3DUnetCNN | Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation | Machine Learning library
kandi X-RAY | 3DUnetCNN Summary
kandi X-RAY | 3DUnetCNN Summary
Pytorch 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation
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Top functions reviewed by kandi - BETA
- Run inference
- Generate filenames from multiple datasets
- Generate filenames from subject identifiers
- Generate filenames
- Make predictions from a config file
- Fetch a model by name
- Build or load a trained model
- Train Keras training
- Build a model
- Write training data
- Compute predictions with permutation keys
- Visualize the Gaussian distribution
- Calculates Volumetric predictions
- Zero - one window
- Train a model
- Parse the command line options
- Compute the Fourier Transform
- Compute predictions for a given subject
- Predict super resolution
- Augments autoimplementation of a case
- Generate an Isensee19 model
- Get training and validation data
- Perform a single - volume Zstat
- Constructs a unet - trained Convolutional model
- Crop the image
- Estimate the Gaussian distribution
3DUnetCNN Key Features
3DUnetCNN Examples and Code Snippets
Community Discussions
Trending Discussions on 3DUnetCNN
QUESTION
I have a model that can train fine on a single GPU, but when I try to fit it using multi_gpu_model, I get this CUDA error before the script exits:
...ANSWER
Answered 2018-Oct-19 at 13:58It turned out that the .fit()
method of a multi_model_gpu
did not like it when the number of samples in the dataset was not a multiple of the batch_size
, i.e., the number of GPUs in my case. Ditching a sample from my dataset solved my issue. I reported this bug here.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install 3DUnetCNN
You can use 3DUnetCNN 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.
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