  The highest accuracy object detectors to date are based either on a two-stage
approach such as Fast R-CNN or one-stage detectors such as Retina-Net or SSD
with deep and complex backbones. In this paper we present TigerNet - simple yet
efficient FPN based network architecture for Amur Tiger Detection in the wild.
The model has 600k parameters, requires 0.071 GFLOPs per image and can run on
the edge devices (smart cameras) in near real time. In addition, we introduce a
two-stage semi-supervised learning via pseudo-labelling learning approach to
distill the knowledge from the larger networks. For ATRW-ICCV 2019 tiger
detection sub-challenge, based on public leaderboard score, our approach shows
superior performance in comparison to other methods.
