https://github.com/isaaccorley/pytorch-modulation-recognition
PyTorch Implementation Modulation Recognition Networks on the RadioML2016 Dataset
https://github.com/isaaccorley/pytorch-modulation-recognition
artificial-intelligence modulation-recognition pytorch signal-processing
Last synced: about 1 year ago
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PyTorch Implementation Modulation Recognition Networks on the RadioML2016 Dataset
- Host: GitHub
- URL: https://github.com/isaaccorley/pytorch-modulation-recognition
- Owner: isaaccorley
- License: mit
- Created: 2020-04-15T18:51:50.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-09-16T17:23:19.000Z (almost 6 years ago)
- Last Synced: 2025-03-26T11:11:16.092Z (about 1 year ago)
- Topics: artificial-intelligence, modulation-recognition, pytorch, signal-processing
- Language: Python
- Homepage:
- Size: 18.6 KB
- Stars: 32
- Watchers: 3
- Forks: 3
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# pytorch-modulation-recognition
PyTorch Implementation of various Modulation Recognition Networks benchmarked on the RadioML2016 Dataset
## Install Requirements
```bash
# Base requirements
pip3 install -r requirements.txt
# Required to use train.py
pip3 install poutyne
# Required to run the demo web app
pip3 install streamlit
```
## Train
```bash
# Train VTCNN2
python train.py --model vtcnn --epochs 25 --batch_size 512 --split 0.8
# Train MRResNet
python train.py --model mrresnet --epochs 25 --batch_size 512 --split 0.8
```
### Results
After training Tensorboard logs will be located in the logs directory and the models in the models directory. Run the below command to start Tensorboard and point your browser to localhost:6006.
```bash
tensorboard --logdir=logs
```
### Demo
To run the web app for visualizing the In-Phase (I) and Quadrature (Q) channels of signals at various modulations and signal-to-noise ratios, use the following command.
```bash
streamlit run app.py
```