The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop

Jong-Chyi Su; Subhransu Maji

The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop

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arxiv 2106.01364 v1 pith:242RKW7Z submitted 2021-06-02 cs.CV cs.LG

Jong-Chyi Su , Subhransu Maji This is my paper · ORCID

classification cs.CVcs.LG

keywords datasetimagessemi-supervisedchallengedifferentfgvc8labelsout-of-class

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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}.

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