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---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
---
<div align="center">
<img width="640" alt="jigarsiddhpura/IPD" src="https://huggingface.co/datasets/jigarsiddhpura/IPD/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['dry-person', 'object', 'wet-swimmer']
```
### Number of Images
```json
{'test': 77, 'valid': 153, 'train': 1608}
```
### 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("jigarsiddhpura/IPD", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/resq/tiny-people-detection-rpi/dataset/1](https://universe.roboflow.com/resq/tiny-people-detection-rpi/dataset/1?ref=roboflow2huggingface)
### Citation
```
@misc{ tiny-people-detection-rpi_dataset,
title = { Tiny people detection RPI Dataset },
type = { Open Source Dataset },
author = { ResQ },
howpublished = { \\url{ https://universe.roboflow.com/resq/tiny-people-detection-rpi } },
url = { https://universe.roboflow.com/resq/tiny-people-detection-rpi },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { sep },
note = { visited on 2024-02-11 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.com on February 10, 2024 at 7:28 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 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 1838 images.
People 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)
The following augmentation was applied to create 3 versions of each source image:
* Randomly crop between 0 and 67 percent of the image
* Salt and pepper noise was applied to 4 percent of pixels
The following transformations were applied to the bounding boxes of each image:
* Random shear of between -5° to +5° horizontally and -5° to +5° vertically
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