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fiftyone-eval-only-0.14.3
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مشاهده بیشترتوضیحات
FiftyOne, for evaluation only.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fiftyone-eval-only-0.14.3 |
| نام | fiftyone-eval-only |
| نسخه کتابخانه | 0.14.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Voxel51, Inc. |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/fiftyone-eval-only/ |
| مجوز | Apache |
<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**
---
<!-- 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>
[](https://pypi.org/project/fiftyone)
[](https://pypi.org/project/fiftyone)
[](https://pepy.tech/project/fiftyone)
[](https://github.com/voxel51/fiftyone/actions?query=workflow%3ABuild)
[](LICENSE)
[](https://join.slack.com/t/fiftyone-users/shared_invite/zt-gtpmm76o-9AjvzNPBOzevBySKzt02gg)
[](https://medium.com/voxel51)
[](https://share.hsforms.com/1zpJ60ggaQtOoVeBqIZdaaA2ykyk)
[](https://twitter.com/voxel51)
[](https://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](https://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:
[](https://join.slack.com/t/fiftyone-users/shared_invite/zt-gtpmm76o-9AjvzNPBOzevBySKzt02gg)
[](https://medium.com/voxel51)
[](https://twitter.com/voxel51)
[](https://www.linkedin.com/company/voxel51)
[](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 opening a Python shell and running the snippet
below, which downloads a
[small dataset](https://voxel51.com/docs/fiftyone/user_guide/dataset_zoo/datasets.html#quickstart)
and launches the
[FiftyOne App](https://voxel51.com/docs/fiftyone/user_guide/app.html) so you
can explore it:
```py
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](https://colab.research.google.com/github/voxel51/fiftyone-examples/blob/master/examples/quickstart.ipynb)
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](https://voxel51.com/docs/fiftyone/user_guide/app.html#creating-a-session)
for more information.
## Documentation
Full documentation for FiftyOne is available at
[fiftyone.ai](https://fiftyone.ai). 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` 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:
```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}
}
```
نیازمندی
| مقدار | نام |
|---|---|
| - | aiofiles |
| - | argcomplete |
| - | boto3 |
| - | Deprecated |
| - | eventlet |
| - | future |
| >=7.0.0 | ipywidgets |
| - | Jinja2 |
| - | kaleido |
| - | matplotlib |
| ==0.20.0 | mongoengine |
| <3,>=2.3 | motor |
| - | numpy |
| - | opencv-python-headless |
| - | packaging |
| - | pandas |
| >=6.2 | Pillow |
| <5,>=4.14 | plotly |
| - | pprintpp |
| - | psutil |
| <4,>=3.11 | pymongo |
| - | pytz |
| - | PyYAML |
| - | retrying |
| - | scikit-learn |
| - | scikit-image |
| - | setuptools |
| - | tabulate |
| <7,>=5.1.1 | tornado |
| - | xmltodict |
| <2,>=1.0.1 | universal-analytics-python3 |
| <0.7,>=0.6.1 | voxel51-eta |
| <0.8,>=0.7.2 | fiftyone-brain |
| <0.4,>=0.3 | fiftyone-db |
| <0.20,>=0.19.1 | fiftyone-desktop |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl fiftyone-eval-only-0.14.3:
pip install fiftyone-eval-only-0.14.3.whl
نصب پکیج tar.gz fiftyone-eval-only-0.14.3:
pip install fiftyone-eval-only-0.14.3.tar.gz