SFOOD combines existing food datasets with self-collected hyperspectral images to create a six-task benchmark, and its evaluations suggest spectral bands improve sweetness and herbal classification while current models still lag on food attributes.
Food-101 – mining discriminative components with random forests
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SFOOD: A Multimodal Benchmark for Comprehensive Food Attribute Analysis Beyond RGB with Spectral Insights
SFOOD combines existing food datasets with self-collected hyperspectral images to create a six-task benchmark, and its evaluations suggest spectral bands improve sweetness and herbal classification while current models still lag on food attributes.