  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.
