CuisineNet: Food Attributes Classification using Multi-scale Convolution Network

TL;DR

This paper introduces CuisineNet, a multi-scale convolutional neural network for classifying food attributes like cuisine and flavor, utilizing a new dataset and achieving superior accuracy over existing models.

Contribution

The paper proposes a novel multi-scale convolutional network architecture and a joint loss function for multi-label food attribute classification, along with a new dataset Yummly48K.

Findings

CuisineNet achieves 65% average F1 score on validation set.

CuisineNet outperforms state-of-the-art models.

The new dataset Yummly48K supports food attribute classification.

Abstract

Diversity of food and its attributes represents the culinary habits of peoples from different countries. Thus, this paper addresses the problem of identifying food culture of people around the world and its flavor by classifying two main food attributes, cuisine and flavor. A deep learning model based on multi-scale convotuional networks is proposed for extracting more accurate features from input images. The aggregation of multi-scale convolution layers with different kernel size is also used for weighting the features results from different scales. In addition, a joint loss function based on Negative Log Likelihood (NLL) is used to fit the model probability to multi labeled classes for multi-modal classification task. Furthermore, this work provides a new dataset for food attributes, so-called Yummly48K, extracted from the popular food website, Yummly. Our model is assessed on the constructed Yummly48K dataset. The experimental results show that our proposed method yields 65% and 62% average F1 score on validation and test set which outperforming the state-of-the-art models.