Video Segmentation with Python using Deep Learning Real-Time

Video Segmentation with Python using Deep Learning Real-Time, available at $54.99, has an average rating of 4.71, with 28 lectures, based on 36 reviews, and has 195 subscribers.

You will learn about Real-Time Video Instance Segmentation with Python and Pytorch using Deep Learning Build, Train, & Test Deep Learning Models on Custom Data & Deploy to Your Own Projects Introduction to YOLOv8 and its Deep Learning Architecture Video Instance Segmentation using YOLOv8 with Python Introduction to Mask RCNN and its Deep Learning Architecture Instance Segmentation using Mask RCNN with Python Configuration of Custom Vehicles Dataset with Annotations for Instance Segmentation HyperParameters Settings for Training Instance Segmentation Models Training Instance Segmentation YOLOv8 and Mask RCNN Models on Custom Datasets Testing Instance Segmentation Trained Models on Videos and Images Perform Car, Motorbike, and Truck Instance Segmentation Deploy Trained Instance Segmentation Models This course is ideal for individuals who are This course is tailored for aspiring Computer Vision and Deep Learning enthusiasts, students, and researchers eager to delve into the world of Video Instance Segmentation with Python. or Whether youre a beginner looking to unlock the mysteries of pixels in motion or a seasoned professional aiming to expand your skill set, this course offers a dynamic learning experience. If youre passionate about mastering deep learning techniques for video analysis and Instance Segmentation, this course is designed just for you. It is particularly useful for This course is tailored for aspiring Computer Vision and Deep Learning enthusiasts, students, and researchers eager to delve into the world of Video Instance Segmentation with Python. or Whether youre a beginner looking to unlock the mysteries of pixels in motion or a seasoned professional aiming to expand your skill set, this course offers a dynamic learning experience. If youre passionate about mastering deep learning techniques for video analysis and Instance Segmentation, this course is designed just for you.

Enroll now: Video Segmentation with Python using Deep Learning Real-Time

Summary

Title: Video Segmentation with Python using Deep Learning Real-Time

Price: $54.99

Average Rating: 4.71

Number of Lectures: 28

Number of Published Lectures: 28

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

Real-Time Video Instance Segmentation with Python and Pytorch using Deep Learning

Build, Train, & Test Deep Learning Models on Custom Data & Deploy to Your Own Projects

Introduction to YOLOv8 and its Deep Learning Architecture

Video Instance Segmentation using YOLOv8 with Python

Introduction to Mask RCNN and its Deep Learning Architecture

Instance Segmentation using Mask RCNN with Python

Configuration of Custom Vehicles Dataset with Annotations for Instance Segmentation

HyperParameters Settings for Training Instance Segmentation Models

Training Instance Segmentation YOLOv8 and Mask RCNN Models on Custom Datasets

Testing Instance Segmentation Trained Models on Videos and Images

Perform Car, Motorbike, and Truck Instance Segmentation

Deploy Trained Instance Segmentation Models

Who Should Attend

This course is tailored for aspiring Computer Vision and Deep Learning enthusiasts, students, and researchers eager to delve into the world of Video Instance Segmentation with Python.

Whether youre a beginner looking to unlock the mysteries of pixels in motion or a seasoned professional aiming to expand your skill set, this course offers a dynamic learning experience. If youre passionate about mastering deep learning techniques for video analysis and Instance Segmentation, this course is designed just for you.

Target Audiences

This course is tailored for aspiring Computer Vision and Deep Learning enthusiasts, students, and researchers eager to delve into the world of Video Instance Segmentation with Python.

Whether youre a beginner looking to unlock the mysteries of pixels in motion or a seasoned professional aiming to expand your skill set, this course offers a dynamic learning experience. If youre passionate about mastering deep learning techniques for video analysis and Instance Segmentation, this course is designed just for you.

Introduction: Step into the dynamic realm of computer vision and get ready to be the maestro of moving pixels! Dive into the world of ‘Video Instance Segmentation with Python Using Deep Learning.’ Unleash the magic hidden in each frame, master the art of dynamic storytelling, and decode the dance of pixels with the latest in deep learning techniques. This course is your passport to unlocking the secrets hidden within the pixels of moving images. Whether you’re a novice or an enthusiast eager to delve into the intricacies of video analysis, this journey promises to demystify the world of deep learning in the context of dynamic visual narratives.

Instance segmentation is a computer vision task to detect and segment individual objects at a pixel level. Unlike semantic segmentation, which assigns a class label to each pixel without distinguishing between object instances, instance segmentation aims to differentiate between each unique object instance in the image. Instance segmentation is a computer vision task to detect and segment individual objects at a pixel level. Instance segmentation goes a step further than object detection and involves identifying individual objects and segment them from the rest of the region. The output of an instance segmentation model is a set of masks or contours that outline each object in the image, along with class labels and confidence scores for each object. Instance segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is. So, Instance segmentation provides a more detailed understanding of the scene by recognizing and differentiating between specific instances of objects. This fine-grained recognition is essential in applications where precise object localization is required. For example In the context of autonomous vehicles, instance segmentation is valuable for understanding the surrounding environment. It helps in identifying and tracking pedestrians, vehicles, and other obstacles with high precision, contributing to safe navigation.

Deep learning is one of the most effective approach to Instance segmentation, which involves training a neural network to learn complex relationships between pixels and able to learn rich feature representations. The goal of Instance segmentationis to train a Deep Learning modelwhich can look at the image of multiple objects and able to detect and recognize individual objects at pixel level. In this course, you will perform real time video Instance segmentation with latest YOLO8 which is a deep CNN and you will also do instance segmentation using Mask RCNN which is a region based CNN.

