YOLOv10 vs EfficientDet: A Detailed Comparison for Object Detection
Choosing the right object detection model is crucial for computer vision projects. This page provides a technical comparison between YOLOv10 and EfficientDet, two popular models known for their efficiency and accuracy. We will delve into their architectures, performance metrics, and ideal applications to help you make an informed decision.
YOLOv10: The Latest Real-Time Detector
YOLOv10 represents the cutting edge in real-time object detection within the YOLO family. Building upon previous YOLO versions, YOLOv10 focuses on enhancing efficiency and speed without sacrificing accuracy. A key architectural innovation in YOLOv10 is its anchor-free design, simplifying the model and reducing computational overhead. This architecture leads to faster inference times, making it suitable for real-time applications and edge devices.
In terms of performance, YOLOv10 achieves impressive results, particularly in speed. As shown in the comparison table below, YOLOv10 models offer a range of sizes and performance levels. For example, YOLOv10n achieves a mAP of 39.5 while maintaining a very small model size and fast inference speed, making it ideal for resource-constrained environments. While specific CPU ONNX speeds are not listed in the provided table, TensorRT speeds on T4 GPUs are remarkably low, indicating its real-time capability.
YOLOv10's strengths lie in its speed and efficiency, making it excellent for applications requiring rapid object detection such as robotics, autonomous vehicles, and real-time security systems. However, for applications demanding the highest possible accuracy and less constrained by speed, other models might be considered.
EfficientDet: Accuracy Through Efficient Scaling
EfficientDet, developed by Google, is designed to achieve state-of-the-art accuracy with remarkable efficiency. Its architecture introduces several key innovations, notably BiFPN (Bidirectional Feature Pyramid Network) and compound scaling. BiFPN enables efficient and effective feature fusion across different network levels, enhancing the model's ability to detect objects at various scales. Compound scaling systematically scales up all dimensions of the network – depth, width, and resolution – to optimize for both accuracy and efficiency.
EfficientDet models, ranging from d0 to d7, offer a spectrum of performance trade-offs. EfficientDet-d7, the largest variant, achieves a mAP of 53.7, comparable to the larger YOLOv10 models, but generally at slower inference speeds and larger model sizes. The smaller EfficientDet variants like d0 and d1 offer faster inference and smaller sizes but with lower mAP. As seen in the table, EfficientDet models generally exhibit slower inference speeds compared to YOLOv10, particularly on TensorRT.
The strength of EfficientDet is its emphasis on accuracy and efficient feature fusion. It is well-suited for applications where high detection accuracy is paramount, such as medical image analysis, high-resolution satellite imagery analysis, and detailed quality control in manufacturing. However, for real-time applications with strict latency requirements, YOLOv10 may present a more advantageous option.
Model Comparison Table
| Model | size (pixels) |
mAPval 50-95 |
Speed CPU ONNX (ms) |
Speed T4 TensorRT10 (ms) |
params (M) |
FLOPs (B) |
|---|---|---|---|---|---|---|
| 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-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 YOLOv10 and EfficientDet are powerful object detection models, each with unique strengths. YOLOv10 excels in real-time performance and efficiency, making it suitable for applications where speed is critical. EfficientDet prioritizes accuracy through its sophisticated feature fusion and scaling techniques, making it ideal for tasks requiring precise detection.
Choosing between YOLOv10 and EfficientDet depends on your specific project requirements. If speed and resource efficiency are paramount, YOLOv10 is likely the better choice. If accuracy is the top priority and you have more computational resources, EfficientDet or larger YOLOv10 models could be more appropriate.
Users interested in exploring other models within the Ultralytics ecosystem might consider YOLOv8, YOLOv9, YOLO11, YOLO-NAS, and RT-DETR, each offering different trade-offs between speed and accuracy. You can also explore comprehensive YOLO tutorials to further understand and optimize model performance for your specific needs.
