deepali | Image , point set , and surface registration in PyTorch

by BioMedIA Python Version: 0.2.0 License: Apache-2.0

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

kandi X-RAY | deepali Summary

deepali is a Python library. deepali has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install deepali' or download it from GitHub, PyPI.

At a granular level, deepali is a library that consists of the following components:.

Support

Quality

Security

License

Reuse

Support

deepali has a low active ecosystem.

It has 11 star(s) with 2 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of deepali is 0.2.0

Quality

deepali has no bugs reported.

Security

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

License

deepali is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

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deepali Key Features

No Key Features are available at this moment for deepali.

deepali Examples and Code Snippets

No Code Snippets are available at this moment for deepali.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install deepali

This Python package can be installed with pip directly from the GitHub repository, i.e.,. Alternatively, it can be installed from a previously cloned local Git repository using. This will install missing dependencies in the current Python environment from PyPI. To use conda for installing the required dependencies, create a conda environment with pre-installed dependencies before running pip install. For further information on how to create and manage project dependencies using conda, see conda/README.md.

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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