Installation#

From PyPI#

Install the latest published release in your Python environment:

python -m pip install cellseg-models-pytorch

Optional features#

Install the features you need:

Feature	Command	Includes
Training datasets and augmentation	`python -m pip install "cellseg-models-pytorch[training]"`	Albumentations and PyTables
ONNX export	`python -m pip install "cellseg-models-pytorch[onnx]"`	ONNX and ONNX Script
OpenSlide reading	`python -m pip install "cellseg-models-pytorch[openslide]"`	OpenSlide Python and native libraries

Combine features with commas, for example "cellseg-models-pytorch[training,onnx]". Install ONNX Runtime separately to run exported models. cuCIM and BioIO require their own setup.

From source with uv#

Install uv, then download the repository and install its dependencies:

git clone https://github.com/okunator/cellseg_models.pytorch.git
cd cellseg_models.pytorch
uv sync --locked

Run Python from this environment with uv run python. Continue with the quick start.

Add optional features with uv sync --locked --extra training, using the same training, onnx and openslide names as above. Combine flags to keep several features, for example uv sync --locked --extra training --extra onnx. Running uv sync without these flags removes the optional packages.

These pages describe version 0.2.0. Installations are tested on Python 3.10–3.14. Python 3.14.1 is excluded by torchvision. For an NVIDIA GPU, follow PyTorch’s installation guidance for your hardware. Examples use CUDA when available and otherwise use CPU.