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image_id
int64
0
13.8k
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imagewidth (px)
640
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int32
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int32
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9,128
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640
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9,882
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640
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4,747
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687
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12,083
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640
{ "id": [ 33702 ], "area": [ 36000 ], "bbox": [ [ 141, 43, 225, 160 ] ], "category": [ 0 ] }
11,236
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640
{ "id": [ 31389, 31390 ], "area": [ 6483, 2835 ], "bbox": [ [ 205, 153, 66.5, 97.5 ], [ 394, 166, 45, 63 ] ], "category": [ 0, 0 ] }
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keremberke/hard-hat-detection

Dataset Labels

['hardhat', 'no-hardhat']

Number of Images

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

How to Use

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.

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