From 8ce8e5ecc4c7d4fb66b4820bce76086de170474a Mon Sep 17 00:00:00 2001 From: Muhammad Rizwan Munawar Date: Sat, 20 Jul 2024 21:57:13 +0500 Subject: [PATCH] Add https://youtu.be/ziJR01lKnio to docs (#14554) --- docs/en/models/yolov9.md | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/docs/en/models/yolov9.md b/docs/en/models/yolov9.md index 88fa66a9..99897431 100644 --- a/docs/en/models/yolov9.md +++ b/docs/en/models/yolov9.md @@ -63,6 +63,27 @@ GELAN represents a strategic architectural advancement, enabling YOLOv9 to achie ![YOLOv9 architecture comparison](https://github.com/ultralytics/ultralytics/assets/26833433/286a3971-677b-45e6-a90b-4b6bd565a7af) +## YOLOv9 Benchmarks + +Benchmarking in YOLOv9 using [Ultralytics](https://docs.ultralytics.com/modes/benchmark/) involves evaluating the performance of your trained and validated model in real-world scenarios. This process includes: + +- **Performance Evaluation:** Assessing the model's speed and accuracy. +- **Export Formats:** Testing the model across different export formats to ensure it meets the necessary standards and performs well in various environments. +- **Framework Support:** Providing a comprehensive framework within Ultralytics YOLOv8 to facilitate these assessments and ensure consistent and reliable results. + +By benchmarking, you can ensure that your model not only performs well in controlled testing environments but also maintains high performance in practical, real-world applications. + +

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+ Watch: How to Benchmark the YOLOv9 Model Using the Ultralytics Python Package +

+ ## Performance on MS COCO Dataset 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.