Dataset Open Access


Tuggener, Lukas; Satyawan, Yvan Putra; Pacha, Alexander; Schmidhuber, Jürgen

Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Tuggener, Lukas</dc:creator>
  <dc:creator>Satyawan, Yvan Putra</dc:creator>
  <dc:creator>Pacha, Alexander</dc:creator>
  <dc:creator>Schmidhuber, Jürgen</dc:creator>
  <dc:description>The DeepScoresV2 Dataset for Music Object Detection contains digitally rendered images of written sheet music, together with the corresponding ground truth to fit various types of machine learning models. A total of 151 Million different instances of music symbols, belonging to 135 different classes are annotated. The total Dataset contains 255,385 Images. For most researches, the dense version, containing 1714 of the most diverse and interesting images, is a good starting point. 

The dataset contains ground in the form of:

	Non-oriented bounding boxes
	Oriented bounding boxes
	Semantic segmentation
	Instance segmentation

The accompaning paper The DeepScoresV2 Dataset and Benchmark for Music Object Detection published at ICPR2020 can be found here:


A toolkit for convenient loading and inspection of the data can be found here:

Code to train baseline models can be found here:


  <dc:description>The authors are grateful for the support through Innosuisse grant No. 34301.1 IP-ICT "RealScore", EuropeanResearch Council Advanced Grant 742870, and the continued fruitful collaboration with ScorePad AG.</dc:description>
  <dc:subject>Music Object Regognition</dc:subject>
  <dc:subject>Deep Learning</dc:subject>
  <dc:subject>Object Detection</dc:subject>
  <dc:subject>Deep Scores</dc:subject>
All versions This version
Views 577577
Downloads 1,8041,804
Data volume 116.6 TB116.6 TB
Unique views 482482
Unique downloads 462462


Cite as