Deep Learning For Object Detection With Python And Pytorch

Object Detection for Computer Vision using Deep Learning with PyTorch & Python. Train & Deploy Models (Detectron2, RCNN)

What you'll learn

Learn Object Detection with Python and Pytorch Coding

Learn Object Detection using Deep Learning Models

Introduction to Convolutional Neural Networks (CNN)

Learn RCNN, Fast RCNN, Faster RCNN and Mask RCNN Architectures

Perform Object Detection with Fast RCNN and Faster RCNN

Introduction to Detectron2 by Facebook AI Research (FAIR)

Preform Object Detection with Detectron2 Models

Explore Custom Object Detection Dataset with Annotations

Perform Object Detection on Custom Dataset using Deep Learning

Train, Test, Evaluate Your Own Object Detection Models and Visualize Results

Requirements

Object Detection using Deep Learning with Python and PyTorch is taught in this course by following a complete pipeline from Zero to Hero

No prior knowledge of Semantic Segmentation is assumed. Everything will be covered with hands-on trainings

A Google Gmail account is required to get started with Google Colab to write Python Code

Description

Are you ready to dive into the fascinating world of object detection using deep learning? In our comprehensive course "Deep Learning for Object Detection with Python and PyTorch", we will guide you through the essential concepts and techniques required to detect, classify, and locate objects in images. Object Detection has wide range of potential real life application in many fields. Object detection is used for autonomous vehicles to perceive and understand their surroundings. It helps in detecting and tracking pedestrians, vehicles, traffic signs, traffic lights, and other objects on the road. Object Detection is used for surveillance and security using drones to identify and track suspicious activities, intruders, and objects of interest. Object Detection is used for traffic monitoring, helmet and license plate detection, player tracking, defect detection, industrial usage and much more.With the powerful combination of Python programming and the PyTorch deep learning framework, you'll explore state-of-the-art algorithms and architectures like R-CNN, Fast RCNN and Faster R-CNN. Throughout the course, you'll gain a solid understanding of Convolutional Neural Networks (CNNs) and their role in Object Detection. You'll learn how to leverage pre-trained models, fine-tune them for Object Detection using Detectron2 Library developed by by Facebook AI Research (FAIR).The course covers the complete pipeline with hands-on experience of Object Detection using Deep Learning with Python and PyTorch as follows:Learn Object Detection with Python and Pytorch CodingLearn Object Detection using Deep Learning ModelsIntroduction to Convolutional Neural Networks (CNN)Learn RCNN, Fast RCNN, Faster RCNN and Mask RCNN ArchitecturesPerform Object Detection with Fast RCNN and Faster RCNNIntroduction to Detectron2 by Facebook AI Research (FAIR)Preform Object Detection with Detectron2 ModelsExplore Custom Object Detection Dataset with AnnotationsPerform Object Detection on Custom Dataset using Deep LearningTrain, Test, Evaluate Your Own Object Detection Models and Visualize ResultsBy the end of this course, you'll have the knowledge and skills you need to start applying Deep Learning to Object Detection problems in your own work or research. Whether you're a Computer Vision Engineer, Data Scientist, or Developer, this course is the perfect way to take your understanding of Deep Learning to the next level. Let's get started on this exciting journey of Deep Learning for Object Detection with Python and PyTorch.

Overview

Section 1: Introduction to Course

Lecture 1 Introduction

Section 2: Object Detection and How it Works

Lecture 2 What is Object Detection and How it Works

Section 3: Convolutional Neural Network (CNN)

Lecture 3 Deep Convolutional Neural Network (VGG, ResNet, GoogleNet)

Section 4: Deep Learning Architectures for Object Detection (R-CNN Family)

Lecture 4 RCNN Deep Learning Architectures

Lecture 5 Fast RCNN Deep Learning Architecture

Lecture 6 Faster RCNN Deep Learning Architectures

Lecture 7 Mask RCNN Deep Learning Architectures

Section 5: Google Colab for Writing Python Code

Lecture 8 Set-up Google Colab for Writing Python Code

Lecture 9 Connect Google Colab with Google Drive to Read and Write Data

Section 6: Detectron2 for Ojbect Detection

Lecture 10 Detectron2 for Ojbect Detection with PyTorch

Lecture 11 Perform Object Detection using Detectron2 Pretrained Models

Lecture 12 Python and PyTorch Code

Section 7: Custom Dataset for Object Detection

Lecture 13 Custom Dataset for Object Detection

Section 8: Training, Evaluating and Visualizing Object Detection on Custom Dataset

Lecture 14 Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset

Lecture 15 Python and PyTorch Code

Section 9: Resources: Code and Custom Dataset for Object Detection

Lecture 16 Resources: Code and Custom Dataset for Object Detection

This course is designed for a wide range of Students and Professionals, including but not limited to: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers who want to learn how to use PyTorch to build and train deep learning models for Object Detection,In general, the course is for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Object Detection using Python and PyTorch