semvec/pypi-packages huggingface dataset:
gunpowder 1.4.3
A library to facilitate machine learning on large, multi-dimensional images.
License: MIT
Requires Python: >=3.9

![gunpowder](docs/source/img/gunpowder.svg)

[![Tests](https://github.com/funkey/gunpowder/actions/workflows/test.yml/badge.svg)](https://github.com/funkey/gunpowder/actions/workflows/test.yml)

A library to facilitate machine learning on large, multi-dimensional images.

`gunpowder` allows you to assemble a pipeline from
[data loading](http://funkelab.github.io/gunpowder/api.html#source-nodes)
over
[pre-processing](http://funkelab.github.io/gunpowder/api.html#image-processing-nodes),
[random batch sampling](http://funkelab.github.io/gunpowder/api.html#randomlocation),
[data augmentation](http://funkelab.github.io/gunpowder/api.html#augmentation-nodes),
[pre-caching](http://funkelab.github.io/gunpowder/api.html#precache),
[training/prediction](http://funkelab.github.io/gunpowder/api.html#training-and-prediction-nodes), to
[storage of results](http://funkelab.github.io/gunpowder/api.html#output-nodes)
on arbitrarily large volumes of
multi-dimensional images. `gunpowder` is not tied to a particular learning
framework, and thus complements libraries like
[`torch`](https://pytorch.org/),
[`tensorflow`](https://www.tensorflow.org/).

The full documentation can be found [here](https://funkelab.github.io/gunpowder).

`gunpowder` was originally written by Jan Funke and is inspired by
[`PyGreentea`](https://github.com/TuragaLab/PyGreentea) by William Grisaitis,
Fabian Tschopp, and Srini Turaga.