Teaching resources for image segmentation with U-Nets (a type of deep neural network).
There are five courses that can be accessed and run online through Google Colab
This repository contains versions of those courses that may be adapted for your own purposes, running on the computers you manage and have access to.
Step 1: get the code
open an anaconda command window
git clone --depth 1 https://github.com/MARDAScience/UNets4IntertidalReefs.git
Step 2: create the conda environment
cd UNets4IntertidalReefs
conda env create -f unet_imseg.yml
conda activate unet_imseg
Now, you have a few options
- Run through the workflows (parts 1 through 5) as python scripts, in sequence
cd scripts
python _part1_of_5.py
python _part2_of_5.py
python _part3_of_5.py
python _part4_of_5.py
python _part5_of_5.py
- Run through the workflows as jupyter notebooks
cd notebooks
jupyter notebook
(open the notebooks from your browser)
- Train a model on your own data
a. make a new folder inside data and organize your images and label images similarly, into train, test and validation folders b. make a config file like those for the other data sets c. copy and adapt the workflow to your needs, modifying (at least) the paths to your data, and other specifics