Training on New Data

For training new models we rely on the pytorch-3dunet. A similar configuration file can be used for training on new data and all the instructions can be found in the repo.

Adding Models

1. Put these three files in one directory:
   1. configuration file used for training: config_train.yml
   2. snapshot of the best model across training: best_checkpoint.pytorch
   3. snapshot of the last model saved during training: last_checkpoint.pytorch
2. Click "Add Custom Model" in the GUI and follow the instruction

Alternative Old Method

When the network is trained, it is enough to create ~/.plantseg_models/MY_MODEL_NAME directory and copy the following files into it:

• configuration file used for training: config_train.yml
• snapshot of the best model across training: best_checkpoint.pytorch
• snapshot of the last model saved during training: last_checkpoint.pytorch

The later two files are automatically generated during training and contain all neural networks parameters.

Now you can simply use your model for prediction by setting the config.yaml key to MY_MODEL_NAME.

If you want your model to be part of the open-source model zoo provided with this package, please contact us.