The iNaturalist Species Classification and Detection Dataset

CVPR 2018 Grant Van Horn • Oisin Mac Aodha • Yang Song • Yin Cui • Chen Sun • Alex Shepard • Hartwig Adam • Pietro Perona • Serge Belongie

Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories. In contrast, the natural world is heavily imbalanced, as some species are more abundant and easier to photograph than others. To encourage further progress in challenging real world conditions we present the iNaturalist species classification and detection dataset, consisting of 859,000 images from over 5,000 different species of plants and animals.

Full paper

Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Image Classification iNaturalist IncResNetV2 SE Top 1 Accuracy 67.3% # 1
Image Classification iNaturalist IncResNetV2 SE Top 5 Accuracy 87.5% # 1