Neural Network Signal Processing on Torch

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

Table of contents

• Authors
• Installation
• A simple example
• Available components
• 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

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]

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}}
}