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YOLO11 vs YOLOv10: A Technical Comparison for Object Detection

This page provides a detailed technical comparison between two state-of-the-art object detection models from Ultralytics: YOLO11 and YOLOv10. Both models are designed for high-performance computer vision tasks, but they incorporate distinct architectural choices and offer different performance profiles, making them suitable for varied use cases. This comparison will delve into their architecture, performance metrics, and ideal applications to help you choose the right model for your project.

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
YOLO11n 640 39.5 56.1 1.5 2.6 6.5
YOLO11s 640 47.0 90.0 2.5 9.4 21.5
YOLO11m 640 51.5 183.2 4.7 20.1 68.0
YOLO11l 640 53.4 238.6 6.2 25.3 86.9
YOLO11x 640 54.7 462.8 11.3 56.9 194.9
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

Architectural Differences

YOLO11, the latest iteration in the YOLO series, builds upon the foundation of previous models like YOLOv8, focusing on enhanced feature extraction and improved parameter efficiency. A key architectural highlight of YOLO11 is its refined network structure, designed to capture more intricate details with fewer parameters. This is achieved through advancements in network layers and optimization techniques, leading to a model that is both accurate and computationally efficient. YOLO11 supports all the standard YOLO computer vision tasks including object detection, instance segmentation, image classification, and pose estimation.

Learn more about YOLO11

YOLOv10, while also striving for high performance, emphasizes real-time efficiency and speed. It introduces innovations aimed at reducing inference latency, making it particularly suitable for applications requiring rapid object detection. One of the notable architectural changes in YOLOv10 is the elimination of Non-Maximum Suppression (NMS) in post-processing, streamlining the inference pipeline and contributing to faster processing times. This architectural choice directly addresses the need for speed in real-time applications. Like YOLO11, YOLOv10 is versatile and capable of handling various computer vision tasks.

Learn more about YOLOv10

Performance Metrics

Looking at the performance metrics, both YOLO11 and YOLOv10 demonstrate strong object detection capabilities, but with trade-offs.

Accuracy (mAP): YOLO11 models generally achieve slightly higher mAP (mean Average Precision) scores, indicating superior accuracy in object detection, particularly in the larger model sizes like YOLO11x. For instance, YOLO11m achieves a mAPval50-95 of 51.5, slightly better than YOLOv10m's 51.3. This edge in accuracy makes YOLO11 preferable when precision is paramount. To understand more about mAP and other metrics, refer to the YOLO Performance Metrics guide.

Inference Speed: YOLOv10 excels in inference speed, especially when measured with TensorRT on NVIDIA T4 GPUs. For example, YOLOv10n and YOLOv10s show slightly faster inference times (1.56ms and 2.66ms respectively) compared to YOLO11n and YOLO11s (1.5ms and 2.5ms). The removal of NMS in YOLOv10 directly contributes to this speed advantage, making it more suitable for real-time processing environments. For optimizing inference speed, consider exploring OpenVINO Latency vs Throughput Modes.

Model Size and Parameters: YOLO11 models are designed to be parameter-efficient. For example, YOLO11m has 20.1 million parameters, which is fewer than YOLOv10m's 15.4 million parameters. However, YOLOv10 generally has smaller model sizes across comparable variants. This difference in parameter count and model size can influence deployment decisions, especially on resource-constrained devices. Model size is an important factor in model deployment options.

Strengths and Weaknesses

YOLO11 Strengths:

  • High Accuracy: Offers superior accuracy, making it ideal for applications where precise object detection is critical.
  • Parameter Efficiency: Achieves high performance with a relatively smaller number of parameters, leading to efficient resource utilization.
  • Versatility: Supports a wide array of computer vision tasks, providing flexibility for diverse applications.

YOLO11 Weaknesses:

  • Slightly slower inference speed compared to YOLOv10, which might be a limitation in ultra-real-time applications.
  • Potentially larger model sizes compared to YOLOv10, which could be a concern for very memory-constrained devices.

YOLOv10 Strengths:

  • Real-time Speed: Exceptional inference speed, making it perfect for real-time object detection and processing.
  • NMS-Free Architecture: Simplifies the pipeline and enhances speed by eliminating Non-Maximum Suppression.
  • Efficiency: Optimized for speed and resource efficiency, suitable for edge devices and applications with strict latency requirements.

YOLOv10 Weaknesses:

  • Marginally lower accuracy compared to YOLO11 in some configurations.
  • The NMS-free approach might impact accuracy in scenarios with highly overlapping objects, although optimizations are in place to mitigate this.

Ideal Use Cases

YOLO11 Use Cases:

  • High-Precision Applications: Medical imaging analysis (AI in Healthcare), quality control in manufacturing (AI in Manufacturing), and detailed satellite image analysis (Using Computer Vision to Analyse Satellite Imagery), where accuracy is paramount.
  • Complex Object Detection: Scenarios requiring detection of small or occluded objects where higher mAP is beneficial.
  • Resource-Aware Deployments: Applications where parameter efficiency is valued, allowing for deployment on devices with moderate computational resources.

YOLOv10 Use Cases:

Other YOLO Models

Besides YOLO11 and YOLOv10, Ultralytics offers a range of YOLO models, each with unique strengths:

  • YOLOv8: A versatile model known for its balance of speed and accuracy, suitable for a wide range of applications (Ultralytics YOLOv8 Turns One).
  • YOLOv9: Introduces innovations like Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN) for enhanced efficiency and accuracy (YOLOv9).
  • YOLOv7: Known for its speed and accuracy, making it a strong contender for real-time object detection tasks (YOLOv7).
  • YOLOv6: Focuses on striking a balance between speed and accuracy, offering various model sizes to suit different needs (YOLOv6).
  • YOLOv5: A widely-used model celebrated for its ease of use and deployment flexibility (YOLOv5).

Choosing between YOLO11 and YOLOv10, or other YOLO models, depends on the specific requirements of your project. If accuracy is the top priority, and computational resources are sufficient, YOLO11 is an excellent choice. If real-time speed and efficiency are paramount, especially in edge deployments, YOLOv10 provides a compelling advantage. Carefully consider your application's needs and performance trade-offs to select the most appropriate model.

📅 Created 1 year ago ✏️ Updated 1 month ago

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