---
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