openmic-annotator | Annotation framework for annotating data for OpenMIC | Data Labeling library

by cosmir Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | openmic-annotator Summary

openmic-annotator is a Python library typically used in Artificial Intelligence, Data Labeling, Deep Learning, Tensorflow applications. openmic-annotator has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However openmic-annotator build file is not available. You can download it from GitHub.

This CAS architecture can be described in the following (approximately) sequential manner, where the corresponding functions are numerated in turn:.

Support

Quality

Security

License

Reuse

Support

openmic-annotator has a low active ecosystem.

It has 55 star(s) with 1 fork(s). There are 40 watchers for this library.

It had no major release in the last 6 months.

There are 17 open issues and 20 have been closed. On average issues are closed in 65 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of openmic-annotator is current.

Quality

openmic-annotator has 0 bugs and 0 code smells.

Security

openmic-annotator has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

openmic-annotator code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

openmic-annotator is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

openmic-annotator releases are not available. You will need to build from source code and install.

openmic-annotator has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

openmic-annotator saves you 576 person hours of effort in developing the same functionality from scratch.

It has 1344 lines of code, 123 functions and 27 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed openmic-annotator and discovered the below as its top functions. This is intended to give you an instant insight into openmic-annotator implemented functionality, and help decide if they suit your requirements.

Start OpenMic
Launches the WSGI server
Kill processes
Join a list of data objects
Submit an annotation
Return a shallow copy of the object
Generate next task
Get the taxonomic taxonomy
Filters out files that are not in the list
Yield all uris
Example login
Iterate over uris
Retrieve the contents of a key
Download the file as a string
Store a record
Write the collection to disk
Upload bytes to bucket
Upload a file from a string
Creates a directory if necessary
Configure application
Return the taxonomy for the instrument
Parse log file

Get all kandi verified functions for this library.

openmic-annotator Key Features

No Key Features are available at this moment for openmic-annotator.

openmic-annotator Examples and Code Snippets

No Code Snippets are available at this moment for openmic-annotator.

Community Discussions

Trending Discussions on Data Labeling

How can I do this split process in Python?

Replacing a character with a space and dividing the string into two words in R

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

QUESTION

How can I do this split process in Python?

Asked 2021-Dec-30 at 14:06

I'm trying to make a data labeling in a table, and I need to do it in such a way that, in each row, the index is repeated, however, that in each column there is another Enum class.

What I've done so far is make this representation with the same enumerator class.

A solution using the column separately as a list would also be possible. But what would be the best way to resolve this?

...

ANSWER

Answered 2021-Dec-30 at 13:57

Instead of using Enum you can use a dict mapping. You can avoid loops if you flatten your dataframe:

Source https://stackoverflow.com/questions/70532286

QUESTION

Replacing a character with a space and dividing the string into two words in R

Asked 2020-Nov-18 at 07:32

I have a dataframe that contains a column that includes strings separeted with semi-colons and it is followed by a space. But unfortunately in some of the strings there is a semi-colon that is not followed by a space.

In this case, This is what i'd like to do: If there is a space after the semi-colon we do not need a change. However if there are letters before and after the semi-colon, we should change semi-colon with space

i have this:

...

ANSWER

Answered 2020-Nov-16 at 07:24

Try something like:

Source https://stackoverflow.com/questions/64853962

QUESTION

Azure ML FileDataset registers, but cannot be accessed for Data Labeling project

Asked 2020-Oct-28 at 20:31

Objective: Generate a down-sampled FileDataset using random sampling from a larger FileDataset to be used in a Data Labeling project.

Details: I have a large FileDataset containing millions of images. Each filename contains details about the 'section' it was taken from. A section may contain thousands of images. I want to randomly select a specific number of sections and all the images associated with those sections. Then register the sample as a new dataset.

Please note that the code below is not a direct copy and paste as there are elements such as filepaths and variables that have been renamed for confidentiality reasons.

...

ANSWER

Answered 2020-Oct-27 at 22:39

Is the data behind virtual network by any chance?

Source https://stackoverflow.com/questions/64546521

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install openmic-annotator

You can download it from GitHub.
You can use openmic-annotator like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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