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Two-Stage Model Classifies 46 Produce and Vegetable-Dish Categories at 92.43% Top-1 Accuracy, but Lacks On-Device Validation

The study developed a two-stage ThaiSook pipeline: a binary model first rejects non-produce images, after which a multiclass model identifies 43 fruit and vegetable types and three vegetable-heavy dishes. The 40,595-image dataset contained 11,210 labelled and 29,385 unlabelled user images. With label smoothing and temperature scaling, the pipeline reached 92.43% top-1, 94.56% top-3, and 94.91% top-5 accuracy; Noisy Student raised top-1 accuracy to 92.59%. However, results rely on one train–test split, without real on-device testing or evidence that classification improves dietary measurement or health outcomes.