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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-08-30 05:52:10 +05:00 • committed by GitHub
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docs/en/models/yolov9.md
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@ -19,7 +19,7 @@ YOLOv9 marks a significant advancement in real-time object detection, introducin
<strong>Watch:</strong> YOLOv9 Training on Custom Data using Ultralytics | Industrial Package Dataset
</p>
![YOLOv9 performance comparison](https://github.com/ultralytics/ultralytics/assets/26833433/9f41ef7b-6008-43eb-8ba1-0a9b89600100)
![YOLOv9 performance comparison](https://github.com/ultralytics/docs/releases/download/0/yolov9-performance-comparison.avif)
## Introduction to YOLOv9
@ -61,7 +61,7 @@ PGI is a novel concept introduced in YOLOv9 to combat the information bottleneck
GELAN represents a strategic architectural advancement, enabling YOLOv9 to achieve superior parameter utilization and computational efficiency. Its design allows for flexible integration of various computational blocks, making YOLOv9 adaptable to a wide range of applications without sacrificing speed or accuracy.
![YOLOv9 architecture comparison](https://github.com/ultralytics/ultralytics/assets/26833433/286a3971-677b-45e6-a90b-4b6bd565a7af)
![YOLOv9 architecture comparison](https://github.com/ultralytics/docs/releases/download/0/yolov9-architecture-comparison.avif)
## YOLOv9 Benchmarks