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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 these results to create powerful AI-driven applications. 🌟YOLO Vision 2024 (YV24), our annual hybrid Vision AI event is just days away! Happening on 27th September 2024 at Google for Startups Campus, Madrid.! Watch live on: 🔗 YouTube: • Ultralytics YOLO Vision 2024: Explore the ... 🔗 Bilibili: https://live.bilibili.com/1921503038 📚 For more in-depth insights, check out these resources: Ultralytics Blog: Extracting Outputs from Ultralytics YOLOv8: https://www.ultralytics.com/blog/extr... Docs: Working with Results in YOLOv8: https://docs.ultralytics.com/referenc... Explore more about our state-of-the-art vision AI solutions and join our community: Ultralytics HUB: https://www.ultralytics.com/hub Ultralytics YOLO: https://www.ultralytics.com/yolo Join Our Team: https://www.ultralytics.com/work 👍 If you found this video helpful, please like, subscribe, and stay tuned for more episodes. Visit our site for additional resources and updates. #YOLOv8 #Ultralytics #ComputerVision #AI #MachineLearning #DeepLearning #ObjectDetection