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Add Docs models JS charts (#18905)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2025-01-26 20:01:56 +01:00 • committed by GitHub
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docs/en/models/yolov9.md
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@ -86,6 +86,11 @@ By benchmarking, you can ensure that your model not only performs well in contro
## Performance on MS COCO Dataset
<script async src="https://cdn.jsdelivr.net/npm/chart.js@3.9.1/dist/chart.min.js"></script>
<script defer src="../../javascript/benchmark.js"></script>
<canvas id="modelComparisonChart" width="1024" height="400" active-models='["YOLOv9"]'></canvas>
The performance of YOLOv9 on the [COCO dataset](../datasets/detect/coco.md) exemplifies its significant advancements in real-time object detection, setting new benchmarks across various model sizes. Table 1 presents a comprehensive comparison of state-of-the-art real-time object detectors, illustrating YOLOv9's superior efficiency and [accuracy](https://www.ultralytics.com/glossary/accuracy).
**Table 1. Comparison of State-of-the-Art Real-Time Object Detectors**