RTCnet | radar based , single-frame , multi-class detection method

by tudelft-iv Python Version: Current License: GPL-3.0

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

kandi X-RAY | RTCnet Summary

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

This repository will contain the code of RTCnet from our paper entitled "CNN-based Road User Detection using the 3D Radar Cube", by TU Delft, Intelligent Vehicles group (The paper was accepted to Robotics and Automation Letters (RA-L) in 2020 January. See the publication here: Here is a short presentation of the methodology: and a short demo video of its performance: Example output at target level: Red/Green stands for bike/pedestrian.

Support

Quality

Security

License

Reuse

Support

RTCnet has a low active ecosystem.

It has 27 star(s) with 7 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 4 have been closed. On average issues are closed in 235 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of RTCnet is current.

Quality

RTCnet has 0 bugs and 0 code smells.

Security

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

RTCnet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

RTCnet is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Train the objective function
Train the model
Evaluate the model
Performs a farward pass through the model
Calculate the accuracy of the evaluation
Saves the checkpoint
Convert to OOV
Perform post clustering
Cluster a cylinder
Runs the test
Perform a farward pass through the model
Compute the features from the input features
Splits a feature into two features
Calculate precision and recall for each class
Calculate precision recall
Train an OLS Estimator
Convert the object to an oo type
Create the path for training
Generate a dataset
Sets the labels and features

Get all kandi verified functions for this library.

RTCnet Key Features

No Key Features are available at this moment for RTCnet.

RTCnet Examples and Code Snippets

No Code Snippets are available at this moment for RTCnet.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install RTCnet

You can download it from GitHub.
You can use RTCnet 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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