Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
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Updated
May 6, 2024 - Python
Mimesis is a powerful Python library that empowers developers to generate massive amounts of synthetic data efficiently.
A procedural Blender pipeline for photorealistic training image generation
Synthetic data generation for tabular data
Conditional GAN for generating synthetic tabular data.
⚗️ distilabel is a framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models. 🤖💤
Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
A multi-purpose LLM framework for RAG and data creation.
Synthetic data generators for structured and unstructured text, featuring differentially private learning.
A lightweight library for generating synthetic instruction tuning datasets for your data without GPT.
A library to model multivariate data using copulas.
[IROS 2020] se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
A library for generating and evaluating synthetic tabular data for privacy, fairness and data augmentation.
Official code for our CVPR '22 paper "Dataset Distillation by Matching Training Trajectories"
Official project website for the CVPR 2020 paper (Oral Presentation) "Cascaded deep monocular 3D human pose estimation wth evolutionary training data"
[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
Synthetic Minority Over-Sampling Technique for Regression
Synthetic Image generation with Flip. Generate thousands of new 2D images from a small batch of objects and backgrounds.
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