Build SuperData for Your AI

The end-to-end platform to annotate, train and automate your AI.

superannotate annotation tool

Level up your ML model performance

Scale annotation and computer vision projects of all sizes using the smartest tools,
robust data management systems, and best-in-class outsourced services.

Robust tooling

Scale your image and video annotation projects with advanced and easy-to-use tools to address even your most sophisticated annotation needs.

Streamlined collaboration and quality management

Multi-level quality management and effective collaboration drive successful projects and boost model performance. Set up a detailed annotation workflow, approve and disapprove instances and images, add comments, monitor project and team analytics, and more.

███████████████████████████████████████████████████████████████| 20/20 [00:12<00:00, 1.59it/s] ['___save.png', '___fuse.png']. SA-PYTHON-SDK - INFO - Uploading 20 images to project Vector Project. 100% | from /Users/varduhi/Desktop/archive to project Vector Project. Excluded file patterns are: ['jpg', 'jpeg', 'png', 'tif', 'tiff', 'webp', 'bmp'] = "./images", for more information. >>> import superannotate as sa >>> sa.upload_images_from_folder_to_project('Vector Project','/Users/john/Desktop/archive') (env3) johnwhick@johns-MacBook-Pro ~ % Python 3.8.6 (default, Oct 8 2020, 14:06:32) [Clang 12.0.0 (clang-1200.0.32.2)] on darwin Type "help", "copyright", "credits" or "license" SA-PYTHON-SDK - INFO - Uploading all images with extensions Images Classes Analytics Contributors Settings Download Workflow Status Assignee Project Name Projects Workflow Download Contributors Classes Image Settings Analytics

Seamless pipeline integration

Work smarter, not harder. Integrate your computer vision pipeline using Python SDKs to streamline and automate your data, user, and project management tasks. You can also import images by linking them from external storages. The linked images are displayed in SuperAnnotate, but they are not stored on our local servers.

Powerful data exploration

Curate a dataset of your project’s images and review the quality of your annotations to create top-performing training models.