Faster R-CNN PyTorch Implementation
In this tutorial, I go step-by-step into how to implement Faster R-CNN for object detection using PyTorch . I cover everything from building Faster R-CNN from scratch to training the model and running object detection.
This video builds the code for Faster R-CNN in Python and provides detailed explanations of different components involved in implementing Faster R-CNN. We start with building RPN with anchor generation and converting anchors to proposals and computing RPN loss, then get into ROI layer and end with building the Faster R-CNN module in PyTorch.
This should provide you with everything you need to implement and train a Faster R-CNN model in PyTorch by yourself on your own dataset for object detection task.
⏱️ Timestamps:
00:00 Intro
01:59 Faster RCNN Implementation Overview
04:25 Region Proposal Network in Faster R CNN
05:52 RPN Implementation
08:19 Anchor Generation Implementation
15:32 Converting Anchors to Proposal Boxes
19:39 Filtering Proposals in Faster RCNN
22:15 Creating Labels for Anchors in RPN
31:38 Creating Regression Targets for Anchors in RPN
34:05 Sampling Anchors for Training RPN
36:32 Implementing RPN loss
38:07 Region Proposal Network Summary
39:07 ROI Head Initialization
40:50 Creating Labels for Proposals in ROI Head
44:02 ROI Pooling
46:12 Implementing Detection Losses for Faster RCNN
49:07 Filtering Proposals for Inference in Faster R-CNN
54:45 Summary of ROI Head for Faster RCNN
55:36 Faster RCNN Module Initialization
57:25 Resizing Image and boxes for Faster R-CNN Module
01:01:47 Predicted Boxes to Output Boxes Transformation
01:03:32 Dataset, Configuration and Training Code
01:05:56 Results for Faster R-CNN
01:06:58 Outro
📖 Resources:
Faster R-CNN Paper - https://tinyurl.com/exai-faster-rcnn-...
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Background Track - Fruits of Life by Jimena Contreras
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