We introduce CzechLynx, the first large-scale, open-access dataset for individual identification, pose estimation, and instance segmentation of the Eurasian lynx (Lynx lynx). CzechLynx contains 39,760 camera trap images annotated with segmentation masks, identity labels, and 20-point skeletons and covers 319 unique individuals across 15 years of systematic monitoring in two geographically distinct regions: southwest Bohemia and the Western Carpathians. In addition to the real camera trap data, we provide a large complementary set of photorealistic synthetic images and a Unity-based generation pipeline with diffusion-based text-to-texture modeling, capable of producing arbitrarily large amounts of synthetic data spanning diverse environments, poses, and coat-pattern variations. To enable systematic testing across realistic ecological scenarios, we define three complementary evaluation protocols: (i) geo-aware, (ii) time-aware open-set, and (iii) time-aware closed-set, covering cross-regional and long-term monitoring settings. With the provided resources, CzechLynx offers a unique, flexible benchmark for robust evaluation of computer vision and machine learning models across realistic ecological scenarios.
@article{arxiv.2506.04931,
title = {CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx},
author = {Lukas Picek and Elisa Belotti and Michal Bojda and Ludek Bufka and Vojtech Cermak and Martin Dula and Rostislav Dvorak and Luboslav Hrdy and Miroslav Jirik and Vaclav Kocourek and Josefa Krausova and Jirı Labuda and Jakub Straka and Ludek Toman and Vlado Trulık and Martin Vana and Miroslav Kutal},
journal= {arXiv preprint arXiv:2506.04931},
year = {2025}
}