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Semi-iNat is a challenging dataset for semi-supervised classification with along-tailed distribution of classes, fine-grained categories, and domain shiftsbetween labeled and unlabeled data. This dataset is behind the second iterationof the semi-supervised recognition challenge to be held at the FGVC8 workshopat CVPR 2021. Different from the previous one, this dataset (i) includes imagesof species from different kingdoms in the natural taxonomy, (ii) is at a largerscale -- with 810 in-class and 1629 out-of-class species for a total of 330kimages, and (iii) does not provide in/out-of-class labels, but provides coarsetaxonomic labels (kingdom and phylum) for the unlabeled images. This documentdescribes baseline results and the details of the dataset which is availablehere: \url{https://github.com/cvl-umass/semi-inat-2021}.

The Semi-Supervised iNaturalist Challenge at the FGVC8 Workshop