Datasets:
Tasks:
Object Detection
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
1K - 10K
License:
metadata
annotations_creators: []
language: en
license: cc0-1.0
task_categories:
- object-detection
task_ids: []
pretty_name: hard-hat-detection
tags:
- fiftyone
- image
- object-detection
dataset_summary: >
![image/png](dataset_preview.gif)
This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 5000
samples.
## Installation
If you haven't already, install FiftyOne:
```bash
pip install -U fiftyone
```
## Usage
```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'split', 'max_samples', etc
dataset = fouh.load_from_hub("voxel51/hard-hat-detection")
# Launch the App
session = fo.launch_app(dataset)
```
Dataset Card for hard-hat-detection
This dataset, contains 5000 images with bounding box annotations in the PASCAL VOC format for these 3 classes:
- Helmet
- Person
- Head
This is a FiftyOne dataset with 5000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
import fiftyone.utils.huggingface as fouh
# Load the dataset
# Note: other available arguments include 'split', 'max_samples', etc
dataset = fouh.load_from_hub("dgural/hard-hat-detection")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
Improve workplace safety by detecting people and hard hats on 5k images with bbox annotations.
- Language(s) (NLP): en
- License: cc0-1.0
Dataset Sources
Source Data
Dataset taken from https://www.kaggle.com/datasets/andrewmvd/hard-hat-detection/data and created by andrewmvd
Citation
BibTeX:
@misc{make ml, title={Hard Hat Dataset}, url={https://makeml.app/datasets/hard-hat-workers}, journal={Make ML}}