WildlifeDatasets: an Open-source Toolkit for Animal Re-identification: Conclusion and References

3 Jul 2024


(1) Vojtech ˇ Cerm ˇ ak, Czech Technical University in Prague (Email: cermavo3@fel.cvut.cz);

(2) Lukas Picek, University of West Bohemia & INRIA (Email: picekl@kky.zcu.cz/lpicek@inria.cz);

(3) Luka´s Adam, Czech Technical University in Prague (Email: lukas.adam.cr@gmail.com);

(4) Kostas Papafitsoros, Queen Mary University of London (Email: k.papafitsoros@qmul.ac.uk).

7. Conclusion

We have introduced the WildlifeDatasets toolkit, an open-source, user-friendly library that provides (i) convenient access and manipulation of all publicly available wildlife datasets for individual re-identification, (ii) access to a variety of state-of-the-art models for animal reidentification, and (iii) simple API that allows inference and matching over new datasets. Besides, we have provided the most comprehensive experimental comparison of these datasets and essential methods in wildlife re-identification using local descriptors and deep learning approaches. Using insights from our ablation studies led to the creation of a MegaDescriptor, the first-ever foundation model for animal re-identification, which delivers state-of-the-art performance on a wide range of species. We anticipate that this toolkit will be widely used by both computer vision scientists and ecologists interested in wildlife re-identification and will significantly facilitate progress in this field.

A. WildlifeDatasets: Supplementary Materials

A.1. Ablation study on model size

To showcase and quantify the performance of different MegaDescriptor flavors, we compare five variants, e.g., Base, Small, Tiny, and Large-224 and Large-384, originating from corresponding variations of the Swin architecture. All the models were trained and evaluated using the same setting. Naturally, the model performance in terms of accuracy increased with an increasing model size, i.e., the MegaDescriptor-L-384 outperformed smaller flavors by a considerable margin in most cases. Overall, higher model complexity achieved higher performance with few exceptions, where it underperformed by a small margin, e.g., by 2.53%, 0.48%, and 0.08% on FriesianCattle2017, LeopardID2022, and MacaqueFaces respectively. This is more or less statistically insignificant, given the poor quality of the data and the data acquisition.

We visualized the accuracy of all provided MegaDescriptor flavors in Figure 9 and Table 5.

A.2. Online Documentation – Dataset samples and tutorials

We provide extensive documentation to give users a better orientation within the WildlifeDatasets toolkit and available features. It covers a wide range of use cases of the toolkit, including a guide to installation and dataset downloading, tutorials, and how to contribute. Notably, the documentation includes a detailed description of the datasets, including image samples.

Table 5. Ablation study on model size/complexity. We compare five MegaDescriptor flavors, e.g., Large, Base, Small, and Tiny, in terms of accuracy. In general, models with a bigger model size or higher input resolution outperformed their smaller variants by a considerable margin. The best-performing model – MegaDescriptor-L-384 – underperformed by 2.53%, 0.48%, and 0.08% on FriesianCattle2017, LeopardID2022, and MacaqueFaces, respectively.


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