Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything

Synthetic data generation for tabular data

A powerful, feature-rich, random test data generator.

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to GitHub repos, papers, and others.

🎨 NeMo Data Designer: Generate high-quality synthetic data from scratch or from seed data.

Conditional GAN for generating synthetic tabular data.

The Declarative Data Generator

GraphGen: Enhancing Supervised Fine-Tuning for LLMs with Knowledge-Driven Synthetic Data Generation

Data generation and property-based testing for Elixir. 🔮

CAIRI Supervised, Semi- and Self-Supervised Visual Representation Learning Toolbox and Benchmark

A library to model multivariate data using copulas.

MockNeat - the modern faker lib.

Generate strings that match a given regular expression

Deep Convolutional Neural Networks for Musical Source Separation

Generate relevant synthetic data quickly for your projects. The Databricks Labs synthetic data generator (aka `dbldatagen`) may be used to generate large simulated / synthetic data sets for test, POCs, and other uses in Databricks environments including in Delta Live Tables pipelines

C++ Faker library for generating fake (but realistic) data.

Genalog is an open source, cross-platform python package allowing generation of synthetic document images with custom degradations and text alignment capabilities.

A novel approach for synthesizing tabular data using pretrained large language models

Random dataframe and database table generator