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YOLOv8: Video Object Detection with Python on Custom Dataset


YOLOv8: Video Object Detection with Python on Custom Dataset

 In this article, we walk through how to train a YOLOv8 object detection model using a custom dataset.

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What you'll learn

  • YOLOv8 for Real-Time Video Object Detection with Python
  • Train, Test YOLO8 on Custom Datasets and Deploy to Your Own Projects
  • Football, Player, and Referee Detection in Videos with Python
  • Vehicles (Ambulance, Bus, Car, Motorcycle, Truck) Detection in Videos
  • What is YOLO and How it Works for Object Detection?
  • Overview of YOLO Family (YOLO2, YOLO3, YOLO4, YOLO5, YOLO6, YOLO7, YOLO8)
  • Overview of CNN, RCNN, Fast RCNN, and Faster RCNN
  • Custom Football Player Dataset Configuration for Vidoes Object Detection
  • Custom Vehicles Dataset Configuration for Video Object Detection
  • YOLOv8 Ultralytics and its HyperParameters Settings
  • Training YOLOv8 for Player, Referee and Football Detection
  • Training YOLOv8 for Vehicles (Ambulance, Bus, Car, Motorcycle, Truck) Detection
  • Testing YOLOv8 Trained Models on Videos and Images
  • Deploy YOLOv8: Export Model to Required Format
  • What are the Performance Metrics for Object Detection
  • Calculate Performance Metrics (Precision, Recall, Mean Average Precision mAP)

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

Unlock the potential of YOLOv8, a cutting-edge technology that revolutionizes video Object Detection. YOLOv8, or "You Only Look Once," is a state-of-the-art Deep Convolutional Neural Network renowned for its speed and accuracy in identifying objects within videos. In our course, "YOLOv8: Video Object Detection with Python on Custom Dataset" you'll explore its applications across various real-world scenarios. In this course, You will have the overview of all YOLO variants Where you will perform the real time video object detection with latest YOLO version 8 which is extremely fast and accurate as compared to the previous YOLO versions. YOLOv8 processes an entire image in a single pass to predict object bounding box and its class, making object detection computationally efficient. YOLOv8 comes in five variants based on the number of parameters – nano(n), small(s), medium(m), large(l), and extra large(x). You can use all the variants for object detection according to your requirement.

YOLOv8 is an AI framework that supports multiple computer vision tasks. YOLO8 can be used to perform Object Detection, Image segmentation, classification, and pose estimation. Speed and Detection accuracy of YOLOv8 makes it so popular for real-time applications such as object detection in videos and surveillance as compared to other object detectors. Imagine deploying YOLOv8 to monitor crowded public spaces for security, effortlessly tracking objects in surveillance videos, or enhancing autonomous vehicles' perception capabilities. Witness its capabilities in sports analytics, precisely detecting players and actions in dynamic game scenarios like football matches. Dive into retail analytics, where YOLOv8 can optimize inventory management and customer experience by tracking products and people movements.

Object detection is a task that involves identifying the location and class of objects in an image or video stream. The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene. This course covers the complete pipeline with hands-on experience of Object Detection using YOLOv8 Deep Learning architecture with Python and PyTorch as follows:

Course Breakdown: Key Learning Outcomes

  • YOLOv8 for Real-Time Video Object Detection with Python
  • Train, Test YOLO8 on Custom Dataset and Deploy to Your Own Projects
  • Introduction to YOLO and its Deep Convolutional Neural Network based Architecture.
  • How YOLO Works for Object Detection?
  • Overview of CNN, RCNN, Fast RCNN, and Faster RCNN
  • Overview of YOLO Family (YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7 )
  • What is YOLOv8 and its Architecture?
  • Custom Football Player Dataset Configuration for Object Detection
  • Setting-up Google Colab for Writing Python code
  • YOLOv8 Ultralytics and its HyperParameters Settings
  • Training YOLOv8 for Player, Referee and Football Detection
  • Testing YOLOv8 Trained Models on Videos and Images
  • Deploy YOLOv8: Export Model to required Format
This course provides you with hands-on experience, enabling you to apply YOLOv8's capabilities to your specific use cases. By mastering video object detection with Python and YOLOv8, you'll be equipped to contribute to innovations in diverse fields, reshaping the future of computer vision applications. Join us and discover the limitless possibilities of YOLOv8 in the real world! I will provide you the complete python code and datasets for real time video Object Detection with Python, so that you can start within no time. Let's enroll now and get started. See you inside the class.

Who this course is for:

  • This course is designed for computer vision enthusiasts, machine learning and deep learning practitioners who want to delve into the realm of video object detection.
  • Whether you're a beginner looking to build a strong foundation in Object Detection or an experienced professional aiming to enhance your skills, this course provides valuable insights and hands-on experience with YOLOv8, a state-of-the-art object detection algorithm.

Get Started -- > YOLOv8: Video Object Detection with Python on Custom Dataset

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