fiftyone

What it is and what it does

FiftyOne is an open-source Python framework designed to streamline the entire computer vision workflow—from dataset curation and annotation through model evaluation and iteration. It provides integrated tools for visualizing image and video data, labeling datasets collaboratively, evaluating model predictions, and identifying data quality issues. The framework sits between raw datasets and model training pipelines, helping teams understand their data and debug model behavior more efficiently than manual inspection.

The package is built on a substantial dependency stack including MongoDB for data persistence, async I/O libraries for concurrent operations, and visualization tools like Plotly. It supports Python 3.10–3.13 and runs on Linux, macOS, and Windows. While the open-source version is suitable for individual and team workflows, the project also offers an enterprise variant for production-scale, cloud-native deployments.

Use it for:

• Visualize and explore large image or video datasets to understand data distribution and identify labeling errors before training.
• Annotate and curate datasets collaboratively, with built-in tools for tagging, filtering, and organizing samples.
• Evaluate model predictions on test sets, compare multiple models, and identify failure modes or systematic biases.
• Debug model performance by correlating prediction errors with dataset characteristics and metadata.
• Manage dataset versioning and track changes across annotation rounds and model iterations.

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

FiftyOne is a Python framework for building, visualizing, and evaluating computer vision datasets and models, with integrated labeling, model evaluation, and data quality tools.

Yes. FiftyOne is actively maintained, has no known vulnerabilities, and offers low install friction. It is well-suited for teams building computer vision systems who need integrated dataset management and model evaluation. The permissive Apache license and broad platform support (Python 3.10–3.13, Linux/macOS/Windows) make it accessible. The large dependency footprint and MongoDB requirement are manageable trade-offs for the functionality gained. Install if you are working with image or video datasets and need visualization, labeling, and evaluation tools in a single framework.

Install

fiftyone on PyPI

pip install fiftyone

uv add fiftyone

poetry add fiftyone

Installing fiftyone

Before you install

Low install friction with a pure-Python wheel distribution. Active maintenance with a recent release (9 days old) and 11015 repository stars. Requires Python 3.10–3.13 and depends on 49 runtime packages including MongoDB integration (mongoengine, motor, pymongo), async utilities, and visualization libraries (plotly, Pillow).

License in practice

Apache License (permissive) allows commercial and private use, modification, and distribution with minimal restrictions, making it suitable for both open-source and proprietary projects.

Quickstart

pip install fiftyone

import fiftyone as fo

dataset = fo.Dataset(name="my_dataset")
print(dataset)

Requires Python 3.10–3.13. MongoDB backend is embedded by default but can be configured to use a self-managed instance. FFmpeg is optional but needed for video dataset support.

Verify before relying

• Whether the 49 runtime dependencies are all required by default or only for specific features
• Performance characteristics and scalability limits for large datasets
• Whether the embedded MongoDB instance is production-ready or intended only for development

Package facts

Evidence: fiftyone-1.20.1-py3-none-any.whl

Tags

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