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Deep Learning for Object Detection with Python and PyTorch

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Deep Learning for Object Detection with Python and PyTorch, available at $49.99, has an average rating of 4.57, with 36 lectures, based on 83 reviews, and has 369 subscribers.

You will learn about Learn Object Detection with Python and Pytorch Coding Learn Object Detection using Deep Learning Models Single-Stage Object Detection vs Two-Stage Objection Detection with Python Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8 Architectures Perform Object Detection with Fast RCNN and Faster RCNN Perform Real-time Video Object Detection with YOLOv8 Train, Test and Deploy YOLOv8 for Video Object Detection Introduction to Detectron2 by Facebook AI Research (FAIR) Preform Object Detection with Detectron2 Models Explore Custom Object Detection Datasets with Annotations Perform Object Detection on Custom Datasets using Deep Learning Train, Test, Evaluate Your Own Object Detection Models and Visualize Results Perform Object Instance Segmentation at Pixel Level using Mask RCNN Perform Object Instance Segmentation on Custom Dataset with Pytorch and Python This course is ideal for individuals who are 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 or 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 It is particularly useful for 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 or 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.

Enroll now: Deep Learning for Object Detection with Python and PyTorch

Summary

Title: Deep Learning for Object Detection with Python and PyTorch

Price: $49.99

Average Rating: 4.57

Number of Lectures: 36

Number of Published Lectures: 36

Number of Curriculum Items: 36

Number of Published Curriculum Objects: 36

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will Learn

  • Learn Object Detection with Python and Pytorch Coding
  • Learn Object Detection using Deep Learning Models
  • Single-Stage Object Detection vs Two-Stage Objection Detection with Python
  • Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8 Architectures
  • Perform Object Detection with Fast RCNN and Faster RCNN
  • Perform Real-time Video Object Detection with YOLOv8
  • Train, Test and Deploy YOLOv8 for Video Object Detection
  • Introduction to Detectron2 by Facebook AI Research (FAIR)
  • Preform Object Detection with Detectron2 Models
  • Explore Custom Object Detection Datasets with Annotations
  • Perform Object Detection on Custom Datasets using Deep Learning
  • Train, Test, Evaluate Your Own Object Detection Models and Visualize Results
  • Perform Object Instance Segmentation at Pixel Level using Mask RCNN
  • Perform Object Instance Segmentation on Custom Dataset with Pytorch and Python

Who Should Attend

  • 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

Target Audiences

  • 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

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 Coding

  • Learn Object Detection using Deep Learning Models

  • Introduction to Convolutional Neural Networks (CNN)

  • Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8 Architectures

  • Perform Object Detection with Fast RCNN and Faster RCNN

  • Perform Real-time Video Object Detection with YOLOv8

  • Train, Test and Deploy YOLOv8 for Video Object Detection

  • 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

  • Perform Object Instance Segmentation at Pixel Level using Mask RCNN

  • Perform Object Instance Segmentation on Custom Dataset with Pytorch and Python

By 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.

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction

Chapter 2: Object Detection and How it Works

Lecture 1: What is Object Detection and How it Works

Chapter 3: Single-shot vs Two-shot Object Detection

Lecture 1: Single-Stage vs Two-Stage Object Detection

Lecture 2: YOLOs vs RCNNs

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

Lecture 1: Introduction to CNN

Lecture 2: RCNN Deep Learning Architectures

Lecture 3: Fast RCNN Deep Learning Architecture

Lecture 4: Faster RCNN Deep Learning Architectures

Lecture 5: Mask RCNN Deep Learning Architectures

Chapter 5: Google Colab for Writing Python Code

Lecture 1: Set-up Google Colab for Writing Python Code

Lecture 2: Connect Google Colab with Google Drive to Read and Write Data

Chapter 6: Detectron2 for Ojbect Detection

Lecture 1: Detectron2 for Ojbect Detection with PyTorch

Lecture 2: Perform Object Detection using Detectron2 Pretrained Models

Lecture 3: Python and PyTorch Code

Chapter 7: Annotation tools to Label Your Own Dataset for Object Detection

Lecture 1: Annotate Your Own Dataset for Object Detection

Chapter 8: Custom Dataset for Object Detection

Lecture 1: Custom Dataset for Object Detection

Lecture 2: Dataset for Object Detection

Chapter 9: Training, Evaluating and Visualizing Object Detection on Custom Dataset

Lecture 1: Train, Evaluate Object Detection Models & Visualizing Results on Custom Dataset

Lecture 2: Python and PyTorch Code

Chapter 10: Complete Code and Custom Dataset for Object Detection

Lecture 1: Resources: Code and Custom Dataset for Object Detection

Chapter 11: Object Instance Segmentation for Detection at Pixel Level

Lecture 1: What is Object Instance Segmentation

Chapter 12: Mask RCNN for Object Detection and Instance Segmentation

Lecture 1: Mask RCNN for Object Detection and Instance Segmentation

Chapter 13: Train, Evaluate& Visualize Object Instance Segmentation on Custom Dataset

Lecture 1: Custom Dataset for Object Instance Segmentation

Lecture 2: Train, Evaluate& Visualize Object Instance Segmentation on Custom Dataset

Lecture 3: Object Instance Segmentation (Pytorch and Python Code)

Chapter 14: Complete Code and Dataset for Object Instance Segmentation

Lecture 1: Resources: Complete Code and Dataset for Object Instance Segmentation

Chapter 15: Real-time Object Detection with YOLOv8

Lecture 1: Introduction to YOLO and its Architecture

Lecture 2: How YOLO Detects Objects

Lecture 3: YOLOv8 and its Architecture

Chapter 16: Video Object Detection with YOLO8

Lecture 1: Custom Dataset for Object Detection

Lecture 2: YOLO8 Settings and Hyperparameters

Lecture 3: Training YOLO8 on Custom Object Detection Dataset

Lecture 4: Testing YOLOv8 on Videos and Images

Chapter 17: Deploy YOLOv8 for Real-time Object Detection

Lecture 1: Deploy YOLOv8 for Real-time Object Detection

Chapter 18: Resources: Complete Code and Dataset for Video Object Detection

Lecture 1: Resources: Complete Code and Dataset for Video Object Detection

Chapter 19: Bonus Lecture: Image & Video Segmentation, and Classification with Python

Lecture 1: Bonus Lecture: Image & Video Segmentation, and Classification with Python

Instructors

Rating Distribution

  • 1 stars: 1 votes
  • 2 stars: 3 votes
  • 3 stars: 5 votes
  • 4 stars: 6 votes
  • 5 stars: 68 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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