About This Architecture

End-to-end face mask detection pipeline integrating data collection, model training with YOLO11s and Faster R-CNN, benchmarking, baseline generation, and FastAPI serving on Google Colab with NVIDIA Tesla T4 GPU. Data flows from Pascal VOC annotations through verification and augmentation stages, with trained models evaluated on accuracy, precision, recall, F1, and inference latency metrics. Production monitoring tracks prediction drift using PSI, KL divergence, and embedding cosine distance stored in SQLite, enabling root cause analysis and dataset improvement cycles. Fork this diagram to customize model selection, add monitoring thresholds, or integrate diagnostic tools like Cleanlab and SHAP for automated data quality assessment.

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How do you build a production-ready face mask detection system with model monitoring and continuous improvement?

This diagram shows a complete ML pipeline: collect Pascal VOC face mask images, prepare and validate data, train YOLO11s and Faster R-CNN on Tesla T4 GPU, benchmark both models on accuracy/latency metrics, serve the best model via FastAPI, and monitor production predictions for data drift using PSI and embedding distance in SQLite.