Metadata-Version: 2.1
Name: fiftyone
Version: 0.8.0
Summary: FiftyOne: the open-source tool for building high-quality datasets and computer vision models
Home-page: https://github.com/voxel51/fiftyone
Author: Voxel51, Inc.
Author-email: info@voxel51.com
License: Apache
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: argcomplete
Requires-Dist: Deprecated
Requires-Dist: eventlet
Requires-Dist: future
Requires-Dist: Jinja2
Requires-Dist: kaleido
Requires-Dist: matplotlib
Requires-Dist: mongoengine (==0.20.0)
Requires-Dist: motor (<3,>=2.3)
Requires-Dist: numpy
Requires-Dist: packaging
Requires-Dist: pandas
Requires-Dist: Pillow (>=6.2)
Requires-Dist: plotly (>=4.14)
Requires-Dist: pprintpp
Requires-Dist: psutil
Requires-Dist: pymongo (<4,>=3.11)
Requires-Dist: retrying
Requires-Dist: scikit-learn
Requires-Dist: scikit-image
Requires-Dist: setuptools
Requires-Dist: tabulate
Requires-Dist: tornado (<7,>=5.1.1)
Requires-Dist: xmltodict
Requires-Dist: universal-analytics-python3 (<2,>=1.0.1)
Requires-Dist: voxel51-eta (<0.5,>=0.4.1)
Requires-Dist: opencv-python-headless (<=4.4.0.46)
Requires-Dist: fiftyone-brain (<0.6,>=0.5)
Requires-Dist: fiftyone-db (<0.3,>=0.2.1)
Provides-Extra: desktop
Requires-Dist: fiftyone-desktop (<0.13.0,>=0.12.0) ; extra == 'desktop'

<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"> &nbsp;
<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**

---

<!-- prettier-ignore -->
<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://medium.com/voxel51">Blog</a> •
<a href="https://join.slack.com/t/fiftyone-users/shared_invite/zt-gtpmm76o-9AjvzNPBOzevBySKzt02gg">Community</a>

[![PyPI python](https://img.shields.io/pypi/pyversions/fiftyone)](https://pypi.org/project/fiftyone)
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[![FiftyOne](https://voxel51.com/images/fiftyone_poster.png)](http://fiftyone.ai)

</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](http://fiftyone.ai) 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](https://img.shields.io/badge/Slack-4A154B?logo=slack&logoColor=white)](https://join.slack.com/t/fiftyone-users/shared_invite/zt-gtpmm76o-9AjvzNPBOzevBySKzt02gg)
[![Medium](https://img.shields.io/badge/Medium-12100E?logo=medium&logoColor=white)](https://medium.com/voxel51)
[![Twitter](https://img.shields.io/badge/Twitter-1DA1F2?logo=twitter&logoColor=white)](https://twitter.com/voxel51)
[![LinkedIn](https://img.shields.io/badge/LinkedIn-0077B5?logo=linkedin&logoColor=white)](https://www.linkedin.com/company/voxel51)
[![Facebook](https://img.shields.io/badge/Facebook-1877F2?logo=facebook&logoColor=white)](https://www.facebook.com/voxel51)

## Installation

You can install the latest stable version of FiftyOne via `pip`:

```shell
pip install fiftyone
```

Consult the
[installation guide](https://voxel51.com/docs/fiftyone/getting_started/install.html)
for troubleshooting and other information about getting up-and-running with
FiftyOne.

## Quickstart

Dive right into FiftyOne by launching the quickstart:

```shell
fiftyone quickstart
```

This command will download a small dataset, launch the App, and print some
suggestions for exploring the dataset.

You can also check out
[this Colab notebook](https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/quickstart.ipynb)
to try some common workflows with the quickstart dataset, or run through
[this Colab notebok](https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/walkthrough.ipynb)
for a more detailed overview of FiftyOne.

## Documentation

Full documentation for FiftyOne is
[available online](https://voxel51.com/docs/fiftyone). In particular, see these
resources:

-   [Tutorials](https://voxel51.com/docs/fiftyone/tutorials/index.html)
-   [Recipes](https://voxel51.com/docs/fiftyone/recipes/index.html)
-   [User Guide](https://voxel51.com/docs/fiftyone/user_guide/index.html)
-   [CLI Documentation](https://voxel51.com/docs/fiftyone/cli/index.html)
-   [API Reference](https://voxel51.com/docs/fiftyone/api/fiftyone.html)

## Examples

Check out the [fiftyone-examples](https://github.com/voxel51/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](https://github.com/voxel51/fiftyone/blob/develop/CONTRIBUTING.md)
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](#source-installs-in-google-colab).

### Prerequisites

You will need:

-   [Python](https://www.python.org) (3.6 or newer)
-   [Node.js](https://nodejs.org) - on Linux, we recommend using
    [nvm](https://github.com/nvm-sh/nvm) to install an up-to-date version.
-   [Yarn](https://yarnpkg.com) - once Node.js is installed, you can install
    Yarn via `npm install -g yarn`
-   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.
    For example:

```shell
# Ubuntu 18.04
sudo apt install libcurl4 openssl

# Fedora 32
sudo dnf install libcurl openssl
```

### Installation

We strongly recommend that you install FiftyOne in a
[virtual environment](https://voxel51.com/docs/fiftyone/getting_started/virtualenv.html)
to maintain a clean workspace. The install script is only supported in
POSIX-based systems (e.g. Mac and Linux).

1. Clone the repository:

```shell
git clone --recursive https://github.com/voxel51/fiftyone
cd fiftyone
```

2. Run the installation script:

```shell
bash install.bash
```

**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-web` in the `./app` directory.

3. If you want to use the `fiftyone.brain` package, you will need to install it
   separately after installing FiftyOne:

```shell
pip install fiftyone-brain
```

### 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:

```shell
git checkout develop
git pull
bash install.bash
```

### Developer installation

If you would like to
[contribute to FiftyOne](https://github.com/voxel51/fiftyone/blob/develop/CONTRIBUTING.md),
you should perform a developer installation using the `-d` flag of the install
script:

```shell
bash install.bash -d
```

### Source installs in Google Colab

You can install from source in
[Google Colab](https://colab.research.google.com) by running the following in a
cell and then **RESTARTING THE RUNTIME**:

```shell
%%shell

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

### Generating documentation

See the
[docs guide](https://github.com/voxel51/fiftyone/blob/develop/docs/docs_guide.md)
for information on building and contributing to the documentation.

## Uninstallation

You can uninstall FiftyOne as follows:

```shell
pip uninstall fiftyone fiftyone-brain fiftyone-db fiftyone-desktop
```

## Citation

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

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


