Star_strip Object Detection Dataset and Pre-Trained Model by self

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Explore Dataset

Here are a few use cases for this project:

Retail Inventory Management: The "Star_strip" model can be used in supermarkets and retail stores to monitor real-time shelf inventories of Nestle products like Nescafe and Maggi, and to promptly alert staff when restocking is needed.
Brand Placement Analysis: Marketing agencies can utilize this model to analyze product visibility and optimal product placement within stores, and to verify if brands like Nestle, Star, and Everyday are correctly positioned and stand out from competitor products.
Supply Chain Verification: Food distributors and warehouses can use the model to verify the correct types of products received in shipments, significantly streamline sorting work, and reduce human error.
Consumer Behavior Studies: The model can be used to track which products are most frequently picked up by customers, providing insights for retail store planograms and offering data for advertising and promotional strategies.
Automated Checkout Systems: The "Star_strip" model can be implemented in autonomous checkout systems to identify and charge for Nestle products like Nestle Good Food Good Life, Nescafe, and Maggi as customers leave the store, reducing the need for human cashiers.

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.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            star_strip_dataset,
                            title = { Star_strip Dataset },
                            type = { Open Source Dataset },
                            author = { self },
                            howpublished = { \url{ https://universe.roboflow.com/self-pmzhg/star_strip } },
                            url = { https://universe.roboflow.com/self-pmzhg/star_strip },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-05-04 },
                            }