Skip to content

Register now for

# EfficientDet vs. YOLO11: A Detailed Technical Comparison

This page offers a detailed technical comparison between Google's EfficientDet and Ultralytics YOLO11, two prominent object detection models. We analyze their architectures, performance benchmarks, and suitability for different applications to assist you in selecting the optimal model for your computer vision needs. While both models aim for efficient and accurate object detection, they stem from different research lines (Google and Ultralytics) and employ distinct architectural philosophies.

## EfficientDet

EfficientDet is a family of object detection models developed by researchers at Google Brain. Introduced in 2019, it set a new standard for efficiency by combining a powerful backbone with a novel feature fusion mechanism and a unique scaling method.

Technical Details:

- Authors: Mingxing Tan, Ruoming Pang, and Quoc V. Le
- Organization: Google
- Date: 2019-11-20
- Arxiv: https://arxiv.org/abs/1911.09070
- GitHub: https://github.com/google/automl/tree/master/efficientdet
- Docs: https://github.com/google/automl/tree/master/efficientdet#readme

### Architecture and Key Features

EfficientDet's architecture is built on three core components:

1. EfficientNet Backbone: It uses the highly efficient EfficientNet as its backbone for feature extraction.
2. BiFPN (Bi-directional Feature Pyramid Network): A novel, weighted feature pyramid network that allows for simple and fast multi-scale feature fusion. It introduces learnable weights to understand the importance of different input features and applies both top-down and bottom-up connections.
3. Compound Scaling: A key innovation where the model depth, width, and resolution are scaled up together using a single compound coefficient. This allows the model family (from D0 to D7) to scale efficiently across a wide range of resource constraints.

### Strengths

- High Efficiency: EfficientDet models are renowned for their low parameter and FLOPs counts, achieving strong accuracy for their computational budget.
- Scalability: The compound scaling method provides a clear path to scale the model up or down, making it adaptable to various hardware profiles, from mobile devices to data centers.
- Strong Academic Benchmark: It was a state-of-the-art model upon release and remains a strong baseline for efficiency-focused research.

### Weaknesses

- Slower GPU Inference: Despite its FLOP efficiency, EfficientDet can be slower in terms of real-world inference latency on GPUs compared to models like YOLO11, which are specifically designed for parallel processing hardware.
- Limited Versatility: EfficientDet is primarily an object detector. It lacks the native support for other tasks like instance segmentation, pose estimation, or classification that is integrated into modern frameworks like Ultralytics.
- Less Maintained Ecosystem: The official repository is not as actively developed as the Ultralytics ecosystem. This can lead to challenges in usability, community support, and integration with the latest tools and deployment platforms.

Learn more about EfficientDet

## Ultralytics YOLO11

Ultralytics YOLO11 is the latest advancement in the YOLO (You Only Look Once) series, developed by Ultralytics. It builds upon the success of its predecessors like YOLOv8, focusing on pushing the boundaries of both accuracy and real-time performance while offering unparalleled ease of use and versatility.

Technical Details:

- Authors: Glenn Jocher, Jing Qiu
- Organization: Ultralytics
- Date: 2024-09-27
- GitHub: https://github.com/ultralytics/ultralytics
- Docs: https://docs.ultralytics.com/models/yolo11/

### Architecture and Key Features

YOLO11 employs a single-stage, anchor-free detector architecture optimized for speed and precision. Its design features refined feature extraction layers and a streamlined network structure, which reduces parameter count and computational load without sacrificing accuracy. This ensures exceptional performance across diverse hardware, from edge devices like the NVIDIA Jetson to powerful cloud servers.

