ultrasound-nerve-segmentation | Kaggle Ultrasound Nerve Segmentation competition | Machine Learning library

 by   EdwardTyantov Python Version: Current License: No License

kandi X-RAY | ultrasound-nerve-segmentation Summary

kandi X-RAY | ultrasound-nerve-segmentation Summary

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

Kaggle Ultrasound Nerve Segmentation competition [Keras]. #Install (Ubuntu {14,16}, GPU). In ~/.keras/keras.json (it's very important, the project was running on theano backend, and some issues are possible in case of TensorFlow). Place train and test data into '../train' and '../test' folders accordingly. Results will be generatated in "res/" folder. res/unet.hdf5 - best model. Generate predection with a model in res/unet.hdf5. Motivation's explained in my internal pres (slides:
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              ultrasound-nerve-segmentation has a low active ecosystem.
              It has 178 star(s) with 62 fork(s). There are 11 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 4 open issues and 9 have been closed. On average issues are closed in 13 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of ultrasound-nerve-segmentation is current.

            kandi-Quality Quality

              ultrasound-nerve-segmentation has 0 bugs and 0 code smells.

            kandi-Security Security

              ultrasound-nerve-segmentation has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              ultrasound-nerve-segmentation code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              ultrasound-nerve-segmentation does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              ultrasound-nerve-segmentation releases are not available. You will need to build from source code and install.
              ultrasound-nerve-segmentation has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions are not available. Examples and code snippets are available.

            Top functions reviewed by kandi - BETA

            kandi has reviewed ultrasound-nerve-segmentation and discovered the below as its top functions. This is intended to give you an instant insight into ultrasound-nerve-segmentation implemented functionality, and help decide if they suit your requirements.
            • Test flow
            • Generate a random transformation
            • Transform an image using elastic transformation
            • Generate the next batch of data
            • Generate the flow of the flow function
            • Performs validation
            • Calculate the length of a label
            • Resize image
            • Load test ids
            • Run the test
            • Load test data
            • Create training images
            • Create test images
            Get all kandi verified functions for this library.

            ultrasound-nerve-segmentation Key Features

            No Key Features are available at this moment for ultrasound-nerve-segmentation.

            ultrasound-nerve-segmentation Examples and Code Snippets

            No Code Snippets are available at this moment for ultrasound-nerve-segmentation.

            Community Discussions

            QUESTION

            Absurd loss and metric values when using flow_from_directory
            Asked 2019-Aug-10 at 16:26

            I am trying to train a UNet for image segmentation in keras using the following custom loss function and metric:

            ...

            ANSWER

            Answered 2019-Aug-10 at 16:26

            Thanks to the insight by @today, I realized both the images and the masks were being loaded as arrays with values ranging from 0 to 255. So I added a preprocessing function to normalize them, which solved my problem:

            Source https://stackoverflow.com/questions/57442975

            QUESTION

            Last convoulutional layer in U-net architecure is expecting wrong dimention
            Asked 2018-Apr-27 at 05:21

            I am trying to implement u-net in Keras,but I got this error while training the model(call model.fit()):

            ValueError: Error when checking target: expected conv2d_302 to have shape > (None, 1, 128, 640) but got array with shape (360, 1, 128, 128)

            And the output of the model.summary() is :

            ...

            ANSWER

            Answered 2018-Apr-26 at 19:13

            It is most certain that the author of the original wanted to concatenate on the channels dimension, not one of the image dimensions.

            The tensors in convolutional networks could be in one of the two formats:

            Source https://stackoverflow.com/questions/50050021

            QUESTION

            Keras + CNTK: TensorSliceWithMBLayoutFor
            Asked 2017-Sep-09 at 04:51

            I am running into a few problems while migrating an image segmentation code done with Keras+Tensorflow backend into Keras+CNTK backend. The code runs perfectly with a TF backend but crashes with CNTK.

            The model was inspired from https://github.com/jocicmarko/ultrasound-nerve-segmentation/blob/master/train.py

            Model inputs are defined as inputs = Input((img_width, img_height, num_channels)), where num_channels = 1.

