优化停车位分类：将集成模型蒸馏为轻量级分类器
Optimizing Parking Space Classification: Distilling Ensembles into Lightweight Classifiers
AI总结：
本文针对基于图像的停车位分类在边缘部署中的数据传输与算力挑战，提出将鲁棒分类器集成作为教师模型，并通过伪标签蒸馏出轻量级学生模型；实验表明学生模型参数量减少26倍，平均准确率达96.6%，优于教师模型的95.3%。
摘要：
在为智慧城市应用（例如基于图像的停车场监控）部署大规模机器学习模型时，数据通常必须发送到中央服务器以执行分类任务。这对城市基础设施构成挑战，因为基于图像的应用需要传输大量数据，从而需要复杂的网络和硬件基础设施来处理这些数据。为了解决基于图像的停车位分类中的这一问题，我们提出构建一个鲁棒的分类器集成作为教师模型（Teacher models）。这些教师模型被蒸馏为轻量级且专用的学生模型（Student models），可直接部署在边缘设备上。知识通过教师模型生成的伪标签样本蒸馏给学生模型，并利用这些样本在目标场景上对学生模型进行微调。我们的结果表明，学生模型的参数量比教师模型少26倍，在目标测试数据集上达到了96.6%的平均准确率，超过了教师模型95.3%的平均准确率。
英文摘要：
When deploying large-scale machine learning models for smart city applications, such as image-based parking lot monitoring, data often must be sent to a central server to perform classification tasks. This is challenging for the city's infrastructure, where image-based applications require transmitting large volumes of data, necessitating complex network and hardware infrastructures to process the data. To address this issue in image-based parking space classification, we propose creating a robust ensemble of classifiers to serve as Teacher models. These Teacher models are distilled into lightweight and specialized Student models that can be deployed directly on edge devices. The knowledge is distilled to the Student models through pseudo-labeled samples generated by the Teacher model, which are utilized to fine-tune the Student models on the target scenario. Our results show that the Student models, with 26 times fewer parameters than the Teacher models, achieved an average accuracy of 96.6% on the target test datasets, surpassing the Teacher models, which attained an average accuracy of 95.3%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