A significant advantage of YOLO11 is its integration within the comprehensive Ultralytics ecosystem. This provides developers with:

- Ease of Use: A simple and intuitive Python API and CLI make training, validation, and inference straightforward.
- Versatility: YOLO11 is a multi-task model supporting object detection, instance segmentation, image classification, pose estimation, and oriented bounding boxes (OBB) within a single unified framework.
- Well-Maintained Ecosystem: The model benefits from active development, a large and supportive open-source community, frequent updates, and seamless integration with tools like Ultralytics HUB for end-to-end MLOps.
- Training and Memory Efficiency: YOLO11 is designed for efficient training, often requiring less CUDA memory and converging faster than alternatives. It comes with readily available pre-trained weights on datasets like COCO.

### Strengths

- State-of-the-Art Performance: Achieves an excellent balance of high mAP scores and fast inference speeds, especially on GPUs.
- Deployment Flexibility: Optimized for a wide range of hardware, with easy export to formats like ONNX and TensorRT for maximum performance.
- User-Friendly Framework: Backed by extensive documentation, tutorials, and a strong community, lowering the barrier to entry for both beginners and experts.
- Multi-Task Support: A single YOLO11 model can be trained for various vision tasks, reducing development complexity and time.

### Weaknesses

- CPU Performance Trade-offs: While highly optimized for GPUs, the larger YOLO11 models can be slower on CPU-only environments compared to the smallest EfficientDet variants.
- Small Object Detection: Like other one-stage detectors, it can sometimes be challenged by detecting extremely small or heavily occluded objects in dense scenes, though continuous improvements are made with each version.

Learn more about YOLO11

## Performance and Benchmarks

The performance comparison on the COCO val2017 dataset highlights the different design philosophies of EfficientDet and YOLO11. EfficientDet excels in theoretical efficiency (mAP per parameter/FLOP), especially with its smaller models. However, when it comes to practical deployment, particularly on GPUs, YOLO11 demonstrates a clear advantage in inference speed.

For example, YOLO11s achieves a comparable mAP (47.0) to EfficientDet-d3 (47.5) but with a staggering 2.9x faster inference speed on a T4 GPU. The largest model, YOLO11x, surpasses all EfficientDet models in accuracy (54.7 mAP) while remaining significantly faster on GPU than even mid-sized EfficientDet models. This makes YOLO11 the superior choice for applications where real-time inference is critical.

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 | 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

## Ideal Use Cases

### EfficientDet

EfficientDet is best suited for scenarios where computational resources are the primary bottleneck and GPU optimization is less critical.

- Academic Research: Excellent for studies focused on model efficiency and architecture design.
- CPU-Bound Applications: Smaller variants (D0-D2) can perform well in environments without dedicated GPUs.
- Cost-Sensitive Cloud Deployment: Where billing is directly tied to FLOPs or CPU usage.

### YOLO11

YOLO11 excels in a vast range of real-world applications that demand high accuracy, speed, and development efficiency.

- Autonomous Systems: Powering robotics and self-driving cars with low-latency perception.
- Security and Surveillance: Enabling real-time monitoring for security systems and public safety.
- Industrial Automation: Used for high-speed quality control and defect detection on production lines.
- Retail Analytics: Driving applications like inventory management and customer behavior analysis.

## Conclusion

EfficientDet is a landmark architecture that pushed the boundaries of model efficiency. Its scalable design remains a valuable contribution to the field, particularly for resource-constrained environments.

However, for developers and researchers seeking a state-of-the-art, versatile, and user-friendly solution, Ultralytics YOLO11 is the clear choice. It offers a superior combination of accuracy and real-world speed, especially on modern hardware. The key advantages of YOLO11 lie not just in its performance but in the robust ecosystem that surrounds it. The streamlined API, extensive documentation, multi-task capabilities, and active community support significantly accelerate the development and deployment lifecycle, making it the most practical and powerful option for a wide array of computer vision challenges today.

## Explore Other Models

For further exploration, consider these comparisons with other state-of-the-art models:

- YOLO11 vs. YOLOv10
- YOLO11 vs. YOLOv9
- YOLO11 vs. RT-DETR
- EfficientDet vs. YOLOv8
- EfficientDet vs. YOLOX

📅 Created 1 year ago ✏️ Updated 1 month ago

## Comments