Wall_Segmentation Instance Segmentation Dataset and Pre-Trained Model by Emre

Images

2015 images

Explore Dataset

Here are a few use cases for this project:

"Interior Design Planning": An application where users can upload images of their rooms and the model identifies the walls, allowing the user clearly visualize different coloring or wallpapering options.
"Architectural Analysis": The model can be used to assess images or videos of buildings in order to automate the process of identifying the number, type and structure of walls, helping architects in their design process.
"Virtual Reality Gaming": Use the model in developing AR/VR experiences, where real-world walls can be translated into game boundaries or virtual obstacles, enhancing immersion and interactivity.
"Structural Damage Assessment": For insurance or real estate inspections, the AI model could assess images in order to identify walls and evaluate their condition, providing a tool for automated preliminary analysis.
"Indoor Robot Navigation": Utilize the model in indoor robotics to help machines identify walls and map indoor spaces for better navigation and obstacle avoidance.

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{
                            wall_segmentation-flyds_dataset,
                            title = { Wall_Segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { Emre },
                            howpublished = { \url{ https://universe.roboflow.com/emre-aer6z/wall_segmentation-flyds } },
                            url = { https://universe.roboflow.com/emre-aer6z/wall_segmentation-flyds },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jun },
                            note = { visited on 2024-05-16 },
                            }