Training the network#

Cellfinder includes a pretrained network for cell candidate classification. This will likely need to be retrained for different applications. Rather than generate training data blindly, the aim is to reduce the amount of hands-on time by only generating training data where cellfinder classified a cell candidate incorrectly.


If you don’t have any data yet, and want to try out the training see Using supplied training data

Generate training data#

To generate training data, you will need:

  • The cellfinder output file, cell_classification.xml (it’s in the points subdirectory).

  • The raw data used initially for cellfinder

To generate training data for a single brain, use the napari plugin.

Start training#

You can then use these yaml files for training, either using the napari plugin, or the following command-line tool.


If you have any yaml files from previous versions of cellfinder, they will continue to work, but are not documented here. Just use them as you would the files from the napari plugin._


If you would like to use the data that was originally used to train the supplied network, please see Using supplied training data

cellfinder_train -y yaml_1.yml  yaml_2.yml -o /path/to/output/directory/


  • -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

  • --network-depth Resnet depth (based on He et al. (2015)). Choose from

    (18, 34, 50, 101 or 152). In theory, a deeper network should classify better,

    at the expense of a larger model, and longer training time. Default: 50

  • --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).

Further help#

All cellfinder_train options can be found by running:

cellfinder_train -h