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.

通过研究垃圾分类中各种迁移学习模型的性能和计算碳排放，我们发现 EfficientNetV2S 是最可持续和准确的模型，其准确率高达 96.41%。这项研究强调了考虑机器学习模型在垃圾分类中的生态影响的重要性。

通过迁移学习管理家庭垃圾

Managing Household Waste through Transfer Learning

Computer vision methods have shown to be effective in classifying garbage
into recycling categories for waste processing, existing methods are costly,
imprecise, and unclear. To tackle this issue, we introduce MWaste, a mobile
application that uses computer vision and deep learning techniques to classify
waste materials as trash, plastic, paper, metal, glass or cardboard. Its
effectiveness was tested on various neural network architectures and real-world
images, achieving an average precision of 92\% on the test set. This app can
help combat climate change by enabling efficient waste processing and reducing
the generation of greenhouse gases caused by incorrect waste disposal.

介绍了一种名为 MWaste 的移动应用程序，使用计算机视觉和深度学习技术将垃圾材料分类为垃圾、塑料、纸张、金属、玻璃或纸板，可以帮助有效处理垃圾、减少温室气体的排放。