keras-fcn | playable implementation of Fully Convolutional Networks | Machine Learning library

by JihongJu Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | keras-fcn Summary

keras-fcn is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. keras-fcn has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

A playable implementation of Fully Convolutional Networks with Keras.

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Quality

Security

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Support

keras-fcn has a low active ecosystem.

It has 207 star(s) with 80 fork(s). There are 12 watchers for this library.

It had no major release in the last 6 months.

There are 19 open issues and 13 have been closed. On average issues are closed in 10 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keras-fcn is current.

Quality

keras-fcn has 0 bugs and 0 code smells.

Security

keras-fcn has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keras-fcn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keras-fcn is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

keras-fcn 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 are not available. Examples and code snippets are available.

keras-fcn saves you 556 person hours of effort in developing the same functionality from scratch.

It has 1300 lines of code, 78 functions and 26 files.

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

Top functions reviewed by kandi - BETA

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

VGG19 model
Wrapper for VGG_UPSampling
Generate the decoded feature
Computes a bilinear up with bilinear up
Compute the weighted freq weight of the confusion matrix
Compute the confusion matrix
Calculate Confusion
Factory for FCN
Convolutional layer
Mean IU
Compute mean accuracy
Compute the accuracy
Performs the standardization of x
Create an IndexIterator from an image set
Save the image
Load an image
Load image from file
Visualize the VGG16 model

Get all kandi verified functions for this library.

keras-fcn Key Features

No Key Features are available at this moment for keras-fcn.

keras-fcn Examples and Code Snippets

No Code Snippets are available at this moment for keras-fcn.

Community Discussions

Trending Discussions on keras-fcn

Make a custom loss function in keras

Why does my neural net identify everything as person or background for a semantic segmentation task?

QUESTION

Make a custom loss function in keras

Asked 2018-Jan-08 at 19:27

Hi I have been trying to make a custom loss function in keras for dice_error_coefficient. It has its implementations in tensorboard and I tried using the same function in keras with tensorflow but it keeps returning a NoneType when I used model.train_on_batch or model.fit where as it gives proper values when used in metrics in the model. Can please someone help me out with what should i do? I have tried following libraries like Keras-FCN by ahundt where he has used custom loss functions but none of it seems to work. The target and output in the code are y_true and y_pred respectively as used in the losses.py file in keras.

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ANSWER

Answered 2017-Oct-06 at 16:03

There are two steps in implementing a parameterized custom loss function in Keras. First, writing a method for the coefficient/metric. Second, writing a wrapper function to format things the way Keras needs them to be.

It's actually quite a bit cleaner to use the Keras backend instead of tensorflow directly for simple custom loss functions like DICE. Here's an example of the coefficient implemented that way:

Source https://stackoverflow.com/questions/45961428

QUESTION

Why does my neural net identify everything as person or background for a semantic segmentation task?

Asked 2017-Dec-11 at 04:42

Firstly, I am newbie to these topics. My neural network classifies everything as person or background on both training and validation sets. Training set is VOC2011.

https://github.com/JihongJu/keras-fcn

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ANSWER

Answered 2017-Nov-21 at 08:59

Regarding the question about visualisation of label images. You do not need your own colour-map with imshow. You can use the cmap keyword argument to imshow, and choose a qualitative colour-map.

Vulnerabilities

No vulnerabilities reported

Install keras-fcn

You can download it from GitHub.
You can use keras-fcn 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.

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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