In this paper, we present WildlifeDatasets
(this https URL) - 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
(this https URL).

该研究论文介绍了 WildlifeDatasets 工具集，一个开源工具集用于生态学家和计算机视觉 / 机器学习研究人员，它使用 Python 编写，提供了简单直接获取公开的野生动物数据集的方法，并提供了各种数据集预处理、性能分析和模型优化方法。我们在不同的场景和基准实验中展示了该工具集，包括了野生动物再识别数据集和方法的最全面比较，包括了局部描述符和深度学习方法。此外，我们提供了首个针对各种动物个体再识别的基础模型 - MegaDescriptor，该模型在动物再识别数据集上表现优异，超过了 CLIP 和 DINOv2 等预训练模型的性能。为了让该模型对大众可用以及方便集成到任何现有的野生动物监测应用中，我们通过 HuggingFace hub 提供了多个 MegaDescriptor 版本（即 Small、Medium 和 Large）。