AarnoStormborn/Smoking-Detection
Cigarette Detection Model deployed over Django backend
Leverages YOLOv5s (7.6M parameters) for real-time single-stage object detection with a CSPDarknet backbone, trained on 1,996 annotated cigarette images at 512×512 resolution achieving 0.9729 precision and 0.9714 mAP@0.5. Exposes inference through a Django REST API supporting batch image/video uploads and live RTSP stream ingestion for surveillance integration, with persistent sample storage and model result tracking via the built-in ORM.
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Apr 04, 2023
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