The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
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Updated
Jul 12, 2024 - Python
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
The open-source tool for building high-quality datasets and computer vision models
Argilla is a collaboration platform for AI engineers and domain experts that require high-quality outputs, full data ownership, and overall efficiency.
A modular active learning framework for Python
MONAI Label is an intelligent open source image labeling and learning tool.
A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convol…
Pool-based active learning in Python
The deep potential generator to generate a deep-learning based model of interatomic potential energy and force field
ALiPy: Active Learning in Python is an active learning python toolbox, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods.
Active learning for systematic reviews
Bayesian active learning library for research and industrial usecases.
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
Machine Learning for Computer Security
A simple client for doccano API.
Active Learning for Text Classification in Python
📈 Adaptive: parallel active learning of mathematical functions
Tools for detecting wildlife in aerial images using active learning
Code and website for DAL (Discriminative Active Learning) - a new active learning algorithm for neural networks in the batch setting. For the blog:
Multi-view face recognition, face cropping and saving the cropped faces as new images on videos to create a multi-view face recognition database.
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