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DAMO-YOLO vs YOLO11: A Detailed Comparison

Explore a detailed technical comparison between DAMO-YOLO and Ultralytics YOLO11, two state-of-the-art computer vision models for object detection. This page provides an in-depth analysis of their architectures, performance metrics, training methodologies, and ideal applications, helping you make an informed decision for your computer vision projects.

DAMO-YOLO and Ultralytics YOLO11 represent significant advancements in real-time object detection, but they cater to different needs and priorities. DAMO-YOLO, known for its efficiency and ease of deployment, contrasts with YOLO11, which emphasizes enhanced accuracy and versatility across various vision AI tasks. This comparison delves into their technical specifications and performance benchmarks to guide users in selecting the optimal model for their specific requirements.

Architectural Overview

DAMO-YOLO, developed by Alibaba DAMO Academy, is designed for efficient inference, particularly on resource-constrained devices. While specific architectural details might vary across versions, DAMO-YOLO generally employs a streamlined network structure to minimize computational overhead. It often leverages techniques like knowledge distillation and model pruning to achieve a smaller model size and faster inference speed. Its architecture is optimized for industrial applications requiring rapid deployment and processing, such as in recycling efficiency or smart retail inventory management.

Ultralytics YOLO11, the latest iteration in the renowned YOLO series, builds upon the strengths of its predecessors like YOLOv8 and YOLOv9. YOLO11 is engineered for superior accuracy and includes features for tasks beyond object detection, such as instance segmentation, pose estimation, and image classification. Its architecture incorporates advancements for enhanced feature extraction and efficient processing across different hardware platforms, from edge devices like Raspberry Pi and NVIDIA Jetson to cloud servers.

Learn more about YOLO11

Performance Analysis

The performance of object detection models is typically evaluated using metrics like mean Average Precision (mAP), inference speed, and model size. The table below provides a comparative overview based on these metrics:

Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
DAMO-YOLOt 640 42.0 - 2.32 8.5 18.1
DAMO-YOLOs 640 46.0 - 3.45 16.3 37.8
DAMO-YOLOm 640 49.2 - 5.09 28.2 61.8
DAMO-YOLOl 640 50.8 - 7.18 42.1 97.3
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

mAP (Mean Average Precision): YOLO11 models generally achieve higher mAP values compared to DAMO-YOLO counterparts of similar size, indicating superior accuracy in object detection. For instance, YOLO11m outperforms DAMO-YOLOm in mAP. This enhanced accuracy is crucial in applications where precision is paramount, such as medical image analysis or quality inspection in manufacturing.

Inference Speed: DAMO-YOLO models are designed for faster inference, especially when considering TensorRT speeds. DAMO-YOLO models exhibit lower latency, making them suitable for real-time applications on less powerful hardware. YOLO11, while also optimized for speed, prioritizes balancing speed with higher accuracy, resulting in slightly slower inference times but improved precision. For applications like security alarm systems or traffic management, both models offer viable options, but the choice depends on the specific trade-off between speed and accuracy required.

Model Size and Parameters: DAMO-YOLO models typically have fewer parameters and smaller model sizes, contributing to their faster inference and easier deployment on edge devices. YOLO11 models, particularly the larger variants, have more parameters to achieve higher accuracy and handle complex tasks. The smaller model size of DAMO-YOLO is advantageous for deployment in environments with limited storage and computational resources, such as AI in consumer electronics or mobile applications.

Use Cases and Applications

DAMO-YOLO Use Cases:

  • Edge Deployment: Ideal for applications requiring real-time object detection on edge devices with limited computational power, such as mobile robots, drones, and embedded systems.
  • Industrial Automation: Well-suited for tasks in manufacturing and logistics where speed and efficiency are critical, including robotic process automation and automated quality control.
  • Resource-Constrained Environments: Effective in scenarios with limited bandwidth and computational resources, such as remote monitoring and IoT applications.

YOLO11 Use Cases:

  • High-Accuracy Demands: Best for applications where accuracy is paramount, such as medical diagnostics, autonomous driving, and advanced surveillance systems.
  • Versatile Vision Tasks: Suitable for projects requiring a model that can handle multiple computer vision tasks beyond object detection, including segmentation and pose estimation.
  • Cloud and Edge Deployment: Flexible enough to be deployed in both cloud environments for high-performance computing and on edge devices where enhanced accuracy is needed.

Strengths and Weaknesses

DAMO-YOLO Strengths:

  • High Inference Speed: Optimized for real-time performance, especially on CPUs and edge devices.
  • Small Model Size: Easier to deploy on resource-constrained systems.
  • Efficient Computation: Requires less computational power, reducing energy consumption.

DAMO-YOLO Weaknesses:

  • Lower Accuracy: Generally lower mAP compared to YOLO11, potentially less precise in complex scenarios.
  • Limited Task Versatility: Primarily focused on object detection, with less emphasis on other vision tasks.

YOLO11 Strengths:

  • Superior Accuracy: Achieves higher mAP, providing more precise object detection results.
  • Task Versatility: Supports multiple vision tasks including detection, segmentation, pose estimation, and classification.
  • Scalability: Performs well across a range of hardware, from edge devices to cloud GPUs.

YOLO11 Weaknesses:

  • Slower Inference Speed: Can be slower than DAMO-YOLO, particularly on CPUs, depending on model size.
  • Larger Model Size: Requires more storage and memory, potentially challenging for very constrained devices.

Similar Models

Users interested in DAMO-YOLO and YOLO11 might also find other Ultralytics models beneficial, depending on their specific needs:

  • YOLOv8: A highly versatile and efficient model, balancing speed and accuracy across various tasks. Explore YOLOv8 documentation.
  • YOLOv10: The cutting-edge, real-time object detector, eliminating NMS for boosted efficiency. Learn more about YOLOv10.
  • YOLO-NAS: By Deci AI, offers a state-of-the-art object detection with quantization support, detailed in YOLO-NAS documentation.
  • RT-DETR: Baidu's real-time detector, based on Vision Transformers, providing high accuracy and adaptable speed, as described in RT-DETR documentation.

By considering these factors and exploring the performance metrics, users can choose between DAMO-YOLO and YOLO11 to best suit their computer vision applications.


Model size
(pixels)
mAPval
50-95
Speed
CPU ONNX
(ms)
Speed
T4 TensorRT10
(ms)
params
(M)
FLOPs
(B)
DAMO-YOLOt 640 42.0 - 2.32 8.5 18.1
DAMO-YOLOs 640 46.0 - 3.45 16.3 37.8
DAMO-YOLOm 640 49.2 - 5.09 28.2 61.8
DAMO-YOLOl 640 50.8 - 7.18 42.1 97.3
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
📅 Created 1 year ago ✏️ Updated 1 month ago

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