https://github.com/cemac/lifd_imagesegmentation
Jupyter notebook tutorial on Image Segmentation
Science Score: 36.0%
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Repository
Jupyter notebook tutorial on Image Segmentation
Basic Info
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Metadata Files
README.md
Leeds Institute for Fluid Dynamics Machine Learning For Earth Sciences
Image Segmentation
This Jupyter notebook demonstrates how artificial neural networks (ANNs) can be applied to image segmentation problems. Segmentation in this context refers to the task of assigning discrete labels to individual pixels or regions of an image. We can use segmentation models to identify and locate features of interest within an image. This notebook contains a simple application to self-driving cars, where we train a segmentation model to identify important features in dashcam footage, as well as a more complicated example, based on the work of Coney et al. (2023), identifying and characterising trapped lee waves over the UK.
Quick look
Quick start
Binder and Colab buttons
Will launch this tutorial in binder (CPU) or Google Colab (GPU).
Running locally
If you're already familiar with Git, Anaconda and virtual environments, the environment you need to create is found in unet.yml and the code below will install, activate and launch the notebook. The .yml file has been tested on the latest Linux, macOS and Windows operating systems.
bash
git clone git@github.com:cemac/LIFD_ImageSegmentation.git
cd LIFD_ImageSegmentation
conda env create -f unet.yml
conda activate unet
jupyter-notebook
Installation and requirements
This notebook is designed to run on a laptop with no special hardware required. However, training of neural networks can take a long time (hours) without dedicated GPU hardware. If you have a GPU, it is recommended to do a local installation as outlined in the repository howtorun and jupyter_notebooks sections. Otherwise, online compute platforms which offer GPU access (e.g. Google Colab) are strongly recommended.
Licence Information
LIFDENVML_NOTEBOOKS by CEMAC are licenced under a Creative Commons Attribution 4.0 International License.
Acknowledgements
Thanks to Jonathan Coney for making available the code on which this notebook is based. This tutorial is part of the LIFDENVML_NOTEBOOKS series. Please refer to the parent repository for full acknowledgements.
Owner
- Name: Centre for Environmental Modelling And Computation
- Login: cemac
- Kind: organization
- Location: Leeds
- Website: www.cemac.leeds.ac.uk
- Twitter: CEMAC_Leeds
- Repositories: 53
- Profile: https://github.com/cemac
software to support environmental science
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Dependencies
- actions/checkout v2 composite
- conda-incubator/setup-miniconda v2 composite