FCN8s | A very simple Pytorch implementation of FCN8s | Machine Learning library

by knn1989 Python Version: Current License: No License

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

kandi X-RAY | FCN8s Summary

FCN8s is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. FCN8s has no bugs, it has no vulnerabilities and it has low support. However FCN8s build file is not available. You can download it from GitHub.

This is an implementation of the FCN8s [Fully Convolutional Networks for Semantic Segmentation] (

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Quality

Security

License

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Support

FCN8s has a low active ecosystem.

It has 4 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

FCN8s has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of FCN8s is current.

Quality

FCN8s has no bugs reported.

Security

FCN8s has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

FCN8s 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

FCN8s releases are not available. You will need to build from source code and install.

FCN8s has no build file. You will be need to create the build yourself to build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed FCN8s and discovered the below as its top functions. This is intended to give you an instant insight into FCN8s implemented functionality, and help decide if they suit your requirements.

Train the model
Create data loader
Saves the model
Convert a sparse matrix to a dense matrix
Convert input to a variable
Compute the mean accuracy of the union
Calculate the ioucorrelation of the preds
Calculate the iourier correlation coefficient
Compute the pixel accuracy
Load feature
Loads the network
Set the epoch

Get all kandi verified functions for this library.

FCN8s Key Features

No Key Features are available at this moment for FCN8s.

FCN8s Examples and Code Snippets

No Code Snippets are available at this moment for FCN8s.

Community Discussions

Trending Discussions on FCN8s

What is the impact of lr_mult = 0?

QUESTION

What is the impact of lr_mult = 0?

Asked 2018-May-16 at 07:25

I'm looking at some Caffe network-building code (in the BerkeleyVision pascalcontext-fcn8s net.py file), and I find this line:

...

ANSWER

Answered 2018-May-16 at 07:25

You are correct. Setting lr_mult=0 freezes the weights of the layer. The weights will be stayed fixed and will not change from their initial values throughout training.
If you follow the code, you'll see a call to surgery.interp, this function sets the initial weights of the upscaling layer before training begins. The weights are remaining fixed to these values due to the lr_mult=0.

Source https://stackoverflow.com/questions/50355013

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

Vulnerabilities

No vulnerabilities reported

Install FCN8s

You can download it from GitHub.
You can use FCN8s 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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