  In this paper, we present WildlifeDatasets
(https://github.com/WildlifeDatasets/wildlife-datasets) - an open-source
toolkit intended primarily for ecologists and computer-vision /
machine-learning researchers. The WildlifeDatasets is written in Python, allows
straightforward access to publicly available wildlife datasets, and provides a
wide variety of methods for dataset pre-processing, performance analysis, and
model fine-tuning. We showcase the toolkit in various scenarios and baseline
experiments, including, to the best of our knowledge, the most comprehensive
experimental comparison of datasets and methods for wildlife re-identification,
including both local descriptors and deep learning approaches. Furthermore, we
provide the first-ever foundation model for individual re-identification within
a wide range of species - MegaDescriptor - that provides state-of-the-art
performance on animal re-identification datasets and outperforms other
pre-trained models such as CLIP and DINOv2 by a significant margin. To make the
model available to the general public and to allow easy integration with any
existing wildlife monitoring applications, we provide multiple MegaDescriptor
flavors (i.e., Small, Medium, and Large) through the HuggingFace hub
(https://huggingface.co/BVRA).
