Supervisely

Categories: Coding & Developer Tools, Data Analysis, Automation / Agents | Pricing: Freemium | Official Website ↗

Supervisely is an all-in-one platform for computer vision, enabling users to curate, label, and build production models for various data types.

Supervisely provides a comprehensive ecosystem for computer vision development, covering the entire lifecycle from data annotation to model deployment. It offers advanced labeling toolboxes for images, videos, 3D point clouds, and medical DICOM scans, enhanced with AI assistance and automation features. The platform supports custom labeling UIs and workflows, ensuring high-quality training data creation. Beyond labeling, Supervisely includes robust data management capabilities for organizing local and cloud data, ensuring quality assurance, and facilitating user collaboration with security features. It also provides infrastructure for training, serving, and applying neural networks, allowing users to query, transform, visualize, and improve models, and even generate synthetic data. The platform is highly customizable with an API, SDK, and AppEngine, and offers enterprise solutions for scalable AI infrastructure.

Key Features

Image labeling with AI assistance
Video labeling with AI tracking and segmentation
3D Point Cloud & LiDAR labeling
DICOM & Volumetric Medical multi-slice scan labeling
Data management and quality assurance
Neural network training, serving, and application
Custom labeling UIs and workflows
API and SDK for automation

Pros

Comprehensive suite for the entire computer vision pipeline
Supports diverse data modalities including images, videos, 3D, and medical scans
Offers advanced AI-assisted labeling and automation tools
Provides robust data management and collaboration features
Highly customizable with API, SDK, and AppEngine

Cons

Pricing for advanced features can increase with add-ons
Requires technical expertise for full utilization of customization options
Free plan has limitations on storage, files, and retention
Video and 3D labeling features are more expensive add-ons
No explicit mention of mobile platform support

Use Cases

Creating high-quality training datasets for computer vision models
Developing and deploying AI models for object detection, segmentation, and tracking
Managing and curating large volumes of image, video, and 3D data
Automating data labeling processes with AI assistance
Collaborating on computer vision projects across teams

Best For

Computer vision engineers
Machine learning researchers
Data scientists
Startups and enterprises developing AI models
Teams requiring high-quality data annotation at scale

Integrations: AWS S3, Google Cloud Storage, Azure, OpenID, LDAP, Microsoft Active Directory, OAuth2

Platforms: Web

Watch demo on YouTube ↗

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