Multi-model adaptation system for YOLOv12
YOLOv12 is designed with a complete model size matrix, including five configurations, Nano, Small, Medium, Large and Extra-Large, forming a solution that covers all types of computing environments. The leanest Nano model parameter count is controlled within 4MB, which is suitable for embedded devices and mobile applications; while the Extra-Large version is optimized for high-performance servers to achieve the most accurate detection results.
The main difference between different scale models is the network depth, width and configuration density of the attention layer, users can choose according to the computational resource constraints of specific application scenarios: Nano or Small models are recommended for low-power devices to balance computational efficiency and accuracy; Medium or Large versions are recommended for automated driving systems to ensure reliability; and Extra-Large is applicable to Extra-Large is suitable for professional image analysis scenarios that require extremely high detection accuracy.
This flexible hierarchical strategy enables the YOLOv12 to be seamlessly deployed on a full range of hardware platforms, from Raspberry Pi to data center servers, demonstrating strong environmental adaptability.
This answer comes from the articleYOLOv12: Open source tool for real-time image and video target detectionThe
