Watch out Venomous Snake Species: A Solution to SnakeCLEF2023

TL;DR

This paper introduces a novel snake species identification method combining CNNs, data augmentation, seesaw loss, and metadata analysis, achieving first place in the SnakeCLEF2023 competition.

Contribution

It presents a new integrated approach utilizing images and metadata with specialized loss and post-processing techniques for improved snake species classification.

Findings

Achieved 91.31% score on private leaderboard

Outperformed other participants in SnakeCLEF2023

Effectively handled long-tailed data distribution

Abstract

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.