Universe

yolo_labels Computer Vision Dataset

byNouran YoussryTask:
Object Detection
License:CC BY 4.070 views3 downloads
See all 2.3k imagesyolo_labels masks sample showing no_maskyolo_labels masks sample showing no_maskyolo_labels masks sample showing incorrect_maskyolo_labels masks sample showing no_maskyolo_labels masks sample showing incorrect_maskyolo_labels masks sample showing incorrect_maskyolo_labels masks sample showing no_maskyolo_labels masks sample showing incorrect_maskyolo_labels masks sample showing incorrect_maskyolo_labels masks sample showing incorrect_maskyolo_labels masks sample showing incorrect_maskyolo_labels masks sample showing no_mask

About yolo_labels Dataset

Here are a few use cases for this project:

  1. Public Health Compliance: This model could be used to monitor public spaces, events, or transportation systems to ensure compliance with public health guidelines, particularly during times of pandemic. It could detect individuals not wearing masks or wearing them incorrectly, allowing authorities to swiftly respond.

  2. Automated Mask Compliance in Workplaces: The model can help ensure employee safety by monitoring mask usage within work environments, especially in healthcare and industrial sectors where safety protocols are stringent.

  3. Education and Training: It could be used in educational videos or applications to demonstrate the correct way to wear masks and emphasize the importance of correct mask wearing.

  4. Social Research: Researchers studying behavioral changes in response to public health crises could use this model to analyze the prevalence and correctness of mask usage in different demographics, locations, or over time.

  5. Retail and Hospitality Management: The model could facilitate the management of retail stores, restaurants, or hotels in ensuring the safety of customers and complying with health guidelines. The system could alert staff when customers are not following mask requirements allowing them to intervene effectively.

Use Free Mask, ' and Incorrect_mask Detection API

Powered by general detection model

Code
pip install inference-sdk
# 1. Import the library
from inference_sdk import InferenceHTTPClient

# 2. Connect to your workspace
client = InferenceHTTPClient(
  api_url="https://serverless.roboflow.com",
  api_key="API_KEY"
)

# 3. Run your workflow on an image
result = client.run_workflow(
  workspace_name="<YOUR_WORKSPACE>",
  workflow_id="<YOUR_WORKFLOW_ID>",
  images={
    "image": "YOUR_IMAGE.jpg"  # Path to your image file
  },
  parameters={
    "classes": "mask, ', incorrect_mask, no_mask"
  },
  use_cache=True  # cache workflow definition for 15 minutes
)

# 4. Get your results
print(result)

Run on custom image

Drop an image here or click to upload

Detecting classes:
Or try a test image 

Cite This Project

LicenseCC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{ yolo_labels_dataset,
  title = { yolo_labels Dataset },
  type = { Open Source Dataset },
  author = { Nouran Youssry },
  howpublished = { \url{ https://universe.roboflow.com/nouran-youssry/yolo_labels } },
  url = { https://universe.roboflow.com/nouran-youssry/yolo_labels },
  journal = { Roboflow Universe },
  publisher = { Roboflow },
  year = { 2021 },
  month = { nov },
  note = { visited on 2026-07-29 },
}

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