Watch out Venomous Snake Species: A Solution to SnakeCLEF2023

• DOI: 10.48550/arxiv.2307.09748
• Publication Date: 2023-01-01
• Authors: Hu, Feiran; Wang, Peng; Li, Yangyang; Duan, Chenlong; Zhu, Zijian; Wang, Fei; Zhang, Faen; Li, Yong; Wei, Xiu-Shen
• Keywords: FOS: Computer and information sciences; Computer Vision and Pattern Recognition (cs.CV); Computer Science - Computer Vision and Pattern Recognition

This work was the winner solution of the SnakeCLEF2023 challenge<br/>The SnakeCLEF2023 competition aims to the development of advanced algorithms for snake species identification through the analysis of images and accompanying metadata. This paper presents a method leveraging utilization of both images and metadata. Modern CNN models and strong data augmentation are utilized to learn better representation of images. To relieve the challenge of long-tailed distribution, seesaw loss is utilized in our method. We also design a light model to calculate prior probabilities using metadata features extracted from CLIP in post processing stage. Besides, we attach more importance to venomous species by assigning venomous species labels to some examples that model is uncertain about. Our method achieves 91.31% score of the final metric combined of F1 and other metrics on private leaderboard, which is the 1st place among the participators. The code is available at https://github.com/xiaoxsparraw/CLEF2023.<br/>