ProjectsSushi Computer Vision Project
Updated 3 years ago
Metrics
Description
Here are a few use cases for this project:
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"Sushi Recognition App": An app that uses "ProjectsSushi" model to identify different types of sushi in an image in real time. This can be helpful in restaurants when customers aren't certain about the type of sushi they are ordering.
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"Diet Tracker": Integrate the "ProjectsSushi" model with diet tracking applications. It could automatically identify the type of sushi and provide the nutritional information, helping users track their calorie intake.
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"Interactive Learning Tool for Culinary Students": This model could be used in an educational setting, where culinary students learning about sushi could confirm if they've created the sushi correctly.
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"Restaurant Inventory Management": Restaurants could use the model to track the sushi on their conveyor belts or displays and keep an accurate real-time inventory of available sushi dishes.
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"Sushi Quality Control": Sushi manufacturing companies could use this model for quality control, identifying whether their products match the standard Sushi classes before being dispatched to market.
Use This Trained Model
Try it in your browser, or deploy via our Hosted Inference API and other deployment methods.
Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
projectssushi_dataset,
title = { ProjectsSushi Dataset },
type = { Open Source Dataset },
author = { Silantiev Maksim },
howpublished = { \url{ https://universe.roboflow.com/silantiev-maksim/projectssushi } },
url = { https://universe.roboflow.com/silantiev-maksim/projectssushi },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { may },
note = { visited on 2025-05-13 },
}