Supervisely
Supervisely is a comprehensive computer vision platform that provides an end-to-end ecosystem for data labeling, neural network training, and application development to accelerate your entire AI development lifecycle.
V7 is an AI data engine providing a unified platform for training data labeling, automated annotation, and model management to accelerate the development of computer vision applications.
Quick Comparison
| Feature | Supervisely | V7 |
|---|---|---|
| Website | supervisely.com | v7labs.com |
| Pricing Model | Freemium | Subscription |
| Starting Price | Free | Free |
| FREE Trial | ✓ 0 days free trial | ✓ 14 days free trial |
| Free Plan | ✓ Has free plan | ✘ No free plan |
| Product Demo | ✓ Request demo here | ✓ Request demo here |
| Deployment | ||
| Integrations | ||
| Target Users | ||
| Target Industries | ||
| Customer Count | 0 | 0 |
| Founded Year | 2017 | 2018 |
| Headquarters | Limassol, Cyprus | London, UK |
Overview
Supervisely
Supervisely provides a unified operating system for computer vision that handles everything from data ingestion to model deployment. You can manage massive datasets, annotate images and videos with AI-assisted tools, and train neural networks without leaving the platform. It eliminates the need to stitch together fragmented tools, allowing your entire team to collaborate in a single environment.
You can customize the platform by building your own apps or using hundreds of pre-built ones from the Supervisely Ecosystem. Whether you are working on autonomous driving, medical imaging, or industrial inspection, the platform scales to meet your specific project requirements. It simplifies the transition from raw data to production-ready AI models while maintaining high data quality standards.
V7
V7 is an automated training data platform designed to help you build and deploy computer vision models faster. You can manage the entire AI lifecycle in one place, from uploading raw images and video to labeling data with AI-powered tools and monitoring model performance. It eliminates the need for fragmented tools by combining data management, manual annotation, and automated workflows into a single, collaborative environment.
You can automate up to 90% of your labeling tasks using the platform's 'Auto-Annotate' feature, which identifies object boundaries with high precision. Whether you are a small research team or a large enterprise in healthcare, manufacturing, or autonomous driving, V7 helps you maintain high data quality while significantly reducing the time spent on manual tasks. It scales with your needs, offering robust API access and seamless team collaboration features.
Overview
Supervisely Features
- AI-Assisted Labeling Speed up your annotation process using interactive AI tools that automatically segment objects and track them across video frames.
- Data Management Organize and visualize millions of images or videos with powerful filtering, tagging, and versioning capabilities to keep your datasets clean.
- Supervisely Ecosystem Access hundreds of open-source apps and neural networks to extend your platform's functionality without writing code from scratch.
- Neural Network Training Train popular models like YOLO or Mask R-CNN directly on your data using integrated training dashboards and GPU monitoring.
- Quality Assurance Set up multi-stage review workflows and automated tests to ensure your labels meet the highest accuracy standards for production.
- Custom App Development Build your own Python-based applications to automate specific tasks or create custom interfaces tailored to your unique business needs.
V7 Features
- AI Auto-Annotation. Create complex polygons and masks in seconds by simply clicking on objects, reducing your manual labeling time by up to 90%.
- Video Labeling. Annotate video files with frame-by-frame precision and use object tracking to automatically follow items across multiple frames.
- Dataset Management. Organize millions of images and videos with powerful filtering, versioning, and metadata tagging to keep your training data structured.
- Real-time Collaboration. Work together with your team in real-time, assign tasks to labelers, and use built-in chat to resolve data ambiguities quickly.
- Quality Control Workflows. Build custom multi-stage review pipelines to ensure every annotation meets your accuracy standards before it reaches your model.
- Model Management. Deploy your trained models as labeling assistants or run them in the cloud to automate your data pipeline end-to-end.
Pricing Comparison
Supervisely Pricing
- Free for individuals
- Access to Ecosystem apps
- Standard labeling tools
- Community support
- SaaS deployment
- Everything in Community, plus:
- Increased storage limits
- Advanced team collaboration
- Priority technical support
- Custom app development
- Enhanced data management
V7 Pricing
- For students and researchers
- Auto-Annotate tool access
- Up to 100 images
- Community support
- Public datasets only
- Everything in Education, plus:
- Private datasets
- Priority support
- Advanced video labeling
- API and CLI access
- Custom workflow stages
Pros & Cons
Supervisely
Pros
- Comprehensive end-to-end workflow in one platform
- Extensive library of pre-built ecosystem applications
- Powerful video annotation and object tracking
- Flexible Python SDK for custom automation
- User-friendly interface for non-technical annotators
Cons
- Significant learning curve for advanced features
- Self-hosting setup requires technical expertise
- Pricing for enterprise tiers is not public
V7
Pros
- Auto-annotate tool is exceptionally fast and accurate
- Intuitive interface makes it easy to onboard new labelers
- Superior handling of high-resolution medical imaging files
- Robust API allows for deep integration into existing pipelines
Cons
- Pricing can be high for very small startups
- Occasional lag when handling extremely large video files
- Learning curve for setting up complex automated workflows