README.md

metadata

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

Dataset Labels

['barricade', 'dumpster', 'excavators', 'gloves', 'hardhat', 'mask', 'no-hardhat', 'no-mask', 'no-safety vest', 'person', 'safety net', 'safety shoes', 'safety vest', 'dump truck', 'mini-van', 'truck', 'wheel loader']

Number of Images

{'train': 307, 'valid': 57, 'test': 34}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("keremberke/construction-safety-object-detection", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/roboflow-universe-projects/construction-site-safety/dataset/1

Citation

@misc{ construction-site-safety_dataset,
    title = { Construction Site Safety Dataset },
    type = { Open Source Dataset },
    author = { Roboflow Universe Projects },
    howpublished = { \\url{ https://universe.roboflow.com/roboflow-universe-projects/construction-site-safety } },
    url = { https://universe.roboflow.com/roboflow-universe-projects/construction-site-safety },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jan },
    note = { visited on 2023-01-26 },
}

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on December 29, 2022 at 11:22 AM 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 unstructured image data
• annotate, and create datasets
• export, train, and deploy computer vision models
• use active learning to improve your dataset over time

It includes 398 images. Construction are annotated in COCO format.

The following pre-processing was applied to each image:

• Auto-orientation of pixel data (with EXIF-orientation stripping)

No image augmentation techniques were applied.