{"id":17053848,"url":"https://github.com/isaaccorley/pytorch-modulation-recognition","last_synced_at":"2025-04-12T16:47:02.014Z","repository":{"id":165368355,"uuid":"256008824","full_name":"isaaccorley/pytorch-modulation-recognition","owner":"isaaccorley","description":"PyTorch Implementation Modulation Recognition Networks on the RadioML2016 Dataset","archived":false,"fork":false,"pushed_at":"2020-09-16T17:23:19.000Z","size":19,"stargazers_count":32,"open_issues_count":3,"forks_count":3,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-26T11:11:16.092Z","etag":null,"topics":["artificial-intelligence","modulation-recognition","pytorch","signal-processing"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/isaaccorley.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-04-15T18:51:50.000Z","updated_at":"2024-12-23T13:43:54.000Z","dependencies_parsed_at":null,"dependency_job_id":"b1be55a8-df48-4dc2-9f68-6eb53b6d99f0","html_url":"https://github.com/isaaccorley/pytorch-modulation-recognition","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isaaccorley%2Fpytorch-modulation-recognition","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isaaccorley%2Fpytorch-modulation-recognition/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isaaccorley%2Fpytorch-modulation-recognition/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/isaaccorley%2Fpytorch-modulation-recognition/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/isaaccorley","download_url":"https://codeload.github.com/isaaccorley/pytorch-modulation-recognition/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248600945,"owners_count":21131578,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["artificial-intelligence","modulation-recognition","pytorch","signal-processing"],"created_at":"2024-10-14T10:13:26.156Z","updated_at":"2025-04-12T16:47:01.981Z","avatar_url":"https://github.com/isaaccorley.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pytorch-modulation-recognition\nPyTorch Implementation of various Modulation Recognition Networks benchmarked on the RadioML2016 Dataset\n\n## Install Requirements\n\n```bash\n# Base requirements\npip3 install -r requirements.txt\n\n# Required to use train.py\npip3 install poutyne\n\n# Required to run the demo web app\npip3 install streamlit\n\n```\n\n## Train\n\n```bash\n# Train VTCNN2\npython train.py --model vtcnn --epochs 25 --batch_size 512 --split 0.8\n\n# Train MRResNet\npython train.py --model mrresnet --epochs 25 --batch_size 512 --split 0.8\n\n```\n\n### Results\n\nAfter 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.\n\n```bash\ntensorboard --logdir=logs\n\n```\n\n### Demo\n\nTo 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.\n\n```bash\nstreamlit run app.py\n\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisaaccorley%2Fpytorch-modulation-recognition","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fisaaccorley%2Fpytorch-modulation-recognition","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fisaaccorley%2Fpytorch-modulation-recognition/lists"}