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
