# PyTorch-2D-3D-UNet-Tutorial

**Repository Path**: automlresearch/PyTorch-2D-3D-UNet-Tutorial

## Basic Information

- **Project Name**: PyTorch-2D-3D-UNet-Tutorial
- **Description**: PyTorch-2D-3D-UNet-Tutorial
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-13
- **Last Updated**: 2021-09-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PyTorch-2D-3D-UNet-Tutorial

This repository contains all files that were used for the blog series 'Creating and training a U-Net model with PyTorch for 2D & 3D semantic segmentation - A guide to semantic segmentation with PyTorch and the U-Net'.
You can find the blog posts [here](https://johschmidt42.medium.com/).

In [requirements.txt](requirements.txt) you'll find the packages for the conda environment that I used. This does not necessarily mean that these will work on your machine/computer (for example torch comes with a specific cuda version).

I have updated the repo, e.g. the [transformations](transformations.py) and added a dataset example for a [3D dataset](Part5-3D-example.ipynb).
There is also an [example](Part6-PL-example.ipynb) now that shows how you can use a segmentation model like the UNet (or any other segmentation model) in PyTorch Lightning in combination with a logger like [neptune.ai](https://neptune.ai/) for experiment tracking.