Synthetic Data
Showing 23 of 23 projects
A procedural Blender pipeline for generating photorealistic training images for computer vision and machine learning.
A Python toolkit for causal and probabilistic reasoning using graphical models like Bayesian Networks and Structural Equation Models.
An open-source synthetic patient population simulator that generates realistic (but not real) patient data and health records in multiple formats.
An open-source tool for PostgreSQL and MySQL database anonymization, synthetic data generation, and logical dumping.
A Python package for generating synthetic tabular and time-series data using state-of-the-art generative models like GANs and Gaussian Mixtures.
A declarative tool for generating realistic, scalable test data from code or existing databases.
A declarative tool for generating realistic, scalable test data from code or existing databases.
A CNN-based captcha solver for Taiwan Railway booking website with a training set generator that mimics captcha style and uses data augmentation.
A Python library for generating high-quality synthetic tabular data using GANs, diffusion models, and large language models.
A Python data validation toolkit that finds data quality issues and generates beautiful, shareable reports for team collaboration.
An AI-powered captcha solver using SimGAN to generate synthetic training data without manual labeling.
A centralized Python framework for agricultural machine learning, providing access to public datasets, benchmarks, pretrained models, and synthetic data generation.
A lightweight Go-based templating system for generating random data with customizable tokens.
A Python framework for generating synthetic log events without requiring actual infrastructure or actions.
A Python package for generating multidimensional synthetic data using Copula and fPCA models to preserve statistical properties.
A natural language generation language for creating training data for intent parsing systems.
Generates synthetic phase contrast or fluorescence microscope images of bacteria for training deep learning segmentation models.
A Python implementation of the clugen algorithm for generating multidimensional clusters with arbitrary distributions.
A Neo4j procedure for generating realistic test data with customizable nodes, relationships, and values directly in the Neo4j browser.
A Julia implementation of the clugen algorithm for generating multidimensional clusters with arbitrary distributions.
A MATLAB/Octave implementation of the clugen algorithm for generating multidimensional clusters with arbitrary distributions.
An R implementation of the clugen algorithm for generating multidimensional clusters with arbitrary distributions.
A general-purpose U-Net implementation in Keras for image segmentation tasks.
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