Object Detection Part 3: Faster R-CNN, Region Proposal Network and Intersection over Union

This is the third video in the object detection series and in it we are exploring how the Faster R-CNN model improves the Fast R-CNN model by removing the selective search algorithm and introducing the region proposal network (RPN) which can be trained to provide better regions for the task we are trying to solve.

References
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"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" paper: https://arxiv.org/abs/1506.01497
"Rich feature hierarchies for accurate object detection and semantic segmentation" paper: https://arxiv.org/abs/1311.2524
"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" paper: https://arxiv.org/abs/1506.01497

Related Videos
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Object Detection Part 1: R-CNN, Sliding Window and Selective Search:    • Object Detection Part 1: R-CNN, Sliding Wi...  
Object Detection Part 2: Fast R-CNN, Region Projection and Region of Interest (RoI) Pooling Layer:    • Object Detection Part 2: Fast R-CNN, Regio...  
Why Neural Networks Can Learn Any Function:    • Why Neural Networks Can Learn Any Function  
Why Deep Neural Networks (DNNs) Underperform Tree-Based Models on Tabular Data:    • Why Deep Neural Networks (DNNs) Underperfo...  
Why Residual Connections (ResNet) Work:    • Why Residual Connections (ResNet) Work  

Contents
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00:00 - Intro
00:23 - R-CNN & Fast R-CNN Recap
01:12 - Faster R-CNN
01:31 - Anchor Points & Boxes
02:07 - Region Proposal Network
03:54 - Training Anchor Boxes with Intersection over Union (IoU)
05:13 - Outro

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