Awesome
<div align="center"> <p align="center"> <!-- prettier-ignore --><img src="https://user-images.githubusercontent.com/25985824/106288517-2422e000-6216-11eb-871d-26ad2e7b1e59.png" height="55px"> <img src="https://user-images.githubusercontent.com/25985824/106288518-24bb7680-6216-11eb-8f10-60052c519586.png" height="50px">
The open-source tool for building high-quality datasets and computer vision models
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<a href="https://voxel51.com/fiftyone">Website</a> • <a href="https://voxel51.com/docs/fiftyone">Docs</a> • <a href="https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/quickstart.ipynb">Try it Now</a> • <a href="https://voxel51.com/docs/fiftyone/tutorials/index.html">Tutorials</a> • <a href="https://github.com/voxel51/fiftyone-examples">Examples</a> • <a href="https://voxel51.com/blog/">Blog</a> • <a href="https://slack.voxel51.com">Community</a>
</p> </div>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:
Installation
You can install the latest stable version of FiftyOne via pip
:
pip install fiftyone
Consult the installation guide for troubleshooting and other information about getting up-and-running with 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.
Documentation
Full documentation for FiftyOne is available at fiftyone.ai. In particular, see these resources:
Examples
Check out the fiftyone-examples repository for open source and community-contributed examples of using FiftyOne.
Contributing to FiftyOne
FiftyOne and FiftyOne Brain are 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.
Prerequisites
You will need:
- Python (3.9 - 3.11)
- 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
- On Linux, you will need at least the
openssl
andlibcurl
packages. On Debian-based distributions, you will need to installlibcurl4
orlibcurl3
instead oflibcurl
, depending on the age of your distribution. For example:
# Ubuntu
sudo apt install libcurl4 openssl
# Fedora
sudo dnf install libcurl openssl
Installation
We strongly recommend that you install FiftyOne in a virtual environment to maintain a clean workspace.
First, clone the repository:
git clone https://github.com/voxel51/fiftyone
cd fiftyone
Then run the install script:
# Mac or Linux
bash install.bash
# Windows
.\install.bat
NOTE: 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: The install script adds to your nvm
settings in your ~/.bashrc
or
~/.bash_profile
, 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 ./app
directory.
Upgrading your source installation
To upgrade an existing source installation to the bleeding edge, simply pull
the latest develop
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 -d
flag of the install
script:
# Mac or Linux
bash install.bash -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.bash
# Windows
.\install.bat
Docker installs
Refer to these instructions to see how to build and run Docker images containing source or release builds of FiftyOne.
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
Contributors
Special thanks to these amazing people for contributing to FiftyOne! 🙌
<a href="https://github.com/voxel51/fiftyone/graphs/contributors"> <img src="https://contrib.rocks/image?repo=voxel51/fiftyone" /> </a>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}
}