Pytorch-UNet | PyTorch implementation of the U-Net for image | Machine Learning library

by milesial Python Version: v3.0 License: GPL-3.0

X-Ray Key Features Code Snippets(3)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | Pytorch-UNet Summary

Pytorch-UNet is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. Pytorch-UNet has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has medium support. You can download it from GitHub.

This model was trained from scratch with 5k images and scored a Dice coefficient of 0.988423 on over 100k test images. It can be easily used for multiclass segmentation, portrait segmentation, medical segmentation, ...

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Pytorch-UNet has a medium active ecosystem.

It has 6899 star(s) with 2104 fork(s). There are 69 watchers for this library.

It had no major release in the last 12 months.

There are 56 open issues and 334 have been closed. On average issues are closed in 137 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of Pytorch-UNet is v3.0

Quality

Pytorch-UNet has 0 bugs and 0 code smells.

Security

Pytorch-UNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

Pytorch-UNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

Pytorch-UNet is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

Pytorch-UNet releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

Top functions reviewed by kandi - BETA

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

Train network
Evaluate the validation set
Calculate the dice coefficient
Calculate the dice coefficient of the dice coefficient
Computes the dice loss between input and target
Compute predictions for the given image
Parse command line arguments
Plot image and mask
Returns output filenames
Convert a numpy array to an Image

Get all kandi verified functions for this library.

Pytorch-UNet Key Features

No Key Features are available at this moment for Pytorch-UNet.

Pytorch-UNet Examples and Code Snippets

pytorch-UNet,U-Net quickstart,Training

Python

Lines of Code : 9

License : Permissive (MIT)

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images_folder
   |-- images
       |-- img001.png
       |-- img002.png
       |-- ...
   |-- masks
       |-- img001.png
       |-- img002.png
       |-- ...

3. Tests

Python

Lines of Code : 6

License : No License

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kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5))
y_pred[:, :, -1] = cv2.morphologyEx(y_pred[:, :, -1], cv2.MORPH_OPEN, kernel)

blurred = cv2.GaussianBlur(test_dataset[n],(21,21),0)
dst = cv2.bitwise_and(blurred, blurred, mask=~out[0][:,

1. Loss

Python

Lines of Code : 6

License : No License

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dice_loss = (2. * intersection + eps) / (union + eps)
loss = w * BCELoss + (1 - w) * log(dice_loss) * (-1)

def get_mask_weight(mask):
    mask_ = cv2.erode(mask, kernel=np.ones((8,8),np.uint8), iterations=1)
    mask_ = mask-mask_
    return mask_ +

Community Discussions

Trending Discussions on Pytorch-UNet

Modify existing Pytorch code to run on multiple GPUs

Pytorch summary only works for one specific input size for U-Net

QUESTION

Modify existing Pytorch code to run on multiple GPUs

Asked 2020-Oct-03 at 23:52

I'm trying to run Pytoch UNet from the following link on 2 or more GPUs

Pytorch-UNet github

the changes the I did till now is:

1. from:

...

ANSWER

Answered 2020-Oct-03 at 23:52

My mistake was changing output = net(input) (commonly named as model) to:

output = net.module(input)

you can find information here

Source https://stackoverflow.com/questions/64185568

QUESTION

Pytorch summary only works for one specific input size for U-Net

Asked 2020-Feb-14 at 01:13

I am trying to implement the UNet architecture in Pytorch. When I print the model using print(model) I get the correct architecture:

but when I try to print the summary using (or any other input size for that matter):

...

ANSWER

Answered 2020-Feb-14 at 01:13

This UNet architecture you provided doesn't support that shape (unless the depth parameter is <= 3). Ultimately the reason for this is that the size of a downsampling operation isn't invertible since multiple input shapes map to the same output shape. For example consider

Source https://stackoverflow.com/questions/60199088

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

Vulnerabilities

No vulnerabilities reported

Install Pytorch-UNet

Install Docker 19.03 or later:
Install the NVIDIA container toolkit:
Download and run the image:
Download the data and run training:

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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