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.

翻译：卷积神经网络（CNN）模型通常缺乏整合人类输入的能力，而增强现实（AR）头显可以解决这一问题。然而，当前AR头显在计算能力方面存在局限，这阻碍了研究人员在AR头显上使用CNN执行实时、复杂的图像识别任务。本文提出了一种在AR头显上部署CNN模型的方法：先在计算机上训练模型，再将优化后的权重矩阵传输到头显。该方法将图像数据和CNN层转换为一维格式，以适应AR平台。我们通过在MNIST数据集上使用PyTorch训练LeNet-5 CNN模型，并将其部署在HoloLens AR头显上，验证了此方法。结果表明，该模型保持了约98%的准确率，与其在计算机上的性能相近。这种CNN与AR的结合实现了在AR头显上的实时图像处理，使得将人类输入整合到AI模型中成为可能。