Abstract
<title>Abstract</title> <p>Recognising individual animals from photographs is an important practical challenge in wildlife conservation. Camera traps can capture thousands of images, but researchers still need reliable ways to tell whether two photographs show the same animal, not only the same species. This helps with population monitoring, movement tracking, territory studies, and long-term conservation decisions. The problem is difficult because the same animal may appear from different angles, in different lighting, or partly hidden, while different animals can look very similar. This study presents a practical computer vision workflow for animal re-identification using a small, labelled dataset, with jaguars used as the main case study. The research compares two broad approaches. The first fine-tunes specialised or large vision models for the jaguar task. The second keeps a strong general-purpose vision model frozen, uses it to extract image features once, and trains only a small metric-learning head on top. In the recorded experiments, the frozen DINOv2-Giant approach achieved stronger benchmark results than the earlier fine-tuned MegaDescriptor-L system, reaching 0.958 public identity-balanced mAP, compared with 0.840 for the prior champion. These results suggest that, for small wildlife datasets, fine-tuning a large, specialised model is not always necessary; a powerful frozen foundation model can sometimes deliver better inference quality with much lower training overhead. The broader contribution is not a claim that one model will always win, but a reproducible model-selection workflow that ML engineers and conservation technologists can adapt to other pattern-bearing species such as tigers, leopards, zebras, giraffes, and whale sharks. Because AI and machine learning are evolving quickly, practitioners should treat this study as a decision framework and benchmark example, while also evaluating newer models, training methods, and deployment practices as they become available.</p>