Mutual-Channel-Loss | Code release for The Devil is in the Channels | Machine Learning library

by PRIS-CV Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | Mutual-Channel-Loss Summary

Mutual-Channel-Loss is a Python library typically used in Artificial Intelligence, Machine Learning, Bert applications. Mutual-Channel-Loss has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Mutual-Channel-Loss build file is not available. You can download it from GitHub.

Code release for The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification (TIP 2020)

Support

Quality

Security

License

Reuse

Support

Mutual-Channel-Loss has a low active ecosystem.

It has 164 star(s) with 28 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 20 have been closed. On average issues are closed in 9 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of Mutual-Channel-Loss is current.

Quality

Mutual-Channel-Loss has 0 bugs and 0 code smells.

Security

Mutual-Channel-Loss has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Mutual-Channel-Loss code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Mutual-Channel-Loss 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

Mutual-Channel-Loss releases are not available. You will need to build from source code and install.

Mutual-Channel-Loss 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.

Mutual-Channel-Loss saves you 237 person hours of effort in developing the same functionality from scratch.

It has 577 lines of code, 33 functions and 3 files.

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

Top functions reviewed by kandi - BETA

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

Perform the forward computation
Supervisor
Mask nb_batch
Train the network
Run the test
Parse parameters
Construct a ResNet - 18 model

Get all kandi verified functions for this library.

Mutual-Channel-Loss Key Features

No Key Features are available at this moment for Mutual-Channel-Loss.

Mutual-Channel-Loss Examples and Code Snippets

No Code Snippets are available at this moment for Mutual-Channel-Loss.

Community Discussions

Trending Discussions on Mutual-Channel-Loss

matplotlib - segmentation fault (core dumped) while trying to plot a grid

QUESTION

matplotlib - segmentation fault (core dumped) while trying to plot a grid

Asked 2020-Jul-10 at 07:48

I'm trying to plot a grid but exists with the error message -

...

ANSWER

Answered 2020-Jul-10 at 07:48

The unit used by the attribute figsize is the inch. So an image of size 300x500 with dpi=300 is insanely huge (45M pixels) and mpl cannot handle that. If you want a 300 by 500 (pixels) image you have to first convert it to inches. In this case it would be something like ~1x1.7.

Source https://stackoverflow.com/questions/62829316

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

Vulnerabilities

No vulnerabilities reported

Install Mutual-Channel-Loss

You can download it from GitHub.
You can use Mutual-Channel-Loss 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.