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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
The inaturalist species classification and detection dataset
Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie · 2018
Earlier work this paper cites.

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A realistic evaluation of semi-supervised learning for fine-grained classification
Jong-Chyi Su, Zezhou Cheng, and Subhransu Maji · 2021
Cited alongside, same era.
https://www.inaturalist.org
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The semi-supervised inaturalist-aves challenge at fgvc7 workshop
Original
Jong-Chyi Su and Subhransu Maji · 2021
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2021

The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop

Su, Jong-Chyi, Maji, Subhransu

Understand

Semi-iNat is a challenging dataset for semi-supervised classification with a long-tailed distribution of classes, fine-grained categories, and domain shifts between labeled and unlabeled data.

This dataset is behind the second iteration of the semi-supervised recognition challenge to be held at the FGVC8 workshop at CVPR 2021.
Different from the previous one, this dataset (i) includes images of species from different kingdoms in the natural taxonomy, (ii) is at a larger scale -- with 810 in-class and 1629 out-of-class species for a total of 330k images, and (iii) does not provide in/out-of-class labels, but provides coarse taxonomic labels (kingdom and phylum) for the unlabeled images.
This document describes baseline results and the details of the dataset which is available here: \url{https://github.com/cvl-umass/semi-inat-2021}.