https://github.com/rostepifanov/nnspt

A Python library for signal processing with PyTorch. Useful for machine learning.
https://github.com/rostepifanov/nnspt

deep-learning ecg eeg emg machine-learning python signal-processing

Last synced: about 2 months ago
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A Python library for signal processing with PyTorch. Useful for machine learning.

• Host: GitHub
• URL: https://github.com/rostepifanov/nnspt
• Owner: rostepifanov
• License: mit
• Created: 2023-12-18T13:21:21.000Z (about 2 years ago)
• Default Branch: master
• Last Pushed: 2024-07-27T08:20:41.000Z (over 1 year ago)
• Last Synced: 2024-09-12T10:58:19.128Z (over 1 year ago)
• Topics: deep-learning, ecg, eeg, emg, machine-learning, python, signal-processing
• Language: Python
• Homepage:
• Size: 63.5 KB
• Stars: 3
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Neural Network Signal Processing on Torch

![Python version support](https://img.shields.io/pypi/pyversions/nnspt)

[![PyPI version](https://badge.fury.io/py/nnspt.svg)](https://badge.fury.io/py/nnspt)

[![Downloads](https://pepy.tech/badge/nnspt/month)](https://pepy.tech/project/nnspt?versions=0.0.*)

NNSPT is a Python library for neural network signal processing on PyTorch.

## Table of contents

- [Authors](#authors)

- [Installation](#installation)

- [A simple example](#a-simple-example)

- [Available components](#available-components)

- [Citing](#citing)

## Authors

[**Rostislav Epifanov** — Researcher in Novosibirsk]()

## Installation

Installation from PyPI:

```

pip install nnspt

```

Installation from GitHub:

```

pip install git+https://github.com/rostepifanov/nnspt

```

## A simple example

```python

from nnspt.segmentation.unet import Unet

model = Unet(encoder='tv-resnet34')

```

## Available components

#### Encoders

  *  ResNet

      - tv-resnet18

      - tv-resnet34

      - tv-resnet50

      - tv-resnet101

      - tv-resnet152

  

  *  ResNeXt

      - tv-resnext50_32x4d

      - tv-resnext101_32x4d

      - tv-resnext101_32x8d

      - tv-resnext101_32x16d

      - tv-resnext101_32x32d

      - tv-resnext101_32x48d

  

  *  DenseNet

      - tv-densenet121

      - tv-densenet169

      - tv-densenet201

      - tv-densenet161

  

  *  EfficientNetV1

      - timm-efficientnet-b0

      - timm-efficientnet-b1

      - timm-efficientnet-b2

      - timm-efficientnet-b3

      - timm-efficientnet-b4

      - timm-efficientnet-b5

      - timm-efficientnet-b6

      - timm-efficientnet-b7

  

  *  EfficientNetLite

      - timm-efficientnet-lite0

      - timm-efficientnet-lite1

      - timm-efficientnet-lite2

      - timm-efficientnet-lite3

      - timm-efficientnet-lite4

  

#### Pretraining

  * Autoencoder

#### Segmentation

  * Unet [[paper](https://arxiv.org/abs/1505.04597)]

## Citing

If you find this library useful for your research, please consider citing:

```

@misc{epifanov2023ecgmentations,

  Author = {Rostislav Epifanov},

  Title = {NNSTP},

  Year = {2023},

  Publisher = {GitHub},

  Journal = {GitHub repository},

  Howpublished = {\url{https://github.com/rostepifanov/nnspt}}

}

```