google/uncertainty-baselines
High-quality implementations of standard and SOTA methods on a variety of tasks.
This project offers standardized, high-quality implementations of methods for assessing and improving the reliability of machine learning models. It takes raw training data and model configurations, and outputs performance metrics like accuracy, calibration error, and negative log-likelihood. This tool is designed for machine learning researchers and practitioners who need to evaluate model robustness and uncertainty in a consistent way.
1,568 stars. Actively maintained with 1 commit in the last 30 days. Available on PyPI.
Use this if you are a machine learning researcher or practitioner who needs a solid starting point to experiment with and compare different methods for quantifying model uncertainty and improving robustness.
Not ideal if you are looking for a plug-and-play solution for immediate deployment without deep involvement in model architecture and training specifics.
Stars
1,568
Forks
216
Language
Python
License
Apache-2.0
Last pushed
Feb 02, 2026
Commits (30d)
1
Dependencies
5
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