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.

通过图像和元数据分析，本研究提出了一种使用现代CNN模型和强大的数据增强方法，以实现对蛇物种进行识别的高级算法，进一步利用seesaw损失解决长尾分布的挑战，并通过从CLIP中提取的元数据特征设计轻量级模型计算先验概率，在后处理阶段为毒蛇物种赋予更高的重要性，最终在私有排行榜上获得91.31%的F1和其他指标的综合得分，成为参赛者中的第一名。

警惕有毒蛇种类：解决SnakeCLEF2023的方法