daml 0.40.2

DAML provides a simple interface to characterize image data and its impact on model performance across classification and object-detection tasks

Data-Analysis Metrics Library (DAML)

About DAML

The Data-Analysis Metrics Library, or DAML, focuses on characterizing image data and its impact on model performance across classification and object-detection tasks.

Model-agnostic metrics that bound real-world performance

• relevance/completeness/coverage
• metafeatures (data complexity)

Model-specific metrics that guide model selection and training

• dataset sufficiency
• data/model complexity mismatch

Metrics for post-deployment monitoring of data with bounds on model performance to guide retraining

• dataset-shift metrics
• model performance bounds under covariate shift
• guidance on sampling to assess model error and model retraining

Getting Started

Installing DAML

To install the package from the GitLab Pypi repository, run the following command in an environment with Python 3.8-3.11 installed:

pip install daml

Additional Tutorials

For more ideas on getting started using DAML in your workflow, additional information and tutorials are in our Sphinx documentation hosted on Read the Docs.

Contributing

For steps on how to get started on contributing to the project, you can follow the steps in CONTRIBUTING.md.

Attribution

This project uses code from the Alibi-Detect python library developed by SeldonIO. Additional documentation from the developers are also available here.

POCs

• POC: Scott Swan @scott.swan
• DPOC: Andrew Weng @aweng