VisionScore README v1.0

README.md

@@ -1,3 +1,47 @@
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-license: cc-by-4.0
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+---
+license: cc-by-4.0
+size_categories:
+- 10K<n<100K
+tags:
+- music
+- art
+---
+
+# VisionScores
+
+**VisionScores** is a novel, system-segmented image score dataset specifically designed for machine learning applications in symbolic music processing. It represents the first dataset that captures the system-level structure of two-handed piano compositions, formatted for compatibility with modern machine learning frameworks.
+
+The dataset was constructed to address the limitations of existing music score datasets, which are predominantly designed for Optical Music Recognition (OMR) tasks. **VisionScores** emphasizes structural consistency and format uniformity while preserving the semantic richness of musical scores. Its development was grounded on two foundational constraints: (1) content consistency, through the selection of two-handed piano compositions, and (2) format regularity, achieved through segmentation of systems from score pages into uniformly sized grayscale images.
+
+The full methodology, motivation, and analysis are presented in the following paper:
+
+**[VisionScores – A System-Segmented Image Score Dataset for Deep Learning Tasks](http://arxiv.org/abs/2506.23030)**
+
+We invite readers to consult the full text for a comprehensive understanding of the dataset’s construction and applications.
+
+
+## Structure of Dataset
+
+Each sample in **VisionScores** is a grayscale image of dimensions **128 × 512 pixels**, saved in `.jpg` format. All samples represent **individual systems** extracted from two-handed piano sheet music. In addition to the image data, **VisionScores** includes detailed **metadata** for each system, comprising the title of the piece, composer, key (if available), IMSLP page reference, and a system index.
+
+To provide both structural similarity and stylistic diversity, the dataset is divided into two distinct scenarios:
+
+* **Sonatinas Scenario**: Contains 14,000 systems extracted from various Sonatina compositions by multiple composers. This scenario emphasizes stylistic similarity across different authors.
+* **Franz Liszt Scenario**: Contains 10,810 systems extracted from diverse works by Franz Liszt, showcasing a wide range of compositional styles from a single composer.
+
+These scenarios were designed to support controlled experimentation in symbolic music generation, layout analysis, and related tasks.
+
+### Disclaimer
+- As noted in the paper, **individual systems do not contain enough information to represent a work type or to characterize a composer**. These attributes emerge only in the context of complete, ordered sequences of systems.
+- The **system index** in the metadata is relative to the set of valid segmented systems for each piece and **may not** match the original order in the score. This limitation results from the exclusion of incomplete or low-quality segments and will be addressed in future dataset updates.
+
+
+## Additional Data
+
+Auxiliary materials including unsegmented full-page scores and non-formatted systems are available through Google Drive due to file size constraints:
+
+* [Google Drive – Auxiliary Files](https://drive.google.com/drive/folders/19ZJEfOZMDByBymQpXw3Y0Ys0Y4IwvZss?usp=drive_link)
+
+Segmentation methods can be found on GitHub repository:
+
+* [VisionScores GitHub](https://github.com/alroamz/VisionScores)