Authors
Abulikemu Abuduweili, Xin Wu, Xingchen Tao
Publication date
2019/7/30
Journal
arXiv preprint
Description
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
Total citations
2021202220232024122
Scholar articles
A Abuduweili, X Wu, X Tao - arXiv preprint arXiv:1907.13037, 2019