README.md

metadata

task_categories:
  - object-detection
tags:
  - roboflow
  - roboflow2huggingface

Dataset Labels

['football', 'player']

Number of Images

{'valid': 87, 'train': 119}

How to Use

• Install datasets:

pip install datasets

• Load the dataset:

from datasets import load_dataset

ds = load_dataset("manot/football-players", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/konstantin-sargsyan-wucpb/football-players-2l81z/dataset/1

Citation

@misc{ football-players-2l81z_dataset,
    title = { football-players Dataset },
    type = { Open Source Dataset },
    author = { Konstantin Sargsyan },
    howpublished = { \\url{ https://universe.roboflow.com/konstantin-sargsyan-wucpb/football-players-2l81z } },
    url = { https://universe.roboflow.com/konstantin-sargsyan-wucpb/football-players-2l81z },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { jun },
    note = { visited on 2023-06-12 },
}

License

MIT

Dataset Summary

This dataset was exported via roboflow.com on June 12, 2023 at 10:10 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 206 images. Players 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)

No image augmentation techniques were applied.