3525 Floor detection Semantic Segmentation Dataset and Pre-Trained Model by Tina Liu

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Explore Dataset

Here are a few use cases for this project:

Retail Analytics: This model can be utilized to analyze the floor plan of retail stores. By identifying floors and shelves, it could provide data on product placement, customer's walking patterns, or best locations for advertising displays.
Robot Navigation: In warehouses or industrial settings, this model could be integrated into autonomous ground vehicles or robots to identify and navigate floor spaces, and avoid shelving units or other obstacles.
Layout Optimization: It can be used by architects, interior designers, or construction planners to optimize the use of space within a building. It can help identify whether a change in physical layout could improve functionality and usability.
Virtual Reality Space Modeling: Using this model, developers could create more realistic virtual environments for VR or AR applications such as simulations, games, or training programs.
Security and Surveillance: Integration into security systems to analyze human traffic, recognize unusual placements, or track an individual's behavior, which could be useful in both retail and security applications.

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{
                            3525-floor-detection_dataset,
                            title = { 3525 Floor detection Dataset },
                            type = { Open Source Dataset },
                            author = { Tina Liu },
                            howpublished = { \url{ https://universe.roboflow.com/tina-liu-suczt/3525-floor-detection } },
                            url = { https://universe.roboflow.com/tina-liu-suczt/3525-floor-detection },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-06-18 },
                            }