kandi X-RAY | faster_rcnn Summary
kandi X-RAY | faster_rcnn Summary
faster_rcnn is a Python library. faster_rcnn has no bugs, it has no vulnerabilities and it has low support. However faster_rcnn build file is not available. You can download it from GitHub.
faster_rcnn
faster_rcnn
Support
Quality
Security
License
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Support
faster_rcnn has a low active ecosystem.
It has 0 star(s) with 1 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
faster_rcnn has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of faster_rcnn is current.
Quality
faster_rcnn has no bugs reported.
Security
faster_rcnn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
faster_rcnn 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.
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faster_rcnn releases are not available. You will need to build from source code and install.
faster_rcnn has no build file. You will be need to create the build yourself to build the component from source.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of faster_rcnn
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of faster_rcnn
faster_rcnn Key Features
No Key Features are available at this moment for faster_rcnn.
faster_rcnn Examples and Code Snippets
No Code Snippets are available at this moment for faster_rcnn.
Community Discussions
No Community Discussions are available at this moment for faster_rcnn.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install faster_rcnn
You can download it from GitHub.
You can use faster_rcnn 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.
You can use faster_rcnn 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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