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Ultralytics YOLO11: Object Detection and Instance Segmentation🤯
In the fast-growing field of computer vision YOLO (You Only Look Once) has been recognized as the most effective method for the detection of objects. Each time, YOLO pushes the boundaries of object detection in real-time, and the most recent version Ultralytics YOLO11 adds better capabilities released by Ultralytics on their annual VisionAI event YOLO VISION 2024.
Glimpse of YOLO VISION 2024
YOLO11 is not just better at detecting objects but also adds advanced features like image segmentation, pose estimation, oriented bounding boxes, and many other awesome features, which makes it a flexible tool for applications in computer vision.
The above picture is a single frame of my LinkedIn post, where I have explained a lot of stuff about YOLO11 models.
In this article, we will dive deep into,
- What’s new in YOLO11?
- How to perform Object Detection using YOLO11?
- How to perform Instance Segmentation with Object Tracking using YOLO11?
- Real World Applications of YOLO11?
Let’s get started 🚀
What’s new in YOLO11?
YOLO11 builds upon the strengths of previous versions, such as Ultralytics YOLOv8, YOLOv9, and YOLOv10. However, it also introduces some major enhancements:
- Precision and accuracy: YOLO11 achieves high detection accuracy while ensuring the speed of processing in real time.
- Improved training techniques: Through the use of more powerful data augmentation techniques and loss functions YOLO11 enhances performance on difficult data sets.
I will not cover minor updates, rather I will focus more on YOLO11 usage and code examples, that you can use to run…
