#Synthetic-data
Showing 60 of 83 repositories tagged #synthetic-data, ranked by stars
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷
Build, Evaluate, and Optimize AI Systems. Includes evals, RAG, agents, fine-tuning, synthetic data generation, dataset management, MCP, and more.
Mimesis is a fast Python library for generating fake data in multiple languages.
Open Source Data Security Platform for Developers to Monitor and Detect PII, Anonymize Production Data and Sync it across environments.
A procedural Blender pipeline for photorealistic training image generation
Synthetic data generation for tabular data
Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
Python Toolkit for Causal and Probabilistic Reasoning
SDG is a specialized framework designed to generate high-quality structured tabular data.
UnrealCV: Connecting Computer Vision to Unreal Engine
🎨 NeMo Data Designer: Generate high-quality synthetic data from scratch or from seed data.
Database anonymization and test data management
Synthetic data curation for post-training and structured data extraction
Synthetic data generators for tabular and time-series data
The Declarative Data Generator
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models. 🤖💤
Generate High-Quality Synthetics, Train, Measure, and Evaluate in a Single Pipeline
[ICLR 2025] Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing. Your efficient and high-quality synthetic data generation pipeline!
A lightweight library for generating synthetic instruction tuning datasets for your data without GPT.
Synthetic Data SDK ✨
Verbalized Sampling, a training-free prompting strategy to mitigate mode collapse in LLMs by requesting responses with probabilities. Achieves 2-3x diversity improvement while maintaining quality. Model-agnostic framework with CLI/API for creative writing, synthetic data generation, and dialogue simulation.
Curated list of open source tooling for data-centric AI on unstructured data.
Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
Official code for MAMMA: Markerless Accurate Multi-person Motion Acquisition.
Synthetic data generators for structured and unstructured text, featuring differentially private learning.
A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.
A library to model multivariate data using copulas.
A multi-purpose LLM framework for RAG and data creation.
Draw a store, generate LLM personas, and watch them shop — an isometric 3D sandbox for synthetic-consumer experiments.
[ICML 2023] The official implementation of the paper "TabDDPM: Modelling Tabular Data with Diffusion Models"
Augmentation pipeline for rendering synthetic paper printing, faxing, scanning and copy machine processes
Synthadoc: An open-source LLM knowledge compilation engine that turns raw documents into structured, local-first wikis. A transparent, human-readable alternative to traditional RAG, which can be self-managed and self-improved without the use of any tools.
Design, conduct and analyze results of AI-powered surveys and experiments. Simulate social science and market research with large numbers of AI agents and LLMs.
Dataset and benchmark for RAG on company internal documents.
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
Official PyTorch implementation of the paper "Dataset Distillation with Neural Characteristic Function: A Minmax Perspective" (NCFM) in CVPR 2025 (Full Score, Highlight).
SynthDet - An end-to-end object detection pipeline using synthetic data
Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.
Open-Source Software, Tutorials, and Research on Data-Centric AI 🤖
A complete end-to-end demonstration in which we collect training data in Unity and use that data to train a deep neural network to predict the pose of a cube. This model is then deployed in a simulated robotic pick-and-place task.
Unity's privacy-preserving human-centric synthetic data generator
This repository provides you with an easy-to-use labeling tool for State-of-the-art Deep Learning training purposes. It supports Auto-Labeling.
Synthetic data for computer vision. An open source toolkit using Blender and Python.
Random dataframe and database table generator
Benchmarking synthetic data generation methods.
Synthetic Image generation with Flip. Generate thousands of new 2D images from a small batch of objects and backgrounds.
AgML is a centralized framework for agricultural machine learning. AgML provides access to public agricultural datasets for common agricultural deep learning tasks, with standard benchmarks and pretrained models, as well the ability to generate synthetic data and annotations.
Generation and evaluation of synthetic time series datasets (also, augmentations, visualizations, a collection of popular datasets) NeurIPS'24
medigan - A Python Library of Pretrained Generative Models for Medical Image Synthesis
This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
Material for QuantUniversity talk on Sythetic Data Generation for Finance.
Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several folders, or covering topics spanning across multiple folders..
(SIGCOMM '22) Practical GAN-based Synthetic IP Header Trace Generation using NetShare
Synthetic Blender Dataset Production
A curated list of awesome resources such as books, tutorials, courses, open-source libraries, exercises, and other materials that support Pythonistas in the making, and Pythonistas migrating into Data Science! 📊
AI-powered synthetic data generation — structured tables, unstructured documents, multi-provider LLM support, referential integrity, and code-gen mode for millions of rows
Synthetic benchmark for privacy-preserving and fairness-aware ranking under signal loss
A simple data generation engine for computer vision, compatible with 🤗 datasets.