Day 17: R-CNN & Fast R-CNN Everything You Need to Know for Object Detection | Computer Vision Course

Welcome to Day 17 of our Free Computer Vision Course! 🚀

Today, we dive deep into the history and architecture of modern Object Detection. If you want to understand how state-of-the-art models like YOLO work today, you must first understand their ancestors: R-CNN and Fast R-CNN.

In this live masterclass, we will break down exactly how these algorithms changed the landscape of Computer Vision, how they work step-by-step, and the critical improvements that led from one to the other.

Note: The lecture and technical explanations are delivered entirely in the Bangla language (বাংলা).

🧠 What You Will Master in This Session:
👉 R-CNN (Regions with CNN features): Understanding the first breakthrough in Deep Learning-based object detection.
👉 Selective Search: How R-CNN proposes regions (and why it was slow).
👉 The Bottleneck: Analyzing the major drawbacks of R-CNN (training time & inference speed).
👉 Fast R-CNN: The game-changer! How it solved R-CNN's speed issues.
👉 RoI Pooling Layer: Deep dive into the most important concept—Region of Interest (RoI) Pooling—and how it allows sharing computation.
👉 Architecture Comparison: A clear comparison between R-CNN vs. Fast R-CNN.

This session is crucial for anyone preparing for Deep Learning interviews or university exams!

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🔴 Keywords Covered in this Video:
👉 R-CNN Architecture Explained
👉 Fast R-CNN vs R-CNN Difference
👉 Selective Search in Object Detection
👉 Region of Interest (RoI) Pooling Explained
👉 How R-CNN Works Step by Step
👉 Object Detection Algorithms Bangla
👉 Deep Learning for Computer Vision Day 17
👉 R-CNN Paper Review Bangla

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