YOLOv9 vs YOLOv10: A Comprehensive Comparison

Enhanced Efficiency:
YOLOv10’s architectural enhancements significantly reduce parameters, FLOPs (Floating Point Operations), and latency across various model scales (N/S/M/B/L/X). For instance, YOLOv10-S is 1.8 times faster than RT-DETR-R18 with similar average precision (AP), and YOLOv10-B exhibits 46% lower latency and 25% fewer parameters compared to YOLOv9-C.
Superior Accuracy:
YOLOv10 achieves state-of-the-art performance across multiple benchmarks, showing notable improvements in AP over previous YOLO versions. YOLOv10-L surpasses YOLOv8-L by 0.3 AP with 1.8 times fewer parameters, highlighting its efficient parameter utilization.
Exceptional Performance:
Speed: YOLOv10 processes images at impressive speeds, taking just 2.0ms for preprocessing, 13.4ms for inference, and 1.3ms for post-processing per image at a resolution of (1, 3, 384, 640). This swift processing capability makes YOLOv10 ideal for applications requiring fast and accurate object detection while maintaining high accuracy. The smallest model processes each image in just 1 millisecond (1000fps), making it perfect for real-time video processing on edge devices. Additionally, it demonstrates high speed on CPUs.
Tech Stack:
Python: Ultralytics, YOLOv10
System: Intel i5 9th Gen, 24 GB RAM, 6 GB GeForce 1660 Ti
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