semvec/pypi-packages huggingface dataset:
tendril-utils-gschem-files 0.2.0
Gschem file manipulation utilities for Tendril
Requires Python: >=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*
Keywords: tendril


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    :target: https://pypi.org/project/tendril-utils-gschem-files

.. image:: https://img.shields.io/pypi/pyversions/tendril-utils-gschem-files.svg?logo=pypi
    :target: https://pypi.org/project/tendril-utils-gschem-files

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    :target: https://travis-ci.org/tendril-framework/tendril-utils-gschem-files

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    :target: https://coveralls.io/github/tendril-framework/tendril-utils-gschem-files

.. image:: https://img.shields.io/codacy/grade/a4e1a69c38064e28987df1f891041005?logo=codacy
    :target: https://www.codacy.com/app/chintal/tendril-utils-gschem-files

.. image:: https://img.shields.io/requires/github/tendril-framework/tendril-utils-gschem-files.svg
    :target: https://requires.io/github/tendril-framework/tendril-utils-gschem-files/requirements

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    :target: https://www.gnu.org/licenses/agpl-3.0.en.html



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Introduction
------------

TODO Some brief introduction


Package Information
-------------------

The latest version of the documentation, including installation, usage, and
API/developer notes can be found at
`ReadTheDocs <https://tendril-utils-gschem-files.readthedocs.io/en/latest/index.html>`_.

The latest version of the sources can be found at
`GitHub <https://github.com/tendril-framework/tendril-utils-gschem-files>`_. Please use 
GitHub's features to report bugs, request features, or submit pull/merge requests.

The principle author for ``tendril-utils-gschem-files`` is Chintalagiri Shashank. The 
author can be contacted if necessary via the information on the
`author's github profile <https://github.com/chintal>`_ . See the AUTHORS file
for a full list of collaborators and/or contributing authors, if any.

``tendril-utils-gschem-files`` is distributed under the terms of the
`AGPLv3 license <https://www.gnu.org/licenses/agpl-3.0.en.html>`_ .
A copy of the text of the license is included along with the sources.
django-library-restful 0.0.11
a django library manager

# Django Library RESTful API

### project description
A django app RESTful api to manage multiple libraries concurrently.
1. Can add many library
2. Book shelves can be added to any library with a specific number
3. Lending books from libraries to one user can be managed

### About
This is a RESTful api to manage multiple libraries that borrow book to user. the [admin.py](https://github.com/MHolger77/django-libraries/blob/master/admin.py)
create good django adminstration for library that you can:
1. Search name , author or user that borrow book
2. Filter author of books
3. Search book and library between list of shelves
4. Filter shelves of specific library
5. Search Library in specific city
6. Look for which library has a specific book
7. Filter borrows list with borrow date and give back date


### Installation

Install using ``pip``::

    pip install djangorestframework
    pip install django-library-restful

1. Add "polls" to your INSTALLED_APPS setting like this::


    INSTALLED_APPS = [
    ...
    'django_library_restful',
    ]

2. Include the library URLconf in your project urls.py like this::

```python
from django.urls import path, include
from django_library_restfull.router import bookstore_api_router

urlpatterns = [
    path('api/library/', include(bookstore_api_router.urls))
]    
```

4. Run `python manage.py makemigrations django_library_restfull` to create the sql.

3. Run `python manage.py migrate` to create the polls models.

4. Use library api


### License
Uses the MIT license.

* Django Library RESTful api : https://github.com/MHolger77/django-libraries
* MIT: http://opensource.org/licenses/MIT
res-facenet 0.0.5
This model has been trained on VGGv2 and tested on LFW with 92% accuracy. The pretrained model can be accessed on res_facenet.model_921 or res_facenet.model_920

# ResFacenet
This package is intended as an __pytorch hub__ entry point for my trained facenet model on this repo [khrlimam/facenet](https://github.com/khrlimam/facenet).
This pretrained model can be used for anyone who want to use it for transfer learning or any other applications.

This model trained on VGGv2 dataset and tested on LFW dataset and gained 92% accuracy.

This work is distributed under MIT license, so I hope you consider the license's policy. Cheers!
facenet-khairulimam 0.0.2
This model has been trained on VGGv2 and tested on LFW with 92% accuracy.

# ResFacenet
This package is intended as an __pytorch hub__ entry point for my trained facenet model on this repo [khrlimam/facenet](https://github.com/khrlimam/facenet).
This pretrained model can be used for anyone who want to use it for transfer learning or any other applications.

This model trained on VGGv2 dataset and tested on LFW dataset and gained 92% accuracy.

This work is distributed under MIT license, so I hope you consider the license's policy. Cheers!