EfficientDet vs YOLO11: Evaluating the Evolution of Object Detection
Selecting the optimal architecture for computer vision applications often involves balancing the trade-off between computational efficiency and detection accuracy. This comprehensive comparison explores the technical differences between EfficientDet, Google's scalable detection architecture from 2019, and YOLO11, a 2024 release from Ultralytics that redefined real-time performance.
While EfficientDet introduced groundbreaking concepts in model scaling, YOLO11 represents a significant leap forward in usability, inference speed, and multi-task versatility. For developers starting new projects in 2026, we also recommend exploring the latest YOLO26, which builds upon the innovations discussed here with native end-to-end processing.
Performance Benchmark Analysis
The landscape of object detection has shifted dramatically from optimizing for theoretical FLOPs to optimizing for real-world latency. The table below highlights the stark contrast in inference speeds. While EfficientDet-d0 requires approximately 10ms for CPU inference, modern architectures like YOLO11n perform similar tasks significantly faster, often under 2ms on comparable hardware, while maintaining competitive mean Average Precision (mAP).
| 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 |
| YOLO11n | 640 | 39.5 | 1.5 | 2.6 | 2.6 | 6.5 |
| YOLO11s | 640 | 47.0 | 2.5 | 9.4 | 9.4 | 21.5 |
| YOLO11m | 640 | 51.5 | 4.7 | 20.1 | 20.1 | 68.0 |
| YOLO11l | 640 | 53.4 | 6.2 | 25.3 | 25.3 | 86.9 |
| YOLO11x | 640 | 54.7 | 11.3 | 56.9 | 56.9 | 194.9 |
EfficientDet: The Compound Scaling Pioneer
EfficientDet, developed by the Google Brain team, emerged as a systematic approach to model scaling. It was built on top of the EfficientNet backbone and introduced the Weighted Bi-directional Feature Pyramid Network (BiFPN), which allows for easy and fast multi-scale feature fusion.
The core innovation was compound scaling, a method that uniformly scales the resolution, depth, and width of the network backbone, feature network, and box/class prediction networks. This allowed the EfficientDet family (D0 through D7) to target a wide range of resource constraints, from mobile devices to high-power GPU servers.
Despite its academic success and high efficiency in terms of FLOPs, EfficientDet often struggles with latency on real-world hardware due to the memory access costs of its complex BiFPN connections and depth-wise separable convolutions, which are not always optimized by accelerators like TensorRT.
EfficientDet Metadata:
- Authors: Mingxing Tan, Ruoming Pang, and Quoc V. Le
- Organization:Google Research
- Date: 2019-11-20
- Arxiv:EfficientDet: Scalable and Efficient Object Detection
- GitHub:google/automl
Ultralytics YOLO11: Redefining Real-Time State-of-the-Art
Released in September 2024, YOLO11 is designed for practical, high-speed object detection and instant deployment. Unlike EfficientDet, which focuses heavily on parameter efficiency, YOLO11 optimizes for hardware utilization, ensuring that the model runs exceptionally fast on both edge CPUs and enterprise GPUs.
YOLO11 introduces architectural refinements such as the C3k2 block and an improved SPPF (Spatial Pyramid Pooling - Fast) module. These changes enhance the model's ability to extract features at various scales without the latency penalty seen in older feature pyramid designs. Furthermore, YOLO11 supports a unified framework for multiple vision tasks, including instance segmentation, pose estimation, and Oriented Bounding Box (OBB) detection, capabilities that require complex custom implementations with EfficientDet.
Ecosystem Advantage
Ultralytics models are fully integrated with the Ultralytics Platform, enabling seamless dataset management, auto-annotation, and one-click model training in the cloud.
YOLO11 Metadata:
- Authors: Glenn Jocher and Jing Qiu
- Organization:Ultralytics
- Date: 2024-09-27
- GitHub:ultralytics/ultralytics
- Docs:YOLO11 Documentation
Key Technical Differences
Architecture and Feature Fusion
EfficientDet relies on BiFPN, a complex weighted feature fusion layer that connects feature maps top-down and bottom-up repeatedly. While theoretically efficient, the irregular memory access patterns can slow down inference on GPUs.
In contrast, YOLO11 utilizes a streamlined PANet (Path Aggregation Network) inspired architecture with C3k2 blocks. This design favors dense, regular memory access patterns that align well with CUDA cores and modern NPU architectures, resulting in the massive speedups observed in the benchmark table (e.g., YOLO11x is vastly faster than EfficientDet-d7 while maintaining higher accuracy).
Training Efficiency and Ease of Use
Training an EfficientDet model typically involves using the TensorFlow Object Detection API or the AutoML library, which can have a steep learning curve and complex configuration files.
Ultralytics prioritizes the developer experience. Training YOLO11 is accessible via a simple Python API or Command Line Interface (CLI). The library handles hyperparameter tuning, data augmentation, and dataset formatting automatically.
from ultralytics import YOLO
# Load a COCO-pretrained YOLO11n model
model = YOLO("yolo11n.pt")
# Train on a custom dataset with a single command
results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
Versatility and Deployment
EfficientDet is primarily an object detection architecture. Adapting it for tasks like segmentation or pose estimation requires significant architectural modification.
YOLO11 is natively multi-modal. The same backbone and training pipeline support:
- Detection: Standard bounding boxes.
- Segmentation: Pixel-level masks for precise object boundaries.
- Classification: Whole-image categorization.
- Pose: Keypoint detection for skeletal tracking.
- OBB: Rotated boxes for aerial imagery and text detection.
This versatility makes YOLO11 a "Swiss Army Knife" for AI engineers, allowing a single repository to power diverse applications from healthcare imaging to autonomous robotics.
Why Choose Ultralytics Models?
When comparing these two architectures for modern production systems, Ultralytics models offer distinct advantages:
- Lower Memory Footprint: YOLO models are optimized to train on consumer-grade hardware. Unlike transformer-based models or older heavy architectures that demand massive CUDA memory, efficient YOLO architectures democratize access to high-end AI training.
- Streamlined Deployment: Exporting to ONNX, TensorRT, CoreML, or TFLite is a single-line command in the Ultralytics library.
- Active Support: The Ultralytics community is vibrant and active. With frequent updates, the framework ensures compatibility with the latest versions of PyTorch and CUDA.
Conclusion: The Modern Choice
While EfficientDet remains an important milestone in the history of computer vision research, demonstrating the power of compound scaling, YOLO11 and the newer YOLO26 are the superior choices for practical deployment today. They offer a better balance of speed and accuracy, a significantly easier user experience, and the flexibility to handle multiple computer vision tasks within a single framework.
For developers looking to stay on the absolute cutting edge, we recommend investigating YOLO26, which introduces an end-to-end NMS-free design for even lower latency and simpler deployment pipelines.
To explore other high-performance options, consider reading our comparisons on YOLOv10 or RT-DETR.