# Managing Household Waste through Transfer Learning

arXiv: https://arxiv.org/abs/2402.09437 · HTML: https://mlsanity.com/paper/2402.09437/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [Why gradient clipping accelerates training: A theoretical justification for adaptivity](https://mlsanity.com/paper/1905.11881/) (2019)
- [Style transfer-based image synthesis as an efficient regularization technique in deep learning](https://mlsanity.com/paper/1905.10974/) (2019)
- [DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet](https://mlsanity.com/paper/2101.05960/) (2021)

## Built on by (most cited first, 5 of 5)

- [GlobalWasteData: A Large-Scale, Integrated Dataset for Robust Waste Classification and Environmental Monitoring](https://mlsanity.com/paper/2602.07463/) (2026)
- [Plastic Waste Classification Using Deep Learning: Insights from the WaDaBa Dataset](https://mlsanity.com/paper/2412.20232/) (2024)
- [DWaste: Greener AI for Waste Sorting using Mobile and Edge Devices](https://mlsanity.com/paper/2510.18513/) (2025)
- [Towards Accurate and Efficient Waste Image Classification: A Hybrid Deep Learning and Machine Learning Approach](https://mlsanity.com/paper/2510.21833/) (2025)
- [A Scoping Review of Deep Learning for Urban Visual Pollution and Proposal of a Real-Time Monitoring Framework with a Visual Pollution Index](https://mlsanity.com/paper/2602.09446/) (2026)

## Abstract (arXiv)

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.