# Comparative Analysis of Multiple Deep CNN Models for Waste Classification

arXiv: https://arxiv.org/abs/2004.02168 · HTML: https://mlsanity.com/paper/2004.02168/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [Comparative Analysis of CPU and GPU Profiling for Deep Learning Models](https://mlsanity.com/paper/2309.02521/) (2023)
- [Inference of Recyclable Objects with Convolutional Neural Networks](https://mlsanity.com/paper/2104.00868/) (2021)
- [Synthetic Data for Object Classification in Industrial Applications](https://mlsanity.com/paper/2212.04790/) (2022)

## Abstract (arXiv)

Waste is a wealth in a wrong place. Our research focuses on analyzing possibilities for automatic waste sorting and collecting in such a way that helps it for further recycling process. Various approaches are being practiced managing waste but not efficient and require human intervention. The automatic waste segregation would fit in to fill the gap. The project tested well known Deep Learning Network architectures for waste classification with dataset combined from own endeavors and Trash Net. The convolutional neural network is used for image classification. The hardware built in the form of dustbin is used to segregate those wastes into different compartments. Without the human exercise in segregating those waste products, the study would save the precious time and would introduce the automation in the area of waste management. Municipal solid waste is a huge, renewable source of energy. The situation is win-win for both government, society and industrialists. Because of fine-tuning of the ResNet18 Network, the best validation accuracy was found to be 87.8%.