5. Run train, predict, session

Once you have added a meaningful number of (extra) examples, you can do a (new) training run. The number of added examples that is meaningful will depend on the case, but a reasonable amount is 50. Once the model is trained, you can continue by running a prediction and if wanted a postprocessing step.

If you ran the sample project, these steps will look very familiar:

1. start a conda command prompt

2. activate the orthoseg environment with:

   conda activate orthoseg
   

3. preload the images so they are ready to detect your {segment_subject} on, using the configuration file {project_dir}{segment_subject}.ini:

   orthoseg_load_images --config {project_dir}{segment_subject}.ini
   

4. train a neural network to detect football fields:

   orthoseg_train --config {project_dir}{segment_subject}.ini
   

5. detect the football fields:

   orthoseg_predict --config {project_dir}{segment_subject}.ini
   

After this completes, the directory {project_dir}/output_vector will contain a .gpkg file with the features found.

Of course it is also possible to script this in your scripting language of choice to automate this further…

Note

Because tasks often take quite a while, orthoseg maximally tries to resume work that was started but was not finished yet. Eg. when predicting a large area, orthoseg will save the prediction per image, so if the prediction process is stopped for any reason and restarted, it will continue where it stopped.