FOOD IMAGE SEGMENTATION YOLOv5 Computer Vision Project

Harsh

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Description

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.

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LICENSE

CC BY 4.0

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 = { jul },
                            note = { visited on 2024-12-22 },
                            }