Importance: Understanding video instance segmentation is at the forefront of technological innovation. It goes beyond mere object detection, offering a pixel-level understanding of each object’s motion and shape over time. The importance of this skill extends across industries, influencing advancements in robotics, autonomous systems, healthcare, entertainment, and more.

Applications:

Surveillance and Security: Contribute to the development of advanced security systems by mastering video instance segmentation for accurate object identification.

Autonomous Systems: Enhance your skills for applications like self-driving cars and drones, where precise object tracking is crucial for decision-making.

Medical Imaging: Dive into the medical field, where pixel-level understanding in video sequences aids in precise localization and tracking for diagnostic purposes.

Entertainment Industry: Join the league of creators in the entertainment industry, mastering the art of visually engaging effects through detailed object segmentation in videos.

Course Key Objectives:

In this course, You will follow a complete pipeline for real time video instance segmentation:

Real-Time Video Instance Segmentation with Python and Pytorch using Deep Learning

Build, Train, & Test Deep Learning Models on Custom Data & Deploy to Your Own Projects

Introduction to YOLOv8 and its Deep Learning Architecture

Introduction to Mask RCNN and its Deep Learning Architecture

Video Instance Segmentation using YOLOv8 with Python

Instance Segmentation using Mask RCNN with Python

Configuration of Custom Vehicles Dataset with Annotations for Instance Segmentation

HyperParameters Settings for Training Instance Segmentation Models

Training Instance Segmentation YOLOv8 and Mask RCNN Models on Custom Datasets

Testing  Instance Segmentation Trained Models on Videos and Images

Perform Car, Motorbike, and Truck Instance Segmentation

Deploy Trained Instance Segmentation Models

So, Are you ready to take your understanding of deep learning to the next level and learn how to apply it to real-world problems? This course is especially designed to give you hands-on experience using Python and Pytorch to build, train and Test deep learning models for Instance segmentation applications.“ At the end of this course, you will be able to perform real time video instance segmentation to your own real word problem on custom datasets using Python. Acquire hands-on experience with Python and deep learning frameworks, gaining a skill set that’s in high demand across industries. Become a visual storyteller, interpreting the language of pixels in moving images. Seize the opportunity to be at the forefront of technological advancements and make a lasting impact in fields where video analysis is the key to unlocking the future.

Embark on this learning journey, where the fusion of Python, deep learning, and video instance segmentation awaits your exploration. Don’t miss your chance to be a part of this transformative experience. Enroll now and turn your passion into expertise!

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction

Chapter 2: What is Video Instance Segmentation

Lecture 1: Introduction to Image Instance Segmentation

Lecture 2: Introduction to Video Instance Segmentation

Chapter 3: Introduction to YOLO and its Architecture

Lecture 1: Introduction to YOLO and its Architecture

Chapter 4: YOLOv8 for Real-time Video Instance Segmentation

Lecture 1: Introduction to YOLOv8 for Real-time Video Instance Segmentation

Lecture 2: Google Colab for Writing Python Code

Lecture 3: Instance Segmentation with Python using YOLOv8

Chapter 5: Custom Vehicles Instance Segmentation Dataset

Lecture 1: Vehicles Dataset for Instance Segmentation

Lecture 2: Vehicles Instance Segmentation Dataset

Chapter 6: Google Colab for Writing Python Code

Lecture 1: Google Colab for Writing Python Code

Lecture 2: Connect Google Colab With Google Drive To Read And Write Data

Chapter 7: HyperParameters for Training Instance Segmentation Model

Lecture 1: HyperParameters for Training Instance Segmentation YOLO8 Model

Lecture 2: Python Code

Chapter 8: Training Instance Segmentation YOLOv8 on Vehicles Data

Lecture 1: Training YOLOv8 for Image and Video Instance Segmentation

Lecture 2: Python Code for Model Training

Chapter 9: Testing Segmentation YOLOv8 on Videos and Images

Lecture 1: Testing Segmentation YOLOv8 on Images

Lecture 2: Testing Segmentation YOLOv8 on Videos

Lecture 3: Python Code to Segment Instances in Videos and Images

Chapter 10: Deploy Trained Video Segmentation Model

Lecture 1: Deploy Trained Video Segmentation Model

Lecture 2: Python Code to Deploy Model

Chapter 11: Resources: Video Segmentation Complete Code and Dataset

Lecture 1: Resources: Video Segmentation Complete Code and Dataset

Chapter 12: Overview of CNN, RCNN, Fast RCNN, and Faster RCNN

Lecture 1: Overview of CNN, RCNN, Fast RCNN, and Faster RCNN

Chapter 13: Mask RCNN for Instance Segmentation

Lecture 1: Introduction to Mask RCNN for Instance Segmentation

Chapter 14: Get Started with PyTorch Facebook Library

Lecture 1: Get Started with PyTorch Facebook Library

Chapter 15: Custom Dataset for Instance Segmentation

Lecture 1: Custom Dataset for Instance Segmentation

Chapter 16: Train, Evaluate & Visualize Instance Segmentation on Custom Dataset

Lecture 1: Train, Evaluate & Visualize Instance Segmentation on Custom Dataset

Chapter 17: Resources: Mask RCNN Complete Code and Segmentation Dataset

Lecture 1: Resources: Mask RCNN Complete Code and Segmentation Dataset

Chapter 18: Bonus Lecture

Lecture 1: Image Semantic Segmentation with Python

Instructors

Dr. Mazhar Hussain
Deep Learning, Computer Vision, AI & Python | CS Lecturer

AI & Computer Science School
Learn AI, Deep Learning, & Computer Vision with Python

Rating Distribution

1 stars: 0 votes

2 stars: 2 votes

3 stars: 1 votes

4 stars: 3 votes

5 stars: 31 votes

Frequently Asked Questions

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