Train a model#
Parameters#
Model name, no spaces: Name of the model as it will appear in the other empanada modules after training.
Train directory: Training directory for finetuning. Must conform to the standard directory structure specified for empanada (as for example is created by the Save finetune/training patches module).
Validation directory (optional): Validation directory. Must conform to the standard directory structure specified for empanada. Can be the same as Train directory.
Model directory: Directory in which to save the trained model definition, weights, and config file. The directory will be created if it doesn’t exist already.
Dataset labels: List of labels in the training dataset. Each line is a comma separated list of three items without spaces: <class_id>,<class_name>,<class_type>. Class IDs must be integers, class names can be anything, class types must be either ‘semantic’ or ‘instance’.
Label divisor: For mutliclass segmentation, the label divisor that was used to offset the labels for each class.
Model architecture: The model architecture to use for training.
Use CEM pretrained weights: If checked the model encoder will be initialized with the latest CEM weights. (CEM weights are created by self-supervised training on the very large and heterogeneous CEM dataset).
Finetunable layers: Layers to unfreeze in the model encoder during training. See insert either best practice or FAQ link with photo description. Ignored if Use CEM pretrained weights isn’t checked.
Iterations: Number of iterations to train the model.
Patch size in pixels: Patch size in pixels to use for random cropping of the image during finetuning. Should be divisible by 16 for PanopticDeepLab model or 128 for PanopticBiFPN models.
Custom config (optional): Use a custom config file to set other model and training hyperparameters. See here for a training template to modify.
Description: Free form text description of the model including details about the training data, model purpose, and other relevant information.
Results#
Saves and registers a .pth torchscript model that has been trained on the provided data. Also saves a .yaml config with parameters necessary for additional finetuning.
