Training

At the beginning of training, cellpose computes the flow field representation for each mask image (dynamics.labels_to_flows).

The cellpose pretrained models are trained using resized images so that the cells have the same median diameter across all images. If you choose to use a pretrained model, then this fixed median diameter is used.

If you choose to train from scratch, you can set the median diameter you want to use for rescaling with the --diameter flag, or set it to 0 to disable rescaling.

The same channel settings apply for training models (see all Command line options).

Note Cellpose expects the labelled masks (0=no mask, 1,2…=masks) in a separate file, e.g:

wells_000.tif
wells_000_masks.tif

If you use the –img_filter option (–img_filter img in this case):

wells_000_img.tif
wells_000_masks.tif

Training-specific options

--test_dir TEST_DIR       folder containing test data (optional)
--n_epochs N_EPOCHS       number of epochs (default: 500)

To train on cytoplasmic images (green cyto and red nuclei) starting with a pretrained model from cellpose (cyto or nuclei):

python -m cellpose --train --dir ~/images_cyto/train/ --test_dir ~/images_cyto/test/ --pretrained_model cyto --chan 2 --chan2 1

You can train from scratch as well:

python -m cellpose --train --dir ~/images_nuclei/train/ --pretrained_model None

You can specify the full path to a pretrained model to use:

python -m cellpose --dir ~/images_cyto/test/ --pretrained_model ~/images_cyto/test/model/cellpose_35_0 --save_png