Allergic-fruit Object Detection Dataset and Pre-Trained Model by UNT

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Explore Dataset

Here are a few use cases for this project:

Dietary Management: Users with specific fruit allergies can use this model to identify fruits in mixed meals or fruit salads, making it easier for them to manage their diet and avoid allergic reactions.
Health and Wellness Apps: The "Allergic-fruit" model can be integrated into health and wellness apps to guide users on their fruit consumption based on their allergies, aiding in better nutrition tracking and meal planning.
Supermarket Checkout Systems: The model can be used by grocery stores in their self-checkout systems to identify the type of fruit the customer is buying, especially useful for loose and unlabelled fruits for correct pricing.
Agriculture Quality Control: The model can help farmers and agricultural businesses to classify and sort their products effectively, increasing operation efficiency.
Educational Purposes: The model can be used in educational tools or games aimed at teaching children about fruit types, or even used as a tool to teach the basics of computer vision.

Note: The example image of a couple in a boat on lake does not relate directly to the "Allergic-fruit" model as it does not contain any fruit images to identify. For the most effective use of the model, images should contain identifiable fruit.

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.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new 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{
                            allergic-fruit-a9ixh_dataset,
                            title = { Allergic-fruit Dataset },
                            type = { Open Source Dataset },
                            author = { UNT },
                            howpublished = { \url{ https://universe.roboflow.com/unt-eh3cu/allergic-fruit-a9ixh } },
                            url = { https://universe.roboflow.com/unt-eh3cu/allergic-fruit-a9ixh },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-04-28 },
                            }