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A framework for benchmarking pose estimation and point tracking methods on animal beheviour videos.

Project description

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poseinterface

poseinterface exists to advance pose estimation and point tracking applications in animal behaviour videos. The project aims to:

  • Build a benchmark dataset with dozens of videos and annotations from multiple institutes, open to external contributions.
  • Develop a general-purpose framework for running pose estimation and point tracking tools on benchmark data, and for comparing their outputs via evaluation metrics.
  • Provide baseline models trained using common pose estimation and tracking frameworks on the benchmark datasets.

Read the documentation for more information.

[!Warning] This project is in early stages of development. The API is not stable and may change without warning. Use with caution.

Installation

We recommend installing poseinterface in a virtual environment, using uv.

In your working directory, create a new environment and activate it:

uv venv --python=3.13
source .venv/bin/activate  # On macOS and Linux
.venv\Scripts\activate     # On Windows PowerShell

Then, install the package directly from the main branch on GitHub:

uv pip install git+https://github.com/neuroinformatics-unit/poseinterface.git@main

If you would like to contribute, see the contributing guide, which includes instructions for setting up a development environment.

License

⚖️ BSD 3-Clause

Package template

This package layout and configuration (including pre-commit hooks and GitHub actions) have been copied from the python-cookiecutter template.

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