R-CNN Explained

This is a R CNN tutorial video in which I dive deep into what is R CNN and cover its basics.
This video is a part of object detection series and the first one in that is RCNN for object detection.

By the end of this video you would be able to understand the R CNN algorithm in detail to understand clearly as to how rcnn works . We start with what selective search is and how rcnn uses selective search to get region proposals . We then move on to the different stages of training RCNN, RCNN architecture, talk about bounding box regressors in R-CNN and lastly discuss the results RCNN gets on object detection task. By the end of this video you should be able to understand all parts of object detection using rcnn .

📖 Resources
RCNN Paper - https://tinyurl.com/exai-rcnn-paper
Graph Segmentation - https://tinyurl.com/exai-rcnn-graph-paper
Selective Search - https://tinyurl.com/exai-rcnn-ss-paper
Selective Search opencv implementation - https://tinyurl.com/exai-rcnn-ss-opencv-code

⏱️ Timestamps

00:00 Introduction
00:30 Classification vs Localization vs Detection
03:40 Object Detection using Sliding Window Approach
06:17 Object detection using RCNN - Introduction
08:11 Selective Search in RCNN for region proposals
13:50 RCNN : Supervised Pre-training and Finetuning
19:12 RCNN : SVM Training
22:20 Why use SVM in R-CNN
26:00 Bounding Box Regression Training in RCNN
28:42 Non-Maximum Suppression | NMS in Object Detection
31:14 RCNN Results
33:18 Outro

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Background Track - Fruits of Life by Jimena Contreras
Email - explainingai.official@gmail.com