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Improving fine-grained food classification using deep residual learning and selective state space models
Chi-Sheng Chen1, Guan-Ying Chen2, Dong Zhou3
1Neuro Industry, Inc., San Francisco, California, United States of America.
Plos One
|May 5, 2025
Summary
We introduce ResVMamba, a novel model for accurate food classification that efficiently captures global and local dependencies. This computational nutrition approach sets a new benchmark for food recognition tasks.
Area of Science:
- Computational Nutrition
- Computer Vision
- Machine Learning
Background:
- Food classification is crucial for computational nutrition and food vision tasks.
- Fine-grained food classification presents challenges for traditional Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) due to complexity and computational cost.
Purpose of the Study:
- To develop an efficient and accurate food classification model.
- To address the limitations of existing models in handling complex food datasets.
Main Methods:
- Proposed the ResVMamba model, integrating a residual learning strategy within a state-space framework.
- Leveraged VMamba for efficient capture of global and local dependencies.
- Introduced and utilized the CNFOOD-241 food dataset for validation.
Main Results:
- ResVMamba achieved a Top-1 accuracy of 81.70% and a Top-5 accuracy of 96.83% on the CNFOOD-241 dataset.
- The model surpassed current state-of-the-art (SOTA) models in food recognition.
- Established a new performance benchmark for the CNFOOD-241 dataset.
Conclusions:
- Pioneered the integration of residual learning within VMamba for enhanced feature extraction.
- The ResVMamba model offers a computationally efficient and highly accurate solution for food classification.
- The study introduces the CNFOOD-241 dataset and provides open-source code for the ResVMamba model.
