Find Open-Source Alternatives
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Discover powerful open-source replacements for popular commercial software. Save on costs, gain transparency, and join a community of developers.
Compare community-driven replacements for Supervisely in data labeling & annotation workflows. We curate active, self-hostable options with transparent licensing so you can evaluate the right fit quickly.
These projects match the most common migration paths for teams replacing Supervisely.
Why teams pick it
Organizations needing on-premise control over labeled data
Run on infrastructure you control
Recent commits in the last 6 months
MIT, Apache, and similar licenses
Counts reflect projects currently indexed as alternatives to Supervisely.
Why teams pick it
Docker images for quick self‑hosted deployment
Collaborative video and image annotation platform for computer vision
Why teams choose it
Watch for
Online free tier limits tasks and data size
Migration highlight
Semantic segmentation dataset creation
Produce high‑quality pixel masks for training segmentation models
Collaborative platform for building high-quality AI datasets
Intuitive Python tool for polygonal image and video annotation
Collaborative web‑based text annotation for fast ML dataset creation
Flexible, multi-type data labeling platform for modern ML pipelines.
Teams replacing Supervisely in data labeling & annotation workflows typically weigh self-hosting needs, integration coverage, and licensing obligations.
Tip: shortlist one hosted and one self-hosted option so stakeholders can compare trade-offs before migrating away from Supervisely.
Why teams choose it
Watch for
No new feature development planned
Migration highlight
Refugee request triage for humanitarian aid
Domain experts classified incoming messages, enabling the Red Cross to route assistance faster and improve response accuracy.
Why teams choose it
Watch for
GPL‑3.0 license may limit use in proprietary software
Migration highlight
Semantic segmentation dataset creation
Generate VOC/COCO masks for training segmentation models
Why teams choose it
Watch for
Requires Python 3.8+ for pip installation
Migration highlight
Sentiment analysis dataset creation
Rapidly label thousands of tweets with positive, neutral, or negative tags using the classification UI.
Why teams choose it
Watch for
Self-hosting requires managing infrastructure and updates
Migration highlight
Image classification dataset creation
Annotators label thousands of images via web UI and export COCO format for training a vision model.