With fine-grained classification, we identify unique characteristics to distinguish among classes of the same super-class. We are focusing on species recognition in Insecta, as they are critical for biodiversity monitoring and at the base of many ecosystems. With citizen science campaigns, billions of images are collected in the wild. Once these are labelled, experts can use them to create distribution maps. However, the labelling process is time-consuming, which is where computer vision comes in. The field of computer vision offers a wide range of algorithms, each with its strengths and weaknesses; how do we identify the algorithm that is in line with our application? To answer this question, we provide a full and detailed evaluation of nine algorithms among deep convolutional networks (CNN), vision transformers (ViT), and locality-based vision transformers (LBVT) on 4 different aspects: classification performance, embedding quality, computational cost, and gradient activity. We offer insights that we haven't yet had in this domain proving to which extent these algorithms solve the fine-grained tasks in Insecta. We found that the ViT performs the best on inference speed and computational cost while the LBVT outperforms the others on performance and embedding quality; the CNN provide a trade-off among the metrics.

翻译：在细粒度分类中，我们识别独特特征以区分同一超类下的不同类别。本研究聚焦昆虫纲的物种识别，因为昆虫对生物多样性监测至关重要，且处于许多生态系统的基础地位。通过公民科学项目，野外采集了数十亿张图像。这些图像经过标记后，专家可据此绘制分布图。然而，标记过程耗时费力，这正是计算机视觉发挥作用之处。计算机视觉领域提供了多种算法，每种算法各有优劣；如何识别最适合我们应用的算法？为回答这一问题，我们对深度卷积网络（CNN）、视觉变换器（ViT）和基于局部性的视觉变换器（LBVT）中的九种算法进行了全面详细的评估，涵盖分类性能、嵌入质量、计算成本和梯度活动四个方面。我们提供了该领域此前尚未获得的见解，揭示了这些算法在昆虫纲细粒度任务中的解决程度。我们发现，ViT在推理速度和计算成本方面表现最佳，而LBVT在性能和嵌入质量上优于其他算法；CNN则在各项指标间提供了折衷方案。