Overview of AnimalCLEF 2025: Recognizing Individual Animals in Images

Abstract

The first edition of the individual animal identification challenge, AnimalCLEF 2025, organized within LifeCLEF,advances the field of animal re-identification using computer vision and machine learning. Building on theWildlifeReID-10k dataset and incorporating new data, AnimalCLEF 2025 challenges participants to recognizeindividual animals from images for three species: lynxes, salamanders and sea turtles. The mix of species withdifferent image capture conditions attempts to make the submitted prediction models generalizable to unseenspecies. The competition attracted 270 participants across 230 teams, with 136 outperforming the provided baselinebased on MegaDescriptor. This overview paper provides (i) a comprehensive description of the challenge andprovided baseline method, (ii) detailed characteristics of the dataset and task specifications, (iii) an examinationof the methods employed by contestants, and (iv) a discussion of the competition outcomes. The results highlightincremental advancements in animal re-identification, showcasing innovative approaches and techniques thatpush the limits of previous work.