Fine-Tuning VGG Neural Network For Fine-grained State Recognition of Food Images

TL;DR

This paper demonstrates that fine-tuning a pre-trained CNN on a small, manually annotated food image dataset effectively recognizes food states, highlighting CNNs' potential in fine-grained food image classification.

Contribution

It shows the effectiveness of transfer learning with CNNs for food state recognition using a small dataset with data augmentation.

Findings

CNN fine-tuning achieves high accuracy on food state recognition

Transfer learning reduces the need for large datasets

Data augmentation improves model performance

Abstract

State recognition of food images can be considered as one of the promising applications of object recognition and fine-grained image classification in computer vision. In this paper, evidence is provided for the power of convolutional neural network (CNN) for food state recognition, even with a small data set. In this study, we fine-tuned a CNN initially trained on a large natural image recognition dataset (Imagenet ILSVRC) and transferred the learned feature representations to the food state recognition task. A small-scale dataset consisting of 5978 images of seven categories was constructed and annotated manually. Data augmentation was applied to increase the size of the data.