train.rst

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 --diam_mean flag. We trained all model zoo models with a diameter of 30.0 pixels, except the nuclei model which used a diameter of 17 pixels, so if you want to start with a pretrained model, it will default to those values.

The models will be saved in the image directory (--dir) in a folder called models/.

The same channel settings apply for training models.

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

You can use a different ending from _masks with the --mask_filter option, e.g. --mask_filter _masks_2022.

Also, you can train a model using the labels from the GUI (_seg.npy) by using the following option --mask_filter _seg.npy.

If you use the --img_filter option (--img_filter _img in this case):

wells_000_img.tif
wells_000_masks.tif

Warning

The path given to --dir and --test_dir should be an absolute path.

To train on cytoplasmic images (green cyto and red nuclei) starting with a pretrained model from cellpose (one of the model zoo models), we also have included the recommended training parameters in the command below:

python -m cellpose --train --dir ~/images_cyto/train/ --test_dir ~/images_cyto/test/ --pretrained_model cyto --chan 2 --chan2 1 --learning_rate 0.1 --weight_decay 0.0001 --n_epochs 100

You can train from scratch as well:

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

To train the cyto model from scratch using the same parameters we did, download the dataset and run

python -m cellpose --train --train_size --use_gpu --dir ~/cellpose_dataset/train/ --test_dir ~/cellpose_dataset/test/ --img_filter _img --pretrained_model None --chan 2 --chan2 1

You can also 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

Training arguments

--train               train network using images in dir
--train_size          train size network at end of training
--test_dir TEST_DIR   folder containing test data (optional)
--mask_filter MASK_FILTER
                        end string for masks to run on. Default: _masks
--diam_mean DIAM_MEAN
                        mean diameter to resize cells to during training -- if
                        starting from pretrained models it cannot be changed
                        from 30.0
--learning_rate LEARNING_RATE
                        learning rate. Default: 0.2
--weight_decay WEIGHT_DECAY
                        weight decay. Default: 1e-05
--n_epochs N_EPOCHS   number of epochs. Default: 500
--batch_size BATCH_SIZE
                        batch size. Default: 8
--min_train_masks MIN_TRAIN_MASKS
                        minimum number of masks a training image must have to
                        be used. Default: 5
--residual_on RESIDUAL_ON
                        use residual connections
--style_on STYLE_ON   use style vector
--concatenation CONCATENATION
                        concatenate downsampled layers with upsampled layers
                        (off by default which means they are added)
--save_every SAVE_EVERY
                        number of epochs to skip between saves. Default: 100
--save_each           save the model under a different filename per
                        --save_every epoch for later comparsion