Food Image Classification: The Benefit of In-Domain Transfer Learning

Description

Monitoring food intake and calories may be fundamental for a healthy lifestyle and preventing nutrition-related illnesses. Recently, deep-learning approaches have been extensively exploited to provide an automatic analysis of food images. However, food image datasets have peculiar challenges, including fine granularity with a high intra-class and low inter-class variability. In this work, we focus on training strategies considering the typical scenario where data availability and computational resources are limited. Exploiting convolutional neural networks, we show that in-domain source datasets provide a better representation with respect to only using ImageNet, bringing a significant increase in test accuracy. We finally show that ensembling different CNN models further improves the learned representation.

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