State-of-the-art animal classification models like SpeciesNet provide predictions across thousands of species but use conservative rollup strategies, resulting in many animals labeled at high taxonomic levels rather than species. We present a hierarchical re-classification system for the Animal Detect platform that combines SpeciesNet EfficientNetV2-M predictions with CLIP embeddings and metric learning to refine high-level taxonomic labels toward species-level identification. Our five-stage pipeline (high-confidence acceptance, bird override, centroid building, triplet-loss metric learning, and adaptive cosine-distance scoring) is evaluated on a segment of the LILA BC Desert Lion Conservation dataset (4,018 images, 15,031 detections). After recovering 761 bird detections from "blank" and "animal" labels, we re-classify 456 detections labeled animal, mammal, or blank with 96.5% accuracy, achieving species-level identification for 64.9 percent

翻译：当前最先进的动物分类模型（如SpeciesNet）能够对数千个物种进行预测，但采用保守的汇总策略，导致许多动物被标记在较高的分类学层级而非物种级别。我们为Animal Detect平台提出了一种分层重分类系统，该系统将SpeciesNet EfficientNetV2-M的预测结果与CLIP嵌入及度量学习相结合，以将高层级分类学标签细化为物种级别的识别。我们的五阶段流程（高置信度接受、鸟类覆盖、质心构建、三元组损失度量学习和自适应余弦距离评分）在LILA BC Desert Lion Conservation数据集的一个子集（4,018张图像，15,031个检测）上进行了评估。在从"空白"和"动物"标签中恢复了761个鸟类检测后，我们对456个标记为动物、哺乳动物或空白的检测进行了重分类，准确率达到96.5%，实现了其中64.9%的检测达到物种级别的识别。