Installation

Dependencies

  • Python (3.6+)
  • Optional: OpenVINO, TensorFlow, PyTorch, MxNet, Caffe, Accuracy Checker

Installation steps

Optionally, set up a virtual environment:

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

Install:

# From PyPI:
pip install datumaro[default]

# From the GitHub repository:
pip install 'git+https://github.com/openvinotoolkit/datumaro[default]'

Read more about choosing between datumaro and datumaro[default] here.

Plugins

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 section.

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.

    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):

    DATUMARO_HEADLESS=1 pip install datumaro --no-binary=datumaro

    This option can’t be covered by extras due to Python packaging system limitations.

  • Although Datumaro excludes pycocotools of version 2.0.2 in requirements, it works with this version perfectly fine. The reason for such requirement is binary incompatibility of the numpy dependency in the TensorFlow and pycocotools binary packages, and the current workaround forces this package to be build from sources on most platforms (see #253). If you need to use 2.0.2, make sure it is linked with the same version of numpy as TensorFlow by reinstalling the package:

    pip uninstall pycocotools
pip install pycocotools --no-binary=pycocotools

  • 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.