FiftyOne

What FiftyOne is

FiftyOne Annotation for images helps AI and machine-learning teams find samples to label, automate annotation with AI, and connect labeling to model evaluation. It supports bounding boxes, polygons, segmentation masks, classifications, polylines, and SAM2 click-to-segment instance segmentation. A labeling agent can create pre-labels from natural-language prompts, which users can combine with human review.

Image projects can use multi-stage workflows with review stages, rejection loops, and quality gates. Listed annotation integrations include CVAT, Label Studio, V7, and Labelbox; vector search integrations include Qdrant, Redis, and Pinecone. Deployment options include cloud or multi-cloud, hybrid, on-premise, air-gapped, and managed services. FiftyOne says datasets stay in the customer's environment and are not stored or transmitted by the platform. It also supports anonymization of personally identifiable and protected health information.

FiftyOne is open source and listed for API, Linux, macOS, self-hosted, web, and Windows. Voxel51 describes it as a platform for AI builders and machine-learning teams working with multimodal data at scale. Team, Growth, and Custom plans are listed with contact-sales or custom-pricing billing; their seat, compute, and deployment allowances vary.

Who it suits

FiftyOne suits AI builders and machine-learning teams that need image annotation workflows connected to model evaluation. Teams can use its AI-assisted pre-labeling, human review stages, integrations, and deployment options for data that remains in their environment.

Limits

The Team plan lists 8 user seats, 16 guest seats, 4 VPUs, 2,800 compute hours per month, one production deployment, and one dev/staging deployment. Growth lists 25 user seats, 100 guest seats, 20 VPUs, 14,000 compute hours per month, and three production and three dev/staging deployments.

Getting started

1. Visit https://voxel51.com/annotation/image.
2. Choose a supported environment: API, Linux, macOS, self-hosted, web, or Windows.
3. Select image samples for annotation.
4. Use a natural-language prompt to generate pre-labels, then combine them with human review.
5. Build a multi-stage workflow with review stages, rejection loops, or quality gates as needed.
6. Choose a deployment option such as cloud, hybrid, on-premise, or air-gapped.

Verdict

Choose FiftyOne if your team needs flexible image annotation, AI-assisted pre-labeling, and connections to model evaluation. Teams comparing paid Team, Growth, or Custom plans should confirm the appropriate seat, compute, and deployment allowances with the provider.