Model Comparison: EfficientDet vs RTDETRv2
EfficientDet and RTDETRv2 are popular object detection models, each offering unique architectural and performance characteristics. This page provides a detailed technical comparison to help users understand their key differences and ideal applications.
EfficientDet
EfficientDet, developed by Google Research, is known for its efficiency and scalability in object detection. It employs a BiFPN (Bidirectional Feature Pyramid Network) for feature fusion and EfficientNet as a backbone network. This architecture achieves a good balance between accuracy and computational cost by efficiently scaling model dimensions. EfficientDet models are designed to be smaller and faster, making them suitable for resource-constrained environments.
Strengths:
- Efficiency: Optimized for computational efficiency, making it suitable for edge devices.
- Balanced Performance: Offers a good trade-off between accuracy and speed.
- Scalability: Compound scaling method effectively scales up model performance.
Weaknesses:
- Speed: May not be as fast as some real-time detectors for high-performance applications.
- Accuracy: While accurate, it may not reach the highest accuracy levels of more complex models.
RTDETRv2
RTDETRv2 (Real-Time DEtection TRansformer v2), developed by Baidu, is a more recent model focusing on real-time object detection using a Vision Transformer architecture. It features a hybrid efficient encoder and decoupled decoder, which contributes to its high inference speed and competitive accuracy. RTDETRv2 is designed for applications that demand both speed and high detection accuracy, leveraging transformer efficiency for real-time performance.
Strengths:
- Real-time Performance: Designed for high-speed inference, suitable for real-time applications.
- High Accuracy: Achieves competitive accuracy compared to other real-time detectors.
- Transformer Architecture: Benefits from the global context understanding of Vision Transformers.
Weaknesses:
- Model Size: Transformer-based models can be larger than CNN-based models like EfficientDet.
- Computational Cost: May require more computational resources compared to EfficientDet, especially for larger model variants.
It's important to note that Ultralytics also offers a range of cutting-edge YOLO models, including YOLOv8 and YOLOv11, which are renowned for their speed and accuracy in object detection tasks. Users interested in real-time performance and ease of use within the Ultralytics ecosystem may find these models highly suitable.
Model Comparison Table
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 |
RTDETRv2-s | 640 | 48.1 | - | 5.03 | 20 | 60 |
RTDETRv2-m | 640 | 51.9 | - | 7.51 | 36 | 100 |
RTDETRv2-l | 640 | 53.4 | - | 9.76 | 42 | 136 |
RTDETRv2-x | 640 | 54.3 | - | 15.03 | 76 | 259 |
This table summarizes the performance metrics of different EfficientDet and RTDETRv2 variants. RTDETRv2 models generally show faster inference speeds, especially on TensorRT, while achieving comparable or slightly better mAP scores, but often at the cost of increased model size and computational parameters (params and FLOPs).
Use Cases
- EfficientDet: Ideal for applications requiring efficient object detection on devices with limited computational resources such as mobile applications, drones, and embedded systems. It's also suitable for scenarios where a balance of accuracy and speed is needed without demanding top-tier performance.
- RTDETRv2: Best suited for real-time object detection tasks where low latency and high accuracy are critical, such as autonomous driving, high-speed video analysis, and advanced surveillance systems. Its transformer-based architecture makes it effective in complex scenarios needing global context understanding.
For users within the Ultralytics ecosystem, exploring YOLOv8 or the latest YOLO11 models might offer a balance of performance and ease of integration, with comprehensive documentation and support available. Consider also exploring other models like YOLOv7 and YOLOv9 for different performance characteristics.