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Article

GMamba: A Lightweight Mamba Model for Garbage Classification

1
College of Engineering and Technology, Jiyang College of Zhejiang A&F University, Zhuji 311800, China
2
School of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
3
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China
4
School of Mathematics and Computer Science, Zhejiang Shuren University, Hangzhou 311300, China
*
Authors to whom correspondence should be addressed.
Sustainability 2026, 18(11), 5397; https://doi.org/10.3390/su18115397
Submission received: 28 April 2026 / Revised: 24 May 2026 / Accepted: 25 May 2026 / Published: 27 May 2026

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.
Keywords: image recognition; garbage classification; Mamba; lightweight; hybrid loss function image recognition; garbage classification; Mamba; lightweight; hybrid loss function

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MDPI and ACS Style

Lin, L.; Ding, Q.; Li, X.; Hu, H.; Wang, Q.; Zhou, H. GMamba: A Lightweight Mamba Model for Garbage Classification. Sustainability 2026, 18, 5397. https://doi.org/10.3390/su18115397

AMA Style

Lin L, Ding Q, Li X, Hu H, Wang Q, Zhou H. GMamba: A Lightweight Mamba Model for Garbage Classification. Sustainability. 2026; 18(11):5397. https://doi.org/10.3390/su18115397

Chicago/Turabian Style

Lin, Lujun, Qifeng Ding, Xinzhan Li, Haoji Hu, Qun Wang, and Houkui Zhou. 2026. "GMamba: A Lightweight Mamba Model for Garbage Classification" Sustainability 18, no. 11: 5397. https://doi.org/10.3390/su18115397

APA Style

Lin, L., Ding, Q., Li, X., Hu, H., Wang, Q., & Zhou, H. (2026). GMamba: A Lightweight Mamba Model for Garbage Classification. Sustainability, 18(11), 5397. https://doi.org/10.3390/su18115397

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