https://github.com/apple/parameterized-transforms
torchvision-based transforms that provide access to parameterization
https://github.com/apple/parameterized-transforms
computer-vision deep-learning machine-learning parameterization torchvision transforms
Last synced: 11 months ago
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torchvision-based transforms that provide access to parameterization
- Host: GitHub
- URL: https://github.com/apple/parameterized-transforms
- Owner: apple
- License: apache-2.0
- Created: 2025-02-13T16:16:45.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-13T16:45:42.000Z (about 1 year ago)
- Last Synced: 2025-02-13T17:39:33.324Z (about 1 year ago)
- Topics: computer-vision, deep-learning, machine-learning, parameterization, torchvision, transforms
- Language: Python
- Homepage: https://apple.github.io/parameterized-transforms/
- Size: 7.33 MB
- Stars: 0
- Watchers: 6
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
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README
# Parameterized Transforms
## Index
1. [About the Package](#about-the-package)
2. [Installation](#installation)
3. [Getting Started](#getting-started)
## About the Package
* 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.
## Installation
- 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
```
## Getting Started
* To understand the structure of parameterized transforms and the details of the package, we recommend the reader to
start with
[The First Tutorial](https://apple.github.io/parameterized-transforms/tutorials/000-About-the-Package.html)
of our
[Tutorial Series](https://apple.github.io/parameterized-transforms/).
* However, for a quick starter, check out [Parameterized Transforms in a Nutshell](https://apple.github.io/parameterized-transforms/tutorials/999-In-a-Nutshell.html).
---
## Acknowledgement
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.
## Citation
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}
}
```
---