README.md

metadata

task_categories:
  - object-detection
tags:
  - roboflow
  - roboflow2huggingface
  - Construction
  - Utilities
  - Manufacturing
  - Logistics
  - Ppe
  - Assembly Line
  - Warehouse
  - Factory
  - Construction
  - Logistics
  - Utilities
  - Damage Risk
  - Ppe

Dataset Labels

['hardhat', 'no-hardhat']

Number of Images

{'test': 2001, 'train': 13782, 'valid': 3962}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("keremberke/hard-hat-detection", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/roboflow-universe-projects/hard-hats-fhbh5/dataset/2

Citation

@misc{ hard-hats-fhbh5_dataset,
    title = { Hard Hats Dataset },
    type = { Open Source Dataset },
    author = { Roboflow Universe Projects },
    howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/hard-hats-fhbh5 } },
    url = { https://universe.roboflow.com/roboflow-universe-projects/hard-hats-fhbh5 },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { dec },
    note = { visited on 2023-01-16 },
}

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on January 16, 2023 at 9:17 PM GMT

Roboflow is an end-to-end computer vision platform that helps you

• collaborate with your team on computer vision projects
• collect & organize images
• understand and search unstructured image data
• annotate, and create datasets
• export, train, and deploy computer vision models
• use active learning to improve your dataset over time

For state of the art Computer Vision training notebooks you can use with this dataset, visit https://github.com/roboflow/notebooks

To find over 100k other datasets and pre-trained models, visit https://universe.roboflow.com

The dataset includes 19745 images. Hardhat-ppe are annotated in COCO format.

The following pre-processing was applied to each image:

• Auto-orientation of pixel data (with EXIF-orientation stripping)
• Resize to 640x640 (Stretch)

No image augmentation techniques were applied.