  Automatic species classification in camera traps would greatly help the
biodiversity monitoring and species analysis in the earth. In order to
accelerate the development of automatic species classification task, "Microsoft
AI for Earth" have prepared a challenge in FGVC6 workshop at CVPR 2019, which
called "iWildCam 2019 competition". In this work, we propose an efficient
method for categorizing animals in the wild. We transfer the state-of-the-art
ImagaNet pretrained models to the problem. To improve the generalization and
robustness of the model, we utilize efficient image augmentation and
regularization strategies, like cutout, mixup and label-smoothing. Finally, we
use ensemble learning to increase the performance of the model. Thanks to
advanced regularization strategies and ensemble learning, we got top 7/336
places in the final leaderboard. Source code of this work is available at
https://github.com/Walleclipse/iWildCam_2019_FGVC6
