neural-style-transfer | TensorFlow implementation of perceptual losses | Computer Vision library
kandi X-RAY | neural-style-transfer Summary
kandi X-RAY | neural-style-transfer Summary
neural-style-transfer is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow, OpenCV applications. neural-style-transfer has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However neural-style-transfer build file is not available. You can download it from GitHub.
This is a TensorFlow implementation for performing style transfer using neural networks. The code uses key ideas from the Perceptual Losses for Real-Time Style Transfer and Super-Resolution and A Neural Algorithm of Artistic Style papers.
This is a TensorFlow implementation for performing style transfer using neural networks. The code uses key ideas from the Perceptual Losses for Real-Time Style Transfer and Super-Resolution and A Neural Algorithm of Artistic Style papers.
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Quality
Security
License
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Support
neural-style-transfer has a low active ecosystem.
It has 4 star(s) with 3 fork(s). There are 3 watchers for this library.
It had no major release in the last 12 months.
There are 0 open issues and 4 have been closed. On average issues are closed in 2 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of neural-style-transfer is v1.0
Quality
neural-style-transfer has 0 bugs and 0 code smells.
Security
neural-style-transfer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
neural-style-transfer code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
neural-style-transfer is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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neural-style-transfer releases are available to install and integrate.
neural-style-transfer 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 neural-style-transfer and discovered the below as its top functions. This is intended to give you an instant insight into neural-style-transfer implemented functionality, and help decide if they suit your requirements.
- Calculate the total loss
- Calculate style reconstruction loss
- Calculate total variation regularization
- Compute the loss loss loss
- Compute the gram matrix
- Create resnet
- Residual block
- A convolutional convolution layer
- Create a convolutional convolution layer
- Generate images from images
- Preprocess image from path
- Compute the grams of the given style image
- Preprocess input image
- Check if the arguments passed to the command line
- Check if a path exists
- Preprocess an image file
- Parse command line arguments
- Return the number of images in the given path
Get all kandi verified functions for this library.
neural-style-transfer Key Features
No Key Features are available at this moment for neural-style-transfer.
neural-style-transfer Examples and Code Snippets
No Code Snippets are available at this moment for neural-style-transfer.
Community Discussions
Trending Discussions on neural-style-transfer
QUESTION
How can I replace the first element of an HTML string with an h1?
Asked 2020-Jun-30 at 13:47
I have some HTML:
...ANSWER
Answered 2020-Jun-30 at 13:47Try this:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install neural-style-transfer
You can download it from GitHub.
You can use neural-style-transfer 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 neural-style-transfer 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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