Freedom251 › Active Learning Tools
Baal
Facts from Baal's own pages
- Active learning methods
- The framework supports Monte-Carlo Dropout, MCDropConnect, deep ensembles, and semi-supervised learning. · github.com, 14 Nov 2026
- Dataset handling
- ActiveLearningDataset splits a dataset into training and unlabeled pool sets. · github.com, 14 Nov 2026
- Deployment
- The repository provides Docker commands to build and run an experiment. · github.com, 14 Nov 2026
- Framework components
- The framework has four main parts: ActiveLearningDataset, Heuristics, ModelWrapper, and ActiveLearningLoop. · github.com, 14 Nov 2026
- Installation
- The README gives pip and Poetry installation methods. · github.com, 14 Nov 2026
- Integrations
- The README links a seminar with Label Studio and describes training with Hugging Face or ModelWrapper. · github.com, 14 Nov 2026
- License
- GitHub lists the project license as Apache-2.0. · github.com, 14 Nov 2026
- Model preparation
- Baal provides modules for preparing models for active learning, including an MCDropoutModule wrapper. · github.com, 14 Nov 2026
- Purpose
- Baal is an active learning library for industrial applications and research. · github.com, 14 Nov 2026
- Python requirement
- Baal requires Python 3.10 or later. · github.com, 14 Nov 2026
- Uncertainty-based selection
- ActiveLearningLoop computes uncertainty and labels the most uncertain items in the pool. · github.com, 14 Nov 2026
Where Baal stands on Freedom251
- Active Learning Tools#3 of 16