The Herbarium 2021 Half–Earth Challenge Dataset and Machine Learning Competition
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The Herbarium 2021 Half–Earth Challenge Dataset and Machine Learning Competition. / de Lutio, Riccardo; Park, John Y.; Watson, Kimberly A.; D'Aronco, Stefano; Wegner, Jan D.; Wieringa, Jan J.; Tulig, Melissa; Pyle, Richard L.; Gallaher, Timothy J.; Brown, Gillian; Guymer, Gordon; Franks, Andrew; Ranatunga, Dhahara; Baba, Yumiko; Belongie, Serge J.; Michelangeli, Fabián A.; Ambrose, Barbara A.; Little, Damon P.
In: Frontiers in Plant Science, Vol. 12, 787127, 01.02.2022, p. 1-15.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - The Herbarium 2021 Half–Earth Challenge Dataset and Machine Learning Competition
AU - de Lutio, Riccardo
AU - Park, John Y.
AU - Watson, Kimberly A.
AU - D'Aronco, Stefano
AU - Wegner, Jan D.
AU - Wieringa, Jan J.
AU - Tulig, Melissa
AU - Pyle, Richard L.
AU - Gallaher, Timothy J.
AU - Brown, Gillian
AU - Guymer, Gordon
AU - Franks, Andrew
AU - Ranatunga, Dhahara
AU - Baba, Yumiko
AU - Belongie, Serge J.
AU - Michelangeli, Fabián A.
AU - Ambrose, Barbara A.
AU - Little, Damon P.
N1 - Funding Information: We would like to thank Kiat Chuan Tan from Google and the team at Kaggle (Walter Reade and Maggie Demkin) for their generous support in making this challenge possible. We would also like to thank everyone who entered the Herbarium 2021 Half?Earth Challenge. We are particularly grateful to the teams that provided detailed information on the model architectures and training strategies behind their winning submissions. Funding Information: This work was partially funded by National Science Foundation (USA) grant DEB 2054684. Publisher Copyright: Copyright © 2022 de Lutio, Park, Watson, D'Aronco, Wegner, Wieringa, Tulig, Pyle, Gallaher, Brown, Guymer, Franks, Ranatunga, Baba, Belongie, Michelangeli, Ambrose and Little.
PY - 2022/2/1
Y1 - 2022/2/1
N2 - Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine–grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.
AB - Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine–grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images.
KW - datasets
KW - fine-grained visual categorization
KW - herbarium specimen image
KW - hierarchical classification
KW - machine learning competition
UR - http://www.scopus.com/inward/record.url?scp=85124583035&partnerID=8YFLogxK
U2 - 10.3389/fpls.2021.787127
DO - 10.3389/fpls.2021.787127
M3 - Journal article
C2 - 35178056
AN - SCOPUS:85124583035
VL - 12
SP - 1
EP - 15
JO - Frontiers in Plant Science
JF - Frontiers in Plant Science
SN - 1664-462X
M1 - 787127
ER -
ID: 300985173