boxes on conv Instance Segmentation Dataset and Pre-Trained Model by Boxes

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Explore Dataset

Here are a few use cases for this project:

Warehouse Inventory Management: This model can be used in automated warehouses to monitor and manage inventory. By identifying boxes on the conveyor belt, it can keep track of products leaving or entering the warehouse.
Occupational Safety: In manufacturing or production facilities, this model can help enforce safety protocols. It can detect whether a person's hand is too close to the conveyor belt which could pose a danger risk.
Efficiency Analysis: Businesses can use this model to analyze their product handling and sorting efficiency. By tracking the amount and speed of the boxes moving on the conveyor belt, they can make necessary adjustments to enhance productivity.
Quality Control: The model can also be used to identify any improperly sealed or damaged boxes on the conveyor belt. This can help enhance quality control by removing faulty products from the process.
Operational Streamlining: Logistics companies could use this model to optimize their parcel handling processes. By identifying boxes and people's interaction with them, the model could help uncover inefficiencies in the 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.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite this Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ boxes-on-conv_dataset,
    title = { boxes on conv Dataset },
    type = { Open Source Dataset },
    author = { Boxes },
    howpublished = { \url{ https://universe.roboflow.com/boxes-jdugd/boxes-on-conv } },
    url = { https://universe.roboflow.com/boxes-jdugd/boxes-on-conv },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { nov },
    note = { visited on 2023-12-04 },
}