Fast R-CNN Explained | ROI Pooling

In this tutorial, I dive deep into Fast R-CNN , explaining its architecture, the role of ROI pooling and how it differs from R-CNN. Through this video you will learn how Fast R-CNN works, understand Region Of Interest (ROI) pooling, and discover the advantages it brings to object detection tasks over previous approaches. I specifically go through how Fast R-CNN compares over R-CNN in terms of performance and speed in detail. By the end of this video, you should have everything you need to master Fast R-CNN. ⏱️ Timestamps 00:00 Introduction 00:37 Problems with RCNN 03:16 Motivation for Region of Interest Pooling 05:32 Dive deep into ROI Pooling 08:34 ROI Pooling Implementation in PyTorch 11:20 Multi Task Training of Fast R-CNN 12:15 Bounding Box Regressor Recap of RCNN 13:08 Initializing Fast R-CNN from Pre-trained Models 15:36 Fine tuning Fast RCNN for Object Detection 19:41 Multi Task Loss of Fast R-CNN 20:51 Scale Invariance for Fast-RCNN 23:13 R-CNN vs Fast R-CNN 23:53 Fast R-CNN Results 28:58 SVD on FC Layers of Fast RCNN 32:29 Outro 📖 Resources Fast RCNN Paper - https://tinyurl.com/exai-fast-rcnn-paper 🔔 Subscribe : https://tinyurl.com/exai-channel-link Background Track - Fruits of Life by Jimena Contreras Email - [email protected]