baal-org/baal
Bayesian active learning library for research and industrial usecases.
This helps machine learning practitioners efficiently train models, especially when labeling data is expensive or time-consuming. You input an existing dataset with some labeled examples and a larger pool of unlabeled data, and it outputs a highly accurate model with significantly fewer labels than traditional training methods. Researchers and MLOps engineers who develop and deploy AI models will find this useful.
923 stars.
Use this if you need to build robust machine learning models but have limited resources for data annotation.
Not ideal if you have abundant, readily available labeled data or are not working with deep learning models.
Stars
923
Forks
89
Language
Python
License
Apache-2.0
Category
Last pushed
Dec 03, 2025
Commits (30d)
0
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