lays Object Detection Dataset and Pre-Trained Model by mehar

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Explore Dataset

Here are a few use cases for this project:

Retail Inventory Management: The "lays" model can be used in supermarkets and convenience stores to manage inventory levels by identifying and counting the number of each variety of Lays chips on the shelves. This can help in re-stocking decisions and ensuring there are sufficient supplies of each flavor.
Customer Preference Analysis: Retailers can use the model to automatically analyze security footage to learn which Lay's flavor is the most popular. It can observe which bags customers pick up the most, leading to the generation of customer preference data.
Smart Vending Machines: In vending machines, "lays" can be used to monitor the available stock and make the vending machine 'aware' of running low or out-of-stock situations, thereby prompting restocking or updating available options to customers.
Waste Management & Recycling: The model can be employed in waste sorting facilities to identify Lays chip packages, enabling better categorization of waste, and contributing to more efficient recycling processes.
Augmented Reality Gaming: In a scavenger hunt-style AR game, players could be tasked with 'collecting' different varieties of Lay's chips. The model could be used to confirm whether the player has found the correct item.

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{
                            lays-txw7q_dataset,
                            title = { lays Dataset },
                            type = { Open Source Dataset },
                            author = { mehar },
                            howpublished = { \url{ https://universe.roboflow.com/mehar/lays-txw7q } },
                            url = { https://universe.roboflow.com/mehar/lays-txw7q },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { mar },
                            note = { visited on 2024-04-29 },
                            }