ETC-Real-time-Per-frame-Semantic-video-segmentation | Enforcing temporal consistency

by irfanICMLL Python Version: Current License: No License

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

kandi X-RAY | ETC-Real-time-Per-frame-Semantic-video-segmentation Summary

ETC-Real-time-Per-frame-Semantic-video-segmentation is a Python library. ETC-Real-time-Per-frame-Semantic-video-segmentation has no bugs, it has no vulnerabilities and it has low support. However ETC-Real-time-Per-frame-Semantic-video-segmentation build file is not available. You can download it from GitHub.

Enforcing temporal consistency in real-time per-frame semantic video segmentation

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Security

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Support

ETC-Real-time-Per-frame-Semantic-video-segmentation has a low active ecosystem.

It has 179 star(s) with 15 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 9 have been closed. On average issues are closed in 59 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ETC-Real-time-Per-frame-Semantic-video-segmentation is current.

Quality

ETC-Real-time-Per-frame-Semantic-video-segmentation has no bugs reported.

Security

ETC-Real-time-Per-frame-Semantic-video-segmentation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

ETC-Real-time-Per-frame-Semantic-video-segmentation 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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ETC-Real-time-Per-frame-Semantic-video-segmentation releases are not available. You will need to build from source code and install.

ETC-Real-time-Per-frame-Semantic-video-segmentation 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.

Top functions reviewed by kandi - BETA

kandi has reviewed ETC-Real-time-Per-frame-Semantic-video-segmentation and discovered the below as its top functions. This is intended to give you an instant insight into ETC-Real-time-Per-frame-Semantic-video-segmentation implemented functionality, and help decide if they suit your requirements.

Main worker function
Train the model
Update the statistics
Get a logger
Calculate average accuracy
Normalize an image
Convert label to trainId
Compute the intersection between two histograms
Calculate loss
Calculate the loss
Performs inference on an image
Merge a cfg from a list
Decode a config value
Ensure that the replacement value is correct
Evaluate the model
Color an image
Predict the output of the image
Backward computation
Apply leaky_relu
Forward computation
Apply the activation function
Transfers a ckpt object
Parse command line arguments
Perform the PSM forward computation

Get all kandi verified functions for this library.

ETC-Real-time-Per-frame-Semantic-video-segmentation Key Features

No Key Features are available at this moment for ETC-Real-time-Per-frame-Semantic-video-segmentation.

ETC-Real-time-Per-frame-Semantic-video-segmentation Examples and Code Snippets

No Code Snippets are available at this moment for ETC-Real-time-Per-frame-Semantic-video-segmentation.

Community Discussions

No Community Discussions are available at this moment for ETC-Real-time-Per-frame-Semantic-video-segmentation.Refer to stack overflow page for discussions.

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

Vulnerabilities

No vulnerabilities reported

Install ETC-Real-time-Per-frame-Semantic-video-segmentation

python tool/train_with_flow.py.
download the Cityscape dataset
python tool/demo.py.

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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