Spectrum Sensing

Forked from U-Net in PyTorch, this implantation is used for mixture signal separation and classification

Usage

Note : Use Python 3

Prediction

To predict and classify a batch of mixture signals

python predict.py --model MODEL.pth

You can specify which model file to use with --model MODEL.pth.

Visualization

To visualize the separated component signals, please run visualization.ipynb

Training

> python train.py -h
usage: train.py [-h] [-e E] [-b [B]] [-l [LR]] [-f LOAD]

Train the UNet on 

optional arguments:
  -h, --help            show this help message and exit
  -e E, --epochs E      Number of epochs (default: 5)
  -b [B], --batch-size [B]
                        Batch size (default: 1)
  -l [LR], --learning-rate [LR]
                        Learning rate (default: 0.1)
  -f LOAD, --load LOAD  Load model from a .pth file (default: False)

Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: https://arxiv.org/abs/1505.04597