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This is yet another PAM annotation tool
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**YAPAT**
*AI-Driven PAM Data Annotation & Visualization*

.. image:: https://img.shields.io/github/license/yapat-app/yapat
   :target: https://github.com/yapat-app/yapat/blob/dev/LICENSE

.. image:: https://img.shields.io/pypi/v/yapat
   :target: https://pypi.org/project/yapat/

.. image:: https://img.shields.io/github/issues/yapat-app/yapat
   :target: https://github.com/yapat-app/yapat/issues

.. image:: https://img.shields.io/github/stars/yapat-app/yapat.svg
   :target: https://github.com/yapat-app/yapat/stargazers

.. caution::
   Under construction (pre-alpha stage)

Designed for efficient analysis of PAM data, YAPAT utilizes machine learning to prioritize samples for expert annotation.
The integrated visualization suite combines embedding, dimensionality reduction, and clustering for dynamic data exploration.

.. toctree::
   :maxdepth: 2
   :caption: Getting Started

   quickstart
   docker

.. toctree::
   :maxdepth: 2
   :caption: User Guide

   datasets
   visualization
   annotation

.. toctree::
   :maxdepth: 2
   :caption: Advanced

   deployment
   contributing

Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`

Read More
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Kath, H., Serafini, P. P., Campos, I. B., Gouvêa, T. S., & Sonntag, D. (2024). Leveraging Transfer Learning and Active Learning for Data Annotation in Passive Acoustic Monitoring of Wildlife. *Ecological Informatics*, 82, 102710. `https://doi.org/10.1016/j.ecoinf.2024.102710 <https://doi.org/10.1016/j.ecoinf.2024.102710>`_

Kath, H., Gouvêa, T. S., & Sonntag, D. (2024b). Active Learning in Multi-label Classification of Bioacoustic Data. In A. Hotho & S. Rudolph (Eds.), *KI 2024: Advances in Artificial Intelligence* (pp. 114–127). Springer Nature Switzerland. `https://doi.org/10.1007/978-3-031-70893-0_9 <https://doi.org/10.1007/978-3-031-70893-0_9>`_

Troshani, I., Gouvêa, T. S., & Sonntag, D. (2024). Leveraging Weakly Supervised and Multiple Instance Learning for Multi-label Classification of Passive Acoustic Monitoring Data. In A. Hotho & S. Rudolph (Eds.), *KI 2024: Advances in Artificial Intelligence* (pp. 260–272). Springer Nature Switzerland. `https://doi.org/10.1007/978-3-031-70893-0_19 <https://doi.org/10.1007/978-3-031-70893-0_19>`_

Kath, H., Gouvêa, T. S., & Sonntag, D. (2024a). Active and Transfer Learning for Efficient Identification of Species in Multi-Label Bioacoustic Datasets. *Proceedings of the 2024 International Conference on Information Technology for Social Good*, 22–25. `https://doi.org/10.1145/3677525.3678635 <https://doi.org/10.1145/3677525.3678635>`_

Kath, H., Serafini, P. P., Campos, I. B., Gouvêa, T. S., & Sonntag, D. (2024). Demo: Enhancing Wildlife Acoustic Data Annotation Efficiency through Transfer and Active Learning. *Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence*, 8691–8695. `https://doi.org/10.24963/ijcai.2024/1010 <https://doi.org/10.24963/ijcai.2024/1010>`_