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Build & Evaluate Real-Time Object Detectors · LearnSpace
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Build & Evaluate Real-Time Object Detectors

Курс от Coursera
Средний≈ 2.8 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Build & Evaluate Real-Time Object Detectors is an intermediate hands-on course for ML engineers who need to deploy fast, accurate object detectors under real-world constraints. When accuracy falls short of KPIs, or FPS drops below target, you need the skills to diagnose metrics, recommend improvements, and evaluate whether a real-time pipeline meets requirements. You'll learn how to compute and interpret detection metrics like mAP and APsmall, identify causes of underperformance, and propose targeted improvements. Then you'll analyze a complete real-time detection pipeline using models like YOLOv8 and trackers like DeepSORT, and evaluate it against throughput requirements such as 25 FPS at 720p. Through short videos, practical readings, analysis-based labs, and a final graded assessment, you will develop the skills to evaluate detectors, recommend optimizations, and assess whether solutions meet real-time demands.

Навыки, которые вы освоите

Model OptimizationSystem RequirementsQuality AssessmentPerformance AnalysisPerformance MeasurementData PipelinesComputer Vision

Программа курса

1 модулей · 17 учебных материалов

01Build & Evaluate Real-Time Object Detectors17 материалов

Understanding Object Detection Metrics and KPIs

Introduction and WelcomeВидеоYour KPI Reality CheckDIALOGUEWhy Evaluation Comes First in Real-Time DetectionВидеоCore Detection Metrics: mAP, APsmall, Precision, RecallЧтение

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Обучение на Coursera

≈ 2.8 ч

1 модулей

Язык: Английский

Субтитры: Дари, Пушту

Часть программы вашего университета
Interpreting mAP: What To Look For in Real ProjectsВидео
HOL: Compute mAP from Provided COCO-Format PredictionsЗадание
Diagnosing Low AP on Small ObjectsЧтение
Reflecting on Labeling and Annotation Challenges DIALOGUE

Designing and Integrating a Real-Time Detection Pipeline

Choosing the Right Model for Real-Time RequirementsВидеоWhere Latency Comes From: IO, Inference, NMS, and TrackingЧтениеTracker Basics: DeepSORT, BYTETrack, OC-SORTВидеоIntegrating YOLOv8 with DeepSORT in OpenCVВидеоHOL: Build a YOLOv8 + DeepSORT Pipeline LoopЗаданиеBenchmarking FPS and Latency on Embedded DeviceЧтениеDoes This Pipeline Meet the 25 FPS Requirement?DIALOGUECongratulations and Continuous Learning JourneyВидеоGraded Quiz: Build & Evaluate Real-Time Object DetectorsЗадание