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# U-Net: Semantic segmentation with PyTorch
<a href="https://hub.docker.com/r/milesial/unet"><img src="https://img.shields.io/docker/cloud/build/milesial/unet?label=docker%20image&logo=Docker&style=for-the-badge" /></a>
<a href="https://hub.docker.com/r/milesial/unet"><img src="https://img.shields.io/badge/PyTorch%20version-v1.9.0-red.svg?logo=PyTorch&style=for-the-badge" /></a>
<a href="https://hub.docker.com/r/milesial/unet"><img src="https://img.shields.io/github/license/milesial/PyTorch-UNet?color=blue&style=for-the-badge" /></a>
<a href="https://hub.docker.com/r/milesial/unet"><img src="https://img.shields.io/badge/docker%20image-available-blue?logo=Docker&style=for-the-badge" /></a>
<a href="https://pytorch.org/"><img src="https://img.shields.io/badge/PyTorch%20version-v1.9.0-red.svg?logo=PyTorch&style=for-the-badge" /></a>
<a href="https://choosealicense.com/licenses/gpl-3.0/"><img src="https://img.shields.io/github/license/milesial/PyTorch-UNet?color=green&style=for-the-badge" /></a>
<a href="#"><img src="https://img.shields.io/badge/python-v3.6+-blue.svg?logo=python&style=for-the-badge" /></a>
![input and output for a random image in the test dataset](https://i.imgur.com/GD8FcB7.png)