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Video Instance Segmentation With Python Using Deep Learning

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Video Instance Segmentation for Computer Vision with Python. Train, Test, Deploy Deep Learning Models YOLOv8, Mask RCNN

What you'll 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
Requirements
A Google Gmail account is required to get started with Google Colab to write Python Code
Python Programming experience is an advantage but not required
Description
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 segmentation is to train a Deep Learning model which 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 LearningBuild, Train, & Test Deep Learning Models on Custom Data & Deploy to Your Own ProjectsIntroduction to YOLOv8 and its Deep Learning ArchitectureIntroduction to Mask RCNN and its Deep Learning ArchitectureVideo Instance Segmentation using YOLOv8 with PythonInstance Segmentation using Mask RCNN with PythonConfiguration of Custom Vehicles Dataset with Annotations for Instance SegmentationHyperParameters Settings for Training Instance Segmentation ModelsTraining Instance Segmentation YOLOv8 and Mask RCNN Models on Custom DatasetsTesting Instance Segmentation Trained Models on Videos and ImagesPerform Car, Motorbike, and Truck Instance SegmentationDeploy Trained Instance Segmentation ModelsSo, 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!
Overview
Section 1: Introduction to Course
Lecture 1 Introduction
Section 2: What is Instance Segmentation
Lecture 2 What is Instance Segmentation
Section 3: Introduction to YOLO and its Architecture
Lecture 3 Introduction to YOLO and its Architecture
Section 4: YOLOv8 for Real-time Video Instance Segmentation
Lecture 4 Introduction to YOLOv8 for Real-time Video Instance Segmentation
Section 5: Vehicles Instance Segmentation Dataset
Lecture 5 Vehicles Dataset for Instance Segmentation
Lecture 6 Vehicles Instance Segmentation Dataset
Section 6: Google Colab for Writing Python Code
Lecture 7 Google Colab for Writing Python Code
Lecture 8 Connect Google Colab With Google Drive To Read And Write Data
Section 7: HyperParameters for Training Instance Segmentation Model
Lecture 9 HyperParameters for Training Instance Segmentation YOLO8 Model
Section 8: Training Instance Segmentation YOLOv8 on Vehicles Data
Lecture 10 Training YOLOv8 for Vehicles Instance Segmentation
Section 9: Testing Segmentation YOLOv8 on Videos and Images
Lecture 11 Testing Segmentation YOLOv8 on Images
Lecture 12 Testing Segmentation YOLOv8 on Videos
Section 10: Deploy Trained Instance Segmentation Model
Lecture 13 Deploy Trained Instance Segmentation Model
Section 11: Resources: YOLOv8 Complete Code and Segmentation Dataset
Lecture 14 Resources: YOLOv8 Complete Code and Segmentation Dataset
Section 12: Overview of CNN, RCNN, Fast RCNN, and Faster RCNN
Lecture 15 Overview of CNN, RCNN, Fast RCNN, and Faster RCNN
Section 13: Mask RCNN for Instance Segmentation
Lecture 16 Introduction to Mask RCNN for Instance Segmentation
Section 14: Get Started with PyTorch Facebook Library
Lecture 17 Get Started with PyTorch Facebook Library
Section 15: Custom Dataset for Instance Segmentation
Lecture 18 Custom Dataset for Instance Segmentation
Section 16: Train, Evaluate & Visualize Instance Segmentation on Custom Dataset
Lecture 19 Train, Evaluate & Visualize Instance Segmentation on Custom Dataset
Section 17: Resources: Mask RCNN Complete Code and Segmentation Dataset
Lecture 20 Resources: Mask RCNN Complete Code and Segmentation Dataset
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 you're 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 you're passionate about mastering deep learning techniques for video analysis and Instance Segmentation, this course is designed just for you.

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