Dataset Open Access

DeepScoresV2

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

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:

https://digitalcollection.zhaw.ch/handle/11475/20647

 

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

https://github.com/yvan674/obb_anns

Code to train baseline models can be found here:

https://github.com/tuggeluk/mmdetection/tree/DSV2_Baseline_FasterRCNN

https://github.com/tuggeluk/DeepWatershedDetection/tree/dwd_old

 

 

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.
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ds2_complete.tar.gz
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