FiftyOne Teams
By Voxel51
FiftyOne Teams is a cloud-based collaborative platform for managing and analyzing visual AI datasets, designed for enterprise teams working on computer vision, medical imaging, and autonomous systems.
Publisher review
FiftyOne Teams is a cloud-based collaborative platform for managing and analyzing visual AI datasets, designed for enterprise teams working on computer vision, medical imaging, and autonomous systems. It extends the open-source FiftyOne toolkit with centralized dataset management, versioning, and role-based access controls. The platform targets ML engineers and data scientists at organizations like Walmart, GM, and Medtronic who need to scale visual AI workflows across distributed teams. Its core use cases include quality inspection in manufacturing, medical diagnosis from imaging data, and 3D object detection for autonomous vehicles.
The platform provides cloud-backed media storage with support for image, video, and 3D point cloud data, plus tools for detection boxes, embeddings visualization, and model comparison via radar charts. Teams can run 2,800 to 14,000 compute hours monthly depending on plan, with automated workflows for annotation and smart data selection. The system identifies labeling errors through its 'mistakenness' scoring algorithm and offers sample-level diagnostics. Enterprise features include SSO, on-premise deployment options, and dedicated customer success engineers for growth-tier subscribers.
FiftyOne competes with Roboflow for dataset management and Prolific for annotation workflows, differentiating with its multimodal data support and physics-based AI focus. Unlike SuperAnnotate's narrower annotation tools, it combines model evaluation metrics (mAP, IoU) with dataset curation in one interface. The platform's GSuite-like collaboration model appeals to teams managing complex visual data pipelines, though it lacks some specialized medical imaging features found in pure healthcare AI tools.
Trade-offs include compute hour limits on lower tiers (2,800/month for Team plan) and no per-user pricing flexibility. While offering unlimited data storage, the platform requires VPU-based compute allocation that may bottleneck large-scale processing. The open-source foundation allows customization but necessitates technical expertise for advanced deployments. Some competitors offer more turnkey solutions for specific verticals like retail or healthcare.
How it works
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Cloud dataset versioning
Tracks changes across dataset iterations with full history, enabling rollbacks and collaborative editing across teams.
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Role-based access control
Provides 8-25 user seats per plan with guest access, enforcing permissions via SSO and enterprise security policies.
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3D object detection
Supports bounding boxes and point cloud annotations for autonomous vehicle and robotics applications.
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Model performance analytics
Generates radar charts comparing metrics like mAP and F1 scores across multiple computer vision models.
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Medical imaging AI
Detects fractures in X-rays with confidence scoring, though lacks some specialty healthcare integrations.
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Automated annotation workflows
Combines manual labeling tools with AI-assisted pre-annotation to accelerate dataset preparation.
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Embeddings visualization
Clusters images by semantic similarity using t-SNE plots to identify dataset gaps or biases.
Strengths and trade-offs
Strengths
- Processes unlimited data storage across all pricing tiers, removing volume constraints for large visual datasets.
- Provides 3 production deployments in Growth tier, enabling parallel testing of computer vision models.
- Identifies labeling errors through proprietary 'mistakenness' scoring that ranks probable annotation mistakes.
- Supports 3D point cloud data and medical imaging natively, unlike many general-purpose ML platforms.
Trade-offs
- Team plan limits compute to 2,800 hours/month, requiring upgrades for large-scale processing workloads.
- Lacks vertical-specific templates for healthcare or retail that some competitors provide.
- VPU-based compute allocation complicates cost forecasting compared to per-user pricing models.
- On-premise deployment requires Growth tier or higher, excluding smaller teams needing air-gapped security.
Pricing context
Team plan: 8 users, 16 guests, 4 VPUs, 2,800 compute hours. Growth plan: 25 users, 100 guests, 20 VPUs, 14,000 compute hours. Custom plan: Unlimited resources with dedicated engineering support.
Getting started with FiftyOne Teams
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Sign up
Create an account on the FiftyOne Teams website. Choose between Team, Growth, or Custom plans based on your user count and compute needs.
