semvec/pypi-packages huggingface dataset:
yaac 0.1.5
YAAC - Python package for loading and using trained AI models
Requires Python: >=3.10
Keywords: machine-learning, deep-learning, pytorch, image-classification, computer-vision

# yaac

[![PyPI version](https://img.shields.io/pypi/v/yaac.svg)](https://pypi.org/project/yaac/)
[![Python 3.10+](https://img.shields.io/pypi/pyversions/yaac.svg)](https://pypi.org/project/yaac/)
[![License](https://img.shields.io/pypi/l/yaac.svg)](https://pypi.org/project/yaac/)

YAAC - Python package for loading and train AI models.

## Installation

Install from PyPI:

```bash
pip install yaac
```

## Requirements

### HuggingFace Token (for DINOv3 ConvNeXt-Tiny backbone)

If you're loading models that use the DINOv3 ConvNeXt-Tiny backbone (`convnext_tiny_dinov3`), you'll need a HuggingFace token because the model repository is gated.

1. **Request access**: Visit https://huggingface.co/facebook/dinov3-convnext-tiny-pretrain-lvd1689m and request access to the repository
2. **Generate a token**: Create a token at https://huggingface.co/settings/tokens
3. **Set environment variable**: Export the token as an environment variable:
   ```bash
   export HUGGINGFACE_TOKEN=your_token_here
   ```

The token is only required when loading models with the ConvNeXt-Tiny backbone. Models using ResNet18 or other backbones don't require a token.

## Quick Start

Load a trained model and run inference:

```python
from yaac.common.model_loader import load_model_from_checkpoint
import torch

# Load your trained model
model, config = load_model_from_checkpoint("path/to/checkpoint", device="cuda")

# Run inference
image = torch.randn(1, 3, 224, 224)  # Your image tensor
with torch.no_grad():
    predictions = model(image)
    processed = model.postprocess(predictions)

print(f"Predictions: {processed}")
```

## What is yaac?

`yaac` is a Python package that provides:

- **Model Loading**: Load trained image classification models from exported checkpoints (safetensors + config.json)
- **Model Interface**: Standardized `TrainableModel` interface for consistent model usage
- **SIC Models**: Support for Simple Image Classifier (SIC) models with configurable backbones and heads

Models are trained using YAAC's infrastructure and exported in a format compatible with this package.

## Documentation

- [Contributing Guide](CONTRIBUTING.md) - For developers who want to contribute
- [Publishing Guide](publish/PUBLISHING.md) - For maintainers publishing new versions

## License

Apache 2.0 - See [LICENSE](LICENSE) for details.
MRArbDcf 1.0.6
Density Compensation Function Design Tool for Arbirary k-Space Trajectoryies.
Keywords: MRI, non-Cartesian, Reconstruction, DCF, Density-Compensation

# Magnetic Resonance Arbitrary Density Compensation Function (MRArbDcf, MAD)

## Introduction
This is the code repository for paper "Sampling Density Compensation using Fast Fourier Deconvolution" [1]. To fill the gap that the previous iterative DCF methods are slow (especially in 3D), this package provides a non-iterative method based on fast Fourier deconvolution. This package supports both CPU and GPU and can derive DCF for a trajectory designed for a 256³ matrix size in 30 seconds on a multi-core CPU or 10 seconds on a GPU.

## Installation
You can install this package either via pip:
```bash
$ pip install mrarbdcf
```
or offline
```bash
$ bash install.bash
```

Optionally, to enable CUDA acceleration in this package, you need to install `cufinufft` and `cupy`. It's recommended to read their official installation guides before installing - pip is not the best source for installation because some dependencies are only available from conda.

## Usage
For tutorials, you can find examples in the `example` folder. Most functions are well-commented in the Google style. We plan to release the documents on `readthedocs.org` in the future.

## Acknowledgement
FINUFFT [2,3] and CUFINUFFT [4] are used as NUFFT operators in this package. We thank the authors for their contributions to create such fast NUFFT libraries.

## Reference
[1] Luo R, Hu P, Qi H. Sampling Density Compensation using Fast Fourier Deconvolution [Internet]. arXiv; 2025 [cited 2025 Oct 17]. Available from: http://arxiv.org/abs/2510.14873

[2] Barnett AH, Magland J, af Klinteberg L. A Parallel Nonuniform Fast Fourier Transform Library Based on an “Exponential of Semicircle" Kernel. SIAM J Sci Comput. 2019 Jan;41(5):C479–504. 

[3] Barnett AH. Aliasing error of the exp(β√(1-z²)) kernel in the nonuniform fast Fourier transform. Applied and Computational Harmonic Analysis. 2021 Mar 1;51:1–16. 

[4] Shih Y hsuan, Wright G, Anden J, Blaschke J, Barnett AH. cuFINUFFT: a load-balanced GPU library for general-purpose nonuniform FFTs. 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 2021 June;688–97.
git-meta 0.3
Git repository manager
Keywords: git

git-meta
========

.. image:: https://travis-ci.org/galactics/git-meta.svg?branch=master
    :alt: Travis tests
    :target: https://travis-ci.org/galactics/git-meta

.. image:: http://readthedocs.org/projects/git-meta/badge/?version=latest
    :alt: Documentation Status
    :target: http://git-meta.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :alt: Code style: black
    :target: https://github.com/ambv/black

Git meta is a small program enabling the power of system-wide status check of
all you git repositories.

.. image:: docs/source/_static/terminal.png
    :alt: Terminal output

It uses `gitpython <https://github.com/gitpython-developers/GitPython>`__.

Installation
------------

.. code-block:: shell

    $ pip install git-meta

Alternatively, if you want to install `git-meta` from the sources:

.. code-block:: shell

    $ python setup.py install

You can also install it in a `virtualenv <https://docs.python.org/3/library/venv.html>`__
in order to test and not mess your system configuration.

Documentation
-------------

The documentation uses `Sphinx <http://sphinx-doc.org/>`__. To generate statics HTML
files, go to the `docs` folder and type

.. code-block:: shell

    $ make html

Unit testing
------------

In order to launch the tests sequence of the package, you need
`Pytest <http://pytest.org/latest/>`__ and
`Pytest-cov <https://pypi.python.org/pypi/pytest-cov/>`__ installed.

Just type

.. code-block:: shell

    $ pytest

at the root of the repository.

Old version of git-meta
-----------------------

The old version of git meta can be found `here <https://github.com/galactics/git-meta-old>`__.