{"id":23878620,"url":"https://github.com/mosesab/yolov11-drowsiness-detection","last_synced_at":"2026-06-23T05:31:23.571Z","repository":{"id":336239239,"uuid":"1010822105","full_name":"mosesab/YOLOV11-Drowsiness-Detection","owner":"mosesab","description":"This repo contains the fine-tune implementation of a drowsy detection system, using a YOLO v11 model.","archived":false,"fork":false,"pushed_at":"2026-02-03T13:17:16.000Z","size":13107,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2026-02-04T02:53:34.788Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"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/mosesab.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-06-29T21:41:19.000Z","updated_at":"2026-02-03T13:18:16.000Z","dependencies_parsed_at":null,"dependency_job_id":"a051d5fc-bdfa-48b6-ac15-8e2365bcc9e4","html_url":"https://github.com/mosesab/YOLOV11-Drowsiness-Detection","commit_stats":null,"previous_names":["mosesab/yolov11-drowsiness-detection"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/mosesab/YOLOV11-Drowsiness-Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mosesab%2FYOLOV11-Drowsiness-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mosesab%2FYOLOV11-Drowsiness-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mosesab%2FYOLOV11-Drowsiness-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mosesab%2FYOLOV11-Drowsiness-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mosesab","download_url":"https://codeload.github.com/mosesab/YOLOV11-Drowsiness-Detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mosesab%2FYOLOV11-Drowsiness-Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34677382,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-23T02:00:07.161Z","response_time":65,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["computer-vision","drowsiness-detection","drowsiness-detection-python","fine-tuning","yolo","yolov11"],"created_at":"2025-01-03T21:46:53.330Z","updated_at":"2026-06-23T05:31:23.566Z","avatar_url":"https://github.com/mosesab.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"### YOLO v11 for Driver Drowsiness Detection\n\n📌 **Notebook moved to Kaggle:**  \n👉 https://www.kaggle.com/code/themosesab/yolov11-for-driver-drowsiness-detection\n\nThis repository contains a complete, end-to-end pipeline for training a YOLO v11 classification model to detect driver drowsiness using the ultralytics library.  \nThe project covers every stage of the machine learning lifecycle, from automated data acquisition and cleaning to model evaluation and explainability using GradCAM.\n\n![alt text](https://img.shields.io/badge/Python-3.10-blue.svg)\n\n\n![alt text](https://img.shields.io/badge/Framework-PyTorch-orange.svg)\n\n\n![alt text](https://img.shields.io/badge/License-MIT-green.svg)\n\n\n![alt text](https://img.shields.io/badge/Data-Kaggle-blue.svg)\n\n\n### Quick Summary\nAccuracy\t99.80%\tOverall correctness on the test set.\nAPCER\t0.00%\tRate of 'Drowsy' drivers missed (False Negatives).\nBPCER\t0.41%\tRate of 'Non Drowsy' drivers flagged (False Positives).\nACER\t0.21%\tAverage of APCER and BPCER.\n\n### Acknowledgements\n\nThis project uses the following datasets from Kaggle:\n- Driver Drowsiness Dataset (DDD) by ISMAIL NASRI.\n- Drowsy Detection Dataset by YASHAR JEBRAEILY.\n\n### License\nThis project is licensed under the MIT License. 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