Abstract
Food Recognition is an essential topic in the area of computer of its target applications is to avoid achieving a cashier at the dining place. In this paper, we investigate the application of Deep Transfer Learning for food recognition. We fine-tune three well learning models namely; AlexNet, GoogleNet, and Vgg16. The fine tuning procedure depends on removing the last three layers of each model and adds another five new layers. The training and validation of each model conducted through food a dataset collected from our university's canteen. The dataset contains 39 food types, 20 images for each type. The fine-tuned models show similar training and validation performance and achieved 100% accuracy over the small-scale dataset.
Highlights
The recent advent of transfer deep learning has achieved successes in many areas such as classification and recognition [1]–[4]
In this paper, we will explain the utilization of deep transfer learning concept for small-scale food dataset captured directly from the tray
Some of the researches mention that the GoogleNet has the highest validation accuracy value, with the lowest number of epochs[10]
Summary
The recent advent of transfer deep learning has achieved successes in many areas such as classification and recognition [1]–[4]. One of the most promising visual object recognition applications is food recognition, since it helps to estimate food calories and analyze eating habits of people to maintain their health [5]. Those applications started to open new challenges to the computer vision and object recognition algorithms. In this paper, we will explain the utilization of deep transfer learning concept for small-scale food dataset captured directly from the tray.
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