DMIT | mapping Image-to-Image Translation via Learning | Computer Vision library

by Xiaoming-Yu Python Version: Current License: MIT

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

kandi X-RAY | DMIT Summary

DMIT is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. DMIT 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.

Pytorch implementation of our paper: "Multi-mapping Image-to-Image Translation via Learning Disentanglement".

Support

Quality

Security

License

Reuse

Support

DMIT has a low active ecosystem.

It has 98 star(s) with 13 fork(s). There are 16 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 10 have been closed. On average issues are closed in 11 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DMIT is current.

Quality

DMIT has 0 bugs and 0 code smells.

Security

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

DMIT code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

DMIT 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

DMIT 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.

DMIT saves you 797 person hours of effort in developing the same functionality from scratch.

It has 1832 lines of code, 148 functions and 26 files.

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

Top functions reviewed by kandi - BETA

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

Update the model
Update the condition of the dis_content
Calculates the generation content for a given pattern
Calculate the genotype image
Display the current results
Add images
Saves the document to disk
Load text data
Build a dictionary
Creates a data loader
Save a checkpoint for each network
Compute latent variables
Print current loss
Creates a new model
Plot the current error
Calculate the convolution layer
Compute the input tensor
Save the document
Translates the input image
Saves images to webpage
Save the image
Get the current examples
Parse the command line options
Builds the models
Forward forward computation
Calculate the translation of the image
Save visual images to page

Get all kandi verified functions for this library.

DMIT Key Features

No Key Features are available at this moment for DMIT.

DMIT Examples and Code Snippets

No Code Snippets are available at this moment for DMIT.

Community Discussions

Trending Discussions on DMIT

Summarizing data by name separated across multiple variables

QUESTION

Summarizing data by name separated across multiple variables

Asked 2018-Jul-18 at 15:41

I'm trying to count totals for goals, primary assists, and secondary assists for each player. My problem is that I can't get my head around the logic to do that, as the data I want to summarize by (player name) is listed across three variables (goal, primary assist and secondary assist)

Here's my reproducible data (it's from dput(), so apologies for the mess).

...

ANSWER

Answered 2018-Jul-18 at 15:28

One way to get the result is to reshape your data with gather()/spread() in addition to your summarize strategy.

Source https://stackoverflow.com/questions/51405462

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

Vulnerabilities

No vulnerabilities reported

Install DMIT

You can download it from GitHub.
You can use DMIT 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.