iNaturalist Competition Dataset

by Grant Van Horn,Oisin Mac Aodha,Yang Song,Yin Cui,Chen Sun,Alex Shepard,Hartwig Adam,Pietro Perona,Serge BelongieUnknown

iNaturalist Competition Dataset

As part of the FGVC4 workshop at CVPR 2017 we are conducting the iNat Challenge 2017 large scale species classification competition, sponsored by Google. It is estimated that the natural world contains several million species of plants and animals. Without expert knowledge, many of these species are extremely difficult to accurately classify due to their visual similarity. The goal of this competition is to push the state of the art in automatic image classification for real world data that features fine-grained categories, big class imbalances, and large numbers of classes. The iNat Challenge 2017 dataset contains 5,089 species, with a combined training and validation set of 675,000 images that have been collected and verified by multiple users from inaturalist.org. The dataset features many visually similar species, captured in a wide variety of situations, from all over the world. Example images, along with their unique GBIF ID numbers (where available), can be viewed here. Teams with top submissions, at the discretion of the workshop organizers, will be invited to present their work at the FGVC4 workshop.

Dataset Attributes

Label SVG
TasksClassification
Label SVG
CategoriesAnimals, Wildlife, Nature
Label SVG
SensorRGB Camera