train-DeepLab | Train DeepLab for Semantic Image Segmentation | Machine Learning library

by martinkersner Python Version: Current License: MIT

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

kandi X-RAY | train-DeepLab Summary

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

This repository contains scripts for training DeepLab for Semantic Image Segmentation using strongly and weakly annotated data. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs and Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image Segmentation papers describe training procedure using strongly and weakly annotated data, respectively. In following tutorial we use couple of shell variables in order to reproduce the same results without any obtacles.

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train-DeepLab has a low active ecosystem.

It has 173 star(s) with 78 fork(s). There are 11 watchers for this library.

It had no major release in the last 6 months.

There are 27 open issues and 11 have been closed. On average issues are closed in 14 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of train-DeepLab is current.

Quality

train-DeepLab has 0 bugs and 15 code smells.

Security

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

train-DeepLab code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

train-DeepLab 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.

train-DeepLab saves you 259 person hours of effort in developing the same functionality from scratch.

It has 629 lines of code, 55 functions and 10 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

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

Convert Matlab to PNG format
Convert a mat file to a Segmentation object
Modify image name
Check if an image contains a given class
Generate a pascal palette
Convert from color segmentation
Returns a list of id classes
Return a dictionary of all Pascal classes
Process the argument list
Print help message
Match net accuracy
Match iteration
Log an image
Clear the file list log
Convert a segmentation matrix to numpy array
Match loss
Preprocess an image
Return True if two strings are strings
Postprocess a segmentation
Predict the predictions
Return a list of palette names
Load a list from a file
Convert from color segmentation
Create tensorflow tensors
Load segmentation from binary file
Concatenate the result

Get all kandi verified functions for this library.

train-DeepLab Key Features

No Key Features are available at this moment for train-DeepLab.

train-DeepLab Examples and Code Snippets

No Code Snippets are available at this moment for train-DeepLab.

Community Discussions

Trending Discussions on train-DeepLab

how to add customize bn layer to caffe

QUESTION

how to add customize bn layer to caffe

Asked 2017-Oct-24 at 08:08

I download a deeplabV2 project from GitHub and find it does not have "BN" layer.
I want to add it to caffe code but meet this error:

...

ANSWER

Answered 2017-Oct-24 at 08:08

It looks like you're using an old Caffe version that takes 2 parameters for the REGISTER_LAYER_CLASS macro, but BN layer is suitable to a newer Caffe version where the macro was changes to take only one parameter (which is the layer type).
You can either check in other layers what is the other parameter for REGISTER_LAYER_CLASS and add it accordingly to your BN layer, or take an updated version of Caffe and merge deeplab layers into it.
(Alternatively, this appears to be a deeplab2 Caffe repo, up to date, with BN support: https://github.com/xmyqsh/deeplab-v2)

Source https://stackoverflow.com/questions/46901722

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

Vulnerabilities

No vulnerabilities reported

Install train-DeepLab

You should follow instructions for installation. However, if you have already fulfilled all necessary dependencies running following commands from code/ directory should do the job.
Before the first training we have to download several files. Using the command below we download initialization model, definition its network and solver. It will also setup symbolic links in directories where those files are later expected during training. In order to easily switch between datasets we will modify image lists appropriately.

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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