Open-source SuperAnnotate alternatives

Label Studio is an open-source tool for labeling and annotating data. It handles audio, text, images, videos and time series through a straightforward interface, and exports the labels in formats suited to various machine learning models. Teams use it to prepare raw data or to improve existing training data so models become more accurate. It is written mainly in TypeScript and released under the Apache-2.0 license.

You can install it locally with Docker, Docker Compose, pip, poetry or Anaconda, or deploy it on a cloud instance. The Docker Compose option provides a production-ready stack of Label Studio, Nginx and PostgreSQL. By default the Docker image serves the app on port 8080 and stores a SQLite database and uploaded files in a local directory. The README also covers labeling templates, connecting machine learning models and integrating with existing tools, and mentions a Starter Cloud edition with a free trial.

CVAT, the Computer Vision Annotation Tool, is a platform for building labeled datasets for computer vision and other visual AI. The Community edition in this repository is the free, self-hosted open-source version, and it supports image, video and 3D annotation along with dataset management.

Teams can work together with multi-user and multi-organization support, roles, task assignments and review workflows. Users can connect their own machine learning models for detection, segmentation and tracking to speed up labeling, integrate cloud storage, and automate work through SDKs and APIs. Running it in your own infrastructure keeps data inside your environment.

The community core is MIT licensed and written mainly in Python, and it forms the base for CVAT Online, a cloud product, and CVAT Enterprise, which the company also offers alongside labeling services. Topics on the repository cover bounding boxes, semantic segmentation, object detection and tools such as TensorFlow and PyTorch.