Projects sharing features with Lightly Studio

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

CVAT is an open-source, web-based platform designed for annotating images, videos, and 3D point clouds to create high-quality training datasets for machine learning. It functions as a containerized server that orchestrates the entire lifecycle of computer vision data, from initial task creation and manual labeling to quality assurance and final dataset export. The platform distinguishes itself through deep integration with machine learning models, allowing users to deploy custom AI models as serverless functions for automated object detection, tracking, and skeleton annotation. It supports co

PigeonXT is an extention to the original Pigeon, created by Anastasis Germanidis. PigeonXT is a simple widget that lets you quickly annotate a dataset of unlabeled examples from the comfort of your Jupyter notebook.

Data Preparation for Satellite Machine Learning

The AI Datastore for Schemas, BLOBs, and Predictions. Use with your apps or integrate built-in Human Supervision, Data Workflow, and UI Catalog to get the most value out of your AI Data.

We well know GANs for success in the realistic image generation. However, they can be applied in tabular data generation. We will review and examine some recent papers about tabular GANs in action.

Doccano is a collaborative data labeling platform and machine learning dataset management system. It provides a web-based interface for teams to import raw text, mark datasets, and export structured annotations for model training. The project specifically supports text annotation for classification and named entity recognition tasks. It enables teams to coordinate multiple users on a single project to maintain consistent labeling guidelines and increase the speed of dataset creation. The system includes tools for data management and team coordination, providing the ability to import raw data

Image viewer. Fast, easy to use. Optional video support.

An open source online platform for collaborative image labeling

Label Studio is a multi-type data labeling tool and data annotation workspace designed to prepare datasets for machine learning training. It functions as a cloud-integrated data pipeline that imports raw data from storage, manages the annotation process, and exports labels into standardized formats. The platform features a machine learning model integration framework that connects to external model servers. This enables model-assisted annotation and active learning, allowing the system to perform pre-labeling and refine predictions based on human feedback. The software provides project manag

Web labeling tool for bitmap images and point clouds

Library for multiscale visualization of high-resolution multiplexed bioimaging data on the web. Directly renders Zarr and OME-TIFF.

Label Studio is a multi-modal data annotation platform designed to create and manage high-quality training datasets for machine learning. It functions as a self-hosted, containerized environment that supports secure, private deployments, including air-gapped configurations. The platform provides a centralized workspace for labeling diverse media types, such as images, text, audio, and time-series data, to support supervised and reinforcement learning workflows. The platform distinguishes itself through deep integration with machine learning backends, enabling active learning loops, automated

Leverage machine learning algorithms to easily segment, classify, track and count your cells or other experimental data. Most operations are interactive, even on large datasets: you just draw the labels and immediately see the result. No machine learning expertise required.

Label-Detect

Create rich adata annotations in jupyter notebooks.

Argilla is a collaborative AI feedback tool and data curation management system. It serves as a human-in-the-loop dataset platform designed to coordinate workforce annotators and domain experts in labeling, rating, and refining data samples for machine learning projects. The platform focuses on large language model dataset curation and reinforcement learning from human feedback workflows. It provides a shared workspace for integrating human expertise into AI development to validate model outputs and correct data errors. The system manages the end-to-end machine learning data pipeline, includ

Practical and minimal image viewer

A cross-platform desktop image annotation tool for machine learning

An image viewer with vim-like keybindings

OpenLabeler is an open source desktop application for annotating objects for AI appplications

A collaborative tool for labeling image data for yolo