  Food classification is the foundation for developing food vision tasks and
plays a key role in the burgeoning field of computational nutrition. Due to the
complexity of food requiring fine-grained classification, recent academic
research mainly modifies Convolutional Neural Networks (CNNs) and/or Vision
Transformers (ViTs) to perform food category classification. However, to learn
fine-grained features, the CNN backbone needs additional structural design,
whereas ViT, containing the self-attention module, has increased computational
complexity. In recent months, a new Sequence State Space (S4) model, through a
Selection mechanism and computation with a Scan (S6), colloquially termed
Mamba, has demonstrated superior performance and computation efficiency
compared to the Transformer architecture. The VMamba model, which incorporates
the Mamba mechanism into image tasks (such as classification), currently
establishes the state-of-the-art (SOTA) on the ImageNet dataset. In this
research, we introduce an academically underestimated food dataset CNFOOD-241,
and pioneer the integration of a residual learning framework within the VMamba
model to concurrently harness both global and local state features inherent in
the original VMamba architectural design. The research results show that VMamba
surpasses current SOTA models in fine-grained and food classification. The
proposed Res-VMamba further improves the classification accuracy to 79.54\%
without pretrained weight. Our findings elucidate that our proposed methodology
establishes a new benchmark for SOTA performance in food recognition on the
CNFOOD-241 dataset. The code can be obtained on GitHub:
https://github.com/ChiShengChen/ResVMamba.
