u-segnet | SegNet architecture for autoencoder segmentation network | Machine Learning library
kandi X-RAY | u-segnet Summary
kandi X-RAY | u-segnet Summary
u-segnet is a Python library typically used in Telecommunications, Media, Advertising, Marketing, Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. u-segnet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.
Implementation of U-SegNet architecture for autoencoder segmentation network
Implementation of U-SegNet architecture for autoencoder segmentation network
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Quality
Security
License
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Support
u-segnet has a low active ecosystem.
It has 7 star(s) with 1 fork(s). There are 2 watchers for this library.
It had no major release in the last 6 months.
u-segnet has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of u-segnet is current.
Quality
u-segnet has no bugs reported.
Security
u-segnet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
u-segnet 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
u-segnet releases are not available. You will need to build from source code and install.
Build file is available. You can 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 u-segnet and discovered the below as its top functions. This is intended to give you an instant insight into u-segnet implemented functionality, and help decide if they suit your requirements.
- Train the model
- Saves the model to the given session
- Update the statistics
- Train a training step
- Train a single epoch
- Test the loss function
- Build the model
- Unpool a tensor
- Predictor for convolutional predictions
- Max pooling op
- Layer normalization layer
- Builds the dataset api
- Reads and returns image and mask
- Preprocessing
- Load config from a JSON file
- Parse the configuration from a config file
- Create directories
- Get command line arguments
- Resizes an image and saves it to output_dir
Get all kandi verified functions for this library.
u-segnet Key Features
No Key Features are available at this moment for u-segnet.
u-segnet Examples and Code Snippets
No Code Snippets are available at this moment for u-segnet.
Community Discussions
Trending Discussions on u-segnet
QUESTION
Tensorflow unpooling after max_pool_with_argmax using indices
Asked 2018-Dec-26 at 21:26
While trying to implement U-SegNet from the paper by Google, I've got a problem implementing unpooling operation using argmax indices.
The full code:
...ANSWER
Answered 2018-Dec-26 at 16:57Your code is working fine:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install u-segnet
You can download it from GitHub.
You can use u-segnet 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 u-segnet 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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