The open-source tool for building high-quality datasets and computer vision models

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👋 hey there!

We created FiftyOne to supercharge your visual AI development by enabling you to visualize and label your data, evaluate your models, and maximize data and model quality more efficiently than ever before 🤝

If you're looking to scale to production-grade, collaborative, cloud-native enterprise workloads, check out FiftyOne Enterprise 🚀

installation 💻

As simple as:

pip install fiftyone

More details

Installation options

FiftyOne supports Python 3.10 - 3.13.

For most users, we recommend installing the latest release version of FiftyOne via pip as shown above.

If you want to contribute to FiftyOne or install the latest development version, then you can also perform a source install.

See the prerequisites section for system-specific setup information.

We strongly recommend that you install FiftyOne in a virtual environment to maintain a clean workspace.

Consult the installation guide for troubleshooting and other information about getting up-and-running with FiftyOne.

Install from source

Source installations

Follow the instructions below to install FiftyOne from source and build the App.

You'll need the following tools installed:

Python (3.10 - 3.13)
Node.js - on Linux, we recommend using nvm to install an up-to-date version.
Yarn - once Node.js is installed, you can enable Yarn via corepack enable

We strongly recommend that you install FiftyOne in a virtual environment to maintain a clean workspace.

If you are working in Google Colab, skip to here.

First, clone the repository:

git clone https://github.com/voxel51/fiftyone
cd fiftyone

Then run the install script:

# Mac or Linux
bash install.sh

# Windows
.\install.bat

If you run into issues importing FiftyOne, you may need to add the path to the cloned repository to your PYTHONPATH:

export PYTHONPATH=$PYTHONPATH:/path/to/fiftyone

Note that the install script adds to your nvm settings in your ~/.bashrc or ~/.bash_profile, which is needed for installing and building the App.

Upgrading your source installation

To upgrade an existing source installation to the bleeding edge, simply pull the latest main branch and rerun the install script:

git checkout main
git pull

# Mac or Linux
bash install.sh

# Windows
.\install.bat

Rebuilding the App

When you pull in new changes to the App, you will need to rebuild it, which you can do either by rerunning the install script or just running yarn build in the ./app directory.

Developer installation

If you would like to contribute to FiftyOne, you should perform a developer installation using the -d flag of the install script:

# Mac or Linux
bash install.sh -d

# Windows
.\install.bat -d

Although not required, developers typically prefer to configure their FiftyOne installation to connect to a self-installed and managed instance of MongoDB, which you can do by following these simple steps.

Source installs in Google Colab

You can install from source in Google Colab by running the following in a cell and then restarting the runtime:

%%shell

git clone --depth 1 https://github.com/voxel51/fiftyone.git
cd fiftyone

# Mac or Linux
bash install.sh

# Windows
.\install.bat

Generating documentation

See the docs guide for information on building and contributing to the documentation.

Uninstallation

You can uninstall FiftyOne as follows:

pip uninstall fiftyone fiftyone-brain fiftyone-db

Prerequisites for beginners

System-specific setup

Follow the instructions for your operating system or environment to perform basic system setup before installing FiftyOne.

If you're an experienced developer, you've likely already done this.

Linux

1. Install Python and other dependencies

These steps work on a clean install of Ubuntu Desktop 24.04, and should also work on Ubuntu 24.04 and 22.04, and on Ubuntu Server:

sudo apt-get update
sudo apt-get upgrade
sudo apt-get install python3-venv python3-dev build-essential git-all libgl1-mesa-dev

On Linux, you will need at least the openssl and libcurl packages
On Debian-based distributions, you will need to install libcurl4 or libcurl3 instead of libcurl, depending on the age of your distribution

# Ubuntu
sudo apt install libcurl4 openssl

# Fedora
sudo dnf install libcurl openssl

2. Create and activate a virtual environment

python3 -m venv fiftyone_env
source fiftyone_env/bin/activate

3. Install FFmpeg (optional)

If you plan to work with video datasets, you'll need to install FFmpeg:

sudo apt-get install ffmpeg

MacOS

1. Install Xcode Command Line Tools

xcode-select --install

2. Install Homebrew

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

After running the above command, follow the instructions in your terminal to complete the Homebrew installation.

