semvec/pypi-packages huggingface dataset:
green-face-recognition 0.1.6
HungLV package
License: MIT
Requires Python: >=3.6

# [GreenLabs] - Face Recognition 

This library written in MXNET framework. 

**Author:** 			HungLV

**Last Update:**    July 13, 2020

**It features:**

* Face Detection 
* Extract Face Embedding
* Search Face in database

**Code:** 

**Documentation:** 

**User-Guide:** 

**Model zoo:** 

**Tech report:**

## What's new

* `[Jul 9]` Initial version
* `[Jul 13]` Make the models can run on GPU/CPU. Integrated `rcnn` library. 

## Installation 

Make sure `conda` is installed

```
pip install green-face-recognition
```

## Getting started
Prepare the config file as `yaml` type. Take a look [this example](https://github.com/leviethung2103/face-recognition-baseline/blob/master/configs/server_api.yaml).

**Show the list of models**

```python
from face_recognition import models
models.show_avai_models()
```

**Load the models**

```python
retina_model = models.build_model('retina-r50',config_path)
arcface = models.build_model('arc-face',config_path)
```

**Make the prediction**
```python
# get faces and landmarks
retina_model.detect_fast(img,img.shape,0.8,[1],do_flip=False)
```


## Appendix

Generate the distributions for uploading to server

```
python3 setup.py sdist bdist_wheel 
```

Upload to server

```
python3 -m twine upload --repository pypi dist/*
```

## Reference

[1. RetinaFaceModel](https://github.com/deepinsight/insightface/tree/master/RetinaFace)
weasel-cli 1.0.4
Simulate Dawkins' weasel experiment
License: MIT
Requires Python: >=3.6,<4.0

<p align="center"><a href="https://pypi.org/project/weasel-cli/" target="_blank" rel="noopener noreferrer"><img width="100" src="https://raw.githubusercontent.com/wdsrocha/weasel-cli/master/logo.svg" alt="Weasel CLI logo"></a></p>

<p align="center">
  <a href="https://github.com/wdsrocha/weasel-cli/blob/master/LICENSE"><img src="https://img.shields.io/github/license/wdsrocha/weasel-cli?color=blue" alt="license"></a>
  <a href="https://pypi.org/project/weasel-cli/"><img src="https://img.shields.io/pypi/v/weasel-cli" alt="version"></a>
  <a href="https://github.com/wdsrocha/weasel-cli/actions?query=workflow%3Abuild"><img src="https://img.shields.io/github/workflow/status/wdsrocha/weasel-cli/build" alt="build"></a>
  <a href="https://github.com/wdsrocha/weasel-cli/blob/master/CONTRIBUTING.md"><img src="https://img.shields.io/badge/PRs-welcome-brightgreen" alt="prs"></a>
</p>

<h1 align="center">Dawkins' Weasel CLI</h1>

Simple implementation of the classic [weasel program](https://en.wikipedia.org/wiki/Weasel_program).

![demo](https://raw.githubusercontent.com/wdsrocha/weasel-cli/master/cli_demo.png)

## About

Although the weasel simulation really works, I'm using this project mostly to test things like CLI development, Github Actions, PyPI deployment, etc.

## Installation

`pip install weasel-cli`

## Usage

```
$ weasel --help
Usage: weasel [OPTIONS]

  Simulate Dawkins' weasel experiment

Options:
  -t, --target TEXT               [default: METHINKS IT IS LIKE A WEASEL]
  -p, --population-size INTEGER RANGE
                                  [default: 100]
  -r, --mutation-rate FLOAT RANGE
                                  [default: 0.05]
  --color / --no-color            Uses ANSI colors when reporting generation
                                  results  [default: True]

  --help                          Show this message and exit.
```

## Contributing

See [CONTRIBUTING.md](https://github.com/wdsrocha/weasel-cli/blob/master/CONTRIBUTING.md) file for more information.

## License

This project is licensed under the terms of the MIT license - see the `LICENSE` file for details.

---

Icons made by [Freepik](https://www.flaticon.com/authors/freepik) from [www.flaticon.com](https://www.flaticon.com/).
pypong 0.1.0
Yet another pong game written in Python
License: MIT
Requires Python: >=3.7,<4.0
Keywords: pong,game,pygame

PyPONG
=======

Yet another Pong game written in Python.

