Feature-Enhanced TResNet for Accurate Fine-Grained Food Image Classification

Food is not only a core component of humans' daily diets, but also an important carrier of cultural heritage and emotional bonds. With the development of technology, the need for accurate classification of food images has grown, which is crucial for a variety of application scenarios. However, existing Convolutional Neural Networks (CNNs) face significant challenges when dealing with fine-grained food images that are similar in shape but subtle in detail. To address this challenge, this study presents an innovative method for classifying food images, named SDCTres, specifically designed to address fine-grained food images and accurately capture subtle features within them. The SDCTres method is based on the TResNet model and integrates StyleM and DCCA technologies to enhance feature extraction capabilities. In experimental validation on Chinese food image datasets ChineseFoodNet and CNFOOD-241, the SDCTres method significantly improved classification accuracy, achieving rates of 81.37% and 80.29%, respectively, demonstrating its effectiveness and superiority in fine-grained food image classification.