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.

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

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

The Declarative Data Generator

Conditional GAN for generating synthetic tabular data.

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.

Random dataframe and database table generator

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

Generate random data sets

A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.