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 0
s. 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 |
|
|
|
---|---|---|---|
|
2 |
3 |
2 |
|
2 |
3 |
2 |
|
2 |
3 |
2 |
|
2 |
3 |
2 |
|
2 |
3 |
3 |
|
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).