VisionScores -- A system-segmented image score dataset for deep learning tasks
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Abstract (arXiv)
VisionScores presents a novel proposal being the first system-segmented image score dataset, aiming to offer structure-rich, high information-density images for machine and deep learning tasks. Delimited to two-handed piano pieces, it was built to consider not only certain graphic similarity but also composition patterns, as this creative process is highly instrument-dependent. It provides two scenarios in relation to composer and composition type. The first, formed by 14k samples, considers works from different authors but the same composition type, specifically, Sonatinas. The latter, consisting of 10.8K samples, presents the opposite case, various composition types from the same author, being the one selected Franz Liszt. All of the 24.8k samples are formatted as grayscale jpg images of 128 × 512 pixels. VisionScores supplies the users not only the formatted samples but the systems' order and pieces' metadata. Moreover, unsegmented full-page scores and the pre-formatted images are included for further analysis.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 13 references that are in the map. First posted on arXiv 28 Jun 2025.
Builds on
6 of 13 references.
References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2018Music Transformercited by 211
- 2021MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Trainingcited by 89
- 2018DeepScores -- A Dataset for Segmentation, Detection and Classification of Tiny Objectscited by 13
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