asen_ecom Instance Segmentation Dataset and Pre-Trained Model by FIT

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Explore Dataset

Here are a few use cases for this project:

Warehouse Inventory Management: Utilize the "asen_ecom" computer vision model to streamline inventory management in warehouses by automating the identification and tracking of packages with LHSAG labels. This can help improve efficiency, reduce human error, and provide real-time updates on the stock levels of stored items.
E-commerce Order Processing: Leverage the model to automatically recognize and match LHSAG-labeled packages in e-commerce fulfillment centers, speeding up the sorting and shipping process for online orders while reducing mistakes that can lead to customer dissatisfaction.
Smart Retail Solutions: Integrate "asen_ecom" into smart retail solutions to automatically identify LHSAG-labeled products placed on shelves or in shopping carts, enabling intelligent inventory tracking, dynamic pricing updates, and automated checkout systems.
Package Handling Robots: Equip robots in logistics centers and postal facilities with the "asen_ecom" model to swiftly identify LHSAG-labeled packages, enabling them to sort and handle items more effectively, minimizing damage to parcels during transportation.
Customs and Security Inspections: Employ the "asen_ecom" model in customs and security checkpoints to swiftly detect LHSAG-labeled packages that may require additional attention or inspection based on predefined criteria, enhancing security measures and expediting the clearance process.

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{
                            asen_ecom_dataset,
                            title = { asen_ecom Dataset },
                            type = { Open Source Dataset },
                            author = { FIT },
                            howpublished = { \url{ https://universe.roboflow.com/fit-4aelf/asen_ecom } },
                            url = { https://universe.roboflow.com/fit-4aelf/asen_ecom },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { jan },
                            note = { visited on 2024-07-27 },
                            }