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YOLOv5 vs YOLOv10: A Detailed Comparison

Choosing the right computer vision model is crucial for the success of your project. Ultralytics YOLO models are renowned for their speed and accuracy in object detection tasks. This page provides a detailed technical comparison between two popular models: YOLOv5 and YOLOv10, to help you make an informed decision. We will analyze their architectures, performance metrics, and ideal use cases.

YOLOv5: Mature and Versatile

Ultralytics YOLOv5 is a widely adopted object detection model known for its balanced performance and ease of use. It offers a range of model sizes, from YOLOv5n (nano) for resource-constrained environments to YOLOv5x (extra large) for maximum accuracy.

Architecture: YOLOv5 utilizes a single-stage detector architecture, emphasizing speed and efficiency. It employs a CSPDarknet backbone, known for its feature extraction capabilities, and a PANet neck for feature aggregation. The model benefits from techniques like mosaic data augmentation and auto-anchor learning during training, contributing to its robust performance.

Performance: YOLOv5 achieves a strong balance between speed and accuracy. Its various sizes cater to different computational budgets, making it suitable for diverse applications. YOLOv5's performance is well-documented, and it benefits from a large and active community.

Use Cases: YOLOv5 is ideal for a wide range of object detection tasks, from real-time applications on edge devices to high-accuracy tasks in cloud environments. Its versatility makes it a popular choice for industries like:

Learn more about YOLOv5

YOLOv10: Cutting-Edge Efficiency

Ultralytics YOLOv10 represents the latest evolution in real-time object detection. It builds upon the foundation of previous YOLO models, introducing architectural innovations to enhance both speed and accuracy, particularly focusing on efficiency without Non-Maximum Suppression (NMS).

Architecture: YOLOv10 introduces advancements in network architecture and training techniques to achieve state-of-the-art performance. Key innovations include a focus on post-NMS-free design for increased inference speed and optimized network structures for better parameter efficiency. This model is designed to be highly efficient while maintaining or improving accuracy compared to its predecessors.

Performance: YOLOv10 pushes the boundaries of real-time object detection with improved speed and competitive accuracy. It is engineered for tasks where low latency and high throughput are critical. While still new, YOLOv10 shows promising performance metrics, particularly in scenarios demanding fast inference.

Use Cases: YOLOv10 excels in applications that require the fastest possible object detection with good accuracy, especially in resource-constrained environments. It is particularly well-suited for:

  • Edge AI: Deployment on edge devices like Raspberry Pi and NVIDIA Jetson for real-time processing.
  • High-Speed Applications: Systems requiring minimal latency, such as autonomous driving and advanced robotics.
  • Mobile and Embedded Systems: Applications where model size and inference speed are paramount, like mobile applications and drones.
  • Queue Management: Optimizing flow and efficiency in systems requiring real-time analysis of object presence, as in queue management systems.

Learn more about YOLOv10

Performance Metrics Table

This table provides a comparative overview of the performance metrics for YOLOv5 and YOLOv10 model variants.

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
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
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

Conclusion

Both YOLOv5 and YOLOv10 are powerful object detection models from Ultralytics, each with unique strengths. YOLOv5 offers a mature and versatile solution with a wide range of applications and strong community support. YOLOv10, on the other hand, represents the cutting edge in real-time detection, prioritizing speed and efficiency, making it ideal for the most demanding applications.

Consider YOLOv5 if:

  • You need a well-established model with extensive documentation and community support.
  • Your application requires a balance of speed and accuracy across various model sizes.
  • You are working with a wide range of hardware, from edge devices to cloud servers.

Consider YOLOv10 if:

  • Your primary requirement is maximum inference speed and minimal latency.
  • You are deploying on resource-constrained edge devices or mobile platforms.
  • You want to leverage the latest advancements in YOLO architecture for efficiency.

For users interested in exploring other models, Ultralytics also offers YOLOv8 and YOLOv9, which provide different balances of performance and features. Explore the Ultralytics documentation to discover the full range of models and choose the best one for your specific needs.

📅 Created 1 year ago ✏️ Updated 1 month ago

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