Học sâu trong Phát hiện đối tượng với Python và PyTorch

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 Models

• Introduction 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 RCNN

• Perform Real-time Video Object Detection with YOLOv8 and YOLO11

• Train, Test and Deploy YOLOv8 for Video Object Detection

• 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

• Perform Object Instance Segmentation at Pixel Level using Mask RCNN

• Perform Object Instance Segmentation on Custom Dataset with Pytorch and Python

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.

See you inside the class!