Models

from cellpose import models

Built-in models

We have four built-in models available:

  • cpsam_v2: this is the CellposeSAM model released in June 2026 using the SAM-ViTL backbone, it includes a fix in the training for low contrast regions

  • cpdino: this is the CellposeDINO model released in June 2026 using the DINOv3-ViTL backbone

  • cpdino-vitb: this is the CellposeDINO model released in June 2026 using the DINOv3-ViTB backbone (smaller model)

  • cpsam: this is the original CellposeSAM model released in April 2025 using the SAM-ViTL backbone

The DINO-based models use an image tile size of 384x384 by default, but this can be changed through the bsize parameter, e.g. you may want to make bsize larger if there are very large or long objects in the image. The SAM-based models use an image tile size of 256x256 and this cannot be changed - the position embeddings do not support different image sizes.

You can select a model in the GUI in the drop-down, in a notebook with models.CellposeModel(pretrained_model='cpdino'), or on the command line with python -m cellpose --pretrained_model cpdino.

The first time that a model is used, the weights will be downloaded automatically to your models.MODELS_DIR (see Installation instructions for more details on MODELS_DIR). You can also directly download the model by going to the URL, e.g.:

https://huggingface.co/mouseland/cellpose-sam/blob/main/cpdino

These models were trained on images with a range of diameters from 7.5 to 120 pixels, with a mean size of 30 pixels. If your images have even larger diameters, you may want to specify the diameter parameter, e.g. specifying diameter=60 will downsample the image by a factor of 2 (downsampling is with respect to 30 pixels). If you have cells with large diameters, then you may need to increase the niter parameter to run the dynamics step for mask creation for longer.

User-trained models

By default, models are fine-tuned on your images and ROIs with a small range of image resizing in the augmentations. Thus, the testing images should have a similar ROI diameter distribution as the training data.

These models can be loaded and used in the notebook with e.g. models.CellposeModel(pretrained_model='name_in_gui') or with the full path models.CellposeModel(pretrained_model='/full/path/to/model') . If you trained in the command line, you can 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.

Finding models on BioImage.IO

BioImage.IO is a repository for sharing AI models, datasets and tools for bioimage analysis. You may look for Cellpose models on BioImage.IO Model Zoo by searching for the tag cellpose. To download a model, click on the model card, click the download icon, and choose “Download by Weight Format” - “Pytorch State Dict”.

Sharing models on BioImage.IO

You can also share your trained Cellpose models on the BioImage.IO Model Zoo. To do this, you need to export your model in the BioImage.IO format using cellpose/export.py and then upload the packaged model to the Model Zoo.

Detailed steps:

  1. Train a Cellpose model and check if it works well on your data.

  2. Create an environment python -m pip install 'cellpose[bioimageio]' or 'cellpose[all]' if you haven’t already. Note that most users installed 'cellpose[gui]' without the bioimageio packages.

  3. Export the model using export.py script. Use python export.py --help to see the usage, or check the example in its docstring. In short, you need to name your models, specify if the model runs on cytoplasm/nuclei/both, and provide:

    1. a model filepath,

    2. a README.md filepath,

    3. a cover image filepath(s),

    4. a short description string,

    5. a license name like MIT,

    6. a link to your GitHub repo (or Cellpose repo),

    7. information about the authors and what to cite,

    8. tags including cellpose, 2d and 3d (Cellpose models handle both).

  4. If you are updating a uploaded model, you should also specify the model ID and icon. Don’t forget to increment the version number.

  5. Go to BioImage.IO, click “Upload”, and follow the instructions there.