🤖 AI Summary
Existing animal classification models (e.g., SpeciesNet) typically yield coarse-grained taxonomic labels (e.g., order or class), limiting species-level identification. To address this, we propose a five-stage hierarchical reclassification framework that integrates EfficientNetV2-M and CLIP-based visual embeddings, augmented with centroid clustering, triplet-loss-driven metric learning, and an adaptive cosine-distance scoring mechanism—enabling fine-grained mapping from high-level taxa to species. Innovatively, we introduce a bird-priority coverage strategy and a high-confidence filtering mechanism to enhance discriminative robustness. Evaluated on the LILA BC Desert Lion Conservation dataset, our method recovers 761 bird detections and refines 456 ambiguous coarse labels, achieving an overall accuracy of 96.5%, with 64.9% of predictions precisely resolved at the species level—substantially improving both accuracy and practical utility for wildlife image recognition.
📝 Abstract
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