Segmentation Object Detection Dataset and Pre-Trained Model by Disrupt Lab

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Explore Dataset

Here are a few use cases for this project:

Warehouse Management System: The "Segmentation" model could be indispensable in automating inventory control in warehouses or storage areas. The model could identify and locate barcodes or QR codes in different orientations on packages, aiding in real-time tracking and efficient organization of goods.
Retail Point of Sale Systems: By identifying barcodes or QR codes from various orientations, this model can help to accelerate the checkout process, assist in price determination, and work on inventory management by tracking the sale of goods.
Shipping and Logistics: The model could be used in scanning packages, automatically updating tracking information based on detected barcodes or QR codes. This could significantly enhance efficiency and accuracy, minimizing manual input.
Document Management: This model could be employed in sorting and organizing digitized documents. By identifying up/down/left/right digits in documents, the model can assist in quick document retrieval, classification, and indexing.
Anti-Counterfeiting Measures: By identifying all the codes and digits from all angles on product packaging, this model could help in identifying fake or counterfeit products. This will be helpful especially in industries where counterfeiting is prevalent, like luxury goods, electronics, and pharmaceuticals.

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{
                            segmentation-koezk_dataset,
                            title = { Segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { Disrupt Lab },
                            howpublished = { \url{ https://universe.roboflow.com/disrupt-lab-nkhed/segmentation-koezk } },
                            url = { https://universe.roboflow.com/disrupt-lab-nkhed/segmentation-koezk },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-05-14 },
                            }