from cellpose import models
Cellpose 2.0 now has a model zoo and options for user model training. Each model will be downloaded automatically to your models.MODELS_DIR (see Installation instructions for more details on MODELS_DIR). See paper for more details on the model zoo. You can also directly download a model by going to the URL, e.g.:
https://www.cellpose.org/models/MODEL_NAME
All built-in models were trained with the ROIs resized to a diameter of 30.0 (diam_mean = 30), except the 'nuclei' model which was trained with a diameter of 17.0 (diam_mean = 17). The models will internally take care of rescaling the images given a user-provided diameter (or with the diameter from auto-diameter estimation in full models).
There is a suggestion button below the model zoo in the GUI. This runs a general model that has been trained on Cellpose, TissueNet, and LiveCell to obtain the style of the image. It uses this style to suggest which model would be best for the given image (see info in Cellpose 2.0 paper, and runs the suggested model on the image. Make sure the diameter is set to the approximate diameter of the ROIs in the image before clicking the button to ensure best performance.
These models have a size model and 4 different training versions, each trained starting from 4 different random initial parameter sets. This means you can run with diameter=0 or --diameter 0 and the model can estimate the ROI size. Also you can set net_avg=True or --net_avg to average the results of the 4 models.
These models can be loaded and used in the notebook with models.Cellpose(model_type='cyto') or in the command line with python -m cellpose --pretrained_model cyto.
These models' names (to download all the models for a class run with --net_avg): * `'cyto': cytotorch_0, cytotorch_1, cytotorch_2, cytotorch_3, size_cytotorch_0.npy * 'nuclei': nucleitorch_0, nucleitorch_1, nucleitorch_2, nucleitorch_3, size_nucleitorch_0.npy * 'cyto2': cyto2torch_0, cyto2torch_1, cyto2torch_2, cyto2torch_3, size_cyto2torch_0.npy`
The cytoplasm model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel. Here are the options for each: 1. 0=grayscale, 1=red, 2=green, 3=blue 2. 0=None (will set to zero), 1=red, 2=green, 3=blue
Set channels to a list with each of these elements, e.g. channels = [0,0] if you want to segment cells in grayscale or for single channel images, or channels = [2,3] if you green cells with blue nuclei.
The nuclear model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is always set to an array of zeros. Therefore set the first channel as 0=grayscale, 1=red, 2=green, 3=blue; and set the second channel to zero, e.g. channels = [0,0] if you want to segment nuclei in grayscale or for single channel images, or channels = [3,0] if you want to segment blue nuclei.
The cytoplasm 2.0 model in cellpose is trained on two-channel images, where the first channel is the channel to segment, and the second channel is an optional nuclear channel, as the cytoplasm model.
In addition to the training data in our dataset, it was trained with user-submitted images.
These models do not have a size model and 4 different training versions. If the diameter is set to 0.0, then the model uses the default diam_mean for the diameter (30.0).
These models can be loaded and used in the notebook with e.g. models.CellposeModel(model_type='tissuenet') or models.CellposeModel(model_type='LC2'), or in the command line with python -m cellpose --pretrained_model tissuenet.
These models' names are the same as their strings in the GUI.
The 'tissuenet' model was trained on all training images from the tissuenet dataset. These images have a cytoplasm channel and a nuclear channel. The other tissuenet models ('TN1', 'TN2', and 'TN3') were trained on subsets of the tissuenet dataset that had similar characteristics.
The 'livecell' model was trained on all training images from the livecell dataset. These images only have a cytoplasm channel. The other livecell models ('LC1', 'LC2', 'LC3', and 'LC4') were trained on subsets of the livecell dataset that had similar characteristics.
By default, models are trained with the ROIs resized to a diameter of 30.0 (diam_mean = 30) -- this is necessary if you want to start from a pretrained cellpose model. If you want to use a different diameter and use pretraining, we recommend performing training yourself on the cellpose dataset with that diameter so the model learns objects at that size. All user-trained models will save the diam_mean so it will be loaded automatically along with the model weights.
Each model also saves the diam_labels which is the mean diameter of the ROIs in the training images. This value is auto-loaded into the GUI for use with the model, or will be used if the diameter is 0 (diameter=0 or --diameter 0).
These models can be loaded and used in the notebook with e.g. models.CellposeModel(model_type='name_in_gui') or with the full path models.CellposeModel(pretrained_model='/full/path/to/model') . If you trained in the GUI, you can automatically use the model_type argument. If you trained in the command line, you need to first add the model to the cellpose path either in the GUI in the Models menu, or using the command line: python -m cellpose --add_model /full/path/to/model.
Or these models can be used in the command line with python -m cellpose --pretrained_model name_in_gui or python -m cellpose --pretrained_model /full/path/to/model .