training-styletransfer | Style Transfer training and using the model | Machine Learning library
kandi X-RAY | training-styletransfer Summary
kandi X-RAY | training-styletransfer Summary
training-styletransfer is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. training-styletransfer has no bugs, it has no vulnerabilities, it has build file available and it has low support. However training-styletransfer has a Non-SPDX License. You can download it from GitHub.
Style Transfer training and using the model in ml5js
Style Transfer training and using the model in ml5js
Support
Quality
Security
License
Reuse
Support
training-styletransfer has a low active ecosystem.
It has 65 star(s) with 28 fork(s). There are 6 watchers for this library.
It had no major release in the last 6 months.
There are 5 open issues and 3 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 training-styletransfer is current.
Quality
training-styletransfer has 0 bugs and 0 code smells.
Security
training-styletransfer has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
training-styletransfer code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
training-styletransfer has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
Reuse
training-styletransfer 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, examples and code snippets are available.
training-styletransfer saves you 278 person hours of effort in developing the same functionality from scratch.
It has 672 lines of code, 36 functions and 8 files.
It has low code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed training-styletransfer and discovered the below as its top functions. This is intended to give you an instant insight into training-styletransfer implemented functionality, and help decide if they suit your requirements.
- Optimizes a training set
- Network layer
- Transpose a convolution layer
- Convolution layer
- Instance normalization
- Get an image
- Dump checkpoint files
- Remove optimizer variables
- Convert a variable name to a filename
- Perform ffwd on different images
- Wrapper for fwd
- Build argument parser
- Wrapper function for ffwd
- Wrapper for ffmpeg
- Check options
- List all files in_path
Get all kandi verified functions for this library.
training-styletransfer Key Features
No Key Features are available at this moment for training-styletransfer.
training-styletransfer Examples and Code Snippets
No Code Snippets are available at this moment for training-styletransfer.
Community Discussions
Trending Discussions on training-styletransfer
QUESTION
How to convert from scipy.misc.imresize to imageio
Asked 2021-Jan-20 at 17:00
Hi I'm running a slightly expensive aws... And trying to solve old scipy.imread to the new imagio.read standard.
In this file https://github.com/ml5js/training-styletransfer/blob/master/src/utils.py
...ANSWER
Answered 2021-Jan-20 at 17:00imresize is not part of scipy anymore. You can either downgrade to scipy i.e. 1.2.1 or install scikit-image and call skimage.transform.resize instead
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install training-styletransfer
Start by downloading or cloning this repository:.
This step is required only if you are running this without the Docker image. You will need to get the complete COCO Dataset, about 14GB of data. This is a requirement for training. You can download the data by running:.
Add the package repositories. Install nvidia-docker2 and reload the Docker daemon configuration.
This step is required only if you are running this without the Docker image. You will need to get the complete COCO Dataset, about 14GB of data. This is a requirement for training. You can download the data by running:.
Add the package repositories. Install nvidia-docker2 and reload the Docker daemon configuration.
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 .
Find more information at:
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