3. Install Python

brew install python@3.10
brew install protobuf

4. Create and activate a virtual environment

python3 -m venv fiftyone_env
source fiftyone_env/bin/activate

5. Install FFmpeg (optional)

If you plan to work with video datasets, you'll need to install FFmpeg:

brew install ffmpeg

Windows

1. Install Python

⚠️ The version of Python that is available in the Microsoft Store is not recommended ⚠️

Download a Python 3.10 - 3.13 installer from python.org. Make sure to pick a 64-bit version. For example, this Python 3.10.11 installer.

Double-click on the installer to run it, and follow the steps in the installer.

Check the box to add Python to your PATH
At the end of the installer, there is an option to disable the PATH length limit. It is recommended to click this

2. Install Microsoft Visual C++

Download Microsoft Visual C++ Redistributable. Double-click on the installer to run it, and follow the steps in the installer.

3. Install Git

Download Git from this link. Double-click on the installer to run it, and follow the steps in the installer.

4. Create and activate a virtual environment

Press Win + R. type cmd, and press Enter. Alternatively, search Command Prompt in the Start Menu.
Navigate to your project. cd C:\path\to\your\project
Create the environment python -m venv fiftyone_env
Activate the environment typing this in the command line window fiftyone_env\Scripts\activate
After activation, your command prompt should change and show the name of the virtual environment (fiftyone_env) C:\path\to\your\project

5. Install FFmpeg (optional)

If you plan to work with video datasets, you'll need to install FFmpeg.

Download an FFmpeg binary from here. Add FFmpeg's path (e.g., C:\ffmpeg\bin) to your PATH environmental variable.

Docker

Refer to these instructions to see how to build and run Docker images containing release or source builds of FiftyOne.

quickstart 🚀

Dive right into FiftyOne by opening a Python shell and running the snippet below, which downloads a small dataset and launches the FiftyOne App so you can explore it:

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("quickstart")
session = fo.launch_app(dataset)

Then check out this Colab notebook to see some common workflows on the quickstart dataset.

Note that if you are running the above code in a script, you must include session.wait() to block execution until you close the App. See this page for more information.

key features 🔑

Native Annotation: Create and edit 2D and 3D labels directly in the App or integrate with your favorite annotation tools — then curate, QA, and iterate, all in one platform.

https://github.com/user-attachments/assets/b06bcdac-d64f-4465-8668-12007dc0eeaa

Visualize Complex Datasets: Easily explore images, videos, and associated labels in a powerful visual interface.

https://github.com/user-attachments/assets/9dc2db88-967d-43fa-bda0-85e4d5ab6a7a

Explore Embeddings: Select points of interest and view the corresponding samples/labels.

https://github.com/user-attachments/assets/246faeb7-dcab-4e01-9357-e50f6b106da7

Analyze and Improve Models: Evaluate model performance, identify failure modes, and fine-tune your models.

https://github.com/user-attachments/assets/8c32d6c4-51e7-4fea-9a3c-2ffd9690f5d6

Advanced Data Curation: Quickly find and fix data issues, annotation errors, and edge cases.

https://github.com/user-attachments/assets/24fa1960-c2dd-46ae-ae5f-d58b3b84cfe4

Rich Integrations: Works with popular deep learning libraries like PyTorch, Hugging Face, Ultralytics, and more.

https://github.com/user-attachments/assets/de5f25e1-a967-4362-9e04-616449e745e5

Open and Extensible: Customize and extend FiftyOne to fit your specific needs.

https://github.com/user-attachments/assets/c7ed496d-0cf7-45d6-9853-e349f1abd6f8

documentation 🪪

Check out these resources to get up and running with FiftyOne:

