metadata
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
Dataset Labels
['dry-person', 'object', 'wet-swimmer']
Number of Images
{'test': 77, 'valid': 153, 'train': 1608}
How to Use
- Install datasets:
pip install datasets
- Load the dataset:
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
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