{"id":21239509,"url":"https://github.com/hemanthh17/malaria-detection-torch-docker","last_synced_at":"2026-04-16T12:02:29.804Z","repository":{"id":214797727,"uuid":"736373608","full_name":"hemanthh17/malaria-detection-torch-docker","owner":"hemanthh17","description":"Using Docker, and ML conepts to deploy an app on local server.","archived":false,"fork":false,"pushed_at":"2023-12-31T13:55:54.000Z","size":26,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-15T03:41:25.519Z","etag":null,"topics":["docker","flask","machinelearning","pytorch"],"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/hemanthh17.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":"2023-12-27T18:10:19.000Z","updated_at":"2023-12-31T13:57:59.000Z","dependencies_parsed_at":"2025-03-15T03:41:28.351Z","dependency_job_id":"b8045fe8-2e98-407d-a039-63a0b801b2a4","html_url":"https://github.com/hemanthh17/malaria-detection-torch-docker","commit_stats":null,"previous_names":["hemanthh17/malaria-detection-torch-docker"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/hemanthh17/malaria-detection-torch-docker","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemanthh17%2Fmalaria-detection-torch-docker","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemanthh17%2Fmalaria-detection-torch-docker/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemanthh17%2Fmalaria-detection-torch-docker/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemanthh17%2Fmalaria-detection-torch-docker/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/hemanthh17","download_url":"https://codeload.github.com/hemanthh17/malaria-detection-torch-docker/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/hemanthh17%2Fmalaria-detection-torch-docker/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31884929,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-16T11:36:10.202Z","status":"ssl_error","status_checked_at":"2026-04-16T11:36:09.652Z","response_time":69,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["docker","flask","machinelearning","pytorch"],"created_at":"2024-11-21T00:43:54.492Z","updated_at":"2026-04-16T12:02:29.770Z","avatar_url":"https://github.com/hemanthh17.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Malaria Detection using Pytorch\nFor this project blood samples with and without parasites have been considered. The overall goal is to make sure we are able to classify whether the person has Malaria or not based on the blood image samples.\n\n## Model Structure\nThe project involved using of pretrained model architecture from timm library which is associated with Pytorch. The pretrained model which was used is the Vision Transformer. \nThe model was able to provide predictions with F1 score of 0.808. \nThe number of epochs trained is 10.\n\n![0 YRDqyaLnCJscrYWV](https://github.com/hemanthh17/malaria-detection-torch-docker/assets/49975886/81303bef-61ec-40df-ba5c-314d6c065cdb)\n\n\n## Process Involved\nThe dataset is from [Kaggle](https://www.kaggle.com/datasets/nipunarora8/malaria-detection-dataset). The data was preprocessed and resized to uniformity to (224,224), all of the training and other parameters can be found in the scripts/config.py file.\nPost training, the model was saved, and a seprate script to define the model and the function call to classify the given image was initialised. The further step is to design a Flask app in order to locally host the application. The results are displayed in a new web page.\n\n## Dockerizing\nIn order to keep the dependencies uniform, the entire environment was dockerized. The image of this can be found in the [Docker Hub](https://hub.docker.com/r/hemanthh17/torchmalaria). \n```\ndocker pull docker pull hemanthh17/torchmalaria:v1.1\n```\nTo use this application the image can be pulled and a new container cna be created at the destination to run the application. \n```\ndocker run --name malariadetection -p 5000 hemanthh17/torchmalaria:latest\n```\nIf you want to build the image from scratch\n```\ndocker build -t malariatorch .\n```\n\nTo scout for any vulnerabilities\n```\ndocker scout malariatorch -quickview\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhemanthh17%2Fmalaria-detection-torch-docker","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhemanthh17%2Fmalaria-detection-torch-docker","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhemanthh17%2Fmalaria-detection-torch-docker/lists"}