Metadata-Version: 2.4
Name: libphysneurallib
Version: 0.1.1
Summary: Deep learning library for biosignal processing.
Author-email: Mariana Dias <mariana.dias@vohcolab.org>, Nianfei Ao <n.ao@fct.unl.pt>, Hugo Gamboa <hgamboa@fct.unl.pt>
License: MIT
Project-URL: Homepage, https://github.com/novabiosignals/NeuralLib
Project-URL: Source, https://github.com/novabiosignals/NeuralLib
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: calflops==0.3.2
Requires-Dist: transformers
Requires-Dist: huggingface_hub==0.36.0
Requires-Dist: matplotlib==3.10.8
Requires-Dist: numpy==2.4.2
Requires-Dist: pytorch_lightning==2.5.5
Requires-Dist: PyYAML==6.0.3
Requires-Dist: scikit_learn==1.8.0
Requires-Dist: scipy==1.17.0
Requires-Dist: torch==2.7.1
Provides-Extra: dev
Requires-Dist: pytest==8.4.1; extra == "dev"

# NeuralLib


`NeuralLib` is a Python library designed for advanced biosignal processing using neural networks. The primary objective is to establish a modular, efficient, generalizable framework for biosignal processing using DL.
The core concept of `NeuralLib` revolves around creating, training, and managing neural network models and leveraging their components for transfer learning (TL). This allows for the reusability of pre-trained models or parts of them to create new models and adapt them to different tasks or datasets efficiently.

The library supports:

- Training and testing `Architectures` from scratch for specific biosignals processing tasks.
- Using trained models (`ProductionModels`) to process biosignals.
- Adding tested models to hugging face repositories to create new `ProductionModels` and share them with the community for public usage.
- Extracting trained components from production models using `TLFactory`.
- Combining, freezing, or further fine-tuning pre-trained components to train`TLModels`.


## Tutorials

Explore the [`tutorials/`](./tutorials) folder for several hands-on examples demonstrating how to use the core functionalities of NeuralLib.

## 📖 Documentation

Comprehensive documentation is available here:  
[NeuralLib Documentation](https://novabiosignals.github.io/NeuralLib-docs/)

## Pre-trained Models

Collection of pre-trained models on Hugging Face:  
[NeuralLib DL Models for Biosignals](https://huggingface.co/collections/novabiosignals/neurallib-deep-learning-models-for-biosignals-processing-6813ee129bc1bba8210b6948)
