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 🔔 Subscribe : https://tinyurl.com/exai-channel-link Background Track - Fruits of Life by Jimena Contreras Email - explainingai.official@gmail.com