Run train, predict,... session - orthoseg/orthoseg GitHub Wiki

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:

start a conda command prompt
activate the orthoseg environment with
```
conda activate orthoseg
```
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
```

train a neural network to detect football fields.

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

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...

Remark: 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.