Скачать или смотреть Object Detection Pipeline Simplified for Interviews

Are you preparing for a deep learning or computer vision interview and want to confidently explain how object detection works? 👀
This video is for you — whether you’re a beginner just starting out or a pro brushing up for a big interview! 💪

In this fun, visual, and easy-to-follow explanation, we’ll break down the Detection Pipeline Basics using two of the most popular architectures: YOLO and Faster R-CNN.

You’ll learn:
🔹 The step-by-step object detection pipeline:
 → Feature Extraction 🧠
 → Region Proposal / Anchors 📦
 → Classification + Bounding Box Regression 🏷️
 → Post-processing (NMS) 🧹

🔹 The key differences between YOLO and Faster R-CNN
 ✅ YOLO = One-stage detector → super fast, real-time performance ⚡
 ✅ Faster R-CNN = Two-stage detector → high accuracy, slightly slower 🎯

🔹 How to answer this question perfectly in AI or ML interviews 💬

By the end of this video, you’ll be able to confidently say:

“Object detection = Feature Extraction + Region Proposal + Classification + NMS” —
and back it up with solid understanding 💡

Whether you’re learning, revising, or prepping for your next big tech interview, this explanation will help you stand out and sound like a pro! 🚀

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