In recent years, the field of computer vision has seen remarkable advancements, particularly with the rise of deep learning techniques. Object detection, a challenging task in image analysis, has benefited from these developments. This thesis investigates the application of object detection models, specifically You Only Look Once (YOLO), in the context of food recognition and health assessment based on the Swedish plate model. The study aims to assess the effectiveness of YOLO models in predicting the healthiness of food compositions according to the guidelines provided by the Swedish plate model. The research utilizes a custom dataset comprising 3707 images with 42 different food classes. Various preprocessing- and augmentation techniques are applied to enhance dataset quality and model robustness. The performance of the three YOLO models (YOLOv7, YOLOv8, and YOLOv9) are evaluated using precision, recall, mean Average Precision (mAP), and F1 score metrics. Results indicate that YOLOv8 showed higher performance, making it the recommended choice for further implementation in dietary assessment and health promotion initiatives. The study contributes to the understanding of how deep learning models can be leveraged for food recognition and health assessment. Overall, this thesis underscores the potential of deep learning in advancing computational approaches to dietary assessment and promoting healthier eating habits.