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Datasets:
manot
/
football-players

Tasks:
Object Detection
Tags:
roboflow
roboflow2huggingface
Dataset card Viewer Files Community
Dataset Viewer
Auto-converted to Parquet
Viewer
image_id
int64
0
118
image
imagewidth (px)
640
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width
int32
640
640
height
int32
640
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manot/football-players

Dataset Labels 

['football', 'player']

Number of Images 

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

How to Use 

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.

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