Keras-FCN | tensorflow implementation of Fully Convolutional Networks | Machine Learning library

by aurora95 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 vulnerabilities, it has a Permissive License and it has low support. However Keras-FCN has 1 bugs and it build file is not available. You can download it from GitHub.

Keras-tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation（Unfinished）

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Keras-FCN has a low active ecosystem.

It has 646 star(s) with 279 fork(s). There are 35 watchers for this library.

It had no major release in the last 6 months.

There are 49 open issues and 26 have been closed. On average issues are closed in 58 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Keras-FCN is current.

Quality

Keras-FCN has 1 bugs (1 blocker, 0 critical, 0 major, 0 minor) and 128 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.

Keras-FCN 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.

Keras-FCN saves you 629 person hours of effort in developing the same functionality from scratch.

It has 1462 lines of code, 59 functions and 15 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.

Train a model
Generate a SegDirectory from a directory
Returns a batch of the transformed images
Generate a random transform
Random crop
Center center of image
Wrapper for inference
Transformer convolution layer
A block of convolutional block
Construct the identity block
A dense dense network
Conv2D convolutional layer
Resnet with Resnet50
A function to construct an identity block
FCN residuals
A DenseNetFCN
Fit the model
Evaluate inference
Performs inference
Calculate the iou covm
Wrapper for resize
Resizes images to bilinear
Returns the path to resnet weights
Returns the path to the VGG16 weights

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

Useful setup scripts for Ubuntu 14.04 and 16.04 can be found in the robotics_setup repository. First use that to install CUDA, TensorFlow,.

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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