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YOLOv9 vs YOLOv5: A Detailed Comparison

Ultralytics YOLO models are renowned for their speed and accuracy in object detection tasks. This page provides a technical comparison between two popular models: YOLOv9 and YOLOv5, focusing on their architecture, performance, and ideal applications.

YOLOv9: High Accuracy Object Detection

YOLOv9 represents a significant advancement in object detection, focusing on enhancing accuracy while maintaining reasonable speed. It introduces architectural innovations aimed at improving feature extraction and information preservation throughout the network. While specific architectural details of YOLOv9 might be evolving, its goal is clear: to push the boundaries of detection accuracy for complex vision tasks.

Strengths:

  • Enhanced Accuracy: YOLOv9 is designed for superior accuracy, achieving higher mAP scores, particularly in challenging datasets.
  • Advanced Feature Extraction: Likely incorporates cutting-edge techniques for more effective feature learning, leading to better object representation and detection.
  • Suitable for Complex Scenes: Excels in scenarios demanding high precision, such as detailed medical image analysis or intricate industrial inspections.

Weaknesses:

  • Potentially Slower Inference Speed: The focus on accuracy might lead to a trade-off with inference speed compared to faster models like YOLOv5.
  • Larger Model Size: More complex architectures often result in larger models, requiring more computational resources.

Learn more about YOLOv9

YOLOv5: Speed and Efficiency for Real-time Applications

YOLOv5 is well-established for its exceptional balance of speed and accuracy, making it a favorite for real-time object detection applications. Its architecture is designed for efficiency, allowing for fast inference times on various hardware platforms, including edge devices. YOLOv5's versatility and ease of use have contributed to its widespread adoption across numerous industries. You can explore its architecture and capabilities further in the YOLOv5 documentation.

Strengths:

  • High Inference Speed: YOLOv5 is optimized for speed, enabling real-time object detection at high frames per second (FPS).
  • Efficient Architecture: Designed for computational efficiency, making it suitable for deployment on resource-constrained devices like Raspberry Pi and NVIDIA Jetson.
  • Smaller Model Sizes: Generally, YOLOv5 models are smaller, facilitating easier deployment and faster loading times.
  • Versatile Use Cases: Ideal for applications requiring rapid detection, such as real-time security systems, robotics, and autonomous vehicles.

Weaknesses:

  • Lower Accuracy Compared to YOLOv9: While highly accurate, YOLOv5 may not reach the absolute highest mAP levels achieved by models like YOLOv9 in certain complex scenarios.

Learn more about YOLOv5

Performance Metrics Comparison

The table below summarizes the performance metrics for different variants of YOLOv9 and YOLOv5 models. Note the trade-offs between model size, speed, and accuracy.

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLOv9t 640 38.3 - 2.3 2.0 7.7
YOLOv9s 640 46.8 - 3.54 7.1 26.4
YOLOv9m 640 51.4 - 6.43 20.0 76.3
YOLOv9c 640 53.0 - 7.16 25.3 102.1
YOLOv9e 640 55.6 - 16.77 57.3 189.0
YOLOv5n 640 28.0 73.6 1.12 2.6 7.7
YOLOv5s 640 37.4 120.7 1.92 9.1 24.0
YOLOv5m 640 45.4 233.9 4.03 25.1 64.2
YOLOv5l 640 49.0 408.4 6.61 53.2 135.0
YOLOv5x 640 50.7 763.2 11.89 97.2 246.4

Choosing the Right Model

  • Select YOLOv9 when accuracy is paramount and computational resources are less of a constraint. Ideal for applications like detailed inspection, advanced surveillance, and high-precision medical imaging.
  • Opt for YOLOv5 for real-time applications, edge deployment, and scenarios where speed and efficiency are critical. Suitable for robotics, mobile applications, and rapid object detection tasks.

Users may also be interested in other YOLO models like YOLOv8, YOLOv7, YOLOv6 or YOLO-NAS, each offering different trade-offs between speed and accuracy. Explore the Ultralytics Models documentation to find the best model for your specific computer vision needs.

For further details, refer to the Ultralytics YOLO Docs and the Ultralytics GitHub repository.

📅 Created 1 year ago ✏️ Updated 1 month ago

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