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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43972904" target="_blank" >RIV/49777513:23520/24:43972904 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/24:00377620

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10483925" target="_blank" >https://ieeexplore.ieee.org/document/10483925</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/WACV57701.2024.00585" target="_blank" >10.1109/WACV57701.2024.00585</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

In 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 show-case 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

In 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 show-case 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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

ISBN

979-8-3503-1892-0

ISSN

2472-6737

e-ISSN

2642-9381

Počet stran výsledku

11

Strana od-do

5941-5951

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

Piscataway

Místo konání akce

Waikoloa, HI, USA

Datum konání akce

3. 1. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001222964606009