click here to download project abstract of yolo v5
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ABSTRACT
In recent decades, a notable shift towards prioritizing health and monitoring calorie intake has emerged. To address this concern, a model for estimating calorie content in various foods was developed, utilizing object detection technology. Leveraging image-based analysis and previously gathered calorie data, this model accurately identifies dishes and provides corresponding calorie estimates, aiming to offer tailored meal planning advice.
The approach involved collecting a diverse array of food images through web scraping techniques. These images were annotated using “makesense.ai,” associating them with their respective dish names. To accomplish real-time object detection and classification, the YOLO-V5 (You Only Look Once) model was employed, ensuring efficient processing.
Furthermore, this model doesn’t merely stop at calorie estimation; it extends its utility by providing tailored meal plans. By considering diverse user groups and their specific dietary needs, the system offers personalized recommendations based on the identified dishes and their calorie content. This feature adds depth and practicality to the tool, aiming to assist users beyond mere calorie tracking.
Moreover, the integration of meal planning guidance adds significant value. It moves beyond mere calorie tracking, offering personalized recommendations tailored to diverse user groups.
Ultimately, the initiative stands as a testament to leveraging technology to promote health and wellness. By amalgamating innovative methodologies, it not only assists in calorie estimation but also serves as a catalyst for informed decision-making, supporting individuals on their journey towards healthier dietary habits.
