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YOLOv10 vs YOLOv5: A Detailed Comparison

This page provides a technical comparison between Ultralytics YOLOv10 and Ultralytics YOLOv5, two state-of-the-art object detection models. We analyze their architectural differences, performance metrics, and suitability for various computer vision applications.

YOLOv10: The Cutting Edge

YOLOv10 represents the latest evolution in the YOLO series, focusing on enhanced efficiency and accuracy. It introduces several architectural innovations designed to optimize performance without significantly increasing computational cost. Key improvements in YOLOv10 include:

  • Backbone and Neck Enhancements: YOLOv10 incorporates advanced backbone architectures and neck designs for more efficient feature extraction and aggregation. This leads to improved representation learning, particularly for complex scenes and small objects.
  • Loss Function Refinements: YOLOv10 employs refined loss functions that contribute to better localization and classification accuracy. These advancements enable the model to learn more robust and discriminative features.
  • Optimized for Speed: A primary design goal of YOLOv10 is real-time performance. Through architectural optimizations and efficient computation, it achieves faster inference speeds compared to its predecessors while maintaining high accuracy.

YOLOv10 is ideally suited for applications demanding high accuracy and real-time processing, such as:

  • Autonomous Systems: Self-driving cars and drones benefit from YOLOv10's speed and precision for object detection in dynamic environments. Explore how Vision AI is crucial for AI in Self-Driving.
  • Advanced Robotics: In robotics, YOLOv10 enhances perception capabilities for tasks requiring precise object recognition and interaction.
  • High-Resolution Surveillance: For security and surveillance, YOLOv10's accuracy is critical for reliable object detection in high-definition video streams. Learn more about computer vision for enhancing security.

While YOLOv10 offers state-of-the-art performance, it is a newer model, and the ecosystem and community support are still growing compared to the more established YOLOv5.

Learn more about YOLOv10

YOLOv5: The Versatile Workhorse

YOLOv5 is a widely adopted and mature object detection model known for its balance of speed, accuracy, and ease of use. It has become a popular choice in both research and industry due to its:

  • Efficient Architecture: YOLOv5 utilizes an efficient single-stage detector architecture, making it fast and suitable for real-time applications.
  • Scalability and Flexibility: YOLOv5 offers a range of model sizes (n, s, m, l, x), allowing users to select the best trade-off between speed and accuracy for their specific needs.
  • Strong Community and Ecosystem: YOLOv5 boasts a large and active community, providing extensive resources, tutorials, and support. The Ultralytics YOLO Docs offer comprehensive guides and examples.

YOLOv5 is highly versatile and applicable across a broad spectrum of use cases, including:

  • Retail Analytics: YOLOv5 is used in retail for AI for smarter retail inventory management, customer behavior analysis, and optimizing store layouts.
  • Environmental Monitoring: Applications in protecting biodiversity and wildlife conservation benefit from YOLOv5's robustness and efficiency in processing visual data.
  • Industrial Quality Control: In manufacturing, YOLOv5 aids in AI in manufacturing for automated visual inspection and defect detection.

YOLOv5's strength lies in its maturity, extensive community support, and proven track record across diverse applications. However, for tasks demanding the absolute highest accuracy, newer models like YOLOv10 may offer advantages.

Performance Metrics Comparison

The table below summarizes the performance metrics of different variants of YOLOv10 and YOLOv5 models. These metrics provide a quantitative comparison of their capabilities.

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLOv10n 640 39.5 - 1.56 2.3 6.7
YOLOv10s 640 46.7 - 2.66 7.2 21.6
YOLOv10m 640 51.3 - 5.48 15.4 59.1
YOLOv10b 640 52.7 - 6.54 24.4 92.0
YOLOv10l 640 53.3 - 8.33 29.5 120.3
YOLOv10x 640 54.4 - 12.2 56.9 160.4
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

Key Metrics:

  • mAP (Mean Average Precision): Indicates the accuracy of object detection. Higher mAP values signify better accuracy. Refer to the YOLO Performance Metrics guide for a detailed explanation.
  • Speed (Inference Time): Measured in milliseconds (ms), this indicates the time taken for a model to process one image. Lower values mean faster inference, crucial for real-time applications.
  • Params (Parameters): The number of parameters in a model. Smaller models are generally faster and require less memory.
  • FLOPs (Floating Point Operations): A measure of computational complexity. Lower FLOPs indicate more efficient computation.

Conclusion

Choosing between YOLOv10 and YOLOv5 depends on the specific requirements of your project.

  • Select YOLOv10 if your priority is the highest possible accuracy and cutting-edge performance, and you are comfortable with a newer model with a growing ecosystem.
  • Choose YOLOv5 for its proven versatility, ease of use, strong community support, and excellent balance of speed and accuracy across a wide range of applications.

Both models are powerful tools for object detection. Ultralytics HUB supports training and deployment for both YOLOv10 and YOLOv5, simplifying the development process. You might also explore other models like YOLOv8, YOLOv9, YOLO-NAS, and RT-DETR to find the best fit for your computer vision needs.

📅 Created 1 year ago ✏️ Updated 1 month ago

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