Datasetpublic

keremberke/nfl-object-detection

Dataset Labels ['helmet', 'helmet-blurred', 'helmet-difficult', 'helmet-partial', 'helmet-sideline'] Number of Images {'valid': 1989, 'train': 6963, 'test': 995} How to Use Install datasets: pip install datasets Load the dataset: from datasets import load_dataset ds = load_dataset("keremberke/nfl-object-detection", name="full") example = ds['train'][0] Roboflow Dataset Page… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/nfl-object-detection.

sourceHugging Faceupdated 4y agoView on Hugging Face

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Dataset Card

Dataset Labels

['helmet', 'helmet-blurred', 'helmet-difficult', 'helmet-partial', 'helmet-sideline']

Number of Images

json

{'valid': 1989, 'train': 6963, 'test': 995}

How to Use

—Install datasets:

bash

pip install datasets

—Load the dataset:

python

from datasets import load_dataset

ds = load_dataset("keremberke/nfl-object-detection", name="full")
example = ds['train'][0]

Roboflow Dataset Page

https://universe.roboflow.com/home-mxzv1/nfl-competition/dataset/1

Citation

@misc{ nfl-competition_dataset,
    title = { NFL-competition Dataset },
    type = { Open Source Dataset },
    author = { home },
    howpublished = { \\url{ https://universe.roboflow.com/home-mxzv1/nfl-competition } },
    url = { https://universe.roboflow.com/home-mxzv1/nfl-competition },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { sep },
    note = { visited on 2023-01-18 },
}

License

Public Domain

Dataset Summary

This dataset was exported via roboflow.com on December 29, 2022 at 8:12 PM 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 unstructured image data
—annotate, and create datasets
—export, train, and deploy computer vision models
—use active learning to improve your dataset over time

It includes 9947 images. Helmets 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 1280x720 (Stretch)

No image augmentation techniques were applied.