YOLOv10 vs. DAMO-YOLO: Innovations in Real-Time Object Detection
The landscape of object detection is characterized by a constant pursuit of the optimal balance between inference speed and detection accuracy. Two significant contributions to this field are YOLOv10, developed by researchers at Tsinghua University, and DAMO-YOLO, created by the Alibaba Group. Both models introduce novel architectural strategies to reduce latency while maintaining high precision on benchmarks like COCO.
This detailed comparison explores their architectural differences, training methodologies, and performance metrics to help developers choose the right model for their computer vision applications.
Model Overview and Origins
Understanding the design philosophy behind these models requires looking at their origins and primary goals.
YOLOv10
Released in May 2024, YOLOv10 marks a significant shift in the YOLO lineage by introducing a NMS-free training capability. By utilizing consistent dual assignments, it removes the need for Non-Maximum Suppression (NMS) during inference, a post-processing step that often bottlenecks deployment speed on edge devices.
- Authors: Ao Wang, Hui Chen, Lihao Liu, et al.
- Organization: Tsinghua University
- Date: 2024-05-23
- Links:Arxiv Paper | GitHub Repository
DAMO-YOLO
DAMO-YOLO, released in late 2022, focuses on uncovering efficient architectures through Neural Architecture Search (NAS). It introduces technologies like MAE-NAS backbones and a heavy reliance on distillation to boost the performance of smaller models without increasing inference cost.
- Authors: Xianzhe Xu, Yiqi Jiang, Weihua Chen, et al.
- Organization: Alibaba Group
- Date: 2022-11-23
- Links:Arxiv Paper | GitHub Repository
Performance Metrics and Efficiency
When deploying models for tasks such as autonomous vehicles or robotics, raw metrics are critical. YOLOv10 generally offers superior parameter efficiency and mAP accuracy, particularly in the smaller model variants (Nano and Small), which are crucial for edge AI.
The table below highlights the performance differences on the COCO validation set.
| 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 |
Analysis
YOLOv10 demonstrates significant efficiency gains. For example, YOLOv10s achieves a higher mAP (46.7) than DAMO-YOLOs (46.0) while using fewer than half the parameters (7.2M vs 16.3M) and significantly fewer FLOPs. This efficiency is attributed to YOLOv10's holistic model design and rank-guided block improvements.
Architectural Innovations
YOLOv10: End-to-End and Holistic Design
The defining feature of YOLOv10 is its Consistent Dual Assignment strategy. During training, the model uses a "one-to-many" head for rich supervision and a "one-to-one" head to align predictions with ground truth. During inference, only the one-to-one head is used, eliminating the need for NMS.
Key architectural features include:
- Large-Kernel Convolutions: Improves the receptive field for better small object detection.
- Partial Self-Attention (PSA): Enhances global representation learning with minimal computational cost.
- Spatial-Channel Decoupled Downsampling: Reduces information loss during feature map reduction.
DAMO-YOLO: Neural Architecture Search
DAMO-YOLO relies heavily on automated search to find efficient structures.
- MAE-NAS Backbone: Uses Method of Auxiliary Edges (MAE) to search for optimal backbone structures under latency constraints.
- RepGFPN: An efficient neck architecture that upgrades the standard Feature Pyramid Network with reparameterization techniques for better feature fusion.
- ZeroHead: A lightweight detection head design intended to maximize speed.
Training Methodologies and Ease of Use
The ecosystem surrounding a model significantly impacts its usability for developers. Ultralytics models, including YOLOv10, benefit from a unified, well-maintained framework.
Ultralytics Ecosystem Advantage
YOLOv10 is integrated directly into the ultralytics Python package. This provides seamless access to training, validation, and export modes across various formats (ONNX, TensorRT, CoreML).
Streamlined Deployment
Because YOLOv10 removes NMS, exporting the model to formats like ONNX or TensorRT is significantly simpler. There is no need to append complex NMS plugins to the model graph, making it natively compatible with more inference engines.
Conversely, DAMO-YOLO typically requires utilizing its specific codebase, which may have different dependencies and API structures compared to the standardized Ultralytics workflow. This can increase the "time-to-first-inference" for new projects.
Code Example: Running YOLOv10
The following code snippet demonstrates how easily YOLOv10 can be used for object detection within the Ultralytics environment:
from ultralytics import YOLO
# Load a pre-trained YOLOv10n model
model = YOLO("yolov10n.pt")
# Run inference on an image
results = model.predict("path/to/image.jpg")
# Display results
results[0].show()
Advancing the State-of-the-Art: YOLO26
While YOLOv10 introduced the NMS-free paradigm, YOLO26 represents the next generation of this technology, refining the end-to-end approach for even greater performance and versatility.
Released in January 2026, YOLO26 builds upon the foundations laid by YOLOv10 and models like YOLO11. It incorporates several groundbreaking improvements:
- Natively End-to-End: Like YOLOv10, YOLO26 is NMS-free, but it further optimizes the architecture to remove Distribution Focal Loss (DFL), simplifying the export process for edge devices.
- MuSGD Optimizer: Inspired by LLM training (specifically Moonshot AI's Kimi K2), YOLO26 utilizes a hybrid SGD and Muon optimizer for faster convergence.
- Enhanced Loss Functions: The integration of ProgLoss and STAL (Soft-Target Anchor Loss) provides notable improvements in small-object recognition, a critical requirement for aerial imagery and IoT.
- CPU Inference Speed: YOLO26 is specifically optimized for CPUs, offering up to 43% faster inference than previous generations, making it ideal for devices like Raspberry Pi.
Conclusion
Both YOLOv10 and DAMO-YOLO represent significant engineering achievements. DAMO-YOLO showcases the power of Neural Architecture Search for finding efficient structures. However, YOLOv10 stands out for its end-to-end NMS-free design and superior parameter efficiency, particularly in the Nano and Small variants.
For developers seeking a balance of high performance, ease of use, and a robust support ecosystem, the Ultralytics models—starting with YOLOv10 and evolving into the cutting-edge YOLO26—offer the most compelling solution. The seamless integration with datasets and deployment tools ensures that researchers can focus on solving real-world problems rather than managing complex model dependencies.
For those interested in other high-performance options, the YOLO11 and YOLO-World models also provide specialized capabilities for open-vocabulary detection and general computer vision tasks.