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Upload datasets
Connect your cloud storage or upload image, video, or 3D point cloud files directly. Organize datasets into projects with descriptive names.
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Set permissions
Configure role-based access for team members and guests. Assign viewer, editor, or admin roles through the SSO integration.
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Annotate data
Draw bounding boxes on images or mark points in 3D space. Use AI-assisted tools to pre-label and manually refine annotations.
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Compare models
Upload model predictions to generate radar charts. Analyze mAP and IoU metrics across different computer vision models.
Frequently Asked Questions
What is FiftyOne Teams used for?
FiftyOne Teams is a cloud platform for managing visual AI datasets across teams, supporting computer vision, medical imaging, and autonomous systems. It handles image, video, and 3D point cloud data with tools for annotation, model comparison, and error detection. Enterprise features include versioning and role-based access controls. (50 words)
How does FiftyOne Teams handle medical imaging data?
The platform processes medical imaging like X-rays with confidence scoring for fractures, though lacks some specialty healthcare integrations. It supports dataset versioning and annotations for diagnosis workflows, making it suitable for medical AI teams needing collaborative tools without full PACS system functionality. (48 words)
What are the compute limits for FiftyOne Teams?
The Team plan provides 2,800 compute hours monthly, while Growth offers 14,000 hours. Compute uses VPU-based allocation rather than per-user pricing. Large-scale processing may require plan upgrades, though all tiers include unlimited data storage. Custom plans remove these limits. (48 words)
How does FiftyOne compare to Roboflow or Labelbox?
Unlike Roboflow's dataset focus or Labelbox's annotation tools, FiftyOne combines model evaluation metrics with dataset management. It uniquely supports 3D point clouds and offers physics-based AI features, though lacks some vertical-specific templates competitors provide for retail or healthcare. (47 words)
Can FiftyOne Teams run on-premise?
On-premise deployment requires at least the Growth plan (25 users) or custom enterprise contracts. Smaller teams needing air-gapped security face limitations, as the Team plan only supports cloud deployment. All plans include SSO and role-based access controls for security. (47 words)
What visualization tools does FiftyOne offer?
The platform provides radar charts for model comparison (mAP, IoU), t-SNE plots for embeddings visualization, and tools for 3D bounding boxes in point clouds. Its 'mistakenness' scoring highlights probable labeling errors, while smart sampling helps identify dataset gaps. (48 words)
Alternatives
How FiftyOne Teams compares
Direct head-to-head against 3 competitors. Picked by 7wData.
FiftyOne Teams
- Pricing
- Team plan: 8 users, 16 guests, 4 VPUs, 2,800 compute hours. Growth plan: 25 users, 100 guests, 20 VPUs, 14,000 compute hours. Custom plan: Unlimited resources with dedicated engineering support.
- Target
- FiftyOne Teams is a cloud-based collaborative platform for managing and analyzing visual AI datasets, designed for enterprise teams working on computer vision, medical imaging, and
- Strength
- Processes unlimited data storage across all pricing tiers, removing volume constraints for large visual datasets.
- Watch for
- Team plan limits compute to 2,800 hours/month, requiring upgrades for large-scale processing workloads.
Roboflow
- Pricing
- Free tier, $19/user/month for Pro, custom enterprise
- Target
- Computer vision teams needing end-to-end pipelines
- Deployment
- Cloud, on-prem, hybrid
- Strength
- Pre-built CV workflows with model training
- Watch for
- Compute limits on lower tiers
Labelbox
- Pricing
- $25/user/month Starter, custom enterprise
- Target
- Enterprise ML teams at scale
- Deployment
- Cloud or private cloud
- Strength
- Granular quality controls for annotations
- Watch for
- Minimum $50k annual commitment
Encord
- Pricing
- Custom/Contact sales
- Target
- Medical imaging and multimodal AI teams
- Deployment
- Cloud, on-prem, air-gapped
- Strength
- Specialized for DICOM and DICOM-like data
- Watch for
- Steep learning curve for non-medical users
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Sources
Reporting on this tool draws on these publicly available sources.