Mission and Values of DeepLabCut#
This document is meant to help guide decisions about the future of DeepLabCut, be it in terms of
whether to accept new functionality, changes to the styling of the code or graphical user interfaces (GUI),
or whether to take on new dependencies, when to break into other repos, among other things. It serves as a point of reference for core developers actively working on the project, and an introduction for
newcomers who want to learn a little more about where the project is going and what the team’s
values are. You can also learn more about how the project is managed by looking at our governance model.
Our founding principles#
The founding DeepLabCut team came together around a shared vision for building the first open-source animal pose estimation framework that is:
user defined pose estimation - i.e. species or object agnostic.
access to SOTA deep learning models that can be swiftly re-trained for customized applications
fast (GPU-powered)
scalable (project focused for ease of portability and sharability)
As the project has grown we’ve turned these original principles into the mission statement and set of values that we described below.
Our mission#
DeepLabCut aims to be the animal pose software package for Python and to provide access to deep learning-based pose estimation for people to use in their daily work without the need to be able to program in a deep learning framework. We hope to accomplish this by:
being easy to use and install. We are careful in taking on new dependencies, sometimes making them optional, and aim support a fully (Python) packaged installation that works cross-platform.
being well-documented with comprehensive tutorials and examples. All functions in our API have thorough docstrings clarifying expected inputs and outputs, and we maintain a separate tutorials and information website.
providing GUI access to all critical functionality so DeepLabCut can be used by people without coding experience.
being interactive and highly performant in order to support large data pipelines.
providing a consistent and stable API to enable plugin developers to build on top of DeepLabCut without their code constantly breaking and to enable advanced users to build out sophisticated Python workflows, if needed.
ensuring correctness. We strive for complete test coverage of both the code and GUI, with all code reviewed by a core developer before being included in the repository.
Our values#
We are inclusive. We welcome newcomers who are making their first contribution and strive to grow our most dedicated contributors into core developers. We have a Code of Conduct to make DeepLabCut a welcoming place for all.
We are community-engaged. We respond to feature requests and proposals on our issue tracker.
We serve scientific applications primarily, over “consumer or commercial” pose estimation tools. This often means prioritizing core functionality support, and rejecting implementations of “flashy” features that have little scientific value.
We are domain agnostic within the sciences. Functionality that is highly specific to particular scientific domains belongs in plugins, whereas functionality that cuts across many domains and is likely to be widely used belongs inside DeepLabCut.
We value education and documentation. All functions should have docstrings, preferably with examples, and major functionality should be explained in our tutorials. Core developers can take an active role in finishing documentation examples.
Acknowledgements#
We share a lot of our mission and values with napari and scikit-image and acknowledge the influence of their mission and values statements on this document.