April 29, 2021 Dataset Open Access

Camille Garcin; Alexis Joly; Pierre Bonnet; Antoine Affouard; Jean-Christophe Lombardo; Mathias Chouet; Maximilien Servajean; Titouan Lorieul; Joseph Salmon

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  <identifier identifierType="DOI">10.5281/zenodo.5645731</identifier>
  <creators>
    <creator>
      <creatorName>Camille Garcin</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4504-7040</nameIdentifier>
      <affiliation>IMAG, Univ Montpellier, Inria, CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Alexis Joly</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2161-9940</nameIdentifier>
      <affiliation>Inria, LIRMM, Univ Montpellier, CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Pierre Bonnet</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2828-4389</nameIdentifier>
      <affiliation>CIRAD, AMAP</affiliation>
    </creator>
    <creator>
      <creatorName>Antoine Affouard</creatorName>
      <affiliation>CIRAD, AMAP, Inria, LIRMM, Univ Montpellier, CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Jean-Christophe Lombardo</creatorName>
      <affiliation>Inria, LIRMM, Univ Montpellier, CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Mathias Chouet</creatorName>
      <affiliation>CIRAD, AMAP, Inria, LIRMM, Univ Montpellier, CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Maximilien Servajean</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-9426-2583</nameIdentifier>
      <affiliation>LIRMM, AMIS, UPVM, Univ Montpellier, CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Titouan Lorieul</creatorName>
      <affiliation>Inria, LIRMM, Univ Montpellier, CNRS</affiliation>
    </creator>
    <creator>
      <creatorName>Joseph Salmon</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-3181-0634</nameIdentifier>
      <affiliation>IMAG, Univ Montpellier, CNRS</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Pl@ntNet-300K image dataset</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>plant</subject>
    <subject>images</subject>
    <subject>identification</subject>
    <subject>classification</subject>
    <subject>Pl@ntNet</subject>
    <subject>species</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-04-29</date>
  </dates>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/5645731</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4726652</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.1</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;This paper presents a novel image dataset with high intrinsic ambiguity and a long-tailed distribution built from the database of Pl@ntNet citizen observatory. It consists of 306146&amp;nbsp;plant images covering 1081&amp;nbsp;species. We highlight two particular features of the dataset, inherent to the way the images are acquired and to the intrinsic diversity of plants morphology:&lt;/p&gt;

&lt;p&gt;&amp;nbsp; &amp;nbsp; (i) the dataset has a strong class imbalance, i.e. a few species account for most of the images, and,&lt;/p&gt;

&lt;p&gt;&amp;nbsp; &amp;nbsp; (ii) many species are visually similar, rendering identification difficult even for the expert eye.&lt;/p&gt;

&lt;p&gt;&amp;nbsp; &amp;nbsp; These two characteristics make the present dataset well suited for the evaluation of set-valued classification methods and algorithms. Therefore, we recommend two set-valued evaluation metrics associated with the dataset (macro-average top-k accuracy&amp;nbsp;and macro-average average-k accuracy) and we provide baseline results established by training deep neural networks using the cross-entropy loss.&lt;/p&gt;

&lt;p&gt;A full description of the dataset as well as baseline experiments can be found in the following&amp;nbsp;publication:&lt;/p&gt;

&lt;p&gt;&amp;quot;&lt;a href="https://openreview.net/forum?id=eLYinD0TtIt"&gt;Pl@ntNet-300K: a plant image dataset with high label ambiguity and a long-tailed distribution&lt;/a&gt;&amp;quot;, Camille Garcin, Alexis Joly, Pierre Bonnet, Antoine Affouard, Jean-Christophe Lombardo, Mathias Chouet, Maximilien Servajean,&amp;nbsp;Titouan Lorieul and Joseph Salmon, in Proc. of Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track, 2021.&lt;/p&gt;

&lt;p&gt;&amp;nbsp;Please cite the above&amp;nbsp;reference for any publication using the dataset.&lt;/p&gt;

&lt;p&gt;Utilities to load the data and train models with pytorch can be found here: &lt;a href="https://github.com/plantnet/PlantNet-300K/"&gt;https://github.com/plantnet/PlantNet-300K/&lt;/a&gt;&lt;/p&gt;</description>
  </descriptions>
  <fundingReferences>
    <fundingReference>
      <funderName>European Commission</funderName>
      <funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
      <awardNumber awardURI="info:eu-repo/grantAgreement/EC/Horizon 2020 Framework Programme - Research and Innovation action/863463/">863463</awardNumber>
      <awardTitle>Co-designed Citizen Observatories Services for the EOS-Cloud</awardTitle>
    </fundingReference>
  </fundingReferences>
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