cistardist-pytorch 0.1.0

PyTorch-only StarDist 2D inference for Keras H5 StarDist weights.

cistardist_pytorch

PyTorch-only StarDist 2D inference for existing TensorFlow/Keras StarDist .h5 weights. The first target model is models/SD_Nuclei_Versatile.

This repo intentionally does not depend on TensorFlow, Keras, CSBDeep, or the upstream stardist Python package.

Install

PyTorch is intentionally installed from requirements.txt so you can choose the CUDA build explicitly.

conda create -n cistardist_pytorch python=3.10
conda activate cistardist_pytorch
pip install -r requirements.txt
pip install -e . --no-deps

After the package is published, install it with:

pip install cistardist_pytorch

If you need the CUDA-specific PyTorch wheel, install PyTorch first using this repo's requirements.txt or your own environment policy.

Convert and Predict

cistardist-convert models/SD_Nuclei_Versatile
cistardist-predict models/SD_Nuclei_Versatile data/nuclei.tif --out outputs/nuclei_labels.tif

The converter reads config.json, thresholds.json, and weights_best.h5. It writes a PyTorch checkpoint next to the source model as weights_best.pt.

Python API

import tifffile
from cistardist_pytorch import StarDist2D

model = StarDist2D.from_folder("models/SD_Nuclei_Versatile")
image = tifffile.imread("data/nuclei.tif")
labels, details = model.predict_instances(image)

Current Scope

• 2D grayscale inference
• Keras .h5 Conv2D weight conversion via h5py
• StarDist-style polygon postprocessing with vendored BSD-compatible 2D geometry
• No training, no TensorFlow reference tests, no 3D, no multiclass models

Attribution

The 2D geometry and NMS behavior follows the BSD-3-Clause upstream StarDist project: https://github.com/stardist/stardist