Datasets:
Tasks:
Object Detection
Size:
< 1K
File size: 2,559 Bytes
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---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
- Construction
- Logistics
- Utilities
- Damage Risk
- Ppe
- Construction
- Utilities
- Manufacturing
- Logistics
- Ppe
- Assembly Line
- Warehouse
- Factory
---
<div align="center">
<img width="640" alt="keremberke/construction-safety-object-detection" src="https://huggingface.co/datasets/keremberke/construction-safety-object-detection/resolve/main/thumbnail.jpg">
</div>
### 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
```json
{'train': 307, 'valid': 57, 'test': 34}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
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](https://universe.roboflow.com/roboflow-universe-projects/construction-site-safety/dataset/1?ref=roboflow2huggingface)
### 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.
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