Models#

All 2D models originally published in the Cellpose and Omnipose papers use nchan=2. This is because Cellpose defaults are set to train models that use two channels for segmentation (usually cytoplasm and nucleus). Images without a second channel are just padded with 0s. I think most users will train Omnipose on mono-channel images, so now nchan=1 by default.

Tip

Always specify nchan and nclasses when training and evaluating models.

Omnipose used to have a boundary prediction, so nclasses=3 (flow field, distance field, and boundary field in 2D). The current version of Omnipose no longer needs a boundary prediction, so nclasses=2 is the default.

See the table below for named models and their corresponding nchan, nclasses.

Pretrained models#

model

nchan

nclasses

dim

bact_phase_omni

2

3

2

bact_fluor_omni

2

3

2

cyto2_omni

2

3

2

worm_omni

2

3

2

plant_omni

2

3

3

bact_phase_omni_2

1

2

2

Cellpose models all have nchan=2, nclasses=2, and dim=2 (3D Cellpose uses 2D models to approximate 3D output). This means that if you wanted to, you could train an Omnipose model based on a Cellpose model using these hyperparameters (see Transfer learning).