A Dish Recognition Framework Using Transfer Learning
Truong Thanh Tai, Dang N. H. Thanh, Nguyen Quoc Hung
Abstract
Dish understanding from digital media is an interesting problem, but it also contains a big challenge. The challenge comes from the complexity of ingredients in the dish. With the development of deep learning, several effective tools can solve the problem partially. In this work, the task of dish recognition is considered. A novel dish recognition method based on EfficientNet architecture and transfer learning is proposed. First, we modify the EfficientNet-B0 by adding several important layers. Second, we use transfer learning to utilize optimal parameters obtained from pretraining the model on ImageNet, and then retrain it on a new dataset of dish images, i.e., UEH-VDR dataset. The UEH-VDR dataset contains images about Vietnamese dishes collected from various sources. Experimental results show that the proposed method can achieve an accuracy of 92.33% for the task of recognizing a dish. It also works more effectively than other models based on popular convolutional neural networks such as VGG and ResNet. In addition, a mobile application is also developed based on the trained data to serve visitors who want to discover the Vietnamese culinary culture.