Dataset Open Access

DeepScoresV2

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


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  <identifier identifierType="DOI">10.5281/zenodo.4012193</identifier>
  <creators>
    <creator>
      <creatorName>Tuggener, Lukas</creatorName>
      <givenName>Lukas</givenName>
      <familyName>Tuggener</familyName>
      <affiliation>ZHAW Datalab &amp; USi</affiliation>
    </creator>
    <creator>
      <creatorName>Satyawan, Yvan Putra</creatorName>
      <givenName>Yvan Putra</givenName>
      <familyName>Satyawan</familyName>
      <affiliation>ZHAW Datalab</affiliation>
    </creator>
    <creator>
      <creatorName>Pacha, Alexander</creatorName>
      <givenName>Alexander</givenName>
      <familyName>Pacha</familyName>
      <affiliation>TU Wien</affiliation>
    </creator>
    <creator>
      <creatorName>Schmidhuber, Jürgen</creatorName>
      <givenName>Jürgen</givenName>
      <familyName>Schmidhuber</familyName>
      <affiliation>The Swiss AI Lab IDSIA (USI &amp; SUPSI)</affiliation>
    </creator>
  </creators>
  <titles>
    <title>DeepScoresV2</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2020</publicationYear>
  <subjects>
    <subject>Music Object Regognition</subject>
    <subject>Deep Learning</subject>
    <subject>Object Detection</subject>
    <subject>OMR</subject>
    <subject>DeepScores</subject>
    <subject>Deep Scores</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2020-09-02</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/4012193</alternateIdentifier>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4012192</relatedIdentifier>
  </relatedIdentifiers>
  <version>2.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;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.&amp;nbsp;&lt;/p&gt;

&lt;p&gt;The dataset contains ground in the form of:&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;Non-oriented bounding boxes&lt;/li&gt;
	&lt;li&gt;Oriented bounding boxes&lt;/li&gt;
	&lt;li&gt;Semantic segmentation&lt;/li&gt;
	&lt;li&gt;Instance segmentation&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;The accompaning paper &lt;em&gt;The DeepScoresV2 Dataset and Benchmark for Music Object Detection &lt;/em&gt;published at ICPR2020 can be found here:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://digitalcollection.zhaw.ch/handle/11475/20647"&gt;https://digitalcollection.zhaw.ch/handle/11475/20647&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;A toolkit for convenient loading and inspection of the data can be found here:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/yvan674/obb_anns"&gt;https://github.com/yvan674/obb_anns&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;Code to train baseline models can be found here:&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/tuggeluk/mmdetection/tree/DSV2_Baseline_FasterRCNN"&gt;https://github.com/tuggeluk/mmdetection/tree/DSV2_Baseline_FasterRCNN&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&lt;a href="https://github.com/tuggeluk/DeepWatershedDetection/tree/dwd_old"&gt;https://github.com/tuggeluk/DeepWatershedDetection/tree/dwd_old&lt;/a&gt;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;

&lt;p&gt;&amp;nbsp;&lt;/p&gt;</description>
    <description descriptionType="Other">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.</description>
  </descriptions>
</resource>
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