Product_it Object Detection Dataset and Pre-Trained Model by ITSQProduct

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Explore Dataset

Here are a few use cases for this project:

E-commerce Inventory Management: The "Product_it" model could be helpful for e-commerce companies to automate their inventory management process. The model can classify stock photos into respective categories such as 'Bread', 'BarChair', 'iPad_Pro11', and so on. This can help companies accurately track and manage stock levels of their different products.
Retail Store Automation: This model can be used in retail stores for automatic item identification at checkout points. It avoids the need for manual barcode scanning and can swiftly identify multiple items, even differentiating between different models of the same product (e.g., iPhone_14ProMax vs iPhone_13mini).
Automated Surveillance: The model can also help in situations requiring surveillance, able to identify things like "HiViewCCTV", "SonyCCTV", "DahuaCCTV", and coin-operated games. This could be useful in both security and customer behavior studies in public places like malls and arcades.
Household Management: This model could be applied in a smart home context, automatically identifying and cataloging belongings to aid in organization or insurance processes. It could identify everything from electronics (like iPads, speakers, or laptops) to smaller personal items (like hand cream or tissues).
Health Care and Rehabilitation: The model could assist in healthcare and rehabilitation processes, as it's capable of identifying body parts (like 'Knee', 'Elbow', 'TwistedAnkle'). This could facilitate the development of rehabilitation exercises or tools that detect and analyze motion based on these body parts.

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{
                            product_it_dataset,
                            title = { Product_it Dataset },
                            type = { Open Source Dataset },
                            author = { ITSQProduct },
                            howpublished = { \url{ https://universe.roboflow.com/itsqproduct/product_it } },
                            url = { https://universe.roboflow.com/itsqproduct/product_it },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-04-23 },
                            }