unet | U-Net Biomedical Image Segmentation | Machine Learning library

 by   IntelAI Jupyter Notebook Version: v2.0 License: Apache-2.0

kandi X-RAY | unet Summary

kandi X-RAY | unet Summary

unet is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow, Keras applications. unet has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

U-Net Biomedical Image Segmentation
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              unet has a low active ecosystem.
              It has 178 star(s) with 82 fork(s). There are 14 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 1 open issues and 22 have been closed. On average issues are closed in 66 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of unet is v2.0

            kandi-Quality Quality

              unet has 0 bugs and 0 code smells.

            kandi-Security Security

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

            kandi-License License

              unet is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              unet releases are available to install and integrate.
              It has 2410 lines of code, 88 functions and 20 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

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            unet Key Features

            No Key Features are available at this moment for unet.

            unet Examples and Code Snippets

            No Code Snippets are available at this moment for unet.

            Community Discussions

            QUESTION

            How can I sort datapath in numerical order?
            Asked 2022-Mar-17 at 05:31

            I have 10000119~10000130 patient 3D ct images I've sliced 3D CT images into 250 slices for each patient data along the z axis.

            I want to sort this data path in numerical order for each slice for each patient. What I want to do is the following:

            ...

            ANSWER

            Answered 2022-Mar-17 at 05:31

            I found my own answer..! I hope this will help other deeplearning engineer too! I hope you have a great day! I explicitly coded to get the intuition at hand.

            Source https://stackoverflow.com/questions/71491907

            QUESTION

            MIDV 500 document localization: fitting problem
            Asked 2022-Mar-12 at 14:14

            I've been experimenting with a part of MIDV 500 dataset, tried to localize document quadrilateral. So, my output is a vector of 8 floats.

            RGB images were scaled to 960 by 540 pixels (960, 540, 3), pixel values were scaled to [0..1]. Target vector also scaled to [0..1] (simply divided by image dims)

            My first approach was pretrained CNN (+ fine-tuning) from Keras applications (tried EfficientNetB0-2) with following Dense head:

            ...

            ANSWER

            Answered 2022-Mar-10 at 13:59

            Two things:

            1. Please check which version of TensorFlow (TF) you are using. I believe that from 2.5, you don't need to rescale the input image to the range [0-1]. The network expects tensors from [0-255]. https://keras.io/api/applications/efficientnet/
            2. Your model architecture and callbacks look all right (I am not an expert on this optimizer + loss though). Thus, I am assuming that the problem might come from your data input. Are you using ImageDataGenerator as input and for splitting the data from training and validation? If not, it might be worth a try. You can specify your validation subset and the generator will split the data for you. More info here: https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/ImageDataGenerator

            Source https://stackoverflow.com/questions/71424152

            QUESTION

            Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)
            Asked 2022-Mar-08 at 14:22

            I am training a Unet segmentation model for binary class. The dataset is loaded in tensorflow data pipeline. The images are in (512, 512, 3) shape, masks are in (512, 512, 1) shape. The model expects the input in (512, 512, 3) shape. But I am getting the following error. Input 0 of layer "model" is incompatible with the layer: expected shape=(None, 512, 512, 3), found shape=(512, 512, 3)

            Here are the images in metadata dataframe.

            Randomly sampling the indices to select the training and validation set

            ...

            ANSWER

            Answered 2022-Mar-08 at 13:38

            Use train_batches in model.fit and not train_images. Also, you do not need to use repeat(), which causes an infinite dataset if you do not specify how many times you want to repeat your dataset. Regarding your labels error, try rewriting your model like this:

            Source https://stackoverflow.com/questions/71395504

            QUESTION

            When does Pytorch initialize parameters?
            Asked 2022-Mar-04 at 09:15

            I’m now writing my own network with Pytorch. And I want to use a pretrained model in my net. Here is my overwriting init() code:

            ...

            ANSWER

            Answered 2022-Mar-04 at 09:15

            For the basic layers (e.g., nn.Conv, nn.Linear, etc.) the parameters are initialized by the __init__ method of the layer.
            For example, look at the source code of class _ConvNd(Module) (the class from which all other convolution layers are derived). At the bottom of its __init__ it calls self.reset_parameters() which initialize the weights.