Getting Started Guides	Tutorials	Recipes	User Guide	Examples	API Reference	CLI Reference

Full documentation is available at fiftyone.ai.

additional resources 🚁

FiftyOne Enterprise	Building Plugins	Vector Search	Dataset Zoo	Model Zoo	FiftyOne Brain	VoxelGPT

FiftyOne Enterprise 🏎️

Want to securely collaborate on billions of samples in the cloud and connect to your compute resources to automate your workflows? Check out FiftyOne Enterprise.

faq & troubleshooting ⛓️‍💥

Refer to our common issues page to troubleshoot installation issues. If you're still stuck, check our frequently asked questions page for more answers.

If you encounter an issue that the above resources don't help you resolve, feel free to open an issue on GitHub or contact us on Discord.

join our community 🤝

Connect with us through your preferred channels:

🎊 Share how FiftyOne makes your visual AI projects a reality on social media and tag us with @Voxel51 and #FiftyOne 🎊

contributors 🧡

FiftyOne and FiftyOne Brain are open source and community contributions are welcome! Check out the contribution guide to learn how to get involved.

Special thanks to these amazing people for contributing to FiftyOne!

citation 📖

If you use FiftyOne in your research, feel free to cite the project (but only if you love it 😊):

@article{moore2020fiftyone,
  title={FiftyOne},
  author={Moore, B. E. and Corso, J. J.},
  journal={GitHub. Note: https://github.com/voxel51/fiftyone},
  year={2020}
}

Metadata

Release files for fiftyone 1.22.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for fiftyone 1.22.0
File	Size	Uploaded
fiftyone-1.22.0.tar.gz	23.8 MB	Sep 12, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for fiftyone 1.22.0
File	Interpreter	ABI	Platform	Reset
fiftyone-1.22.0-py3-none-any.whl 24.0 MB Sep 12, 2026	Python 3	none	any	Details

Total release size: 47.8 MB

Release files / fiftyone-1.22.0.tar.gz

Download URL	fiftyone-1.22.0.tar.gz
Size	23.8 MB
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Upload date	Sep 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/voxel51/fiftyone
Publishing commit: github.com/voxel51/fiftyone/tree/4c3294e0c02dc1fa0aac8a5c381878eedffb39d7

Workflow

Publishing configuration: github.com/voxel51/fiftyone/blob/4c3294e0c02dc1fa0aac8a5c381878eedffb39d7/.github/workflows/publish.yml
Publishing logs: github.com/voxel51/fiftyone/actions/runs/34665872392/attempts/1

Artifact

Subject: fiftyone-1.22.0.tar.gz
SHA-256 checksum: 406f552dab3da1ddc1fded2458f974bf2a50807972adfa6aba49ca6f0605c427 Verifying attestations

Release files / fiftyone-1.22.0-py3-none-any.whl

Download URL	fiftyone-1.22.0-py3-none-any.whl
Size	24.0 MB
Tags	Python 3
SHA-256 checksum How to use checksums	`c82a231fb9c8d381c21efc1293a6fa3d14f6ed1d03853d6e5b7958bba4ceb75b`
BLAKE2b-256 checksum How to use checksums	`aae91fd32e9ccc73f4feac99753cd0fc67be1f0458c42758b669600b78166894`
Upload date	Sep 12, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/voxel51/fiftyone
Publishing commit: github.com/voxel51/fiftyone/tree/4c3294e0c02dc1fa0aac8a5c381878eedffb39d7

Workflow

Publishing configuration: github.com/voxel51/fiftyone/blob/4c3294e0c02dc1fa0aac8a5c381878eedffb39d7/.github/workflows/publish.yml
Publishing logs: github.com/voxel51/fiftyone/actions/runs/34665872392/attempts/1

Artifact

Subject: fiftyone-1.22.0-py3-none-any.whl
SHA-256 checksum: c82a231fb9c8d381c21efc1293a6fa3d14f6ed1d03853d6e5b7958bba4ceb75b Verifying attestations