baybe
modAL
baybe | modAL | |
---|---|---|
1 | 4 | |
185 | 2,146 | |
11.5% | 0.9% | |
9.9 | 1.9 | |
about 5 hours ago | 3 months ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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baybe
modAL
-
modAL VS encord-active - a user suggested alternative
2 projects | 12 Apr 2023
- What are frameworks/tools used for Human-In-The-Loop (active) learning ?
-
Launch HN: Lightly (YC S21): Label only the data which improves your ML model
How does it differentiate from modAL?
https://github.com/modAL-python/modAL
- Active Learning Using Detectron2
What are some alternatives?
emukit - A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.
active_learning - Code for Active Learning at The ImageNet Scale. This repository implements many popular active learning algorithms and allows training with torch's DDP.
vizier - Python-based research interface for blackbox and hyperparameter optimization, based on the internal Google Vizier Service.
GPflowOpt - Bayesian Optimization using GPflow
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
paramonte - ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.
awesome-experimental-standards-deep-learning - Repository collecting resources and best practices to improve experimental rigour in deep learning research.
lightly - A python library for self-supervised learning on images.
pretty-print-confusion-matrix - Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib
DataProfiler - What's in your data? Extract schema, statistics and entities from datasets
Encord Active - Open source active learning toolkit to find failure modes in your computer vision models, prioritize data to label next, and drive data curation to improve model performance.
DIgging - Decision Intelligence for digging best parameters in target environment.