cell_classification.xml
(it's in the points
subdirectory).-y
or --yaml
The path to the yaml files defining training data-o
or --output
Output directory for the trained model (or model weights)--continue-training
Continue training from an existing trained model. If no model or model weights are specified, this will continue from the included model.--trained-model
Path to a trained model to continue training--model-weights
Path to existing model weights to continue training--batch-size
Batch size for training (how many cell candidates to process at once). Default: 16--epochs
How many times to use each sample for training. Default: 1000--test-fraction
What fraction of data to keep for validation. Default: 0.1--learning-rate
Learning rate for training the model--no-augment
Do not use data augmentation--save-weights
Only store the model weights, and not the full model. Useful to save storage space.--no-save-checkpoints
Do not save the model after each training epoch. Useful to save storage space, if you are happy to wait for the chosen number of epochs to complete. Each model file can be large, and if you don't have much training data, they can be generated quickly.--tensorboard
Log to output_directory/tensorboard
. Use tensorboard --logdir outputdirectory/tensorboard
to view.--save-progress
Save training progress to a .csv file (output_directory/training.csv
).cellfinder_train
options can be found by running: