The iNaturalist 2018 dataset (iNat 2018) is a large-scale, fine-grained species classification (and detection) dataset built from observations on the iNaturalist platform. Introduced by Van Horn et al., it emphasizes real-world challenges: long-tailed / highly imbalanced class distributions, many visually similar species, varied image quality and capture conditions, and global coverage. The dataset contains on the order of 0.8–0.9 million images from several thousand species (the paper reports ~859,000 images from over 5,000 species) and was released together with the FGVC / CVPR 2018 challenge (often referenced as the iNaturalist 2018 competition). It has been widely used as a benchmark for fine-grained and long-tailed image classification; variants/splits for the competition (training/validation/test) and detection labels were also provided for challenge participants. Common mirrors / references: the original CVPR paper and arXiv entry (see arXiv:1707.06642), Kaggle competition pages (iNaturalist 2018 / FGVC5), and dataset builders in TFDS and community uploads on Hugging Face.
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