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

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

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Nothing hinders the success of machine learning systems more than poor quality data. And without the right tools, improving a model can be time-consuming and inefficient.

FiftyOne supercharges your machine learning workflows by enabling you to visualize datasets and interpret models faster and more effectively.

Use FiftyOne to get hands-on with your data, including visualizing complex labels, evaluating your models, exploring scenarios of interest, identifying failure modes, finding annotation mistakes, and much more!

You can get involved by joining our Slack community, reading our blog on Medium, and following us on social media:

Slack Medium Twitter LinkedIn Facebook


You can install the latest stable version of FiftyOne via

pip install fiftyone

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


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

to block execution until you close the App. See this page for more information.


Full documentation for FiftyOne is available at In particular, see these resources:


Check out the fiftyone-examples repository for open source and community-contributed examples of using FiftyOne.

Contributing to FiftyOne

FiftyOne is open source and community contributions are welcome!

Check out the contribution guide to learn how to get involved.

Installing from source

The instructions below are for macOS and Linux systems. Windows users may need to make adjustments. If you are working in Google Colab, skip to here.


You will need:

  • Python (3.6 or newer)
  • Node.js - on Linux, we recommend using nvm to install an up-to-date version.
  • Yarn - once Node.js is installed, you can install Yarn via
    npm install -g yarn
  • On Linux, you will need at least the
    packages. On Debian-based distributions, you will need to install
    instead of
    , depending on the age of your distribution. For example:
# Ubuntu 18.04
sudo apt install libcurl4 openssl

Fedora 32

sudo dnf install libcurl openssl


We strongly recommend that you install FiftyOne in a virtual environment to maintain a clean workspace. The install script is only supported in POSIX-based systems (e.g. Mac and Linux).

  1. Clone the repository:
git clone --recursive
cd fiftyone
  1. Run the installation script:
bash install.bash

NOTE: The install script adds to your

settings in your
, which is needed for installing and building the App

NOTE: 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

Upgrading your source installation

To upgrade an existing source installation to the bleeding edge, simply pull the latest

branch and rerun the install script:
git checkout develop
git pull
bash install.bash

Developer installation

If you would like to contribute to FiftyOne, you should perform a developer installation using the

flag of the install script:
bash install.bash -d

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:


git clone --depth 1 cd fiftyone bash install.bash

Generating documentation

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


You can uninstall FiftyOne as follows:

pip uninstall fiftyone fiftyone-brain fiftyone-db fiftyone-desktop


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

  author={Moore, B. E. and Corso, J. J.},
  journal={GitHub. Note:},

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