SEGIN | The paper can be found here

by HideUnderBush Python Version: Current License: No License

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

kandi X-RAY | SEGIN Summary

SEGIN is a Python library. SEGIN has no bugs, it has no vulnerabilities and it has low support. However SEGIN build file is not available. You can download it from GitHub.

The paper can be found here.

Support

Quality

Security

License

Reuse

Support

SEGIN has a low active ecosystem.

It has 1 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of SEGIN is current.

Quality

SEGIN has no bugs reported.

Security

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

License

SEGIN 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

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

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

Installation instructions, examples and code snippets are available.

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SEGIN Key Features

No Key Features are available at this moment for SEGIN.

SEGIN Examples and Code Snippets

No Code Snippets are available at this moment for SEGIN.

Community Discussions

No Community Discussions are available at this moment for SEGIN.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install SEGIN

In our experiment, we use the edges2photos dataset (edge2shoes, edge2bags) same as the pix2pix. Besides, the anime dataset can be download here, the anime dataset used in DRIT are also recommended. For the anime data, we first use an open resource algorithm to detect and crop the anime face, and then we used HED to extract the edges of the anime. If data in DRIT are used for training, then there is no need to crop the face images. Modify the path in the train.py to save the model and middle results, parameters can also be modified. python train.py Similarly, modify the path for files I/O, simply use python test.py to generate test results. This is a draft version of code for review, a refined one will come up soon, which will support more options with higher flexibility. Code for warpping images and pre-trained model will also be uploaded soon.

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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