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Fix YOLO11 YouTube link (#16542)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-09-30 03:20:57 +02:00 • committed by GitHub
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docs/en/models/yolov6.md
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@ -24,7 +24,7 @@ keywords: Meituan YOLOv6, object detection, real-time applications, BiC module,
YOLOv6 provides various pre-trained models with different scales:
- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA Tesla T4 GPU.
- YOLOv6-N: 37.5% AP on COCO val2017 at 1187 FPS with NVIDIA T4 GPU.
- YOLOv6-S: 45.0% AP at 484 FPS.
- YOLOv6-M: 50.0% AP at 226 FPS.
- YOLOv6-L: 52.8% AP at 116 FPS.
@ -151,7 +151,7 @@ YOLOv6 offers multiple versions, each optimized for different performance requir
- YOLOv6-L: 52.8% AP at 116 FPS
- YOLOv6-L6: State-of-the-art accuracy in real-time scenarios
These models are evaluated on the COCO dataset using an NVIDIA Tesla T4 GPU. For more on performance metrics, see the [Performance Metrics](#performance-metrics) section.
These models are evaluated on the COCO dataset using an NVIDIA T4 GPU. For more on performance metrics, see the [Performance Metrics](#performance-metrics) section.
### How does the Anchor-Aided Training (AAT) strategy benefit YOLOv6?