รายละเอียด : Food image classification is a challenging problem, the solution of which can be of great benefit to many real-world applications such as nutrition and allergy estimation. Most of the previous studies proposed to use variations of convolutional neural networks to tackle the problem. However, due to the limited number of annotated food image datasets, there is still some room for improvement, especially in terms of accuracy and speed. Generally speaking, neural networks trained to solve image classification problems on a small dataset benefit from utilizing the weights of the networks that have been pre-trained on a large image classification dataset such as ImageNet. In this paper, we compare the trade-offs between training networks from scratch, deploying pre-trained networks as feature e
