# MWaste: A Deep Learning Approach to Manage Household Waste

arXiv: https://arxiv.org/abs/2304.14498 · HTML: https://mlsanity.com/paper/2304.14498/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 2 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [Ising-Dropout: A Regularization Method for Training and Compression of Deep Neural Networks](https://mlsanity.com/paper/1902.08673/) (2019)
- [DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet](https://mlsanity.com/paper/2101.05960/) (2021)
- [ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning](https://mlsanity.com/paper/2302.02976/) (2023)

## Abstract (arXiv)

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.