# DeepWaste: Applying Deep Learning to Waste Classification for a Sustainable Planet

arXiv: https://arxiv.org/abs/2101.05960 · HTML: https://mlsanity.com/paper/2101.05960/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 15 Jan 2021.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)

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

- [Managing Household Waste through Transfer Learning](https://mlsanity.com/paper/2402.09437/) (2024)
- [ConvoWaste: An Automatic Waste Segregation Machine Using Deep Learning](https://mlsanity.com/paper/2302.02976/) (2023)
- [MWaste: A Deep Learning Approach to Manage Household Waste](https://mlsanity.com/paper/2304.14498/) (2023)
- [A Computer Vision-Based Quality Assessment Technique for the automatic control of consumables for analytical laboratories](https://mlsanity.com/paper/2404.10454/) (2024)
- [Efficient Waste Sorting for Circular Economy: A Confidence-guided comparison between One-Vs-All and One-Vs-Rest Classification Strategies with Human-in-the-Loop for Automated Waste Sorting](https://mlsanity.com/paper/2607.02230/) (2026)

## Abstract (arXiv)

Accurate waste disposal, at the point of disposal, is crucial to fighting climate change. When materials that could be recycled or composted get diverted into landfills, they cause the emission of potent greenhouse gases such as methane. Current attempts to reduce erroneous waste disposal are expensive, inaccurate, and confusing. In this work, we propose DeepWaste, an easy-to-use mobile app, that utilizes highly optimized deep learning techniques to provide users instantaneous waste classification into trash, recycling, and compost. We experiment with several convolution neural network architectures to detect and classify waste items. Our best model, a deep learning residual neural network with 50 layers, achieves an average precision of 0.881 on the test set. We demonstrate the performance and efficiency of our app on a set of real-world images.