photos Instance Segmentation Dataset and Pre-Trained Model by zenotech

Images

110 images

Explore Dataset

Here are a few use cases for this project:

Smart Photo Album: Utilize the "photos" model to automatically organize and tag images in a digital photo album based on the identified people classes, making it easier to search for specific images or moments involving particular individuals.
Security and Surveillance: Use the "photos" model in video surveillance systems to identify individuals or groups of people, monitor their actions, and detect suspicious behavior or unauthorized presence in certain areas.
Advertising and Marketing: Analyze photos of potential customers or target audiences to understand their demographics and preferences, thereby enabling more personalized and effective ad campaigns, product recommendations, and content creation.
Augmented Reality Assistance: Incorporate the "photos" model into augmented reality (AR) applications for recognizing and providing contextual information about people. For example, display relevant social media profiles, career details, or shared interests when looking at someone through an AR headset or smartphone app.
Assistive Technologies for the Visually Impaired: Integrate the "photos" model into accessibility tools for visually impaired users, enabling the identification of people in their surroundings and providing essential information like gender, age, and location, thus helping them navigate social situations more comfortably.

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{
                            photos-gmmo6_dataset,
                            title = { photos Dataset },
                            type = { Open Source Dataset },
                            author = { zenotech },
                            howpublished = { \url{ https://universe.roboflow.com/zenotech/photos-gmmo6 } },
                            url = { https://universe.roboflow.com/zenotech/photos-gmmo6 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { oct },
                            note = { visited on 2024-04-29 },
                            }