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Open-source alternatives to Optimus
30 open-source projects similar to hi-primus/optimus, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Optimus alternative.
- cleanlab/cleanlab
cleanlab/cleanlab
11,513Ver en GitHubCleanlab is a data-centric AI library and toolkit designed to improve machine learning model performance by detecting label errors and increasing overall dataset quality. It implements a confident learning framework that iteratively refines label noise estimates by comparing model predictions with estimated label probabilities to identify mislabeled examples. The project provides specialized utilities for active learning optimization, allowing for the selection of the most impactful examples for labeling or re-labeling. It also includes an outlier detection tool to identify atypical data poin
Pythonactive-learningannotationanomaly-detection - albumentations-team/albumentations
albumentations-team/albumentations
15,308Ver en GitHubAlbumentations is a computer vision image augmentation library designed to increase training data diversity for deep learning models. It provides a toolset for applying geometric and color transformations to images and annotations, including a specialized collection of 3D operations for volumetric data used in medical and scientific imaging. The library functions as an image mask and bounding box transformer, automatically updating masks, bounding boxes, and keypoints when images undergo geometric changes. This ensures that spatial alterations remain synchronized across images and their assoc
Python - alteryx/featuretools
alteryx/featuretools
7,658Ver en GitHubFeaturetools is an automated feature engineering library and data transformation framework written in Python. It automatically generates machine learning feature vectors from multi-table datasets by applying synthesis patterns to relational and timestamped data. The system functions as a distributed feature synthesis engine, allowing the process of creating feature vectors to scale across multiple cores or clusters to handle large-scale datasets. The library supports the synthesis of multi-table datasets, time series feature generation, and the creation of custom machine learning primitives
Python
