Day 18: Fast R-CNN & Faster R-CNN Explanation in Object Detection | Computer Vision free Course

Welcome to Day 18 of our Free Computer Vision Course! 🚀

In the previous class, we saw how Fast R-CNN improved speed but was still held back by the slow "Selective Search" algorithm. Today, we break that final barrier with Faster R-CNN!

This session is dedicated to the algorithm that truly made real-time object detection possible. We will explore how replacing the external region proposal method with a learning-based Region Proposal Network (RPN) revolutionized the field.

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

🧠 What You Will Master in This Advanced Session:
👉 Fast R-CNN Recap: A quick look at why it was still not "Fast" enough (the bottleneck).
👉 Faster R-CNN Architecture: Understanding the unified network design.
👉 Region Proposal Network (RPN): The heart of Faster R-CNN—learning how to propose regions using a neural network instead of traditional algorithms.
👉 Anchor Boxes: The concept of Anchors and how they handle objects of different scales and aspect ratios.
👉 End-to-End Training: How the RPN and the classifier share convolutional features for maximum efficiency.
👉 Comparison: Fast R-CNN vs. Faster R-CNN performance analysis.
👉 This session is crucial for anyone preparing for Deep Learning interviews or university exams!

If you are aiming for a career in Computer Vision, understanding Faster R-CNN is an absolute requirement!

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🔴 Keywords Covered in this Video:
👉 Faster R-CNN Architecture Explained
👉 Region Proposal Network (RPN) in Depth
👉 Fast R-CNN vs Faster R-CNN Difference
👉 What are Anchor Boxes in Object Detection
👉 Real-time Object Detection Algorithms
👉 Deep Learning Bangla Tutorial
👉 Computer Vision Course Day 18
👉 RPN Loss Function

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