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dc.contributor.authorPapafitsoros, K
dc.contributor.author2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2024)
dc.date.accessioned2023-11-17T12:51:24Z
dc.date.available2023-10-25
dc.date.available2023-11-17T12:51:24Z
dc.identifier.urihttps://qmro.qmul.ac.uk/xmlui/handle/123456789/92042
dc.description.abstractIn this paper, we present WildlifeDatasets - 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.
dc.rightsThis item is distributed under the terms of the Creative Commons Attribution 3.0 Unported License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.titleWildlifeDatasets: An open-source toolkit for animal re-identificationen_US
dc.typeConference Proceedingen_US
dc.rights.holder© 2024, The Author(s)
pubs.notesNot knownen_US
pubs.publication-statusAccepteden_US
dcterms.dateAccepted2023-10-25


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