  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.
