fiftyone
FiftyOne is an open-source platform for building, cleaning, and evaluating computer vision datasets and models. It provides visualization, labeling, and quality assessment tools designed to streamline data-centric AI workflows.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | voxel51/fiftyone |
| Owner | voxel51 |
| Primary language | TypeScript |
| License | Apache-2.0 — OSI-approved |
| Stars | 10.9k |
| Forks | 792 |
| Open issues | 670 |
| Latest release | v1.18.0 (2026-07-02) |
| Last updated | 2026-07-08 |
| Source | https://github.com/voxel51/fiftyone |
What fiftyone is
Built primarily in TypeScript with Python support, FiftyOne offers dataset management, model evaluation, active learning, and vector search capabilities. It integrates MongoDB for persistence and supports deployment via pip/Docker with a web-based UI.
Get the fiftyone source
Clone the repository and explore it locally.
git clone https://github.com/voxel51/fiftyone.gitcd fiftyone# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Python 3.10–3.12 and Node.js for source builds; plan virtual environment isolation and dependency management.
- MongoDB dependency: confirm internal MongoDB availability or plan containerized MongoDB deployment for persistent dataset storage.
- Web UI requires Yarn build step; validate CI/CD pipeline integration for automated app rebuilds when pulling from develop branch.
- Open-source lacks multi-user collaboration, authentication, and fine-grained access control; supplementary auth/proxy layer may be needed.
- Test with your dataset size and model count to assess performance; no published benchmarks for concurrent user load or dataset scale limits provided.
When to avoid it — and what to weigh
- Centralized Enterprise Governance Required — FiftyOne open-source lacks built-in RBAC, audit logging, and multi-tenant controls. FiftyOne Enterprise exists for such needs, but open-source deployment may not meet compliance frameworks.
- Heavy Real-Time Inference Serving — FiftyOne is designed for offline dataset/model analysis, not production model serving or real-time batch scoring. Use model serving frameworks instead.
- Non-Vision Unstructured Data at Scale — While tagged for unstructured data, FiftyOne is optimized for computer vision. Text, audio, or multi-modal workflows may lack native tooling.
- Minimal DevOps/Infrastructure Tolerance — Self-hosted deployments require MongoDB setup, Python environment management, and Node.js for the UI. Cloud-native orchestration support is not clearly stated.
License & commercial use
Licensed under Apache License 2.0, a permissive OSI license allowing commercial use, modification, and distribution with attribution and liability disclaimers.
Apache 2.0 permits commercial use without royalties. However, the README references FiftyOne Enterprise as a separate product for production-grade, collaborative, cloud-native workloads. Evaluate whether open-source or Enterprise tier aligns with your SLA, support, and feature requirements before committing.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
MongoDB should be secured (authentication, network isolation) if exposed. Web UI served locally by default but no built-in authentication in open-source. Data classification, encryption at rest/in transit, and access controls are not detailed; assess compliance needs before handling sensitive datasets. Third-party dependency vulnerabilities should be monitored via pip/npm audits.
Alternatives to consider
Roboflow
Cloud-native, multi-user dataset management with built-in annotation and model deployment. Better for teams needing SaaS collaboration but less control over infrastructure.
Label Studio
Open-source annotation platform with broader data type support (text, audio, images). More focused on labeling than model evaluation and curation.
Encord
Enterprise-grade data platform with video/3D annotation, QA workflows, and access controls. Proprietary but purpose-built for regulated environments.
Build on fiftyone with DEV.co software developers
Start with pip install fiftyone and explore the Colab quickstart. For production-grade collaboration and enterprise controls, evaluate FiftyOne Enterprise.
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fiftyone FAQ
Can we use FiftyOne open-source in production?
What are the infrastructure requirements?
How does FiftyOne handle large datasets?
Is there technical support?
Work with a software development agency
Need help beyond evaluating fiftyone? DEV.co is a software development agency offering software development services and web development for teams of every size. Our software developers and web developers build custom software, web applications, APIs, and vector databases integrations — and maintain them long-term.
Ready to refine your vision AI datasets?
Start with pip install fiftyone and explore the Colab quickstart. For production-grade collaboration and enterprise controls, evaluate FiftyOne Enterprise.