del_xb Instance Segmentation Dataset and Pre-Trained Model by FOCR

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Explore Dataset

Here are a few use cases for this project:

Waste Sorting: This model could be employed in automated waste sorting systems for recognizing and classifying different waste types based on the labels detected on white plastic bags, enhancing the efficiency of recycling efforts.
Retail Inventory Management: The "del_xb" model can be used for identifying and tracking items in a retail environment based on their labels. It can assist in maintaining accurate stock counts, preventing theft, and reordering inventory.
Product Comprehension for Visually Impaired: The model can be integrated into applications designed to help visually impaired individuals identify products by reading and describing the labels on them.
Quality Control in Manufacturing: The model can be used on production lines to ensure that products are correctly labeled before shipment, helping detect and correct mislabeling errors in real time.
Shelf Stocking Assistance: The model can be utilized to assist in the efficient restocking of shelves in a supermarket or warehouse setting by identifying different products via their labels.

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{
                            del_xb_dataset,
                            title = { del_xb Dataset },
                            type = { Open Source Dataset },
                            author = { FOCR },
                            howpublished = { \url{ https://universe.roboflow.com/focr/del_xb } },
                            url = { https://universe.roboflow.com/focr/del_xb },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { jan },
                            note = { visited on 2024-04-20 },
                            }