tchibocoffee Object Detection Dataset and Pre-Trained Model by PRofiFLITZER

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Explore Dataset

Here are a few use cases for this project:

Inventory Management: Utilize the tchibocoffee computer vision model in retail stores or warehouses to monitor the stock levels of various coffee products and identify empty shelf spaces, automating the re-ordering process and optimizing inventory management.
Product Recommendations: Integrate the model into a smartphone app or an in-store kiosk that assists customers in finding their desired coffee products quickly and easily by scanning the store shelves and providing personalized suggestions based on their preferences.
Quality Assurance: Use the tchibocoffee model to monitor the integrity of product packaging in production lines, ensuring that only correctly labeled and packaged coffee products are shipped to stores.
Smart Vending Machines: Implement the tchibocoffee model in vending machines that provide freshly ground coffee according to users' preferences, streamlining efficient and personalized coffee dispensing.
Store Layout Optimization: Analyze the placement of different coffee products on store shelves, utilizing insights from the tchibocoffee model to reorganize and optimize product arrangement for better customer experiences and increased sales.

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{
                            tchibocoffee_dataset,
                            title = { tchibocoffee Dataset },
                            type = { Open Source Dataset },
                            author = { PRofiFLITZER },
                            howpublished = { \url{ https://universe.roboflow.com/profiflitzer-p3zeg/tchibocoffee } },
                            url = { https://universe.roboflow.com/profiflitzer-p3zeg/tchibocoffee },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-05-06 },
                            }