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Recent advances in artificial intelligence promote a wide range of computervision applications in many different domains. Digital cameras, acting as humaneyes, can perceive fundamental object properties, such as shapes and colors,and can be further used for conducting high-level tasks, such as imageclassification, and object detections. Human perceptions have been widelyrecognized as the ground truth for training and evaluating computer visionmodels. However, in some cases, humans can be deceived by what they have seen.Well-functioned human vision relies on stable external lighting while unnaturalillumination would influence human perception of essential characteristics ofgoods. To evaluate the illumination effects on human and computer perceptions,the group presents a novel dataset, the Food Vision Dataset (FVD), to create anevaluation benchmark to quantify illumination effects, and to push forwarddevelopments of illumination estimation methods for fair and reliable consumeracceptability prediction from food appearances. FVD consists of 675 imagescaptured under 3 different power and 5 different temperature settings everyalternate day for five such days.

A novel illumination condition varied image dataset-Food Vision Dataset (FVD) for fair and reliable consumer acceptability predictions from food