Object Detection with Python using Deep Learning Models
Object Detection for Computer Vision using Deep Learning with PyTorch & Python. Train & Deploy YOUR Own Models (Detectron2, RCNN Family)
Deep Learning,Computer Vision,Python,CNN models,Face Detection,
Lectures -19
Resources -6
Duration -1.5 hours
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Course 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 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
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.
Goals
What will you learn in this course:
Learn Object Detection with Python and Pytorch Coding
Learn Object Detection using Deep Learning Models
Introduction to Convolutional Neural Networks (CNN)
Introduction to Region-based Convolutional Neural Networks (RCNN)
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 and Test Your Own Object Detection Models and Visualize Results
Evaluate & Deploy Your Own Object Detection Models and Visualize Results
Prerequisites
What are the prerequisites for this course?
- 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
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction to Course
1 Lectures
- Introduction to Course 02:32 02:32
Object Detection and How it Works
1 Lectures
Convolutional Neural Network (CNN)
1 Lectures
Deep Learning Architectures for Object Detection (R-CNN Family)
1 Lectures
Fast RCNN Deep Learning Architecture
1 Lectures
Faster RCNN Deep Learning Architecture
1 Lectures
Mask RCNN Deep Learning Architectures
1 Lectures
Google Colab for Writing Python Code
2 Lectures
Detectron2 for Ojbect Detection
3 Lectures
Custom Dataset for Object Detection
2 Lectures
Training, Evaluating and Visualizing Object Detection on Custom Dataset
2 Lectures
Resources: Complete Code and Dataset for Object Detection
3 Lectures
Instructor Details
Mazhar Hussain
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