iNaturalist 2017 (iNat 2017) is a large-scale fine-grained species classification dataset released for the iNaturalist 2017 challenge. It contains 5,089 categories (species) with approximately 579,184 training images and 95,986 validation images (total ~675k images). Images were contributed by citizen scientists and exhibit varying image quality, heavy class imbalance (long-tailed distribution), and many visually similar species, making the benchmark challenging for real-world species classification. The original release also included some bounding-box annotations, though most uses are image-level (single-label) classification; test labels were not publicly released by the organizers. Introduced in Van Horn et al., “The iNaturalist Species Classification and Detection Dataset” (arXiv:1707.06642, CVPR 2018).
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