Deep-learning-based food recognition system

Deep learning has achieved remarkable results in many fields, and food recognition technology is widely used and promising in health management, catering industry, and food safety supervision. In this paper, we focus on the field of food recognition and construct a set of food recognition system based on deep learning. In the process of system construction, a large amount of food image data is collected for preprocessing. TensorFlow is used to build the CNN model, set up the convolutional layer, pooling layer and fully connected layer, carry out training and optimization, and train the model with the cross-entropy loss function combined with L2 regularization and Adam optimizer, and finally evaluate the model performance through the results of the dataset. It is experimentally verified that the system achieves an average accuracy of 87% on a specific test set, and the confusion matrix also visualizes the specifics of recognizing different food categories, which effectively proves the effectiveness and reliability of the system in the food recognition task.