NU-ResNet: Deep Residual Networks for Thai Food Image Recognition

To improve the recognition accuracy of a convolutional neural network, the number of the modules inside the network is normally increased so that the whole network becomes a deeper network. By doing such, it does not always guarantee that the accuracy will be improved. In addition, adding more modules to the network, the required parameter size and processing time are certainly increased. These then result in a significant drawback if such network is utilized in a smartphone in which the computational resources are limited. In this paper, another technique called Identity mapping, which is from the Residual networks, is adopted and added to the network. This technique is applied to the Deep NU-InNet with a depth of 4, 8, and 12 in order to increase the recognition accuracy while the depth is kept constant. Testing this proposed network; that is, NU-ResNet, with THFOOD-50 dataset, which contains various images of 50 Thai famous dishes, the improvement in terms of the recognition accuracy is obtained. With a depth of 4 for NU-ResNet, the achieved Top-1 accuracy and Top-5 accuracy are 83.07% and 97.04%, respectively. The parameter size of the network is only 1.48×106, which is quite small for being used with a smartphone application. Moreover, the average processing time per image is 44.60 ms, which can be practically used in an image recognition application. These results show a promising performance of the proposed network to be used with a Thai food image recognition application in a smartphone.