Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch
Abstract
We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs) in PyTorch, a class of Neural Networks designed specifically for efficient inference in both online and batch processing scenarios. We offer a comprehensive introduction and guide to CINs and their implementation in practice, and provide best-practices and code examples for composing complex modules for modern Deep Learning. Continual Inference is readily downloadable via the Python Package Index and at \url{www.github.com/lukashedegaard/continual-inference}.
Cite
Text
Hedegaard and Iosifidis. "Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch." European Conference on Computer Vision Workshops, 2022. doi:10.1007/978-3-031-25082-8_2Markdown
[Hedegaard and Iosifidis. "Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch." European Conference on Computer Vision Workshops, 2022.](https://mlanthology.org/eccvw/2022/hedegaard2022eccvw-continual/) doi:10.1007/978-3-031-25082-8_2BibTeX
@inproceedings{hedegaard2022eccvw-continual,
title = {{Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch}},
author = {Hedegaard, Lukas and Iosifidis, Alexandros},
booktitle = {European Conference on Computer Vision Workshops},
year = {2022},
pages = {21-34},
doi = {10.1007/978-3-031-25082-8_2},
url = {https://mlanthology.org/eccvw/2022/hedegaard2022eccvw-continual/}
}