ResNeSt | PyTorch implementation of ResNeSt : Split-Attention Networks | Machine Learning library

by STomoya Python Version: Current License: Apache-2.0

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

kandi X-RAY | ResNeSt Summary

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

PyTorch implementation of ResNeSt : Split-Attention Networks [1]. This implementation is only for my understanding of the architecture of ResNeSt. Mostly the radix-major implementation of the bottleneck block.

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Quality

Security

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Support

ResNeSt has a low active ecosystem.

It has 11 star(s) with 3 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

ResNeSt has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ResNeSt is current.

Quality

ResNeSt has 0 bugs and 0 code smells.

Security

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

ResNeSt code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ResNeSt is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

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

It has 346 lines of code, 13 functions and 2 files.

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

Top functions reviewed by kandi - BETA

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

Initialize resNeSt .
forward computation .
Create nn layers .

Get all kandi verified functions for this library.

ResNeSt Key Features

No Key Features are available at this moment for ResNeSt.

ResNeSt Examples and Code Snippets

No Code Snippets are available at this moment for ResNeSt.

Community Discussions

Trending Discussions on ResNeSt

The grammar explanation of torch[cpuType]

QUESTION

The grammar explanation of torch[cpuType]

Asked 2017-Aug-04 at 09:05

I first see the usage in lua like torch[cpuType] in the file dataloader.lua of fb.resnest.torch:

...

ANSWER

Answered 2017-Jul-18 at 18:12

From my knowledge in pytorch, which is pretty much very similar to Lua Torch (I tried lua torch too), I would say it specifies where you want this tensor to be stored. Note that torch cannot perform an operation stored two different processing unit. There are methods to move data between cpu (net≈ß.cpu()) and gpu [net.cuda()].

Source https://stackoverflow.com/questions/45084936

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

Vulnerabilities

No vulnerabilities reported

Install ResNeSt

You can download it from GitHub.
You can use ResNeSt 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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