10.5281/zenodo.4012193
https://zenodo.org/records/4012193
oai:zenodo.org:4012193
Tuggener, Lukas
Lukas
Tuggener
ZHAW Datalab & USi
Satyawan, Yvan Putra
Yvan Putra
Satyawan
ZHAW Datalab
Pacha, Alexander
Alexander
Pacha
TU Wien
Schmidhuber, Jürgen
Jürgen
Schmidhuber
The Swiss AI Lab IDSIA (USI & SUPSI)
Stadelmann, Thilo
Thilo
Stadelmann
ZHAW Datalab
DeepScoresV2
Zenodo
2020
Music Object Regognition
Deep Learning
Object Detection
OMR
DeepScores
Deep Scores
2020-09-02
eng
10.5281/zenodo.4012192
2.0
Creative Commons Attribution 4.0 International
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, should suffice.
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.