Comparison of convolutional neural network models for food image classification

According to some estimates of World Health Organization, in 2014, more than 1.9 billion adults were overweight. About 13% of the world's adult population were obese. 39% of adults were overweight. The worldwide prevalence of obesity more than doubled between 1980 and 2014. Nowadays, mobile applications recording food intake of people become popular. If an improved food classification system is introduced, users take the photo of their meals and system classifies photos into the categories. Hence, we proposed a deep convolutional neural network structure trained from scratch and compared its performance with pre-trained structures Alexnet and Caffenet in INISTA 2017. This study is the extended version of it. Three different deep convolutional neural networks were trained from scratch by using different learning methods: stochastic gradient descent, Nesterov's accelerated gradient and Adaptive Moment Estimation, and compared with Alexnet and Caffenet fine-tuned with the same learning algorithms. Train, validation and test datasets were generated from Food11 and Food101 datasets. All tests were implemented through NVIDIA Digit interface on GeForce GTX1070. According to the test results, although pre-trained models provided better results than proposed structures, their performances were comparable. Moreover, learning optimization methods accelerated and improved the performances of all the compared models.