- The package provides a uniform, modular, and easily extendable implementation of
torchvision-based transforms that provides access to their parameterization. - With this access, the transforms enable users to achieve the following two important functionalities--
- Given an image, the transform can return an augmentation along with the parameters used for the augmentation.
- Given an image and augmentation parameters, the transform can return the corresponding augmentation.
- To install the package directly, run the following commands:
git clone https://github.com/apple/parameterized-transforms
cd parameterized-transforms
pip install -e .
- To install the package via
pip, run the following command:
pip install --upgrade https://github.com/apple/parameterized-transforms
- If you want to run unit tests locally, run the following steps:
git clone https://github.com/apple/parameterized-transforms
cd parameterized-transforms
pip install -e .
pip install -e '.[test]'
pytest
- To understand the structure of parameterized transforms and the details of the package, we recommend the reader to start with The First Tutorial of our Tutorial Series.
- However, for a quick starter, check out Parameterized Transforms in a Nutshell.
In its development, this project received help from multiple researchers, engineers, and other contributors from Apple. Special thanks to: Tim Kolecke, Jason Ramapuram, Russ Webb, David Koski, Mike Drob, Megan Maher Welsh, Marco Cuturi Cameto, Dan Busbridge, Xavier Suau Cuadros, and Miguel Sarabia del Castillo.
If you find this package useful and want to cite our work, here is the citation:
@software{Dhekane_Parameterized_Transforms_2025,
author = {Dhekane, Eeshan Gunesh},
month = {2},
title = {{Parameterized Transforms}},
url = {https://github.com/apple/parameterized-transforms},
version = {1.0.0},
year = {2025}
}