Food Dish Pedictor using YOLOv4

Abstract

Abstract: In recent years, the demand for automated food recognition systems has increased due to the large variety of dishes available especially in Indian cuisine and also due to the growing awareness of the importance of a healthy diet. Identifying multiple food items from an image is a challenging task, especially when it comes to Indian cuisine, which is known for its diverse range of dishes and ingredients. The goal of this paper is to create a food dish predictor for five common Indian cuisines utilizing the cutting-edge object detection algorithm - YOLOv4. The five foods selected for this study are Aloo paratha, Biryani, Poha, Khichdi, and Chapati. A dataset is created by taking the images of selected dishes from various platforms such as social media and forming classes, which was then used to train the YOLOv4 model. To accurately train the machine to identify the dishes, the dataset was manually labeled. The YOLOv4-based food dish predictor that has been proposed could be put to use in a number of applications. The predictor can assure proper order fulfillment in food delivery services by recognising food plates in real-time from customer-provided photos. The predictor's capacity to automatically extract dish information can assist menu recognition software, saving restaurant personnel time and allowing for rapid menu revisions. Meal recommendation engines can utilize the predictor to provide personalized meal choices based on user preferences and previous eating experiences. Overall, the YOLOv4-based food dish predictor helps multiple sectors of the food business to increase productivity, consumer experiences, and decision-making capacities. The findings of this investigation show how well YOLOv4 can recognise and identify food items, particularly Indian food items.