Supervisely is a comprehensive computer vision platform that combines data annotation, model training, and deployment into a unified web-based operating system — providing AI-assisted annotation tools (smart polygon snapping, interactive segmentation), a plugin marketplace for custom functionality, and native support for 3D volumetric data (LiDAR point clouds, medical CT/MRI scans), making it the preferred platform for autonomous driving, medical imaging, and agricultural computer vision teams that need end-to-end ML workflows.

What Is Supervisely?

• Definition: A web-based "Operating System for Computer Vision" that provides integrated tools for data annotation, dataset management, model training, and deployment — unlike annotation-only tools, Supervisely covers the complete CV pipeline from raw data to deployed model.
• Smart Annotation Tools: AI-powered labeling tools that accelerate annotation — Smart Tool (click an object, the polygon snaps to its edges using edge detection), Interactive Segmentation (SAM-based click-to-segment), and AI-assisted tracking for video sequences.
• Apps Ecosystem: A plugin marketplace (like an app store) where teams can add custom functionality — custom neural network training apps, data augmentation pipelines, format converters, and quality assurance tools, all running as Docker containers within the platform.
• 3D and Volumetric: Native support for LiDAR point cloud annotation (3D bounding boxes, cuboids), medical imaging (DICOM viewers for CT/MRI with slice-by-slice annotation), and multi-sensor fusion (camera + LiDAR synchronized annotation).

Key Features

• Annotation Types: 2D (bounding boxes, polygons, polylines, keypoints, bitmap masks), 3D (cuboids, point cloud segmentation), video (object tracking, temporal segmentation), and medical (DICOM slice annotation, volumetric segmentation).
• Team Collaboration: Role-based access control (admin, manager, annotator, reviewer), project-level permissions, labeling job queues with assignment and deadline tracking, and real-time collaboration on shared datasets.
• Neural Network Integration: Train YOLO, Mask R-CNN, UNet, and custom architectures directly within the platform — use trained models as Smart Tools for AI-assisted annotation, creating a feedback loop between annotation and model improvement.
• Data Versioning: Git-like versioning for datasets — track changes, create snapshots, compare annotation versions, and roll back to previous states.

Supervisely Use Cases

Supervisely is the end-to-end computer vision platform that unifies annotation, training, and deployment — providing AI-assisted labeling tools, 3D volumetric support, and a plugin ecosystem that enables CV teams to build complete machine learning pipelines from raw sensor data to deployed models without switching between disconnected tools.