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YOLO11 vs. EfficientDet: A Technical Comparison for Object Detection

When choosing a computer vision model for object detection, developers often weigh factors like accuracy, speed, and model size. This page provides a detailed technical comparison between Ultralytics YOLO11 and EfficientDet, two popular choices, to help you make an informed decision. We will analyze their architectures, performance metrics, training methodologies, and ideal applications to highlight their respective strengths and weaknesses.

Ultralytics YOLO11

Ultralytics YOLO11 is the latest iteration in the YOLO (You Only Look Once) series, renowned for its real-time object detection capabilities. YOLO11 builds upon the advancements of previous versions, offering enhanced accuracy and efficiency.

Architecture and Key Features

YOLO11 maintains a single-stage detector architecture, optimizing for speed by performing object detection in one pass. Key architectural improvements in YOLO11 include enhanced feature extraction and a refined network structure that reduces parameter count while increasing accuracy. This model is designed for versatility, supporting tasks beyond object detection such as instance segmentation, image classification, and pose estimation. For a deeper dive into the architectural improvements, refer to the Ultralytics YOLO11 announcement blog post.

Performance Metrics

YOLO11 demonstrates a strong balance of speed and accuracy. As indicated in the comparison table below, YOLO11 models achieve high mAP scores while maintaining fast inference times. For instance, YOLO11m attains a 51.5 mAPval 50-95. The efficiency of YOLO11 is further highlighted by its reduced parameter count compared to previous YOLO versions while achieving superior performance. For detailed performance metrics and benchmarks, see the Ultralytics YOLO Docs Guides.

Use Cases and Strengths

YOLO11 is particularly well-suited for applications requiring real-time object detection and high accuracy. Its speed and efficiency make it ideal for deployment on both edge devices and cloud platforms. Industries such as robotics, security systems, and autonomous vehicles can greatly benefit from YOLO11's capabilities. Its strengths include:

  • High Speed Inference: Optimized for real-time applications.
  • Excellent Accuracy: Achieves state-of-the-art mAP scores.
  • Versatility: Supports multiple computer vision tasks.
  • Efficient Resource Utilization: Balances performance with computational cost.

Learn more about YOLO11

EfficientDet

EfficientDet, developed by Google, focuses on creating highly efficient object detection models by systematically scaling network width, depth, and resolution.

Architecture and Key Features

EfficientDet utilizes a bi-directional feature pyramid network (BiFPN) and compound scaling to optimize both accuracy and efficiency. The BiFPN enables efficient multi-scale feature fusion, while compound scaling ensures a balanced scaling of network dimensions to maximize performance gains without excessive computational cost. This design philosophy allows EfficientDet to offer a range of models from D0 to D7, catering to different resource constraints and performance needs.

Performance Metrics

EfficientDet models are known for their efficiency and scalability, offering a good trade-off between accuracy and computational resources. As shown in the table, EfficientDet-D4 achieves a mAPval 50-95 of 49.7, with varying speeds depending on the specific variant. EfficientDet's strength lies in its ability to provide competitive accuracy with fewer parameters and FLOPs compared to some larger models.

Use Cases and Strengths

EfficientDet is particularly effective in scenarios where computational resources are limited, such as mobile devices and edge computing environments. Its efficiency makes it suitable for applications like mobile object detection and embedded systems. EfficientDet's strengths include:

  • High Efficiency: Designed for resource-constrained environments.
  • Scalability: Offers a range of models to suit different needs.
  • Good Accuracy for Size: Achieves competitive mAP with fewer parameters.

Model Comparison Table

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
EfficientDet-d0 640 34.6 10.2 3.92 3.9 2.54
EfficientDet-d1 640 40.5 13.5 7.31 6.6 6.1
EfficientDet-d2 640 43.0 17.7 10.92 8.1 11.0
EfficientDet-d3 640 47.5 28.0 19.59 12.0 24.9
EfficientDet-d4 640 49.7 42.8 33.55 20.7 55.2
EfficientDet-d5 640 51.5 72.5 67.86 33.7 130.0
EfficientDet-d6 640 52.6 92.8 89.29 51.9 226.0
EfficientDet-d7 640 53.7 122.0 128.07 51.9 325.0

Conclusion

Both YOLO11 and EfficientDet offer compelling solutions for object detection, each with unique strengths. YOLO11 excels in scenarios demanding high speed and top-tier accuracy, making it suitable for real-time and performance-critical applications. EfficientDet, on the other hand, shines in resource-constrained environments, providing a range of efficient models that balance accuracy and computational cost effectively.

For users interested in exploring other models within the Ultralytics ecosystem, consider investigating YOLOv8, YOLOv9, YOLOv10, YOLO-NAS, RT-DETR, and FastSAM for a variety of object detection, segmentation, and real-time performance needs. The choice between YOLO11 and EfficientDet, or other models, should be guided by the specific requirements of your project, including the balance between accuracy, speed, and resource availability.

📅 Created 1 year ago ✏️ Updated 1 month ago

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