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Introduction

The wildlife-tools library offers a simple interface for various tasks in the wildlife re-identification domain. Its main features are:

  • It covers use cases such as training, feature extraction, similarity calculation, image retrieval, and classification.
  • It provides traning codes and usage examples for our models MegaDescriptor and WildFusion.
  • It complements the WildlifeDatasets library, which acts as dataset repository.

Installation

Install wildlife-tools using pip

pip install git+https://github.com/WildlifeDatasets/wildlife-tools

or clone the repository using git and install it.

git clone git@github.com:WildlifeDatasets/wildlife-tools.git

cd wildlife-tools
pip install -e .

Modules in the in the wildlife-tools

  • The data module provides tools for creating instances of datasets.
  • The train module offers tools for fine-tuning feature extractors.
  • The features module provides tools for extracting features using various extractors.
  • The similarity module provides tools for constructing a similarity matrix from query and database features.
  • The inference module offers tools for creating predictions using the similarity matrix.

Relations between modules

  graph TD;
      A[Data]-->|ImageDataset|B[Features]
      A-->|ImageDataset|C;
      C[Train]-->|finetuned extractor|B;
      B-->|query and database features|D[Similarity]
      D-->|similarity matrix|E[Inference]

Citation

If you like our package, please cite us.

@InProceedings{Cermak_2024_WACV,
    author    = {\v{C}erm\'ak, Vojt\v{e}ch and Picek, Luk\'a\v{s} and Adam, Luk\'a\v{s} and Papafitsoros, Kostas},
    title     = {{WildlifeDatasets: An Open-Source Toolkit for Animal Re-Identification}},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2024},
    pages     = {5953-5963}
}

@article{cermak2024wildfusion,
  title={WildFusion: Individual animal identification with calibrated similarity fusion},
  author={Cermak, Vojt{\v{e}}ch and Picek, Lukas and Adam, Luk{\'a}{\v{s}} and Neumann, Luk{\'a}{\v{s}} and Matas, Ji{\v{r}}{\'\i}},
  journal={arXiv preprint arXiv:2408.12934},
  year={2024}
}