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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    <subfield code="a">&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;</subfield>
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