Updated datasetcard

README.md

@@ -1,11 +1,35 @@
 ---
 license: mit
 pretty_name: >-
-  DEAR: Dataset for Evaluating the Aesthetics of Rendering
+  DEAR: Dataset for Evaluating the Aesthetics of Rendering (100-scene sample)
+tags:
+- computer-vision
+- image-quality-assessment
+- aesthetics
+- rendering
+- human-preference
+task_categories:
+- image-quality-assessment
+- aesthetic-quality-assessment
+size_categories:
+- 1K-10K
 ---
-Preliminary release of the Dataset for Evaluating the Aesthetics of Rendering
-It contains 100 scenes (600 images) along with pairwise markup and metadata from adobe5k
-|style 1|style 2|style 3|style 4|style 5|neutral|
+
+
+# Dataset for Evaluating the Aesthetics of Rendering (DEAR) - 100-scene Sample
+
+This dataset is a preliminary release of the **Dataset for Evaluating the Aesthetics of Rendering (DEAR)**, designed to model human aesthetic judgments of image rendering styles. Built upon the MIT-Adobe FiveK dataset, DEAR provides a foundation for training and evaluating models that can assess subjective rendering preferences rather than just technical image quality.
+
+## Dataset Overview
+
+This sample contains **100 scenes** (600 total images) with **pairwise human preference annotations** collected through large-scale crowdsourcing. Each scene is rendered in **6 distinct styles**:
+
+- **Style A-E**: Five different photographer-specific renderings (Expert A through Expert E from the original MIT-Adobe FiveK dataset)
+- **Neutral**: Adobe Photoshop auto mode rendering (serves as a baseline/reference style)
+
+Each pair of renderings for the same scene was evaluated by multiple human annotators who indicated which version they preferred or if they considered them equally appealing. This rich preference data enables training models to predict aesthetic preferences across different rendering styles.
+
+|style a|style b|style c|style d|style e|neutral|
 |-------|-------|-------|-------|-------|-------|
 |<img src="./images/tiff16_a/a2031-WP_CRW_5715.jpeg">|<img src="./images/tiff16_b/a2031-WP_CRW_5715.jpeg">|<img src="./images/tiff16_c/a2031-WP_CRW_5715.jpeg">|<img src="./images/tiff16_d/a2031-WP_CRW_5715.jpeg">|<img src="./images/tiff16_e/a2031-WP_CRW_5715.jpeg">|<img src="./images/original/a2031-WP_CRW_5715.jpeg">|
 |<img src="./images/tiff16_a/a3214-KE_-8375.jpeg">|<img src="./images/tiff16_b/a3214-KE_-8375.jpeg">|<img src="./images/tiff16_c/a3214-KE_-8375.jpeg">|<img src="./images/tiff16_d/a3214-KE_-8375.jpeg">|<img src="./images/tiff16_e/a3214-KE_-8375.jpeg">|<img src="./images/original/a3214-KE_-8375.jpeg">|
@@ -14,7 +38,30 @@ It contains 100 scenes (600 images) along with pairwise markup and metadata from
 |<img src="./images/tiff16_a/a3308-Ja_Pe-23.jpeg">|<img src="./images/tiff16_b/a3308-Ja_Pe-23.jpeg">|<img src="./images/tiff16_c/a3308-Ja_Pe-23.jpeg">|<img src="./images/tiff16_d/a3308-Ja_Pe-23.jpeg">|<img src="./images/tiff16_e/a3308-Ja_Pe-23.jpeg">|<img src="./images/original/a3308-Ja_Pe-23.jpeg">|
 |<img src="./images/tiff16_a/a0742-IMG_2429.jpeg">|<img src="./images/tiff16_b/a0742-IMG_2429.jpeg">|<img src="./images/tiff16_c/a0742-IMG_2429.jpeg">|<img src="./images/tiff16_d/a0742-IMG_2429.jpeg">|<img src="./images/tiff16_e/a0742-IMG_2429.jpeg">|<img src="./images/original/a0742-IMG_2429.jpeg">|
 
+## Key Features
 
----
-license: mit
----
+- **Human-centered evaluation**: Captures nuanced aesthetic preferences rather than technical distortions
+- **Multiple rendering styles**: Six distinct interpretations per scene allow for fine-grained preference analysis
+- **Expert vs. automated rendering**: Comparison between professional photographer styles and Adobe's auto mode
+- **Diverse content**: Scenes span portraits, landscapes, food, animals, night photography, and general vernacular subjects
+
+## Metadata
+
+Each image includes comprehensive metadata extracted from the original TIFF XMP data, including:
+
+- **Technical parameters**: Exposure settings, white balance, contrast, saturation, and tone curve adjustments
+- **Rendering information**: Software version, processing history, and renderer identifiers
+- **Content categories**: Auto-generated scene classifications (people, animals, food, night scenes, etc.)
+- **Image properties**: Dimensions, color profiles, and format specifications
+- **Source information**: Original filename, capture device, and photographer attribution
+
+The metadata provides valuable context for understanding how specific parameter adjustments influence aesthetic preferences and enables researchers to analyze correlations between technical settings and human preferences.
+
+## Applications
+
+This dataset enables research in:
+- **Aesthetic preference prediction**: Training models to predict which rendering style users will prefer
+- **Style-aware image enhancement**: Developing algorithms that adapt to user aesthetic preferences
+- **Personalized rendering**: Creating systems that learn individual aesthetic tastes
+- **Benchmarking aesthetic models**: Evaluating existing and new approaches to aesthetic quality assessment
+- **Human-AI collaboration**: Understanding how human preferences can guide AI rendering decisions