Mask_RCNN Object Detection Dataset and Pre-Trained Model by esprit

Images

1570 images

Explore Dataset

Here are a few use cases for this project:

Automation in Warehousing: This model could be used to automate stock tracking and pallet movement in warehouses. By identifying and monitoring pallets, one could design a system where forklifts are autonomously guided to the correct pallet locations, increasing efficiency, and reducing errors.
Material Recyclability: Given that there are pallets made of different materials, another use-case could be in recycling facilities to sort different types of pallets automatically for effective processing.
Manufacturing Quality Control: Manufacturers could use the model to automatically inspect and sort wooden pallets based on criteria such as quality of construction, dimensions, and type. This would reduce the cost of manual inspection.
Retail and Logistics: In a retail or logistics context, this model could help in improving efficiency in unloading or loading freight by identifying type and position of pallets, thus improving load balance and transport safety.
Online Pallet Marketplace: Websites selling or reusing pallets could use this model to classify the pallets in their images. This would make it easier for customers to search for, find, and understand the products they're interested in purchasing.

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{ mask_rcnn-73vt5_dataset,
    title = { Mask_RCNN Dataset },
    type = { Open Source Dataset },
    author = { esprit },
    howpublished = { \url{ https://universe.roboflow.com/esprit-moito/mask_rcnn-73vt5 } },
    url = { https://universe.roboflow.com/esprit-moito/mask_rcnn-73vt5 },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { nov },
    note = { visited on 2023-12-02 },
}