tile best Object Detection Dataset and Pre-Trained Model by bruh

Images

774 images

Explore Dataset

Here are a few use cases for this project:

Home Renovation Planning: Users can search and identify various tile designs to plan and visualize their home renovation projects, such as creating unique backsplashes or flooring patterns in kitchens and bathrooms, using the "tile best" computer vision model.
Architecture and Interior Design: Architects and interior designers can utilize the model to quickly explore and select appropriate tile materials and aesthetics for their projects, making the design process more efficient and tailored to clients' preferences.
Automated Tile Quality Control: Tile manufacturers can use the "tile best" model to automatically inspect and categorize different tile classes during the manufacturing process, ensuring that only high-quality tiles reach the market and minimizing possible defects.
Tile Retail Inventory Management: Tile stores can implement the model for their inventory management system to automatically recognize and sort various tile classes, streamlining the process of organizing, restocking, and displaying their products, contributing to a better customer experience.
Augmented Reality Applications: Developers can integrate the "tile best" model into augmented reality (AR) applications for home improvement, allowing users to virtually try out various tile styles and classes in their own space before making a purchase, ultimately enhancing the decision-making 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{
                            tile-best_dataset,
                            title = { tile best Dataset },
                            type = { Open Source Dataset },
                            author = { bruh },
                            howpublished = { \url{ https://universe.roboflow.com/bruh-s0vei/tile-best } },
                            url = { https://universe.roboflow.com/bruh-s0vei/tile-best },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { feb },
                            note = { visited on 2024-04-28 },
                            }