FOOD CALORIE ESTIMATION USING DEEP LEARNING AND COMPUTER VISION

In later a long time, progressions in counterfeit insights and computer vision have empowered computerized
arrangements for wellbeing observing and dietary evaluation. In this article, a deep learning-based system for
estimating food calories is presented with the help of object recognition and image processing. The proposed
system uses the Yolov5 model for food detection, OpenCV for the preprocessing of the interactive user interface,
and power supplies. By using pre-formed models and pre-defined calorie data sets, the system recognizes food
and provides approximate calorie counts based on volume estimation techniques. This paper presents a
profound learning-based framework for evaluating nourishment calories utilizing question location and picture
handling. The proposed framework utilizes the YOLOv5 demonstrate for nourishment acknowledgment,
OpenCV for picture preprocessing, and Stream lit for an intelligently client interface. The comes about
demonstrate that this approach is productive and adaptable for real-time applications in individual wellbeing
tracking