onetwoskmaersk Object Detection Dataset and Pre-Trained Model by blyolo

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Explore Dataset

Here are a few use cases for this project:

Maritime Logistics Optimization: The model can be utilized by shipping companies to automate the recognition of key information such as shipper, consignee, port of loading and unloading, vessel, etc, from the bill of lading, thus eliminating manual entry and enhancing the efficiency and accuracy of their logistics operations.
Document Management Systems: This model can be implemented into existing Document Management Systems (DMS) to auto-extract and catalog key data from physical or digital shipping documents, thereby improving searchability and organization within the system.
Supply Chain Automation: For businesses handling import-export operations, the model can be utilized to extract and automate information flow from the shipping bill into their Enterprise Resource Planning (ERP) or Supply Chain Management (SCM) systems, reducing manual intervention.
Customs Verification & Compliance: Customs departments could use the model to identify key information automatically from a cargo's shipping bill, speeding up verification processes, ensuring compliance and enhancing border security measures.
Predictive Analytics and Forecasting: The model can contribute to the big data repository of shipping logistics where machine learning algorithms are applied for predicting shipment arrival times, identifying potential bottlenecks at certain ports, analyzing supply chain performance, etc.

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{
                            onetwoskmaersk_dataset,
                            title = { onetwoskmaersk Dataset },
                            type = { Open Source Dataset },
                            author = { blyolo },
                            howpublished = { \url{ https://universe.roboflow.com/blyolo/onetwoskmaersk } },
                            url = { https://universe.roboflow.com/blyolo/onetwoskmaersk },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-04-26 },
                            }