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XAITK - Saliency

The xaitk-saliency package is an open source, Explainable AI (XAI) framework for visual saliency algorithm interfaces and implementations, built for analytics and autonomy applications.

See here for a more formal introduction to the topic of XAI and visual saliency explanations.

This framework is a part of the Explainable AI Toolkit (XAITK).

Supported Algorithms

The xaitk-saliency package provides saliency algorithms for a wide range of image understanding tasks, including image classification, image similarity, object detection, and reinforcement learning. The current list of supported saliency algorithms can be found here.

Target Audience

This toolkit is intended to help data scientists and developers who want to add visual saliency explanations to their workflow or product. Functionality provided here is both directly accessible for targeted experimentation, and through Strategy and Adapter patterns to allow for modular integration into systems and applications.

Installation

xaitk-saliency installation has been tested on Unix and Linux systems.

To install the current version via pip:

pip install xaitk-saliency[<extra1>,<extra2>,...]

To install the current version via conda-forge:

conda install -c conda-forge xaitk-saliency

Certain plugins may require additional runtime dependencies. Details on these requirements can be found here.

For more detailed installation instructions, visit the installation documentation.

Getting Started

Explore usage examples of the xaitk-saliency package in various contexts using the Jupyter notebooks provided in the ./docs/examples/ directory.

Documentation

Documentation for both release snapshots and the latest main branch is available on ReadTheDocs.

To build the Sphinx-based documentation locally for the latest reference:

# Install dependencies
poetry install --sync --with main,linting,tests,docs
# Navigate to the documentation root
cd docs
# Build the documentation
poetry run make html
# Open the generated documentation in your browser
firefox _build/html/index.html

Contributing

Contributions are encouraged!

The following points help ensure contributions follow development practices.

• Follow the JATIC Design Principles.
• Adopt the Git Flow branching strategy.
• Detailed release information is available in docs/release_process.rst.
• Additional contribution guidelines and issue reporting steps can be found in CONTRIBUTING.md.

Developer Tools

Ensure the source tree is acquired locally before proceeding.

Poetry Install

You can install using Poetry:

Warning

Users unfamiliar with Poetry should use caution. See installation documentation for more information.

poetry install --with main,linting,tests,docs --extras "<extra1> <extra2> ..."

Pre-commit Hooks

Pre-commit hooks ensure that code complies with required linting and formatting guidelines. These hooks run automatically before commits but can also be executed manually. To bypass checks during a commit, use the --no-verify flag.

To install and use pre-commit hooks:

# Install required dependencies
poetry install --sync --with main,linting,tests,docs
# Initialize pre-commit hooks for the repository
poetry run pre-commit install
# Run pre-commit checks on all files
poetry run pre-commit run --all-files

Example: A First Look at xaitk-saliency

This associated project provides a local web-application that provides a demonstration of visual saliency generation in a user-interface. This provides an example of how visual saliency, as generated by this package, can be utilized in a user-interface to facilitate model and results exploration. This tool uses the trame framework.

License

Apache 2.0

All development prior to Nov 19, 2024 falls under BSD-3-Clause

Contacts

Principal Investigator / Product Owner: Brian Hu (Kitware) @brian.hu

Scrum Master / Maintainer: Brandon RichardWebster (Kitware) @b.richardwebster

Deputy Scrum Master / Maintainer: Emily Veenhuis (Kitware) @emily.veenhuis

Project Manager: Keith Fieldhouse (Kitware) @keith.fieldhouse

Program Representative: Austin Whitesell (MITRE) @awhitesell

Acknowledgment

This material is based upon work supported by the Chief Digital and Artificial Intelligence Office under Contract No. 519TC-23-9-2032. The views and conclusions contained herein are those of the author(s) and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government.