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Hello-Hello, YOLOv11 is out! In this video, we compare the accuracy of the latest YOLOv11 model with its predecessors—YOLOv10, YOLOv9, and YOLOv8. We test these YOLO object detection models in real-world conditions to see how YOLOv11 performs compared to earlier versions. Here’s what we cover: 1. Daylight conditions: Evaluate YOLOv11 and the other models’ accuracy in well-lit environments. 2. Low-light conditions: Analyze how YOLOv11 improves performance in dim lighting compared to YOLOv10, YOLOv9, and YOLOv8. 3. Blurry, fast-moving objects: Test how YOLOv11 handles motion blur and fast object detection. 4. Multiple objects in frame: Compare how YOLOv11 performs when detecting many objects at once. This video is focused on understanding how these models perform in terms of accuracy, with a special focus on the YOLOv11 model’s improvements. Watch to see how well YOLOv11 detects objects across these challenging scenarios and find out which model delivers the best results! Want to learn YOLOv11 quickly and apply it fast in real project? You will also have 4 other previous YOLO version in one single course. Click 👉 https://bit.ly/YOLO_MEGA_Course #yolov11 #yolov11comparison #yolov11accuracy #yolov10 #yolov9 #yolov8 #objectdetection Disclaimer: We choose YOLOv11L, YOLOv10L, YOLOv9C and YOLOv8L since they have equal quantitative accuracy, according to their repos.