Work

GMamba: A Lightweight Mamba Model for Garbage Classification

Year: 2026

Type: article

Abstract: With the rapid increase in urban waste, efficient and accurate garbage classification has become pivotal for sustainable development. However, existing methods often grapple with high computational complexity, limited adaptability to diverse waste types, and challenges in deploying on resource-constrained devices. To address these issues, this study proposes GMamba, a lightweight garbage classification model based on the Mamba architecture. GMamba employs a hierarchical structure, integrating two modules, the GML Block for efficient local–global feature fusion and the GMC Block for fine-grained spatial dependency modeling, achieving robust feature aggregation while minimizing computational redundancy. Evaluations on the Huawei Cloud Garbage Classification dataset and the custom MixTrash dataset demonstrate that GMamba, with only 17.18 M parameters, achieves Top-1 accuracies of 92.75% and 92.58%, respectively. While scaling evaluations indicate that VMamba maintains a marginal lead in absolute Top-1 accuracy, the proposed GMamba delivers a substantially superior balance between accuracy and computational efficiency, reducing parameter count by 45% and FLOPs by 47.3%, thus demonstrating promising deployment potential for resource-constrained edge systems.

Source: Sustainability

DOI: 10.3390/su18115397

Authors: Lujun Lin, Qifeng Ding, Xinzhan Li, Haoji Hu, Qun Wang, Houkui Zhou

Institutions: Jiyang College of Zhejiang A&F University, Zhejiang A & F University, Zhejiang University, Zhejiang Shuren University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Responsible consumption and production, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,896