DAMO-YOLO vs. YOLOv10: Architectural Evolution in Real-Time Object Detection
The landscape of real-time object detection has evolved rapidly, moving from manual architecture design to neural architecture search (NAS) and, more recently, to end-to-end paradigms that eliminate complex post-processing. This comparison explores two significant milestones in this journey: DAMO-YOLO, developed by Alibaba Group, and YOLOv10, created by researchers at Tsinghua University.
While DAMO-YOLO introduced cutting-edge reparameterization and NAS techniques in late 2022, YOLOv10 (released in 2024) pushed the boundary further by introducing an NMS-free training strategy. This analysis breaks down their architectural choices, performance metrics, and suitability for deployment, helping you choose the right model for your computer vision applications.
Performance Metrics Comparison
The following table contrasts the performance of DAMO-YOLO and YOLOv10 on the COCO dataset. It highlights the progression in efficiency, with YOLOv10 generally offering lower latency and reduced parameter counts for comparable or superior accuracy.
| 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 |
| 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-YOLO: Neural Architecture Search Meets Efficiency
DAMO-YOLO was proposed in November 2022 by researchers from Alibaba Group. It aimed to strike a balance between detection accuracy and inference speed by leveraging Neural Architecture Search (NAS) and advanced reparameterization techniques.
- Authors: Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang, and Xiuyu Sun
- Organization:Alibaba Group
- Date: 2022-11-23
- Arxiv:arXiv:2211.15444v2
- GitHub:tinyvision/DAMO-YOLO
Key Architectural Features
DAMO-YOLO introduced MAE-NAS, a method to automatically search for efficient backbones under specific latency constraints. Unlike models with manually designed blocks, DAMO-YOLO's structure is derived to maximize information flow while minimizing computational cost.
The model utilizes RepGFPN (Efficient Reparameterized Generalized Feature Pyramid Network), which improves feature fusion across different scales. This neck architecture controls the model size effectively while maintaining high accuracy. Additionally, it employs a ZeroHead design and AlignedOTA for label assignment, which were significant innovations for stabilizing training and improving convergence speed at the time of its release.
Legacy of Innovation
While DAMO-YOLO introduced powerful concepts like efficient FPNs, the reliance on Neural Architecture Search can make the training pipeline complex to reproduce or modify for custom datasets compared to the streamlined experience of Ultralytics models.
YOLOv10: The End-to-End NMS-Free Revolution
YOLOv10, released in May 2024 by Tsinghua University, represents a paradigm shift in the YOLO family. It addresses the bottleneck of Non-Maximum Suppression (NMS)—the post-processing step required to filter overlapping bounding boxes—by creating a natively end-to-end detector.
- Authors: Ao Wang, Hui Chen, Lihao Liu, et al.
- Organization:Tsinghua University
- Date: 2024-05-23
- Arxiv:arXiv:2405.14458
- Docs:Ultralytics YOLOv10 Documentation
Architectural Breakthroughs
YOLOv10 eliminates the need for NMS inference through Consistent Dual Assignments. During training, the model uses two heads: a one-to-many head (for rich supervision) and a one-to-one head (for end-to-end prediction). These heads are aligned using a consistent matching metric, allowing the model to learn to suppress duplicates internally.
Furthermore, YOLOv10 incorporates a Holistic Efficiency-Accuracy Design. This includes lightweight classification heads using depth-wise separable convolutions and Rank-Guided Block Design to reduce redundancy in specific stages of the model. For enhanced feature extraction, it utilizes Partial Self-Attention (PSA) modules, which boost global representation learning with minimal computational overhead compared to full transformers.
Detailed Comparison: Strengths and Weaknesses
1. Latency and Inference Speed
DAMO-YOLO was optimized for low latency using NAS, achieving impressive speeds on T4 GPUs. However, YOLOv10 generally outperforms it, particularly in the "latency-forward" metrics, because it removes the NMS step entirely. NMS can be a variable time cost depending on the number of objects detected; by removing it, YOLOv10 offers more deterministic and stable inference times, which is crucial for real-time applications.
2. Training Efficiency and Usability
One of the primary advantages of utilizing YOLOv10 within the Ultralytics ecosystem is the ease of use. Training a YOLOv10 model requires minimal setup and code, whereas reproducing DAMO-YOLO results often involves complex environment configurations and NAS search phases.
Ultralytics models benefit from efficient training routines that optimize GPU memory usage. This stands in contrast to many transformer-heavy or NAS-based architectures that may require significantly more CUDA memory to reach convergence.
3. Deployment and Versatility
YOLOv10, supported by Ultralytics, can be easily exported to numerous formats including ONNX, TensorRT, CoreML, and TFLite using the export mode. This flexibility ensures that developers can deploy models to edge devices, mobile phones, or cloud servers without friction.
from ultralytics import YOLO
# Load a pre-trained YOLOv10 model
model = YOLO("yolov10s.pt")
# Export to ONNX for cross-platform deployment
model.export(format="onnx")
While DAMO-YOLO focuses strictly on detection, the Ultralytics framework surrounding YOLOv10 (and its successors like YOLO11 and YOLO26) supports a broader range of tasks, including instance segmentation and pose estimation, making the ecosystem more versatile for complex projects.
Ideal Use Cases
When to use DAMO-YOLO
- Research: If you are studying the impact of Neural Architecture Search on object detection backbones.
- Legacy Systems: If you have an existing pipeline built specifically around the Alibaba TinyVision codebase.
When to use YOLOv10 (Ultralytics)
- Edge Deployment: The removal of NMS makes YOLOv10 ideal for low-power devices where post-processing CPU cycles are scarce.
- Real-Time Systems: For applications like autonomous driving or robotics where deterministic latency is required.
- Rapid Development: When you need to go from dataset to deployed model quickly using a streamlined API.
The Future is NMS-Free
The end-to-end approach pioneered in YOLOv10 has influenced the development of YOLO26. YOLO26 builds upon this by optimizing the loss functions (ProgLoss) and introducing the MuSGD optimizer, offering even faster CPU inference and higher accuracy.
Conclusion
Both DAMO-YOLO and YOLOv10 have contributed significantly to the field of computer vision. DAMO-YOLO demonstrated the power of automated architecture search, while YOLOv10 successfully tackled the long-standing challenge of NMS dependence.
For most developers and researchers today, YOLOv10 (and the newer YOLO26) is the superior choice. The integration with the Ultralytics ecosystem ensures you have access to a well-maintained suite of tools, from data annotation to easy model export. The balance of speed, accuracy, and ease of use provided by Ultralytics models makes them the standard for modern object detection workflows.
For those looking for the absolute latest in performance, we recommend exploring YOLO26, which refines the end-to-end capabilities of YOLOv10 with updated optimizers and improved small-object detection.
For further reading on other architectures, explore our documentation on YOLOv8, YOLOv9, and RT-DETR.