            Therefore, if your nn.Module does not have any "independent" nn.Parameters, only trainable parameters inside sub-nn.Modules, when you construct your network, all weights of the sub modules are being initialized as the sub modules are constructed. That is, once you call h_model = H_model() the weights of h_model are already initialized to their default values. Calling h_model.load_state_dict(...) overrides these values to the desired pre-trained weights.

            Source https://stackoverflow.com/questions/71348500

            QUESTION

            How can we write our own custom AT commands?
            Asked 2022-Feb-26 at 19:33

            Is there a way that we can write a custom AT command for unetstack/subnero modems ? I refered Chapter 12 but could not find this information.

            ...

            ANSWER

            Answered 2022-Feb-26 at 19:33

            AT command shells (and Groovy shells) can be extended with shell extensions. Shell extensions implement a org.arl.fjage.shell.ShellExtension tag interface. Static methods (and attributes) of the shell extension class are made available in the shell as commands (and variables/constants). In the AT command shell, they are called using AT commands as briefly described in section 12.3 of the Unet handbook.

            For example, the handbook shows an example of loading the PhysicalShellExt via:

            Source https://stackoverflow.com/questions/71261130

            QUESTION

            Shape Error while implementing U-Net (Encoder Part) in Pytorch
            Asked 2022-Feb-23 at 08:54

            I am trying to learn build a U-NET architecture from scratch. I have written this code but the problem is that when I try to run to check the output of the encoder part, I am having issues with it. When you the run the code below , you'll get

            ...

            ANSWER

            Answered 2022-Feb-23 at 08:54

            There was a code logic mistake in the forward of Encoder

            I did:

            Source https://stackoverflow.com/questions/71232961

            QUESTION

            UNET prediction
            Asked 2022-Jan-18 at 20:17

            I use unet for image segmentation my question is what does below code mean

            ...

            ANSWER

            Answered 2022-Jan-18 at 19:36

            About first question test_img_norm=test_img[:,:,0][:,:,None], test_img[:,:,0] will copy first channel of image and test_img[:,:,0][:,:,None] will add one channel to it. for example if you have an image with shape (256, 256, 3), test_img_norm shape will be (256, 256, 1).

            About second part of question, model.predict(test_img_other_input)[0,:,:,0] > 0.2 will give you a boolean array. For every element in output of UNet, if element is less than 0.2, output would be True, otherwise would be False. And finally .astype(np.uint8) make booleans to zero or one.

            Source https://stackoverflow.com/questions/70761109

            QUESTION

            Implementing Dice Lose
            Asked 2022-Jan-13 at 08:15

            I would like to implement Dice Lose like this from dice_loss_for_keras.py:

            ...

            ANSWER

            Answered 2022-Jan-13 at 08:15

            You need to convert y_true to 1-hot representation in order to apply per-class dice loss. It seems like you have tf.one_hot function that does it for you.

            Once you have y_true in the same shape as y_pred, you can use your code to compute the dice score for each class separately, and then combine the scores of all classes to get the final scalar loss.

            Source https://stackoverflow.com/questions/70528006

            QUESTION

            How to feed tfrecord file in a model and train?
            Asked 2022-Jan-13 at 06:49

            I wrote a tfrecord file and fed in my Unet model but got a problem with the input shape. Below is my code.

            About the data:

            • 484 training images, each has a shape of (240, 240, 155, 4), these 4 numbers are the height, width, number of layers and channels respectively.
            • 484 labels, each has a shape of (240, 240, 155)

            I used the first 2 examples:

            ...

            ANSWER

            Answered 2021-Nov-29 at 16:49

            Your model expects the shape (samples, 240, 240, 155, 4), so try something like this:

            Source https://stackoverflow.com/questions/70158072

            QUESTION

            Trying to use Dice Loss with UNET
            Asked 2022-Jan-04 at 00:18

            I'm trying to implement the UNET at the keras website:

            Image segmentation with a U-Net-like architecture

            With only one change. use Dice loss instead of "sparse_categorical_crossentropy". However, every time I try something, I get different error. I'm coding on google colab using Tensorflow 2.7.

            For example, I tried using

            ...

            ANSWER

            Answered 2021-Dec-31 at 10:24

            You are passing 1-dimensional vectors to K.dot, while the ValueError is saying that K.dot requires arrays with 2-dimensions.

            You can replace it with element-wise multiplication, i.e. intersection = K.sum(targets *inputs)

            Source https://stackoverflow.com/questions/70514123

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

            Vulnerabilities

            No vulnerabilities reported

            Install unet

            You can download it from GitHub.

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