Food Classification Using Deep Learning Algorithm

Monitoring of food plays a significant role in leading health-related issues and tasks. With its multiple applications and features, image processing emerges to be an interesting field in the process of identifying food items. In this paper, a technique has been presented for classifying the food image using the You Only Look Once (YOLO) algorithm. Unlike the conventional artificial neural networks, the YOLO algorithm has more efficiency, and it has been trained on a loss function that corresponds straight to detection, and the complete model is trained with 6000 epochs. Due to the high variance in the alike domain of food images, food classification becomes a difficult task but it has a significant role in lives at the present time as it can be utilized by numerous sources. In this paper, a comparison of the working of the YOLO algorithm with other techniques that are used in image processing such as ResNet-50, VGG-16, ImageNet, and Inception has been elaborated. In this work, the famous dataset from Kaggle is used for implementation purposes. The dataset consists of 4000 Indian Food Image 80 different categories or classes. The proposed model is giving 99% accuracy for classifying the food.