SliceSamp, a plug-and-play downsampling module, offers the capability to deploy AI models on edge computing devices, enabling neural networks to operate with lighter weights, lower computational costs, and higher performance.
Lianlian He, Ming Wang*
[Paper] [Project] [Dataset] [BibTeX]
The code requires python>=3.7, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. jupyter is also required to run the example notebooks.
pip install opencv-python pycocotools matplotlib
If you use SliceSamp in your research, please use the following BibTeX entry.
@article{SliceSamp,
title={SliceSamp: A Promising Downsampling Alternative for Retaining Information in a Neural Network},
author={Lianlian He, Ming Wang},
journal={Applied Sciences},
year={2023}
}