PyTorch implementation of 1D, 2D and 3D U-Net.
The U-Net architecture was first described in Ronneberger et al. 2015, U-Net: Convolutional Networks for Biomedical Image Segmentation. The 3D version was described in Çiçek et al. 2016, 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation.
Installation
pip install unet
Credits
If you used this code for your research, please cite this repository using the information available on its Zenodo entry:
Pérez-García, Fernando. (2020). fepegar/unet: PyTorch implementation of 2D and 3D U-Net (v0.7.5). Zenodo. https://doi.org/10.5281/zenodo.3697931
1 maintainer
Metadata
1 maintainer
Release files for unet 0.8.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| unet-0.8.1.tar.gz | 8.8 kB | Without provenance | Details for unet-0.8.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| unet-0.8.1-py3-none-any.whl | Python 3 | none | any | Details for unet-0.8.1-py3-none-any.whl |
Total release size: 16.2 kB
Release files / unet-0.8.1.tar.gz
| Download URL | unet-0.8.1.tar.gz |
|---|---|
| Size | 8.8 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
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uv/0.5.9
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Release files / unet-0.8.1-py3-none-any.whl
| Download URL | unet-0.8.1-py3-none-any.whl |
|---|---|
| Size | 7.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.5.9
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Security for unet 0.8.1
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