Faster R-CNN PyTorch Implementation

ExplainingAI · Beginner ·🧬 Deep Learning ·2y ago

About this lesson

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

Original Description

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

Watch on YouTube ↗ (saves to browser)

Sign in to unlock AI tutor explanation · ⚡30

Related Reads

Understanding Deep Learning Through Four Interactive Experiments

Explore deep learning concepts through interactive experiments to gain hands-on understanding

Medium · Data Science

Understanding Deep Learning Through Four Interactive Experiments

Explore deep learning through interactive experiments to gain hands-on understanding

Medium · Deep Learning

Optimizers in Deep Learning: From Gradient Descent to Adam

Learn how optimizers in deep learning work, from basic Gradient Descent to advanced Adam optimizer, to improve model training

Medium · Deep Learning

The Meta-Architecture of Interface Fracture: High-Dimensional Logical Stress and Systemic Collapse…

Learn about the meta-architecture of interface fracture and its relation to high-dimensional logical stress and systemic collapse in deep learning systems

Medium · Deep Learning

Chapters (24)

Intro

1:59 Faster RCNN Implementation Overview

4:25 Region Proposal Network in Faster R CNN

5:52 RPN Implementation

8:19 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

1:01:47 Predicted Boxes to Output Boxes Transformation

1:03:32 Dataset, Configuration and Training Code

1:05:56 Results for Faster R-CNN

1:06:58 Outro

Image Classification with ml5.js

The Coding Train