YOLOv10 vs YOLOv8: A Detailed Comparison
Ultralytics YOLO models are renowned for their cutting-edge performance in real-time object detection. This page provides a technical comparison between two prominent models: YOLOv10 and YOLOv8, highlighting their architectural differences, performance metrics, and ideal applications.
YOLOv10: The Next Generation Object Detector
YOLOv10 represents the latest iteration in the YOLO series, focusing on efficiency and speed without compromising accuracy. It introduces advancements in network architecture and training methodologies to achieve state-of-the-art real-time object detection. A key innovation in YOLOv10 is its NMS-free approach, which streamlines post-processing and reduces latency, making it exceptionally fast for inference. This is achieved through architectural improvements that allow the model to directly predict bounding boxes without relying on Non-Maximum Suppression (NMS).
YOLOv10 is particularly well-suited for applications where ultra-fast inference speeds are critical, such as autonomous driving, real-time video analytics, and high-throughput industrial automation. Its efficiency also makes it ideal for deployment on edge devices with limited computational resources.
YOLOv8: Balancing Accuracy and Speed
YOLOv8, the predecessor to YOLOv10, is also a state-of-the-art object detection model known for its balance between accuracy and speed. It builds upon the successes of previous YOLO versions and offers a comprehensive suite of models ranging in size and complexity to cater to diverse computational needs. YOLOv8 models incorporate a refined architecture and optimized training techniques, making them highly versatile for a wide range of object detection tasks.
YOLOv8's versatility makes it a strong choice for applications like security systems, retail analytics, and robotics where a combination of high accuracy and reasonable speed is required. It is also widely adopted due to its ease of use and extensive documentation, making it accessible for both research and industry applications. You can deploy YOLOv8 on various platforms, from cloud servers to edge devices like NVIDIA Jetson and Raspberry Pi.
Model Comparison Table
| 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 |
| YOLOv8n | 640 | 37.3 | 80.4 | 1.47 | 3.2 | 8.7 |
| YOLOv8s | 640 | 44.9 | 128.4 | 2.66 | 11.2 | 28.6 |
| YOLOv8m | 640 | 50.2 | 234.7 | 5.86 | 25.9 | 78.9 |
| YOLOv8l | 640 | 52.9 | 375.2 | 9.06 | 43.7 | 165.2 |
| YOLOv8x | 640 | 53.9 | 479.1 | 14.37 | 68.2 | 257.8 |
Conclusion
Both YOLOv10 and YOLOv8 are powerful object detection models from Ultralytics. YOLOv10 prioritizes extreme speed and efficiency through its NMS-free architecture, making it suitable for real-time, latency-sensitive applications. YOLOv8 offers a robust balance of accuracy and speed, providing versatility for a broader range of use cases where both factors are important. The choice between YOLOv10 and YOLOv8 depends on the specific requirements of your application, particularly the trade-off between inference speed and accuracy.
For users seeking other high-performance object detection models, Ultralytics also offers YOLOv9, YOLOv7, YOLOv6, and YOLOv5, each with its own strengths and characteristics. Exploring these models can provide further options tailored to specific project needs.
