用激活函数搜索技术，让YOLO模型更快更省内存。
ActNAS : Generating Efficient YOLO Models using Activation NAS
- 用NAS自动寻找最优混合激活函数组合
- 在NPU上提速22.28%，内存减少64.15%，精度略升
- 适合边缘设备部署的高效目标检测需求
激活函数为神经网络引入非线性，使其能够学习复杂模式。不同激活函数在速度与精度间存在权衡，例如ReLU虽快但精度较低，而SiLU或SELU则更慢但更准确。通常整个模型使用统一激活函数。本文系统研究了在基于YOLO的模型中采用混合激活函数的影响，评估其在CPU、NPU和GPU等边缘设备上的延迟、内存占用和精度表现。我们提出一种新颖方法，利用神经架构搜索（NAS）设计带有优化混合激活函数的YOLO模型。在参考NPU设备上，该方法生成的最佳模型相比基线模型（使用SiLU）在均值平均精度（mAP）上略有提升，同时速度提升22.28%，内存消耗降低64.15%。
原文摘要 · Abstract (English)
Activation functions introduce non-linearity into Neural Networks, enabling them to learn complex patterns. Different activation functions vary in speed and accuracy, ranging from faster but less accurate options like ReLU to slower but more accurate functions like SiLU or SELU. Typically, same activation function is used throughout an entire model architecture. In this paper, we conduct a comprehensive study on the effects of using mixed activation functions in YOLO-based models, evaluating their impact on latency, memory usage, and accuracy across CPU, NPU, and GPU edge devices. We also propose a novel approach that leverages Neural Architecture Search (NAS) to design YOLO models with optimized mixed activation functions.The best model generated through this method demonstrates a slight improvement in mean Average Precision (mAP) compared to baseline model (SiLU), while it is 22.28% faster and consumes 64.15% less memory on the reference NPU device.
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