reseg | A Recurrent Neural Network for Object Segmentation | Machine Learning library

by fvisin Python Version: Current License: GPL-3.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | reseg Summary

reseg is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. reseg has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However reseg build file is not available. You can download it from GitHub.

If you use this code, please cite one of the following papers:.

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Quality

Security

License

Reuse

Support

reseg has a low active ecosystem.

It has 121 star(s) with 34 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 8 have been closed. On average issues are closed in 28 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of reseg is current.

Quality

reseg has 0 bugs and 0 code smells.

Security

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

reseg code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

reseg is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

Computes the padding of the input array
Zero pad the array
Compute the convolution of the input array
Get the equivalent input padding
Compute the equivalent input padding
Return the output of the input array
Return the output shape for the given input shape
Compute zero - padded image shape
Return the output shape for a given input shape
Get the output shape for a given input shape

Get all kandi verified functions for this library.

reseg Key Features

No Key Features are available at this moment for reseg.

reseg Examples and Code Snippets

No Code Snippets are available at this moment for reseg.

Community Discussions

Trending Discussions on reseg

ParameterError: Mono data must have shape (samples,). Received shape=(1, 87488721)

QUESTION

ParameterError: Mono data must have shape (samples,). Received shape=(1, 87488721)

Asked 2020-Jul-29 at 00:48

Currently I am working speaker Diarization on python where I am using pyannote for embedding. My embedding function looks like this:

...

ANSWER

Answered 2020-Jul-29 at 00:48

I got the same error too, but i have found a workaround. For me, the error got triggered in "pyannote/audio/features/utils.py", when it is trying to resample the audio using this line y = librosa.core.resample(y.T, sample_rate, self.sample_rate).T

This is my workaround

Source https://stackoverflow.com/questions/63128049

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

Vulnerabilities

No vulnerabilities reported

Install reseg

Download Theano and make sure it's working properly. All the information you need can be found by following this link: http://deeplearning.net/software/theano/. This software relies on some amazing third-party software libraries. You can install them with pip: pip install <--user> lasagne matplotlib Pillow progressbar2 pydot-ng retrying scikit-image scikit-learn tabulate (Use the --user option if you don't want to install them globally or you don't have sudo privileges on your machine.). Download the CamVid dataset from http://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/. Resize the images to 480X360 resolution. The program expects to find the dataset data in ./datasets/camvid/. You can change this path modifying camvid.py if you want. Download the VGG weights for Lasagne from: https://s3.amazonaws.com/lasagne/recipes/pretrained/imagenet/vgg16.pkl. Once downloaded, rename them as w_vgg16.pkl and put them in the root directory of this code.

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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