pyplatypus

Computer vision for medical imaging — the engine behind the platypus R package.

0.2.0a1 — an alpha. This replaces the 2022 TensorFlow package with a PyTorch one. The API will still move and the R surface does not exist yet, so pin the exact version if you build on it.

Everything on PyPI so far is a pre-release, so pip install pyplatypus resolves to this one. pip install pyplatypus==0.1.0rc2 gets the old TensorFlow package.

What works today

Semantic segmentation in 2D, end to end:

• One spec, two ways in. Build it from arguments or load it from YAML — both produce the same object, so nothing downstream can tell which you used.
• Four architectures: U-Net, U-Net++, Res-U-Net, LinkNet, each composable with separable convolutions, spatial dropout, learned or interpolated upsampling, deep supervision and configurable block width.
• Nine losses (IoU, Dice, CCE, CCE-Dice, Focal, Tversky, Focal-Tversky, Combo, Lovász) and three metrics, all reducing over every axis except batch and channel — so they already work on volumes.
• Tiling that goes both ways: cut a large image into a grid instead of shrinking it, and get a full-size mask back.
• Many models from one file, with a comparison table at the end.

3D, object detection, ensembling and pretrained backbones are deliberately out of scope for v0.1. The spec and the model builder already handle volumes; the data pipeline is where 3D stops.

Try it

uv venv --python 3.11 .venv
uv pip install --python .venv/bin/python -e ".[dev]"
.venv/bin/python -m pytest

from pyplatypus import Engine, from_yaml

engine = Engine(from_yaml("examples/data_science_bowl.yaml"))
engine.fit(verbose=True)

for row in engine.evaluate():
    print(row)

masks = engine.predict(engine.best_model("dice"), split="test")

examples/data_science_bowl.yaml trains a U-Net and a LinkNet on the 2018 Data Science Bowl and prints a comparison. On a GTX 1070 that is about 11 seconds per epoch at 160×160.

Requirements

Python ≥ 3.10, and torch ≥ 2.7.

If your GPU is a GTX 10-series (Pascal) or older, install the pascal extra:

pip install "pyplatypus[pascal]"

torch 2.8 and later ship CUDA 13 builds, and CUDA 13 dropped the Maxwell, Pascal and Volta generations outright - no driver update brings them back. The last torch built against CUDA 12 is 2.7.x, which the extra pins. On anything from Turing (RTX 20-series) onwards, ignore this.

Licence

MIT.