# The Garbage Dataset (GD): A Multi-Class Image Benchmark for Automated Waste Segregation

arXiv: https://arxiv.org/abs/2602.10500 · HTML: https://mlsanity.com/paper/2602.10500/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [EfficientNetV2: Smaller Models and Faster Training](https://mlsanity.com/paper/2104.00298/) (2021)
- [TrustMark: Universal Watermarking for Arbitrary Resolution Images](https://mlsanity.com/paper/2311.18297/) (2023)
- [An Analysis of the t-SNE Algorithm for Data Visualization](https://mlsanity.com/paper/1803.01768/) (2018)
- [TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris](https://mlsanity.com/paper/2007.08097/) (2020)
- [TACO: Trash Annotations in Context for Litter Detection](https://mlsanity.com/paper/2003.06975/) (2020)
- [ColorNet: Investigating the importance of color spaces for image classification](https://mlsanity.com/paper/1902.00267/) (2019)
- [Robotic Detection of Marine Litter Using Deep Visual Detection Models](https://mlsanity.com/paper/1804.01079/) (2018)
- [A Multi-Level Approach to Waste Object Segmentation](https://mlsanity.com/paper/2007.04259/) (2020)
- [Seeing and Seeing Through the Glass: Real and Synthetic Data for Multi-Layer Depth Estimation](https://mlsanity.com/paper/2503.11633/) (2025)
- [Generalization Gap in Data Augmentation: Insights from Illumination](https://mlsanity.com/paper/2404.07514/) (2024)
- [Measuring the Effect of Background on Classification and Feature Importance in Deep Learning for AV Perception](https://mlsanity.com/paper/2512.05937/) (2025)

## Abstract (arXiv)

This study introduces the Garbage Dataset (GD), a publicly available image dataset designed to advance automated waste segregation through machine learning and computer vision. It is a diverse dataset that covers 10 categories of common household waste: metal, glass, biological, paper, battery, trash, cardboard, shoes, clothes, and plastic. The dataset comprises 12,259 labeled images collected through multiple methods, including the DWaste mobile app and curated web sources. The methods included rigorous validation through checksums and outlier detection, analysis of class imbalance and visual separability through PCA/t-SNE, and assessment of background complexity using entropy and saliency measures. The dataset was benchmarked using state-of-the-art deep learning models (EfficientNetV2M, EfficientNetV2S, MobileNet, ResNet50, ResNet101) evaluated on performance metrics and operational carbon emissions. The results of the experiment indicate that EfficientNetV2S achieved the highest performance with a accuracy of 95.13% and an F1-score of 0.95 with moderate carbon cost. Analysis revealed inherent dataset characteristics including class imbalance, a skew toward high-outlier classes (plastic, cardboard, paper), and brightness variations that require consideration. The main conclusion is that GD provides a valuable real-world benchmark for waste classification research while highlighting important challenges such as class imbalance, background complexity, and environmental trade-offs in model selection that must be addressed for practical deployment. The dataset is publicly released to support further research in environmental sustainability applications.