Efficient Method for Categorize Animals in the Wild

TL;DR

This paper presents an efficient animal species classification method in the wild, leveraging transfer learning, advanced augmentation, regularization, and ensemble techniques, achieving top performance in a competitive challenge.

Contribution

It introduces a novel combination of transfer learning, augmentation, regularization, and ensemble methods for improved wildlife species classification.

Findings

Achieved top 7 placement in the FGVC6 iWildCam 2019 challenge.

Demonstrated effectiveness of advanced regularization strategies.

Showed that ensemble learning boosts classification accuracy.

Abstract

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