Who this course is for:

• Targeting diverse learners and professionals: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers. Acquire PyTorch skills to construct and train deep learning models for Object Detection.
• Broadly applicable, the course suits those interested in harnessing Deep Learning to derive insights from visual data and delve into the theory and real-world applications of Object Detection through Python and PyTorch.

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)
• Perform Object Detection with Detectron2 Models
• Explore Custom Object Detection Dataset 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 Datasets with Pytorch and Python

Requirements: 

• This course covers the process of Object Detection using Deep Learning in Python and PyTorch, guiding you through a comprehensive pipeline from beginner to advanced levels.
• No previous familiarity with Semantic Segmentation is required. All concepts will be comprehensively addressed through practical hands-on instruction.
• To commence your journey with Google Colab and begin writing Python code, you’ll need a Google Gmail account.

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 a wide range of potential real-life applications 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 and fine-tune them for Object Detection using Detectron2 Library developed by Facebook AI Research (FAIR).

Want to the learn Semantic Segmentation Complete Pipeline and its Real-world Applications with Python and PyTorch using Google Colab? You can also take my other course Practical Deep Learning For Semantic Segmentation With Python And PyTorch.

Our Promise to You

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. 

10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today!