MediaEval2019 | repository contains the implementation of our , team TaiInn

by dbis-uibk Python Version: Current License: BSD-2-Clause

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

kandi X-RAY | MediaEval2019 Summary

MediaEval2019 is a Python library. MediaEval2019 has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However MediaEval2019 build file is not available. You can download it from GitHub.

This repository contains the implementation of our, team TaiInn (Innsbruck), solution to the Emotion and Theme Recognition in Music Using Jamendo task as part of MediaEval 2019.

Support

Quality

Security

License

Reuse

Support

MediaEval2019 has a low active ecosystem.

It has 5 star(s) with 0 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of MediaEval2019 is current.

Quality

MediaEval2019 has no bugs reported.

Security

MediaEval2019 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

MediaEval2019 is licensed under the BSD-2-Clause License. This license is Permissive.

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

Reuse

MediaEval2019 releases are not available. You will need to build from source code and install.

MediaEval2019 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.

Top functions reviewed by kandi - BETA

kandi has reviewed MediaEval2019 and discovered the below as its top functions. This is intended to give you an instant insight into MediaEval2019 implemented functionality, and help decide if they suit your requirements.

Load the validation set
Loads data from a set
Generate a list of windows
Load data from a sample set
Store prediction for the given dataloader
Predict using cached model
Predict the label for each label
Create a dataset
Expand a list column
Plot confusion matrix
Print a confusion matrix
Create the model
Cnn layer
Plot the accuracy of the confusion matrix
Prints the scores per label
Process a single line
Extract features from a song
Generate a pandas data set
Get grid parameters
Returns a dictionary of parameters for fixed split

Get all kandi verified functions for this library.

MediaEval2019 Key Features

No Key Features are available at this moment for MediaEval2019.

MediaEval2019 Examples and Code Snippets

No Code Snippets are available at this moment for MediaEval2019.

Community Discussions

No Community Discussions are available at this moment for MediaEval2019.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install MediaEval2019

You can download it from GitHub.
You can use MediaEval2019 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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