Deep Learning for Object Detection with Python and PyTorch

About this course

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:

Course BreakDown:

• Learn Object Detection with Python and Pytorch Coding

Learn Object Detection using Deep Learning ModelsIntroduction to Convolutional Neural Networks (CNN)Learn RCNN, Fast RCNN, Faster RCNN, Mask RCNN and YOLO8, YOLO11 Architectures

Perform Object Detection with Fast RCNN and Faster RCNNPerform Real-time Video Object Detection with YOLOv8 and YOLO11</

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