30 open-source projects similar to openvinotoolkit/cvat, ranked by shared indexed features. Tags may describe platforms or build tools rather than the same primary purpose. Check each project’s use case, license, and deployment requirements before treating it as a replacement.
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
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.
A collaborative tool for labeling image data for yolo
A data-centric annotation tool to increase the accuracy of your Named Entity Recognition projects which helps
Images to inference with no labeling (use foundation models to train supervised models).
The Bio-Image Indexing and Graphical Labelling Environment (BIIGLE) is a web service for the efficient and rapid annotation of still images and videos. Read the paper or take a look at the manual.
An open source online platform for collaborative image labeling
Collaborative Annotation Toolkit for Massive Amounts of Image Data
:fire: One of the most comprehensive open-source data annotation platform.
A lightweight tool for labeling 3D bounding boxes in point clouds.
DataGym.ai is a modern, web based workbench to label images and videos. It allows you to manage your projects and datasets, label data, control quality and build your own training data pipeline. With DataGym.ai´s API and Python SDK you can integrate it into your toolchain.
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
QSL is a simple, open-source media labeling tool that you can use as a Jupyter widget. More information available at https://qsl.robinbay.com. It supports:
Adala: Autonomous DAta (Labeling) Agent framework
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.
Create rich adata annotations in jupyter notebooks.
:pencil2: Web-based image segmentation tool for object detection, localization, and keypoints
A cross-platform desktop image annotation tool for machine learning
OpenLabeler is an open source desktop application for annotating objects for AI appplications
KNOSSOS is a software tool for the visualization and annotation of 3D image data and was developed for the rapid reconstruction of neural morphology and connectivity.
Label Objects and Save Time (LOST) - Design your own smart Image Annotation process in a web-based environment.
The open platform for image labeling Try it now » Explore docs · Report Bug · Request Feature · Join the Community
Curate, Annotate, and Manage Your Data in LightlyStudio.
A JavaFX desktop application for creating image-object-annotations with bounding boxes.
VoTT is a computer vision annotation software and machine learning dataset preparation tool. It is a desktop application designed for drawing bounding boxes and assigning tags to objects in images and videos to create training datasets for object detection models. The application utilizes a cross-platform desktop interface to manage image and video assets. It features a local-first storage integration to handle large media assets directly from the host machine's file system and includes frame-rate controlled video sampling to extract specific images from video streams for labeling. The softw
To speedup and simplify image labeling/ annotation process with multiple supported formats.
Viewers is a zero-footprint DICOMweb medical imaging viewer and a modular plugin framework. It serves as a diagnostic interface for rendering 2D and 3D medical images, providing a web-based clinical workflow engine to automate image layouts and toolsets. The project distinguishes itself through a highly extensible architecture that allows for the development of custom clinical workflows, specialized viewing modes, and the integration of external functional extensions. It includes a dedicated command line interface for managing these plugins and supports white-labeling through a comprehensive