hairnet Object Detection Dataset and Pre-Trained Model by agts

Images

3643 images

Explore Dataset

Here are a few use cases for this project:

Food Processing Compliance: The computer vision model can be used to monitor and ensure workers in food processing units are wearing hairnets for compliance with hygiene regulations. It can send alerts or notifications if it detects someone without a hairnet.
Healthcare Facilities: In hospitals and healthcare settings, it could be used to enforce wearing of hairnets in sterile environments such as operating rooms and labs to ensure health and safety regulations are followed.
Cooking Classes and Culinary Schools: The model can be used to ensure students are following proper hygiene and safety guidelines during their practice sessions in kitchens.
Industrial Site Safety: In manufacturing or industrial units where hairnets are part of the mandatory safety gear, the Hairnet model could help to comply with safety standards by detecting workers not wearing hairnets.
Hairnet Manufacturing Quality Control: The model can be utilized in quality control processes for the manufacturing of hairnets themselves – detecting if a finished product is defective or not meeting specifications.

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{
                            hairnet_dataset,
                            title = { hairnet Dataset },
                            type = { Open Source Dataset },
                            author = { agts },
                            howpublished = { \url{ https://universe.roboflow.com/agts/hairnet } },
                            url = { https://universe.roboflow.com/agts/hairnet },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { jan },
                            note = { visited on 2024-05-01 },
                            }