ResUnet | Pytorch implementation of ResUnet and ResUnet ++ | Computer Vision library

by rishikksh20 Python Version: Current License: No License

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

kandi X-RAY | ResUnet Summary

ResUnet is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning applications. ResUnet has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Unofficial Pytorch implementation of following papers :.

Support

Quality

Security

License

Reuse

Support

ResUnet has a low active ecosystem.

It has 310 star(s) with 54 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 1 have been closed. On average issues are closed in 27 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ResUnet is current.

Quality

ResUnet has 0 bugs and 0 code smells.

Security

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

ResUnet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ResUnet does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

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

Build file is available. You can 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 ResUnet and discovered the below as its top functions. This is intended to give you an instant insight into ResUnet implemented functionality, and help decide if they suit your requirements.

Perform validation
Writes images to the log
Update the statistics
Log validation errors
Crop an image
Load an image
Generate start points
Log training data

Get all kandi verified functions for this library.

ResUnet Key Features

No Key Features are available at this moment for ResUnet.

ResUnet Examples and Code Snippets

No Code Snippets are available at this moment for ResUnet.

Community Discussions

Trending Discussions on ResUnet

PyTorch - RuntimeError: Sizes of tensors must match except in dimension 2. Got 55 and 54 (The offending index is 0)

QUESTION

PyTorch - RuntimeError: Sizes of tensors must match except in dimension 2. Got 55 and 54 (The offending index is 0)

Asked 2021-Nov-17 at 12:11

I used a 3DUnet with resblock to segment a CT image with input torch size of [1, 1, 96, 176, 176], but it throws the following error:

RuntimeError: Sizes of tensors must match except in dimension 2. Got 55 and 54 (The offending index is 0)

Hence I traced back, I found the error comes from

...

ANSWER

Answered 2021-Nov-17 at 12:11

You can pad your image's dimensions to be multiple of 32's. By doing this, you won't have to change the 3DUnet's parameters.

I will provide you a simple code to show you the way.

Source https://stackoverflow.com/questions/69998250

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

Vulnerabilities

No vulnerabilities reported

Install ResUnet

You can download it from GitHub.
You can use ResUnet 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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