Fix NEW labeling for more consistency in docs (#16674)
Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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@ -22,7 +22,7 @@ Here are some of the key models supported:
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6. **[YOLOv8](yolov8.md)**: The latest version of the YOLO family, featuring enhanced capabilities such as [instance segmentation](https://www.ultralytics.com/glossary/instance-segmentation), pose/keypoints estimation, and classification.
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7. **[YOLOv9](yolov9.md)**: An experimental model trained on the Ultralytics [YOLOv5](yolov5.md) codebase implementing Programmable Gradient Information (PGI).
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8. **[YOLOv10](yolov10.md)**: By Tsinghua University, featuring NMS-free training and efficiency-accuracy driven architecture, delivering state-of-the-art performance and latency.
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9. **[YOLO11](yolo11.md) NEW 🚀**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks.
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9. **[YOLO11](yolo11.md) 🚀 NEW**: Ultralytics' latest YOLO models delivering state-of-the-art (SOTA) performance across multiple tasks.
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10. **[Segment Anything Model (SAM)](sam.md)**: Meta's original Segment Anything Model (SAM).
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11. **[Segment Anything Model 2 (SAM2)](sam-2.md)**: The next generation of Meta's Segment Anything Model (SAM) for videos and images.
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12. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
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