As part of the FGVC6 workshop at CVPR 2019 we are conducting the iNat Challenge 2019 large scale species classification competition. 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 a large number of fine-grained categories.
Previous versions of the challenge have focused on classifying large numbers of species. This year features a smaller number of highly similar categories captured in a wide variety of situations, from all over the world. In total, the iNat Challenge 2019 dataset contains 1,010 species, with a combined training and validation set of 268,243 images that have been collected and verified by multiple users from iNaturalist.
1st: Bo-Yan Zhou, Bo-Rui Zhao, Quan Cui, Yan-Ping Xie, Zhao-Min Chen - Megvii Research Nanjing
2nd: Chen-Wei Xie, Liming Zhao, Dangwei Li, Yun Zheng,Pan Pan - Alibaba Machine Intelligence Technology Lab
3rd: Jeremy Trammell - General Dynamics Mission Systems (GDMS), Deep Learning Analytics Center of Excellence
Grant Van Horn, Oisin Mac Aodha