tf-image-segmentation | Image Segmentation framework based on Tensorflow | Machine Learning library

by warmspringwinds Python Version: Current License: MIT

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

kandi X-RAY | tf-image-segmentation Summary

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

If you used the code for your research, please, cite the paper:.

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tf-image-segmentation has a low active ecosystem.

It has 542 star(s) with 194 fork(s). There are 36 watchers for this library.

It had no major release in the last 6 months.

There are 31 open issues and 12 have been closed. On average issues are closed in 42 days. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of tf-image-segmentation is current.

Quality

tf-image-segmentation has 0 bugs and 4 code smells.

Security

tf-image-segmentation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

tf-image-segmentation code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

tf-image-segmentation 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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tf-image-segmentation releases are not available. You will need to build from source code and install.

tf-image-segmentation 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.

tf-image-segmentation saves you 325 person hours of effort in developing the same functionality from scratch.

It has 781 lines of code, 45 functions and 17 files.

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

Top functions reviewed by kandi - BETA

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

Convenience layer of fcn_8s
Uses bilinear downsampling
Upsample filter
Calculate the kernel size
Implements FCN 2s
Get the names of the segmentation filenames
Get the filenames for the selected image
Get the list of segmentation images
Add full path and extension to filenames
Creates a convolutional layer
Resnet_50_50_50s
Resnet v1s
Resnet v1_50_50
Get a list of filenames for each segmentation image
Add full path to filenames array
Get a list of filenames
Get the validation logits and logits
Given an annotation tensor
Returns the indices of the valid entries in the annotation batch
Get the labels from an annotation batch
Write image pairs to tfrecord
Visualize segmentation using segmentation
Plot discrete matplotlib matplotlib

Get all kandi verified functions for this library.

tf-image-segmentation Key Features

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tf-image-segmentation Examples and Code Snippets

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Community Discussions

Trending Discussions on tf-image-segmentation

How do you make TensorFlow + Keras fast with a TFRecord dataset?

OutOfRangeError: RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 1, current size 0)

QUESTION

How do you make TensorFlow + Keras fast with a TFRecord dataset?

Asked 2019-Jul-11 at 07:25

What is an example of how to use a TensorFlow TFRecord with a Keras Model and tf.session.run() while keeping the dataset in tensors w/ queue runners?

Below is a snippet that works but it needs the following improvements:

Use the Model API
specify an Input()
Load a dataset from a TFRecord
Run through a dataset in parallel (such as with a queuerunner)

Here is the snippet, there are several TODO lines indicating what is needed:

...

ANSWER

Answered 2017-Feb-19 at 21:25

I don't use tfrecord dataset format so won't argue on the pros and cons, but I got interested in extending Keras to support the same.

github.com/indraforyou/keras_tfrecord is the repository. Will briefly explain the main changes.

Dataset creation and loading

data_to_tfrecord and read_and_decode here takes care of creating tfrecord dataset and loading the same. Special care must be to implement the read_and_decode otherwise you will face cryptic errors during training.

Initialization and Keras model

Now both tf.train.shuffle_batch and Keras Input layer returns tensor. But the one returned by tf.train.shuffle_batch don't have metadata needed by Keras internally. As it turns out, any tensor can be easily turned into a tensor with keras metadata by calling Input layer with tensor param.

So this takes care of initialization:

Source https://stackoverflow.com/questions/42184863

QUESTION

OutOfRangeError: RandomShuffleQueue '_2_shuffle_batch/random_shuffle_queue' is closed and has insufficient elements (requested 1, current size 0)

Asked 2018-Jan-24 at 08:16

I am getting the outOfRange error when trying to feed the data to the model. I am guessing that data never reaches the queue, hence the error. Just for the testing I am feeding it the tfrecord with one tuple (image,ground_truth). I also tried tensorflow debugger(tfdbg) but it would also just throw the same error I couldn't see any tensoeflow value.

Tensorflow version: 1.3

Python version: 3.5.3

Os: Windows10

...

ANSWER

Answered 2017-Sep-08 at 17:53

I was creating a dataset for my machine learning project, and it turns out I get above error whenever I was saving the image file in png format from python. I generated the dataset using the mat-lab and error vanished. I am still investigating this and post the updated answer soon. Meanwhile I hope this will be helpful to somebody.

Source https://stackoverflow.com/questions/46099109

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

Vulnerabilities

No vulnerabilities reported

Install tf-image-segmentation

And add models/slim subdirectory to your path:. Or you can install scikit-image, matplotlib, numpy using pip.
Tensorflow r0.12 or later version.
Custom tensorflow/models repository, which might be merged in a future.
Some libraries which can be acquired by installing Anaconda package.
VGG 16 checkpoint file, which you can get from here.
Clone this library:

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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