usage.rst

Usage

This package supports a fully reproducible research experimentation cycle for semantic binary segmentation with support for the following activities:

• Training: Images are fed to a Fully Convolutional Deep Neural Network (FCN), that is trained to reconstruct annotations (pre-segmented binary maps), automatically, via error back propagation. The objective of this phase is to produce an FCN model.
• Inference: The FCN is used to generate vessel map predictions
• Evaluation: Vessel map predictions are used evaluate FCN performance against test data, generate ROC curves or visualize prediction results overlayed on the original raw images.

Each application is implemented as a :ref:`command-line utility <bob.ip.binseg.cli>`, that is configurable using :ref:`Bob's extensible configuration framework <bob.extension.framework>`. In essence, each command-line option may be provided as a variable with the same name in a Python file. Each file may combine any number of variables that are pertinent to an application.

Tip

For reproducibility, we recommend you stick to configuration files when parameterizing our CLI. Notice some of the options in the CLI interface (e.g. --dataset) cannot be passed via the actual command-line as it requires a :py:class:`concrete PyTorch dataset instance <torch.utils.data.dataset.Dataset>`.

We provide a number of :ref:`preset configuration files <bob.ip.binseg.cli.config.list.all>` that can be used in one or more of the activities described in this section. Our command-line framework allows you to refer to these preset configuration files using special names (a.k.a. "resources"), that procure and load these for you automatically. Aside preset configuration files, you may also create your own to extend existing baseline experiments by :ref:`locally copying <bob.ip.binseg.cli.config.copy>` and modifying one of our configuration resources.