furniturelabel Object Detection Dataset and Pre-Trained Model by A304

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Explore Dataset

Here are a few use cases for this project:

Smart Homes: This model can be integrated into smart home systems to help identify furniture items for automation purposes. For example, it could recognize when a chair is unoccupied or a lamp needs replacing, providing an interactive, interconnected living environment.
E-Commerce & Retail: Online sellers or furniture manufacturers can deploy this model to categorize their products more accurately, enabling customers to search and find their desired furniture pieces with more precision.
Interior Design: Designers and homeowners can use the model to try out different furniture arrangements virtually. By recognizing different pieces, the model can suggest optimal layouts or identify space fillers needed in a room.
Assistive Technology: This model can be useful in creating tools for visually impaired individuals, identifying and vocalizing different pieces of furniture for navigation and usage.
Home Inventory Management: Homeowners and renters can use the model to catalog their belongings for insurance purposes, making the process more efficient and accurate.

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{
                            furniturelabel_dataset,
                            title = { furniturelabel Dataset },
                            type = { Open Source Dataset },
                            author = { A304 },
                            howpublished = { \url{ https://universe.roboflow.com/a304-txrwp/furniturelabel } },
                            url = { https://universe.roboflow.com/a304-txrwp/furniturelabel },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { nov },
                            note = { visited on 2024-04-20 },
                            }