.. image:: https://raw.githubusercontent.com/julianolf/pypong/master/screenshot.png
    :width: 640px
    :alt: game play screenshot

Requirements
------------

* Python >= 3.7, < 3.8

Installing
----------

Use ``pip`` to download and install the game. ::

    $ pip install pypong

Running
-------

Just type ``pypong`` to run the game. ::

    $ pypong

Controls
--------

Use the mouse cursor to control the paddle.
stackoverflow-jobs 0.1.0
Simple wrapper for crawling jobs data at Stack Overflow Jobs portal.
License: MIT
Requires Python: >=3
Keywords: Stack Overflow,Jobs,crawler

# stackoverflow-jobs

Simple wrapper for crawling jobs data at Stack Overflow Jobs portal.

## Basic usage

Creating a query is pretty straightforward:

```python
from stackoverflow_jobs.query import Query

q = Query()
```

All query filters are available inside `filters` module. In case you want to
add new filters, all you need to do is to append them to the `Query` object:

```python
from stackoverflow_jobs.query import Query
from stackoverflow_jobs.filters import Role, Remote, Description

q = Query() \
    + Description("Android Developer") \
    + Remote() \
    + Role([Role.Type.MOBILE])
```

After you build your query, call `execute()` to fetch the data:

```python
data = q.execute()
```

By default, queries requests are timed out after 60 seconds. You can change the
timeout value in two different ways: 

1. `Query(timeout)`: Every query will end up after the specified `timeout`.
1. `execute(timeout)`: Set up timeout for a specific query execution.

```python
q1 = Query()   # Timeout default value: 60 seconds
q2 = Query(25) # Timeout custom value: 25 seconds

q1.execute()  # timeout: 60 seconds
q2.execute()  # timeout: 25 seconds

q1.execute(5) # timeout: 5 seconds
q2.execute(5) # timeout: 5 seconds
```

**Important note:** The library queries Stack Overflow Jobs RSS feed. This
means that you need to parse the XML afterwards.

You can also retrieve the query URL using `build_query()` method, in case you
need to:

```python
q.build_query()
>>> 'https://stackoverflow.com/jobs/feed?q=Android+Developer&r=true&dr=MobileDeveloper'
```

## Installing

`stackoverflow-jobs` is available in pypi repository:

```bash
pip install stackoverflow-jobs
```
console-contacts 1.2.0
CLI contacts manager
License: MIT
Keywords: console cli contacts contact addressbook

console contacts
======

A simple python app for you to manage your contacts. Tested on python 3,
probably not compatible with python 2. 

Installation
------------

Fast install:

::

    pip install console-contacts  

Examples
--------

:: console-contacts -l

Here is the output:
contactName1
contactName2
contactName3
contactName4
:: console-contacts -s Mary
Output:
Mary
mary@example.com
1234567890

:: console-contacts -a Bob bob@example.com 0987654321

This adds a contact with the name Bob, the email bob@example.com, and the phone number 0987654321

:: console-contacts -r Bob bob@example.com

This removes Bob from your contacts
midlevel-perception 0.0.1
The official implementation of visual priors from the paper Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies. Arxiv preprint 2018.
License: MIT
Keywords: computer vision,robotics,perception,midlevel,mid-level,reinforcement learning,machine learning,representation learning

# Visual Priors


# Intro
This package contains the code for the paper:

**Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies**. Arxiv 2018/CoRL 2019. Alexander Sax, Jeffrey O. Zhang, Bradley Emi, Amir R. Zamir, Silvio Savarese, Leonidas Guibas, Jitendra Malik.

- For a fuller description and useful results and demos, and much more, see the website [http://perceptual.actor](http://perceptual.actor).
- For the code in this package, see [https://github.com/alexsax/midlevel-reps/tree/visualpriors](https://github.com/alexsax/midlevel-reps/tree/visualpriors).
- For the full code from the paper, dockers to reproduce experiments, and more, see [https://github.com/alexsax/midlevel-reps](https://github.com/alexsax/midlevel-reps).


### Citation
If you find this repository or toolkit useful, then please cite:
```
@inproceedings{midLevelReps2018,
 title={Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies.},
 author={Alexander Sax and Jeffrey O. Zhang and Bradley Emi and Amir R. Zamir and Leonidas J. Guibas and Silvio Savarese and Jitendra Malik},
 year={2018},
}
```