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

Source: [Fine-Tuning VGG Neural Network For Fine-grained State Recognition of Food Images](<https://www.sota2.com/research/paper/68fa64dc1e209131cea06c96>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:12:58.577Z

Authors: Kaoutar Ben Ahmed, Ahmad Babaeian Jelodar

Published: 2018-09-08T22:38:33.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1809.09529>)

[PDF](<https://arxiv.org/pdf/1809.09529.pdf>)