GMamba: A Lightweight Mamba Model for Garbage Classification

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.