Skip to content

Register now for

# YOLOv10 vs. DAMO-YOLO: A Technical Comparison

Selecting the optimal object detection model is a critical decision that balances the trade-offs between accuracy, speed, and computational cost. This page provides a detailed technical comparison between YOLOv10, the latest highly efficient model integrated into the Ultralytics ecosystem, and DAMO-YOLO, a powerful detector from Alibaba Group. We will analyze their architectures, performance metrics, and ideal use cases to help you make an informed choice for your computer vision projects.

## YOLOv10: Real-Time End-to-End Detection

YOLOv10, introduced by researchers at Tsinghua University in May 2024, marks a significant step forward in real-time object detection. Its primary innovation is achieving end-to-end detection by eliminating the need for Non-Maximum Suppression (NMS), which reduces post-processing overhead and lowers inference latency.

Technical Details:

- Authors: Ao Wang, Hui Chen, Lihao Liu, et al.
- Organization: Tsinghua University
- Date: 2024-05-23
- Arxiv: https://arxiv.org/abs/2405.14458
- GitHub: https://github.com/THU-MIG/yolov10
- Docs: https://docs.ultralytics.com/models/yolov10/

### Architecture and Key Features

YOLOv10 is built upon the robust Ultralytics framework, inheriting its ease of use and powerful ecosystem. Its architecture introduces several key advancements for superior efficiency and performance:

- NMS-Free Training: YOLOv10 employs consistent dual assignments for labels during training. This allows the model to produce clean predictions without requiring the NMS post-processing step, simplifying the deployment pipeline and making it truly end-to-end.
- Holistic Efficiency-Accuracy Design: The model architecture was comprehensively optimized to reduce computational redundancy. This includes a lightweight classification head and spatial-channel decoupled downsampling, which enhances both speed and capability.
- Seamless Ultralytics Integration: As part of the Ultralytics ecosystem, YOLOv10 benefits from a streamlined user experience. This includes a simple Python API, extensive documentation, efficient training processes, and readily available pre-trained weights. This integration makes it exceptionally easy for developers to get started and deploy models quickly.

### Strengths and Weaknesses

Strengths:

- State-of-the-Art Efficiency: YOLOv10 delivers an exceptional balance of speed and accuracy, often outperforming competitors with fewer parameters and lower latency, as detailed in the performance table below.
- Ease of Use: The model is incredibly user-friendly thanks to its integration with the Ultralytics ecosystem, which includes Ultralytics HUB for no-code training and deployment.
- End-to-End Deployment: The NMS-free design simplifies the entire workflow from training to inference, making it ideal for real-world applications.
- Lower Memory Requirements: Compared to more complex architectures, YOLOv10 is efficient in its memory usage during both training and inference, making it accessible for users with limited hardware.

Weaknesses:

- Task Specialization: While exceptional for object detection, YOLOv10 is currently focused on this single task, unlike the versatile Ultralytics YOLOv8 which supports segmentation, classification, and pose estimation out-of-the-box.

### Ideal Use Cases

YOLOv10 is the perfect choice for applications where real-time performance and efficiency are paramount:

- Edge AI: Its small footprint and low latency make it ideal for deployment on resource-constrained devices like the NVIDIA Jetson or Raspberry Pi.
- Autonomous Systems: Fast and reliable detection is crucial for applications like self-driving cars and robotics.
- Real-Time Video Analytics: Perfect for high-throughput systems such as traffic management and security surveillance.

Learn more about YOLOv10

## DAMO-YOLO

DAMO-YOLO is a fast and accurate object detection model developed by the Alibaba Group. Released in November 2022, it introduced several new techniques to push the performance boundaries of YOLO-style detectors.

Technical Details:

- Authors: Xianzhe Xu, Yiqi Jiang, Weihua Chen, et al.
- Organization: Alibaba Group
- Date: 2022-11-23
- Arxiv: https://arxiv.org/abs/2211.15444
- GitHub: https://github.com/tinyvision/DAMO-YOLO
- Docs: https://github.com/tinyvision/DAMO-YOLO/blob/master/README.md

### Architecture and Key Features

DAMO-YOLO is a result of exploring advanced techniques to improve the speed-accuracy trade-off. Its architecture is characterized by:

- Neural Architecture Search (NAS): The backbone of DAMO-YOLO was generated using NAS, allowing for a highly optimized feature extractor.
- Efficient RepGFPN Neck: It incorporates a novel feature pyramid network (FPN) design that is both efficient and powerful.
- ZeroHead and AlignedOTA: The model uses a simplified, zero-parameter head and an improved label assignment strategy (AlignedOTA) to enhance detection accuracy.
- Knowledge Distillation: DAMO-YOLO leverages distillation to further boost the performance of its smaller models.

### Strengths and Weaknesses

Strengths:

- High Performance: DAMO-YOLO achieves competitive accuracy and speed, making it a strong contender in the object detection space.
- Innovative Technologies: It incorporates cutting-edge research concepts like NAS and advanced label assignment strategies.

Weaknesses:

- Higher Complexity: The model's architecture and training pipeline are more complex compared to YOLOv10, potentially creating a steeper learning curve for users.
- Ecosystem Limitations: DAMO-YOLO is primarily available within the MMDetection toolbox. This can be a barrier for developers who are not familiar with that ecosystem and prefer a more integrated, user-friendly solution like the one offered by Ultralytics.
- Community and Support: While a significant contribution, it may not have the same level of active community support, frequent updates, and extensive resources as models within the Ultralytics ecosystem.

### Ideal Use Cases

DAMO-YOLO is well-suited for researchers and developers who:

- Prioritize Novel Architectures: For those interested in exploring the latest research trends like NAS-powered backbones.
- Work within MMDetection: Users already comfortable with the MMDetection framework can integrate DAMO-YOLO into their workflows.
- Require High Accuracy: In scenarios where squeezing out the last bit of accuracy is critical and the added complexity is manageable.

Learn more about DAMO-YOLO

## Performance Head-to-Head: YOLOv10 vs. DAMO-YOLO

The following table compares the performance of various YOLOv10 and DAMO-YOLO model sizes on the COCO dataset. YOLOv10 consistently demonstrates superior performance, offering higher accuracy with lower latency and fewer parameters.

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
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

As the data shows, YOLOv10 models consistently outperform their DAMO-YOLO counterparts. For instance, YOLOv10-S achieves a higher mAP (46.7 vs. 46.0) than DAMO-YOLO-S while being significantly faster (2.66 ms vs. 3.45 ms) and having less than half the parameters (7.2M vs. 16.3M). This trend holds across all model sizes, culminating in YOLOv10-X reaching the highest mAP of 54.4.

## Conclusion

Both YOLOv10 and DAMO-YOLO are impressive object detection models, but they cater to different needs. DAMO-YOLO is a strong research model that showcases innovative architectural ideas.

However, for the vast majority of developers, researchers, and businesses, YOLOv10 is the clear choice. Its superior performance, combined with the NMS-free design, makes it faster and more efficient for real-world deployment. More importantly, its seamless integration into the Ultralytics ecosystem provides an unparalleled user experience with extensive documentation, active community support, and a suite of tools like Ultralytics HUB that simplify the entire MLOps lifecycle.

For those looking for other state-of-the-art options, it's also worth exploring Ultralytics YOLOv8 for its versatility across multiple vision tasks or checking out our other model comparisons to find the perfect fit for your project.

📅 Created 1 year ago ✏️ Updated 1 month ago

## Comments