• Python (3.8+)

  • Optional: OpenVINO, TensorFlow, PyTorch, MxNet, Caffe, Accuracy Checker, Git

Installation steps#

Optionally, set up a virtual environment:

python -m pip install virtualenv
python -m virtualenv venv
. venv/bin/activate


# From PyPI:
pip install datumaro[default]
# From the GitHub repository:
pip install 'git+[default]'


Datumaro has many plugins, which are responsible for dataset formats, model launchers and other optional components. If a plugin has dependencies, they can require additional installation. You can find the list of all the plugin dependencies in the [plugins](/docs/user-manual/extending) section.

Optional dependencies These components are only required for plugins and not installed by default:

  • OpenVINO

  • Accuracy Checker

  • TensorFlow

  • PyTorch

  • MxNet

  • Caffe

Customizing installation#

  • Datumaro has the following installation options:

    • pip install datumaro - for core library functionality

    • pip install datumaro[default] - for normal CLI experience

    In restricted installation environments, where some dependencies are not available, or if you need only the core library functionality, you can install Datumaro without extra plugins.

    The CLI variant (datumaro[default]) requires Git to be installed and available to work with Datumaro projects and dataset versioning features. You can find installation instructions for your platform here.

    In some cases, installing just the core library may be not enough, because there can be limited options of installing graphical libraries in the system (various Docker environments, servers etc). You can select between using opencv-python and opencv-python-headless by setting the DATUMARO_HEADLESS environment variable to 0 or 1 before installing the package. It requires installation from sources (using --no-binary): This option can’t be covered by extras due to Python packaging system limitations.

DATUMARO_HEADLESS=1 pip install datumaro --no-binary=datumaro
  • When installing directly from the repository, you can change the installation branch with ...@<branch_name>. Also use --force-reinstall parameter in this case. It can be useful for testing of unreleased versions from GitHub pull requests.