We introduce a new wildlife re-identification dataset WildlifeReID-10k with
more than 214k images of 10k individual animals. It is a collection of 30
existing wildlife re-identification datasets with additional processing steps.
WildlifeReID-10k contains animals as diverse as marine turtles, primates,
birds, African herbivores, marine mammals and domestic animals. Due to the
ubiquity of similar images in datasets, we argue that the standard (random)
splits into training and testing sets are inadequate for wildlife
re-identification and propose a new similarity-aware split based on the
similarity of extracted features. To promote fair method comparison, we include
similarity-aware splits both for closed-set and open-set settings, use
MegaDescriptor - a foundational model for wildlife re-identification - for
baseline performance and host a leaderboard with the best results. We publicly
publish the dataset and the codes used to create it in the wildlife-datasets
library, making WildlifeReID-10k both highly curated and easy to use.

我们推出了一个新的野生动物重新识别数据集 WildlifeReID-10k，包含超过 214k 张 10k 只个体动物的图像。该数据集由 30 个现有的野生动物重新识别数据集加上额外的处理步骤组成。我们认为，由于数据集中相似图像的普遍存在，标准（随机）的训练集和测试集划分对于野生动物重新识别是不适当的，并提出了一种基于提取特征相似性的新的相似性感知划分。为了促进公平的方法比较，我们针对封闭集和开放集设置都提供相似性感知划分，使用 MegaDescriptor 作为野生动物重新识别的基准模型，同时提供最佳结果的排行榜。我们公开发布数据集以及用于创建它的代码在 wildlife-datasets 库中，使得 WildlifeReID-10k 既高度精选又易于使用。