Food Image Classification with Improved MobileNet Architecture and Data Augmentation
Sirawan Phiphiphatphaisit, Olarik Surinta
Abstract
The real-world food image is a challenging problem for food image classification, because food images can be captured from different perspective and patterns. Also, many objects can appear in the image, not just foods. To recognize food images, in this paper, we propose a modified MobileNet architecture that is applies the global average pooling layers to avoid overfitting the food images, batch normalization, rectified linear unit, dropout layers, and the last layer is softmax. The state-of-the-art and the proposed MobileNet architectures are trained according to the fine-tuned model. The experimental results show that the proposed version of the MobileNet architecture achieves significantly higher accuracies than the original MobileNet architecture. The proposed MobileNet architecture significantly outperforms other architectures when the data augmentation techniques are combined.