Convolutional Neural Network (CNN) models often lack the ability to incorporate human input, which can be addressed by Augmented Reality (AR) headsets. However, current AR headsets face limitations in processing power, which has prevented researchers from performing real-time, complex image recognition tasks using CNNs in AR headsets. This paper presents a method to deploy CNN models on AR headsets by training them on computers and transferring the optimized weight matrices to the headset. The approach transforms the image data and CNN layers into a one-dimensional format suitable for the AR platform. We demonstrate this method by training the LeNet-5 CNN model on the MNIST dataset using PyTorch and deploying it on a HoloLens AR headset. The results show that the model maintains an accuracy of approximately 98%, similar to its performance on a computer. This integration of CNN and AR enables real-time image processing on AR headsets, allowing for the incorporation of human input into AI models.

该论文介绍了一种方法，通过在计算机上训练CNN模型并将优化的权重矩阵传输到AR头戴设备，以在AR头戴设备上部署CNN模型。该方法将图像数据和CNN层转换为适合AR平台的一维格式，在HoloLens AR头戴设备上使用PyTorch训练LeNet-5 CNN模型并部署，结果表明模型的准确率约为98%，与在计算机上的表现相似。这种CNN和AR的结合使得AR头戴设备能够实时进行图像处理，实现了人工智能模型与人的交互。

在沉浸式可穿戴设备上部署基于卷积神经网络的计算机视觉模型的方法论