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Cutting-edge deep learning techniques for bioimage analysis
DL4MicEverywhere / u-net-2d-multilabel-zerocostdl4mic / 2.1.4

u-net-2d-multilabel-zerocostdl4mic implementation.

2D semantic segmentation. U-Net is an encoder-decoder architecture originally used for image segmentation. The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.
Tags
AMD64colabnotebookdenoisingZeroCostDL4Mic2Ddl4miceverywhereobject detection
Citation
https://doi.org/10.1038/s41467-021-22518-0von Chamier, L., Laine, R.F., Jukkala, J. et al. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 12, 2276 (2021). https://doi.org/10.1038/s41467-021-22518-0
Olaf Ronneberger, Philipp Fischer, Thomas Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv:1505.04597. https://arxiv.org/abs/1505.04597https://arxiv.org/abs/1505.04597
Solution written by
DL4MicEverywhere team
album team

Arguments

--path
What is your working path? (default value: .)

Usage instructions

Please follow this link for details on how to install and run this solution.