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EfficientDet vs YOLOv10: A Detailed Comparison

In the realm of real-time object detection, both EfficientDet and YOLOv10 represent significant advancements, offering distinct approaches to balancing accuracy and efficiency. This page provides a detailed technical comparison of these two popular models, analyzing their architectural nuances, performance benchmarks, and suitability for various applications. We will delve into their strengths and weaknesses to guide users in selecting the optimal model for their specific computer vision needs.

Architectural Overview

EfficientDet Architecture

EfficientDet, developed by Google, is renowned for its efficient scaling of model architecture using compound scaling. It employs a BiFPN (Bidirectional Feature Pyramid Network) for feature fusion and utilizes compound scaling to uniformly scale up all dimensions of the network—depth, width, and resolution. This methodical scaling approach allows EfficientDet to achieve state-of-the-art accuracy with significantly fewer parameters and FLOPs compared to many contemporary object detectors. The architecture is designed for a balance between accuracy and computational cost, making it suitable for resource-constrained environments.

YOLOv10 Architecture

YOLOv10, the latest iteration in the You Only Look Once (YOLO) series, emphasizes real-time performance and efficiency while maintaining high accuracy. Building upon previous YOLO models, YOLOv10 introduces architectural refinements and optimizations aimed at reducing computational overhead and enhancing inference speed. Key improvements often include streamlined network structures, optimized backbone designs, and efficient handling of Non-Maximum Suppression (NMS) or even NMS-free approaches for faster post-processing. YOLOv10 is engineered for speed, making it ideal for applications requiring rapid object detection.

Learn more about YOLOv10

Performance Comparison

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

EfficientDet Performance

EfficientDet models are characterized by their impressive accuracy relative to their computational cost. As shown in the table, EfficientDet offers a range of models (d0-d7) that scale in both size and performance. EfficientDet-d0 provides a good starting point with a smaller model size and faster inference speed, suitable for edge devices or applications with strict latency requirements. As we move to larger variants like EfficientDet-d7, the mAP increases significantly, reaching state-of-the-art accuracy, albeit with increased model size and slower inference speeds. EfficientDet excels in scenarios where high detection accuracy is paramount, and computational resources are moderately available.

YOLOv10 Performance

YOLOv10 models are engineered for speed and real-time inference. The performance metrics indicate a focus on maximizing frames per second (FPS) while maintaining competitive accuracy. YOLOv10n, the nano version, is particularly noteworthy for its extremely fast inference speed and small model size, making it ideal for deployment on edge devices and mobile platforms where computational resources are limited. Larger YOLOv10 variants like YOLOv10x offer higher mAP, approaching that of larger EfficientDet models, but with a speed advantage, especially on TensorRT. YOLOv10 is optimized for applications where speed is critical, such as real-time surveillance, autonomous driving, and robotics.

Use Cases and Applications

EfficientDet Use Cases

EfficientDet's balanced approach makes it versatile for a range of applications. Its efficiency and accuracy are well-suited for:

  • Mobile and Edge Devices: EfficientDet-d0 and smaller variants are excellent choices for mobile applications and edge computing devices due to their reduced computational demands.
  • Robotics: In robotics, where both accuracy and real-time processing are needed, EfficientDet offers a good compromise, especially in scenarios with limited onboard computing power.
  • High-Accuracy Demanding Tasks: For applications requiring detailed and accurate object detection, such as medical image analysis or high-resolution satellite imagery analysis using computer vision, larger EfficientDet models provide superior performance.

YOLOv10 Use Cases

YOLOv10's emphasis on speed positions it as an ideal choice for applications needing rapid, real-time object detection:

  • Real-time Surveillance Systems: YOLOv10's speed is crucial for processing video feeds in real-time for security and surveillance, enabling immediate threat detection and response.
  • Autonomous Vehicles: In self-driving cars, quick and accurate object detection is essential for navigation and safety. YOLOv10's low latency inference is highly beneficial in these scenarios. Learn more about AI in self-driving.
  • High-Speed Object Tracking: For applications like sports analytics or traffic monitoring, where tracking objects in fast-paced environments is necessary, YOLOv10's speed provides a distinct advantage. Explore object detection and tracking with Ultralytics YOLOv8.
  • Edge AI and AIoT Applications: Integrating Ultralytics YOLO models on Seeed Studios reCamera exemplifies leveraging YOLOv10 for innovative Vision AI applications at the edge, processing data directly on the device.

Strengths and Weaknesses

EfficientDet Strengths and Weaknesses

Strengths:

  • High Accuracy: Achieves state-of-the-art accuracy, particularly with larger models.
  • Efficient Scaling: Compound scaling method effectively balances accuracy and efficiency.
  • Versatility: Suitable for a broad range of applications, from edge devices to high-performance systems.

Weaknesses:

  • Slower Inference Speed: Generally slower than YOLO models, especially the larger variants.
  • Larger Model Size: Larger models can be computationally intensive and less suitable for very resource-constrained devices compared to smaller YOLO models.

YOLOv10 Strengths and Weaknesses

Strengths:

  • Real-time Performance: Exceptional inference speed, optimized for real-time applications.
  • Small Model Sizes: Smaller variants are highly compact, ideal for edge and mobile deployment.
  • Efficiency: Low computational cost, making it suitable for resource-limited environments.

Weaknesses:

  • Accuracy Trade-off: May sacrifice some accuracy compared to the most accurate models like larger EfficientDet variants in favor of speed.
  • Complexity in Training: Achieving optimal balance between speed and accuracy may require careful tuning and optimization.

Conclusion

EfficientDet and YOLOv10 cater to different priorities in object detection. EfficientDet excels when accuracy is paramount and computational resources are moderately available, offering a scalable architecture for various performance levels. YOLOv10, on the other hand, is the champion of speed and efficiency, designed for real-time applications and resource-constrained deployments. The choice between EfficientDet and YOLOv10 depends heavily on the specific application requirements, balancing the need for accuracy against the constraints of speed and computational resources.


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

Learn more about EfficientDet

For users interested in other models within the Ultralytics ecosystem, consider exploring:

  • YOLOv11: The latest Ultralytics YOLO model, offering state-of-the-art performance and features. Discover YOLO11
  • YOLOv9: Known for its advancements in real-time object detection and efficiency. Explore YOLOv9
  • YOLOv8: A versatile and widely-used model with a balance of speed and accuracy across various tasks. Learn about YOLOv8
  • RT-DETR: Real-Time DEtection Transformer, offering transformer-based detection with high accuracy and adaptable speed. Explore RT-DETR
  • YOLO-NAS: A model from Deci AI, focusing on Neural Architecture Search for optimized performance. Discover YOLO-NAS
  • FastSAM and MobileSAM: For segmentation tasks, especially on mobile devices and resource-constrained environments, FastSAM and MobileSAM offer efficient solutions. Learn about SAM models

These models provide a range of capabilities and performance characteristics, catering to diverse computer vision applications and user needs within the Ultralytics framework.

📅 Created 1 year ago ✏️ Updated 1 month ago

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