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- name: color_aes
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dtype: int64
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- name: detail_facticity
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dtype: int64
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- name: safe
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dtype: int64
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- name: body_correctness
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dtype: int64
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- name: shadow_aes
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dtype: int64
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- name: shadow_degree
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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: place
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dtype: int64
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- name: color
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dtype: int64
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- name: face
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dtype: int64
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- name: hand
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dtype: int64
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- name: sharpness
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dtype: int64
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- name: detail_fineness
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dtype: int64
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- name: harm
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dtype: int64
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- name: collocation
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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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For more detailed annotation guidelines, please refer to:
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- annotation_ch.xlsx(Chinese)
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- annotation.xlsx(English)
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<!-- - [English Documentation (Google Docs)](your_google_docs_link_here) -->
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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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- 1: Symmetric
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- 0: Normal
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- -1: Asymmetric
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### 2. Object Composition (collocation)
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- 1: Harmonious
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- 0: Normal
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- -1: Disharmonious
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### 3. Main Object Position (place)
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- 1: Prominent
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- 0: Normal
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- -1: Not prominent
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### 4. 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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### 5. Background Quality (background)
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- 2: Beautiful
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- 1: Somewhat beautiful
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- 0: Normal
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- -1: No background
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### 6. Overall Clarity (sharpness)
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- 2: Very clear
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- 1: Clear
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- 0: Normal
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- -1: Blurry
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- -2: Completely blurry
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### 7. Brightness (color)
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- 1: Bright
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- 0: Normal
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- -1: Dark
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### 8. Color Aesthetics (color_aes)
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- 1: Beautiful colors
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- 0: Normal colors
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- -1: Ugly colors
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### 9. Environmental Light and Shadow Prominence (shadow_degree)
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- 2: Very prominent
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- 1: Prominent
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- 0: Normal
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- -1: No light and shadow
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### 10. Light and Shadow Aesthetics (shadow_aes)
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- 2: Very beautiful
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- 1: Beautiful
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- 0: Normal
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- -1: No light and shadow
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### 11. Emotional Response (emotion)
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- 2: Very positive
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- 1: Positive
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- 0: Normal
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- -1: Negative
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- -2: Very negative
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### 12. Detail Refinement (detail_fineness)
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- 2: Very refined
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- 1: Refined
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- 0: Normal
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- -1: Rough
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- -2: Very rough
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- -3: Hard to recognize
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- -4: Fragmented
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### 13. Detail Authenticity (detail_facticity)
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- 1: Authentic
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- 0: Neutral
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- -1: Inauthentic
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- -2: Very inauthentic
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- -3: Severely inauthentic
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### 14. Human Body Accuracy (body_correctness)
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- 1: No errors
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- 0: Neutral
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- -1: Has errors
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- -2: Has obvious errors
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- -3: Has severe errors
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- -4: No human body
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### 15. Face Quality (face)
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- 2: Very beautiful
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- 1: Beautiful
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- 0: Normal
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- -1: Has errors
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- -2: Has severe errors
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- -3: No face
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### 16. Hand Quality (hand)
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- 1: Perfect
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- 0: Basically correct
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- -1: Minor errors
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- -2: Obvious errors
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- -3: Severe errors
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- -4: No hands
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### 17. Safety Rating (safe)
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- 1: Safe
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- 0: Neutral
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- -1: Potentially harmful
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- -2: Harmful
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- -3: Very harmful
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### 18. Harm Type (harm)
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- 3: Adult content
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- 2: Horror
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- 1: Other
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- 0: Harmless
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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 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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### 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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annotation.xlsx
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annotation_ch.xlsx
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meta_qa_en.txt
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Is the image symmetrical?
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Does the image avoid asymmetry?
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Are the objects well-coordinated?
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Does the image avoid poorly coordinated objects?
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Is the main subject prominent?
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Does the image avoid an unclear main subject?
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Is the image very rich?
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Is the image rich?
|
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Is the image not monotonous?
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Is the image not empty?
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Is the background beautiful?
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Is the background somewhat beautiful?
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Is there a background?
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14 |
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Is the image very clear?
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15 |
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Is the image clear?
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16 |
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Does the image avoid being blurry?
|
17 |
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Does the image avoid being completely blurry?
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18 |
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Are the colors bright?
|
19 |
+
Are the colors not dark?
|
20 |
+
Are the colors beautiful?
|
21 |
+
Are the colors not ugly?
|
22 |
+
Is the lighting and shadow very distinct?
|
23 |
+
Is the lighting and shadow distinct?
|
24 |
+
Is there lighting and shadow?
|
25 |
+
Are the lighting and shadows very beautiful?
|
26 |
+
Are the lighting and shadows beautiful?
|
27 |
+
Can the image evoke a very positive emotional response?
|
28 |
+
Can the image evoke a positive emotional response?
|
29 |
+
Does the image avoid evoking a negative emotional response?
|
30 |
+
Does the image avoid evoking a very negative emotional response?
|
31 |
+
Are the image details very exquisite?
|
32 |
+
Are the image details exquisite?
|
33 |
+
Do the image details avoid being coarse?
|
34 |
+
Do the image details avoid being very coarse?
|
35 |
+
Does the image avoid being hard to recognize?
|
36 |
+
Does the image avoid being fragmented?
|
37 |
+
Are the image details realistic?
|
38 |
+
Do the image details avoid being unrealistic?
|
39 |
+
Do the image details avoid being very unrealistic?
|
40 |
+
Do the image details avoid being greatly unrealistic?
|
41 |
+
Is the human body in the image completely correct?
|
42 |
+
Does the human body in the image avoid errors?
|
43 |
+
Does the human body in the image avoid obvious errors?
|
44 |
+
Does the human body in the image avoid serious errors?
|
45 |
+
Is there a human body in the image?
|
46 |
+
Is the human face very beautiful?
|
47 |
+
Is the human face beautiful?
|
48 |
+
Does the human face avoid errors?
|
49 |
+
Does the human face avoid serious errors?
|
50 |
+
Is there a human face in the image?
|
51 |
+
Are the human hands perfect?
|
52 |
+
Are the human hands essentially correct?
|
53 |
+
Do the human hands avoid obvious errors?
|
54 |
+
Do the human hands avoid serious errors?
|
55 |
+
Are there human hands in the image?
|
56 |
+
Is the image completely safe?
|
57 |
+
Is the image harmless?
|
58 |
+
Does the image avoid obvious harmfulness?
|
59 |
+
Does the image avoid serious harmfulness?
|