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 (but cells.xml can also work).

  • The raw data used initially for cellfinder

To generate training data for a single brain, use cellfinder_curate:

cellfinder_curate signal_images background_images cell_classification.xml


  • Signal images

  • Background images

  • cell_classification.xml file

You must also specify the pixel sizes, see Specifying pixel size


  • -o or --output Output directory for curation results. If this is not given, then the directory containing cell_classification.xml will be used.

  • --symbol Marker symbol (Default: ring)

  • --marker-size Marker size(Default: 15)

  • --opacity Marker opacity (Default: 0.6)

A napari window will then open, showing two tabs on the left hand side:

  • Image Selecting this allows you to change the contrast limits, to better visualise cells

  • Cell candidates This shows the cell candidates than be curated. Cell

    candidates previously classified as cells are shown in yellow, and artifacts

    in blue.

By selecting the Cell candidates tab and then the cell selecting tool (arrow at the top), cell candidates can be selected (either individually, or many by dragging the cursor). There are then four keyboard commands:

  • C Confirm the classification result, and add this to the training set

  • T Toggle the classification result (i.e. change the classification),

    and add this to the training set.

  • Alt+Q Save the results to an xml file

  • Alt+E Finish curating the training dataset. This will carry out three operations:

    • Extract cubes around these points, into two directories (cells and non_cells).

    • Generate a yaml file pointing to these files for use with cellfinder_train (see below)

    • Close the viewer

Once a yaml file has been generated, you can proceed to training. However, it is likely useful to generate yaml files from additional datasets.

Start training

You can then use these yaml files for training

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 fromcellfinder_curate

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