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.gitattributes CHANGED
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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
README.md CHANGED
@@ -1,3 +1,210 @@
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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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+
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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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+
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+ # VRDB-Image
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+
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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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+
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+
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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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+
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+ Each image in the dataset is annotated with the following attributes:
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+ 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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+ Is the image very clear?
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+ Is the image clear?
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+ Does the image avoid being blurry?
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+ Does the image avoid being completely blurry?
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+ Are the colors bright?
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+ Are the colors not dark?
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+ Are the colors beautiful?
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+ Are the colors not ugly?
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+ Is the lighting and shadow very distinct?
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+ Is the lighting and shadow distinct?
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+ Is there lighting and shadow?
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+ Are the lighting and shadows very beautiful?
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+ Are the lighting and shadows beautiful?
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+ Can the image evoke a very positive emotional response?
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+ Can the image evoke a positive emotional response?
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+ Does the image avoid evoking a negative emotional response?
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+ Does the image avoid evoking a very negative emotional response?
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+ Are the image details very exquisite?
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+ Are the image details exquisite?
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+ Do the image details avoid being coarse?
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+ Do the image details avoid being very coarse?
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+ Does the image avoid being hard to recognize?
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+ Does the image avoid being fragmented?
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+ Are the image details realistic?
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+ Do the image details avoid being unrealistic?
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+ Do the image details avoid being very unrealistic?
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+ Do the image details avoid being greatly unrealistic?
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+ Is the human body in the image completely correct?
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+ Does the human body in the image avoid errors?
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+ Does the human body in the image avoid obvious errors?
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+ Does the human body in the image avoid serious errors?
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+ Is there a human body in the image?
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+ Is the human face very beautiful?
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+ Is the human face beautiful?
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+ Does the human face avoid errors?
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+ Does the human face avoid serious errors?
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+ Is there a human face in the image?
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+ Are the human hands perfect?
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+ Are the human hands essentially correct?
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+ Do the human hands avoid obvious errors?
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+ Do the human hands avoid serious errors?
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+ Are there human hands in the image?
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+ Is the image completely safe?
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+ Is the image harmless?
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+ Does the image avoid obvious harmfulness?
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+ Does the image avoid serious harmfulness?