FOOD IMAGE SEGMENTATION YOLOv5 Computer Vision Project
FOOD IMAGE SEGMENTATION will build a food image segmentation model using YOLOv5 to identify and segment different food items in images. This has applications in calorie counting, dietary tracking, food waste reduction, restaurant food ordering, and automated recipe generation. The project will collect and preprocess food image data, train a YOLOv5 model, evaluate its performance, and integrate it into a web or mobile app. Expected outcomes include a robust food image segmentation model and its integration into various food-related applications.
Trained Model API
This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.
YOLOv5
This project has a YOLOv5 model checkpoint available for inference with Roboflow Deploy. YOLOv5 is a proven and tested, production ready, state-of-the-art real-time object detection model.
Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
food-image-segmentation-yolov5_dataset,
title = { FOOD IMAGE SEGMENTATION YOLOv5 Dataset },
type = { Open Source Dataset },
author = { Harsh },
howpublished = { \url{ https://universe.roboflow.com/harsh-avhnv/food-image-segmentation-yolov5 } },
url = { https://universe.roboflow.com/harsh-avhnv/food-image-segmentation-yolov5 },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { mar },
note = { visited on 2024-04-29 },
}
Connect Your Model With Program Logic
Find utilities and guides to help you start using the FOOD IMAGE SEGMENTATION YOLOv5 project in your project.