            The error comes from the line trying to fit the model: model.fit(X_train, Y_train, epochs=trainingEpochs, verbose=2, shuffle=True, validation_data=(X_val, Y_val), callbacks=cb_list)

            Where X_train, Y_train, X_val, Y_val are all of shape (num_slices, img_width, img_height, num_channels)

            The error I keep getting is the following:

            Traceback (most recent call last):
            File "TrainNetwork_CNTK.py", line 188, in
            history = model.fit(X_train, Y_train, epochs=trainingEpochs, verbose=2, shuffle=True, validation_data=(X_val, Y_val), callbacks=cb_list)
            File "C:\Users...\site-packages\keras\engine\training.py", line 1430, in fit
            initial_epoch=initial_epoch)
            File "C:\Users...\site-packages\keras\engine\training.py", line 1079, in _fit_loop
            outs = f(ins_batch)
            File "C:\Users...\site-packages\keras\backend\cntk_backend.py", line 1664, in call
            input_dict, self.trainer_output)
            File "C:\Users...\site-packages\cntk\train\trainer.py", line 160, in train_minibatch
            output_map, device)
            File "C:\Users...\site-packages\cntk\cntk_py.py", line 2769, in train_minibatch
            return _cntk_py.Trainer_train_minibatch(self, *args)
            RuntimeError: Node 'UserDefinedFunction2738' (UserDefinedV2Function operation): TensorSliceWithMBLayoutFor: FrameRange's dynamic axis is inconsistent with data:

            There seems to be very little activity on CNTK issues here in SO, so anything to try to shine some light to this issue would be very helpful!

            ...

            ANSWER

            Answered 2017-Sep-09 at 04:51

            The reason is the loss function:

            Source https://stackoverflow.com/questions/45984253

            QUESTION

            the Keras layers(functions) corresponding to tf.nn.conv2d_transpose
            Asked 2017-Feb-10 at 00:14

            In Keras, what are the layers(functions) corresponding to tf.nn.conv2d_transpose in Tensorflow? I once saw the comment that we can Just use combinations of UpSampling2D and Convolution2D as appropriate. Is that right?

            In the following two examples, they all use this kind of combination.

            1) In Building Autoencoders in Keras, author builds decoder as follows.

            2) In an u-uet implementation, author builds deconvolution as follows

            ...

            ANSWER

            Answered 2017-Feb-09 at 20:32

            The corresponding layers in Keras are Deconvolution2D layers.

            It's worth to mention that you should be really careful with them because they sometimes might behave in unexpected way. I strongly advise you to read this Stack Overflow question (and its answer) before you start to use this layer.

            UPDATE:

            1. Deconvolution is a layer which was add relatively recently - and maybe this is the reason why people advise you to use Convolution2D * UpSampling2D.
            2. Because it's relatively new - it may not work correctly in some cases. It also need some experience to use them properly.
            3. In fact - from a mathematical point of view - every Deconvolution might be presented as a composition of Convolution2D and UpSampling2D - so maybe this is the reason why it was mentioned in texts you provided.

            UPDATE 2:

            Ok. I think I found an easy explaination why Deconvolution2D might be presented in a form of a composition of Convolution2D and UpSampling2D. We would use a definition that Deconvolution2D is a gradient of some convolution layer. Let's consider three most common cases:

            1. The easiest one is a Convolutional2D without any pooling. In this case - as it's the linear operation - its gradient is a function itself - so Convolution2D.
            2. The more tricky one is a gradient of Convolution2D with AveragePooling. So: (AveragePooling2D * Convolution2D)' = AveragePooling2D' * Convolution2D'. But a gradient of AveragePooling2D = UpSample2D * constant - so it's also in this case when the preposition is true.
            3. The most tricky one is one with MaxPooling2D. In this case still (MaxPooling2D * Convolution2D)' = MaxPooling2D' * Convolution2D' But MaxPooling2D' != UpSample2D. But in this case one can easily find an easy Convolution2D which makes MaxPooling2D' = Convolution2D * UpSample2D (intuitively - a gradient of MaxPooling2D is a zero matrix with only one 1 on its diagonal. As Convolution2D might express a matrix operation - it may also represent the injection from a identity matrix to a MaxPooling2D gradient). So: (MaxPooling2D * Convolution2D)' = UpSampling2D * Convolution2D * Convolution2D = UpSampling2D * Convolution2D'.

            The final remark is that all parts of the proof have shown that Deconvolution2D is a composition of UpSampling2D and Convolution2D instead of opposite. One can easily proof that every function of a form a composition of UpSampling2D and Convolution2D might be easily presented in a form of a composition of UpSampling2D and Convolution2D. So basically - the proof is done :)

            Source https://stackoverflow.com/questions/42144191

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

            Vulnerabilities

            No vulnerabilities reported

            Install ultrasound-nerve-segmentation

            You can download it from GitHub.
            You can use ultrasound-nerve-segmentation 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 .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/EdwardTyantov/ultrasound-nerve-segmentation.git

          • CLI

            gh repo clone EdwardTyantov/ultrasound-nerve-segmentation

          • sshUrl

            git@github.com:EdwardTyantov/ultrasound-nerve-segmentation.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link