unet-pytorch | This is a unet-pytorch source code , you can train | Machine Learning library

by bubbliiiing Python Version: v3.0 License: MIT

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

kandi X-RAY | unet-pytorch Summary

unet-pytorch is a Python library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. unet-pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This is a unet-pytorch source code, you can train your own model

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unet-pytorch has a low active ecosystem.

It has 743 star(s) with 191 fork(s). There are 5 watchers for this library.

It had no major release in the last 12 months.

There are 55 open issues and 10 have been closed. On average issues are closed in 1 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of unet-pytorch is v3.0

Quality

unet-pytorch has no bugs reported.

Security

unet-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

unet-pytorch 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

unet-pytorch releases are available to install and integrate.

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

Train one epoch
Compute the MIOU
Convert image to RGB
Return image data
Gets the image for the given epoch
Visualize the MIU result
Draw a plot function
Adjust axes limits
Convert to onnx
Generate network
Creates a VGG16 model
Make a list of layers
Download weights for given backbone
Freeze the backbone parameters
Unfreeze the backbone parameters
Show configuration options
Compute MIUUU
Calculate a learning rate based on lr decay
Get pixel data
Calculate FPS of image
Detect image
Fit one epoch
Set optimizer learning rate

Get all kandi verified functions for this library.

unet-pytorch Key Features

No Key Features are available at this moment for unet-pytorch.

unet-pytorch Examples and Code Snippets

No Code Snippets are available at this moment for unet-pytorch.

Community Discussions

Trending Discussions on unet-pytorch

Pytorch summary only works for one specific input size for U-Net

QUESTION

Pytorch summary only works for one specific input size for U-Net

Asked 2020-Feb-14 at 01:13

I am trying to implement the UNet architecture in Pytorch. When I print the model using print(model) I get the correct architecture:

but when I try to print the summary using (or any other input size for that matter):

...

ANSWER

Answered 2020-Feb-14 at 01:13

This UNet architecture you provided doesn't support that shape (unless the depth parameter is <= 3). Ultimately the reason for this is that the size of a downsampling operation isn't invertible since multiple input shapes map to the same output shape. For example consider

Source https://stackoverflow.com/questions/60199088

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

Vulnerabilities

No vulnerabilities reported

Install unet-pytorch

You can download it from GitHub.
You can use unet-pytorch 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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