YOLOv10 vs RTDETRv2: A Detailed Comparison
This page provides a technical comparison between two state-of-the-art object detection models: YOLOv10 and RTDETRv2. Both models are designed for high-performance computer vision tasks, but they differ significantly in their architecture, performance characteristics, and ideal applications. This comparison aims to highlight these key differences to help users make informed decisions for their specific needs.
YOLOv10
YOLOv10 is the latest iteration in the renowned YOLO (You Only Look Once) series, known for its speed and efficiency in object detection. Building upon previous YOLO models like YOLOv8 and YOLOv9, YOLOv10 introduces architectural enhancements aimed at further optimizing inference speed and accuracy. While specific architectural details of YOLOv10 are still emerging, it generally maintains the one-stage detection paradigm, prioritizing real-time performance.
Strengths:
- Speed: YOLO models are inherently designed for speed, and YOLOv10 likely continues this trend, making it suitable for real-time applications.
- Efficiency: YOLOv10 is expected to be computationally efficient, allowing for deployment on resource-constrained devices.
- Ease of Use: Following the Ultralytics tradition, YOLOv10 is expected to be user-friendly and easily integrable with existing workflows through the Ultralytics HUB.
Weaknesses:
- Accuracy vs. Two-Stage Detectors: One-stage detectors like YOLO sometimes trade off accuracy compared to two-stage detectors, particularly in complex scenes.
- Emerging Model: As a newer model, YOLOv10 might still be under active development, and the full extent of its capabilities and limitations are yet to be completely documented.
Use Cases:
- Real-time object detection applications such as security alarm systems, robotics, and autonomous vehicles.
- Edge deployment scenarios on devices like Raspberry Pi and NVIDIA Jetson.
- Applications requiring a balance of speed and reasonable accuracy.
RTDETRv2
RTDETRv2 (Real-Time Detection Transformer v2) represents a different architectural approach, leveraging Vision Transformers (ViT) for object detection. RT-DETR models, including v2, are known for their strong performance and efficient inference. Unlike traditional CNN-based detectors, RTDETR models use self-attention mechanisms to capture global context, potentially leading to better accuracy in certain scenarios.
Strengths:
- High Accuracy: RTDETR models, benefiting from the transformer architecture, often achieve higher accuracy compared to some one-stage CNN-based detectors, especially in complex scenes.
- Efficient Transformer Implementation: RTDETRv2 is optimized for real-time performance, making transformers viable for time-sensitive applications.
- Strong Feature Representation: Vision Transformers excel at feature extraction, which can lead to robust object detection performance.
Weaknesses:
- Computational Cost: Transformers can be computationally more intensive than CNNs, potentially leading to slower inference speeds compared to the fastest YOLO models, although RTDETRv2 is designed to mitigate this.
- Model Size: Transformer-based models can sometimes be larger in size compared to lightweight CNN-based models.
Use Cases:
- Applications where high accuracy is paramount, such as medical image analysis and quality inspection in manufacturing.
- Scenarios benefiting from strong contextual understanding, like complex scene analysis and crowded environments.
- Deployments where powerful GPUs are available, such as cloud-based inference or high-end edge devices like NVIDIA Jetson Orin Nano.
Performance Metrics Comparison
The table below summarizes the performance metrics for various sizes of YOLOv10 and RTDETRv2 models, providing a quantitative comparison based on metrics like mAP (mean Average Precision), inference speed, and model size.
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 |
RTDETRv2-s | 640 | 48.1 | - | 5.03 | 20 | 60 |
RTDETRv2-m | 640 | 51.9 | - | 7.51 | 36 | 100 |
RTDETRv2-l | 640 | 53.4 | - | 9.76 | 42 | 136 |
RTDETRv2-x | 640 | 54.3 | - | 15.03 | 76 | 259 |
Key Observations:
- mAP: Both model families offer a range of sizes to cater to different accuracy requirements. Larger models in both families generally achieve higher mAP.
- Inference Speed: YOLOv10 models, especially the smaller variants (n, s), tend to have faster inference speeds, particularly on TensorRT, showcasing their real-time capabilities. RTDETRv2 models, while efficient for transformers, are generally slower in inference speed compared to similarly sized YOLOv10 models.
- Model Size and FLOPs: YOLOv10 models generally have fewer parameters and lower FLOPs (Floating Point Operations) compared to RTDETRv2 models with comparable mAP, indicating greater parameter efficiency.
Conclusion
Choosing between YOLOv10 and RTDETRv2 depends heavily on the specific application requirements. If real-time performance and efficiency are the primary concerns, especially for edge deployment, YOLOv10 is a strong contender. For applications prioritizing higher accuracy and where computational resources are less constrained, RTDETRv2 offers a compelling alternative with its transformer-based architecture.
Users may also be interested in exploring other models available in Ultralytics, such as YOLO-NAS, YOLOv7, YOLOv6, and FastSAM depending on their specific needs for speed, accuracy, and task type like segmentation. For further exploration, refer to the Ultralytics Models documentation.