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YOLOv6-3.0 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: YOLOv6-3.0 and YOLOv5, highlighting their architectural differences, performance metrics, and suitable applications.

YOLOv5 Overview

YOLOv5 is a widely adopted one-stage object detection model known for its ease of use and excellent balance of speed and accuracy. It utilizes a PyTorch-based framework and offers various model sizes (n, s, m, l, x) to cater to different computational needs.

Architecture and Key Features:

  • Backbone: CSPDarknet53
  • Neck: PANet
  • Head: YOLOv3 Head
  • Focus Layer: For initial downsampling
  • Adaptive Anchors: Automatically learns optimal anchor boxes during training.
  • Mosaic Augmentation: Combines multiple images into one for richer context during training, enhancing detection of smaller objects.

Performance and Use Cases:

YOLOv5 excels in real-time object detection scenarios due to its speed. Its different model sizes allow for deployment on diverse hardware, from edge devices to cloud servers. It is suitable for applications requiring a balance of speed and moderate accuracy, such as:

  • Real-time video surveillance
  • Robotics and drone vision
  • Automotive applications
  • Industrial inspection

Strengths:

  • Speed: Offers fast inference speeds, crucial for real-time applications.
  • Scalability: Multiple model sizes provide flexibility for different hardware constraints.
  • Ease of Use: Well-documented and easy to implement with Ultralytics Python package.
  • Large Community Support: Benefit from a large and active community for support and resources.

Weaknesses:

  • Accuracy: While accurate, it may be slightly less precise than some later models like YOLOv6-3.0 in certain scenarios, particularly for complex datasets.

Learn more about YOLOv5

YOLOv6-3.0 Overview

YOLOv6 is developed by Meituan and focuses on industrial applications, aiming for a better trade-off between speed and accuracy. Version 3.0 brings significant improvements in performance and efficiency.

Architecture and Key Features:

  • Backbone: EfficientRep Backbone
  • Neck: Rep-PAN Neck
  • Head: Efficient Decoupled Head
  • Hardware-aware Neural Network Design: Optimized for inference speed on various hardware.
  • RepVGG Style Architecture: Utilizes structural re-parameterization for faster inference without sacrificing training accuracy.
  • Enhanced Training Techniques: Advanced techniques to improve convergence and accuracy.

Performance and Use Cases:

YOLOv6-3.0 is designed for scenarios where high accuracy and fast inference are both critical. It is particularly well-suited for industrial applications and edge deployments where efficiency matters. Ideal use cases include:

  • High-precision industrial quality control
  • Advanced robotics and automation
  • Smart retail and inventory management
  • Security systems requiring high accuracy

Strengths:

  • High Accuracy: Achieves superior accuracy compared to YOLOv5, especially in more complex scenarios.
  • Efficient Inference: Optimized for fast inference, making it suitable for real-time systems.
  • Industrial Focus: Designed with industrial application needs in mind.
  • State-of-the-art performance: Competes with or surpasses other YOLO models in speed-accuracy trade-offs.

Weaknesses:

  • Complexity: Might be slightly more complex to implement and fine-tune compared to YOLOv5 due to its more advanced architecture.
  • Community Size: Although growing, its community might be smaller than YOLOv5's, potentially leading to fewer readily available resources and community support.

Learn more about YOLOv6

Performance Comparison Table

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLOv6-3.0n 640 37.5 - 1.17 4.7 11.4
YOLOv6-3.0s 640 45.0 - 2.66 18.5 45.3
YOLOv6-3.0m 640 50.0 - 5.28 34.9 85.8
YOLOv6-3.0l 640 52.8 - 8.95 59.6 150.7
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

Conclusion

Choosing between YOLOv6-3.0 and YOLOv5 depends on the specific requirements of your object detection task. YOLOv5 remains a strong choice for applications prioritizing speed and ease of deployment, with a good balance of accuracy. YOLOv6-3.0 offers enhanced accuracy and efficient inference, making it more suitable for industrial and high-precision applications.

Users may also be interested in exploring other advanced YOLO models available in Ultralytics Docs, such as the cutting-edge YOLOv8, YOLOv9 and YOLOv10 for state-of-the-art performance, or RT-DETR and YOLO-NAS for specialized architectures.

📅 Created 1 year ago ✏️ Updated 1 month ago

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