WildlifeDatasets: An open-source toolkit for animal re-identification

TL;DR

WildlifeDatasets is an open-source Python toolkit that facilitates wildlife re-identification research by providing dataset access, preprocessing tools, baseline experiments, and a novel foundation model called MegaDescriptor that outperforms existing models.

Contribution

The paper introduces WildlifeDatasets, a comprehensive toolkit for wildlife re-identification, and presents MegaDescriptor, a new foundation model achieving state-of-the-art results across species.

Findings

MegaDescriptor outperforms CLIP and DINOv2 in re-identification tasks.

First comprehensive comparison of wildlife datasets and methods.

Toolkit simplifies dataset access and model fine-tuning for ecologists.

Abstract

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).