Food identification.

Creating a dataset for food identification and training a YOLO model.

Overview

This research area combines computer vision, deep learning, and practical applications to solve a critical problem of food type wich containts iron for edutational in anemia context . My work focuses on create a dataset for food identification and training a proper model to detect food items and estimate the iron content, we did a deep exploration about what is the anemia and nutrition mean and how to select the foods available in the south peruvian context, we also did a deep exploration about the YOLO model and how to fine-tune it for our specific use case. and finally make this available in a paper(under review)

Vegetable and Fruit Detection Using YOLO

In this project, I fine-tuned the YOLO (You Only Look Once) algorithm to detect various vegetables and fruits using a local dataset. By leveraging transfer learning techniques, I improved detection accuracy and performance for real-time applications. The model demonstrates robust capabilities in distinguishing between different produce items, making it suitable for applications in agriculture, retail, and smart kitchen.
the image preview of the set of images trained as batch in the left, and test step for the fine-tuned model video(in the right).

For collaboration opportunities or to discuss potential projects, please contact me.