Abstract
Graphene U-Net is a library that provides a simplified platform for training deep neural networks for the task of microscopy image segmentation. It contains functions and classes that make the process of training a neural network simple such that non-ML experts can train and evaluate models on their own datasets. It uses Pytorch as a backend and can run on both CUDA-enable GPUs and CPUs. It contains the main library file Microscopy_Unet.py as well as unet.py which contains the UNET model used for this software. This can be replaced with any other fully convolutional deep learning architecture with relative ease.
- Developers:
-
Sadre, Robbie [1] ; Ophus, Colin [1]
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Release Date:
- 2021-06-08
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231
- Code ID:
- 60277
- Site Accession Number:
- 2021-023
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Sadre, Robbie, and Ophus, Colin.
Graphene U-Net v1.
Computer Software.
https://github.com/lbnlcomputerarch/graphene-u-net.
USDOE.
08 Jun. 2021.
Web.
doi:10.11578/dc.20210701.1.
Sadre, Robbie, & Ophus, Colin.
(2021, June 08).
Graphene U-Net v1.
[Computer software].
https://github.com/lbnlcomputerarch/graphene-u-net.
https://doi.org/10.11578/dc.20210701.1.
Sadre, Robbie, and Ophus, Colin.
"Graphene U-Net v1." Computer software.
June 08, 2021.
https://github.com/lbnlcomputerarch/graphene-u-net.
https://doi.org/10.11578/dc.20210701.1.
@misc{
doecode_60277,
title = {Graphene U-Net v1},
author = {Sadre, Robbie and Ophus, Colin},
abstractNote = {Graphene U-Net is a library that provides a simplified platform for training deep neural networks for the task of microscopy image segmentation. It contains functions and classes that make the process of training a neural network simple such that non-ML experts can train and evaluate models on their own datasets. It uses Pytorch as a backend and can run on both CUDA-enable GPUs and CPUs. It contains the main library file Microscopy_Unet.py as well as unet.py which contains the UNET model used for this software. This can be replaced with any other fully convolutional deep learning architecture with relative ease.},
doi = {10.11578/dc.20210701.1},
url = {https://doi.org/10.11578/dc.20210701.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20210701.1}},
year = {2021},
month = {jun}
}