Segmentation Semantic Segmentation Dataset and Pre-Trained Model by harish kumar

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Explore Dataset

Here are a few use cases for this project:

Shoe Store Recommendations: Implement the Segmentation model in a shopping app or website to identify users' current foot preferences based on their footwear (shoes, slippers, barefoot or socks) and provide personalized shoe or footwear recommendations and promotions.
Health and Hygiene Analysis: Use the Segmentation model in public spaces, like swimming pools or gyms, where users are required to have specific footwear or be barefoot, to monitor compliance with health and hygiene regulations and ensure a safe environment.
Smart Home Assistant Integration: Integrate the Segmentation model into smart home systems to detect foot classes of family members (shoes, barefoot, socks, or slippers). Automatically adjust floor heating or cooling based on the detected foot class to increase energy efficiency and enhance the user's comfort.
Rehabilitation and Physical Therapy: Utilize the Segmentation model in rehabilitation centers to monitor and track patients' foot classes during specific exercises or therapy sessions, providing therapists with valuable information to better tailor their treatments and support recovery goals.
Assistive Technology for Visually Impaired: Integrate the Segmentation model into an assistive device like a smart walking cane or mobile app for visually impaired individuals. The model can help warn the user of inappropriate footwear or the absence of footwear in certain circumstances, promoting safety and proper footing.

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{
                            segmentation-oifnz_dataset,
                            title = { Segmentation Dataset },
                            type = { Open Source Dataset },
                            author = { harish kumar },
                            howpublished = { \url{ https://universe.roboflow.com/harish-kumar-gjulh/segmentation-oifnz } },
                            url = { https://universe.roboflow.com/harish-kumar-gjulh/segmentation-oifnz },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { feb },
                            note = { visited on 2024-04-26 },
                            }