{"id":20162642,"url":"https://github.com/codeamt/fastefficientcoviddeployment","last_synced_at":"2025-03-03T03:10:01.375Z","repository":{"id":201923646,"uuid":"269250086","full_name":"codeamt/FastEfficientCovidDeployment","owner":"codeamt","description":"A COVID-19 Chest XRay classification model deployed as a Streamlit app","archived":false,"fork":false,"pushed_at":"2024-03-10T04:17:40.000Z","size":628333,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-01-13T14:19:29.402Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/codeamt.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2020-06-04T03:18:52.000Z","updated_at":"2024-03-10T04:15:32.000Z","dependencies_parsed_at":null,"dependency_job_id":"c02e1333-50ad-42c4-a1e1-0e599daf5f38","html_url":"https://github.com/codeamt/FastEfficientCovidDeployment","commit_stats":null,"previous_names":["codeamt/fastefficientcoviddeployment"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FFastEfficientCovidDeployment","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FFastEfficientCovidDeployment/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FFastEfficientCovidDeployment/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/codeamt%2FFastEfficientCovidDeployment/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/codeamt","download_url":"https://codeload.github.com/codeamt/FastEfficientCovidDeployment/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241600518,"owners_count":19988715,"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":[],"created_at":"2024-11-14T00:26:06.357Z","updated_at":"2025-03-03T03:10:01.355Z","avatar_url":"https://github.com/codeamt.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fast Efficient Covidnet\nStreamlit inference service deployment submodule for Udacity's Machine Learning Engineer Nanodegree program.\n\n\n \n\u003cp align=\"center\"\u003e\n\u003cimg src=\"https://drive.google.com/uc?export=view\u0026id=1P1g6zNLvHTgCF6E1jnoJ6lqWIHBoCeUI\" width=\"60%\" /\u003e\n\u003c/p\u003e\n\n**DISCLAIMER:** THIS TOOL SHOULD NOT BE USED FOR MEDICAL DIAGNOSIS/REPLACE CONSULTING FROM A MEDICAL EXPERT AND SHOULD SERVE EDUCATIONAL PURPOSES ONLY.\n\n## Related Repos/Files: \n- [All repos](https://github.com/codeamt/FastEfficientCovidNet)\n- [Data Engineering](https://github.com/codeamt/mle-capstone-data)\n- [Model Training](https://github.com/codeamt/mle-capstone-modeling)\n- [Technical Report](https://github.com/codeamt/FastEfficientCovidNet/blob/master/report.pdf)\n\n## About \nThis is a Chest X-Ray (CXR) classification API. Building on previous work of [[1]](https://arxiv.org/pdf/2003.09871v3.pdf), the CovNet model for this ML project utilizes a pre-trained EfficientNet-b1 to extract features and a fine-tuned Fast.ai classifier to differentiate between infection classes (Normal, Viral Pneumonia, or COVID-19) with 95% test accuracy. \n \n## Build Instructions\n\n### Locally\n\n#### Clone this repo:\n```\ngit clone https://github.com/codeamt/mle-capstone-deployment FastEfficientCovidnet \ncd FastEfficientCovidnet\n\n```\n\n#### Install packages:\n```\ncd src\npip3 -r install requirements.txt \n```\n\n### or Dockerized:\n\n```\ndocker build -f Dockerfile -t app:latest .\n```\n\n## Running \n\n### Locally:\nFrom the src of the repo:\n```\nstreamlit run app.py\n```\n### with Docker: \n\n```\ndocker run -p 8501:8501 app:latest\n```\n\n## References\n[1](https://arxiv.org/pdf/2003.09871.pdf) COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases\nfrom Chest X-Ray Image. L. Wang and A. Wong., 2020.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeamt%2Ffastefficientcoviddeployment","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcodeamt%2Ffastefficientcoviddeployment","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcodeamt%2Ffastefficientcoviddeployment/lists"}