Empty Shelves Object Detection Dataset and Pre-Trained Model by ADMath DL2

Images

368 images

Explore Dataset

Here are a few use cases for this project:

Retail Replenishment: The model can aid businesses in maintaining optimal inventory by accurately tracking when shelves become empty and notifying staff for timely restocking.
Smart Shopping Applications: In a smart store setup, the model could alert shoppers about the lack of product availability in real-time or suggest the nearest store location where the product is available.
Theft Detection: By tracking irregular or sudden emptying of shelves, this model could contribute to loss prevention efforts in supermarkets and other retail stores.
Warehouse Management: The model can assist in efficient warehouse management by rapidly identifying empty storage locations and optimizing inventory redistribution.
Supply Chain Management: By integrating this model with an Inventory Management System, organizations can automatically trigger procurement processes once the product stock hits a threshold level, ensuring a seamless supply chain operation.

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{
                            empty-shelves-pelqt_dataset,
                            title = { Empty Shelves Dataset },
                            type = { Open Source Dataset },
                            author = { ADMath  DL2 },
                            howpublished = { \url{ https://universe.roboflow.com/admath-dl2/empty-shelves-pelqt } },
                            url = { https://universe.roboflow.com/admath-dl2/empty-shelves-pelqt },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { jul },
                            note = { visited on 2024-05-06 },
                            }