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.
|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|