BDD_Driving_Model

by gy20073 Python Version: Current License: No License

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

kandi X-RAY | BDD_Driving_Model Summary

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

BDD_Driving_Model

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Quality

Security

License

Reuse

Support

BDD_Driving_Model has a low active ecosystem.

It has 205 star(s) with 76 fork(s). There are 24 watchers for this library.

It had no major release in the last 6 months.

There are 11 open issues and 9 have been closed. On average issues are closed in 163 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of BDD_Driving_Model is current.

Quality

BDD_Driving_Model has 0 bugs and 0 code smells.

Security

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

BDD_Driving_Model code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

BDD_Driving_Model 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

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

BDD_Driving_Model 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.

BDD_Driving_Model saves you 2550 person hours of effort in developing the same functionality from scratch.

It has 5542 lines of code, 277 functions and 25 files.

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

Top functions reviewed by kandi - BETA

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

Train the RNN model
Convert flags to a command line
Generate images for distorted images
Create a batch of num_readers
Calculate city logits
Update the best error
Plot confusion matrix
Continuous interpolation
Reshape x to specified shape
Calculate loss matrices
Visualize stop - go visualization
R Implements perspective changes
Convert course_speed to joint bin location
Be a ffprobe file
Calculate the relative distance from the given speed vectors
Observe a frame
Evaluate the model
Visualize a continuous model
Visualize a continuous colormap
Visualize a continuous image
Calculate the continuous loss
Visualize a discrete colormap
Visualize a time series
Layer local convolutional layer
Visualize a discrete regression
The number of examples per epoch

Get all kandi verified functions for this library.

BDD_Driving_Model Key Features

No Key Features are available at this moment for BDD_Driving_Model.

BDD_Driving_Model Examples and Code Snippets

No Code Snippets are available at this moment for BDD_Driving_Model.

Community Discussions

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

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

Vulnerabilities

No vulnerabilities reported

Install BDD_Driving_Model

First clone the codebase to your local file system at $BDD_ROOT.
Tensorflow 0.11
ffmpeg and ffprobe
Python packages: IPython, PIL, opencv-python, scipy, matplotlib, numpy, sklearn

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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