Baal

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#10 of 16

Chart

Score

5.1

The facts below are what the maker publishes about Baal, checked and dated. Our editors have not written about it yet.

The facts

Each one read from Baal's own pages, with the date it was checked.

Active learning methods: The framework supports Monte-Carlo Dropout, MCDropConnect, deep ensembles, and semi-supervised learning.checked 14 Nov 2026
Dataset handling: ActiveLearningDataset splits a dataset into training and unlabeled pool sets.checked 14 Nov 2026
Deployment: The repository provides Docker commands to build and run an experiment.checked 14 Nov 2026
Framework components: The framework has four main parts: ActiveLearningDataset, Heuristics, ModelWrapper, and ActiveLearningLoop.checked 14 Nov 2026
Installation: The README gives pip and Poetry installation methods.checked 14 Nov 2026
Integrations: The README links a seminar with Label Studio and describes training with Hugging Face or ModelWrapper.checked 14 Nov 2026
License: GitHub lists the project license as Apache-2.0.checked 14 Nov 2026
Model preparation: Baal provides modules for preparing models for active learning, including an MCDropoutModule wrapper.checked 14 Nov 2026
Purpose: Baal is an active learning library for industrial applications and research.checked 14 Nov 2026
Python requirement: Baal requires Python 3.10 or later.checked 14 Nov 2026
Uncertainty-based selection: ActiveLearningLoop computes uncertainty and labels the most uncertain items in the pool.checked 14 Nov 2026

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