基于迁移学习的家庭垃圾管理
Managing Household Waste through Transfer Learning
AI总结：
本研究针对垃圾分类任务，对比多种迁移学习模型的性能与碳排放，发现EfficientNetV2S以96.41%的准确率成为兼顾精度与可持续性的最优模型，凸显了AI模型生态影响的考量价值。
摘要：
随着全球持续面临气候变化挑战，考量我们所用技术的环境影响至关重要。本研究调研了多种用于垃圾分类的迁移学习模型的性能与计算碳排放，研究对象包括MobileNet、ResNet50、ResNet101、EfficientNetV2S和EfficientNetV2M模型。结果显示，EfficientNetV2系列取得了最高的准确率、召回率、F1分数和交并比（IoU）值，但EfficientNetV2M模型耗时更长、碳排放更高。ResNet50在准确率、召回率、F1分数和IoU上优于ResNet110，但碳足迹更大。研究得出结论：EfficientNetV2S是最具可持续性且准确率最高的模型，准确率达96.41%。本研究强调了在垃圾分类任务中考量机器学习模型生态影响的重要性。
英文摘要：
As the world continues to face the challenges of climate change, it is crucial to consider the environmental impact of the technologies we use. In this study, we investigate the performance and computational carbon emissions of various transfer learning models for garbage classification. We examine the MobileNet, ResNet50, ResNet101, and EfficientNetV2S and EfficientNetV2M models. Our findings indicate that the EfficientNetV2 family achieves the highest accuracy, recall, f1-score, and IoU values. However, the EfficientNetV2M model requires more time and produces higher carbon emissions. ResNet50 outperforms ResNet110 in terms of accuracy, recall, f1-score, and IoU, but it has a larger carbon footprint. We conclude that EfficientNetV2S is the most sustainable and accurate model with 96.41% accuracy. Our research highlights the significance of considering the ecological impact of machine learning models in garbage classification.
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。
