keras-semantic-segmentation-example | Example of semantic segmentation in Keras | Machine Learning library

by mrgloom Python Version: Current License: No License

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kandi X-RAY | keras-semantic-segmentation-example Summary

keras-semantic-segmentation-example is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Keras applications. keras-semantic-segmentation-example has no bugs, it has no vulnerabilities and it has low support. However keras-semantic-segmentation-example build file is not available. You can download it from GitHub.

Example of semantic segmentation in Keras

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keras-semantic-segmentation-example has a low active ecosystem.

It has 51 star(s) with 22 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 1 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-semantic-segmentation-example is current.

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keras-semantic-segmentation-example has 0 bugs and 0 code smells.

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keras-semantic-segmentation-example has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-semantic-segmentation-example code analysis shows 0 unresolved vulnerabilities.

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keras-semantic-segmentation-example releases are not available. You will need to build from source code and install.

keras-semantic-segmentation-example has no build file. You will be need to create the build yourself to build the component from source.

keras-semantic-segmentation-example saves you 370 person hours of effort in developing the same functionality from scratch.

It has 882 lines of code, 39 functions and 6 files.

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

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Save the prediction
Generate random image
Convolutional convolutional convolutional layer
Compile the model
Visualize the prediction
Train the model
Generator for random images
Inspect the generated data

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Community Discussions

Trending Discussions on keras-semantic-segmentation-example

What is the meaning of the result of model.predict() function for semantic segmentation?

QUESTION

What is the meaning of the result of model.predict() function for semantic segmentation?

Asked 2019-Jul-12 at 13:58

I use Segmentation Models library for multi-class (in my case 4 class) semantic segmentation. The model (UNet with 'resnet34' backbone) is trained with 3000 RGB (224x224x3) images. The accuracy is around 92.80%.

1) Why model.predict() function requires (1,224,224,3) shaped array as input ? I didn't find the answer even in the Keras documentation. Actually, below code is working, I have no problem with it but I want to understand the reason.

...

ANSWER

Answered 2019-Jul-12 at 13:58

1) Image input shape in your deep neural network architecture is (224,224,3), so width=height=224 and 3 color channels. And you need an additionnal dimension in case you want to give more than one image at a time to your model. So (1,224,224,3) or (something, 224,224,3).

2) According to the doc of Segementation models repo, you can specify the number of classes you want as output model = Unet('resnet34', classes=4, activation='softmax'). Thus if you reshape your labelled image to have a shape (1,224,224,4). The last dimension is a mask channel indicating with a 0 or 1 if pixel i,j belongs to class k. Then you can predict and access to each output mask

Source https://stackoverflow.com/questions/57006542

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You can use keras-semantic-segmentation-example 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.

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