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hoangbang/hard-hat-heroes

Hard Hat Heroes: Construction Safety Detection Dataset Summary A public, viewer-ready educational challenge dataset. Host-only scoring data and hidden targets are excluded. Splits Split Examples Description train 4,000 Labeled training data test 1,000 Public inputs with withheld target labels or annotations Data Fields Field Type image Image image_id string width int64 height int64 objects.bbox… See the full description on the dataset page: https://huggingface.co/datasets/hoangbang/hard-hat-heroes.

sourceHugging Faceotherupdated 2mo agoView on Hugging Face
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Hard Hat Heroes: Construction Safety Detection

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Dataset Summary

A public, viewer-ready educational challenge dataset. Host-only scoring data and hidden targets are excluded.

Splits

SplitExamplesDescription
train4,000Labeled training data
test1,000Public inputs with withheld target labels or annotations

Data Fields

FieldType
imageImage
image_idstring
widthint64
heightint64
objects.bboxlist[list[float32]] (COCO xywh)
objects.categoryClassLabel[3]

Labels

  • helmet
  • head
  • person

Viewer and Loading

The standard repository layout enables the Hugging Face Dataset Viewer after processing. Load with the datasets library using hoangbang/hard-hat-heroes.

python
from datasets import load_dataset

dataset = load_dataset("hoangbang/hard-hat-heroes")

Preparation

  • Tabular data is stored as Parquet.
  • Image classification uses class directories and ImageFolder metadata.
  • Object detection uses pixel-space COCO xywh metadata converted from YOLO annotations.
  • Audio uses WAV folders and split metadata compatible with AudioFolder.
  • groundtruth.csv, hostonly, filename maps, source archives, and local OS files are excluded.

Source, License, and Attribution

  • Source: https://www.kaggle.com/datasets/andrewmvd/hard-hat-detection
  • License note: See original Kaggle source terms and attribution before reuse.
  • Check original source terms before further redistribution or commercial use.

Limitations and Responsible Use

  • Dense scenes contain small overlapping objects.
  • Test annotations are intentionally withheld.