train-CRF-RNN | Train CRF-RNN for Semantic Image Segmentation | Machine Learning library

by martinkersner Python Version: Current License: Non-SPDX

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kandi X-RAY | train-CRF-RNN Summary

train-CRF-RNN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. train-CRF-RNN has no bugs, it has no vulnerabilities and it has low support. However train-CRF-RNN build file is not available and it has a Non-SPDX License. You can download it from GitHub.

Train CRF-RNN for Semantic Image Segmentation

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train-CRF-RNN has a low active ecosystem.

It has 199 star(s) with 90 fork(s). There are 15 watchers for this library.

It had no major release in the last 6 months.

There are 21 open issues and 29 have been closed. On average issues are closed in 15 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of train-CRF-RNN is current.

Quality

train-CRF-RNN has 0 bugs and 0 code smells.

Security

train-CRF-RNN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

train-CRF-RNN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

train-CRF-RNN 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.

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train-CRF-RNN releases are not available. You will need to build from source code and install.

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

Convert images to lmdb
Preprocess image
Preprocess data
Preprocess label image
Create a dense tensor
Get a list of id classes
Returns a dictionary of all the Pascal classes
Load train and test images
Load a text list from a file
Process arguments
Show help
Splits train and test images
Return the number of lines in a file
Create dense tensors
Check if given model exists
Return a list of all palette names
Postprocess segmentation
Remove log entries from class logs
Return True if two strings are strings
Save test images
Determine if there is a class in the given image

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train-CRF-RNN Key Features

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train-CRF-RNN Examples and Code Snippets

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Community Discussions

Trending Discussions on train-CRF-RNN

How to load Image Masks (Labels) for Image Segmentation in Keras

QUESTION

How to load Image Masks (Labels) for Image Segmentation in Keras

Asked 2017-Dec-14 at 03:14

I am using Tensorflow as a backend to Keras and I am trying to understand how to bring in my labels for image segmentation training.

I am using the LFW Parts Dataset which has both the ground truth image and the ground truth mask which looks like this * 1500 training images:

As I understand the process, during training, I load both the

(X) Image
(Y) Mask Image

Doing this in batches to meet my needs. Now my question is, is it sufficient to just load them both (Image and Mask Image) as NumPy arrays (N, N, 3) or do I need to process/reshape the Mask image in some way. Effectively, the mask/labels are represented as [R, G, B] pixels where:

[255, 0, 0] Hair
[0, 255, 0] Face
[0, 0, 255] Background

I could do something like this to normalize it to 0-1, I don't know if I should though:

...

ANSWER

Answered 2017-Oct-06 at 08:56

Keras requires the label to be one-hot encoded. So your input will have to be of (N x N x n_classes) dimension.

Source https://stackoverflow.com/questions/45178513

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

Vulnerabilities

No vulnerabilities reported

Install train-CRF-RNN

After executing commands above you can find in VOCdevkit/VOC2012/SegmentationClass 2913 labels and in VOCdevkit/VOC2012/JPEGImages their corresponding original images2. In order to have a better access to those directories we will create symlinks to them. Therefore, from your cloned repository you should run following commands (replace $DATASETS with your actual path where you downloaded PASCAL VOC dataset).

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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