Published Paper Details:

Obesity and Overweight is a common and severe disease in most of adults and children, which refers to abnormal or excessive accumulation of fat that causes serious health problems. In a study in 2019 around 38.2 million children under the age of 5 were obese or overweight. There is a rise in the number of overweight children under the age of 5 in Africa by nearly 24% since 2000. Nearly half of the children under the age of 5 in Asia were overweight or obese in 2019. The best way to treat obesity is to follow a reduced-calorie diet. Manually tracking the food and calories in our food is a tedious task. There are Convolutional Neural Network (CNN) models such as InceptionV3 and InceptionV4 to recognize the food items. But the existing CNN-based models are less accurate, computationally expensive and require high computational power. Here we are proposing a Convolutional Neural Network algorithm called YOLOV8, which is mostly used for object detection, to detect food items. This paper aims to develop a model that recognizes the food item and estimates its calorie values. This system provides a platform where users can estimate the number of calories in a food item by just providing pictures or videos of the food item and hence they can track the number of calories in their diet.