How to Extract the Outputs from Ultralytics YOLOv8 Model for Custom Projects | Episode 5

Unlock the full potential of Ultralytics YOLOv8 in your custom projects! 🚀 In this fifth episode of our series, Nicolai delves into the intricacies of extracting outputs from a trained YOLOv8 model. Learn how to efficiently obtain bounding boxes, classes, masks, and confidences to integrate into your applications. Watch as Nicolai demonstrates YOLOv8’s prowess in detecting a variety of custom objects in real-time using a webcam. Key topics covered: - Setting up the YOLOv8 model for inference - Extracting various output attributes: bounding boxes, masks, probabilities, and key points - Practical coding examples for real-time object detection and segmentation - Using PyTorch tensors for GPU and CPU processing to manipulate results - Techniques for visualizing and utilizing extracted data in projects This episode is perfect for developers looking to enhance their computer vision tasks with YOLOv8’s advanced capabilities. Don’t miss out on seeing how to use the