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

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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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*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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thank you so much, the explaination and the demonstrations are so much easier to understand than the paper

tfun-efpm

I have 1D Data, I want to apply faster RCNN, any resources for the same ?

ranjan

I was stuck for about an hour or so, looking at the Object classifier and Bounding Box Regressor, thinking that "2k" and "4k" meant 2000 and 4000. Funnily enough, I couldn't get it to make sense in my head. My god, I need to sleep or something...

Bapll

Very well explained as always.
Idea for a video on a point I have trouble with: why infinite width bayesian deep networks are gaussian processes

alexis

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