CoolFace
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keremberke/csgo-object-detection

Dataset Labels ['ct', 'cthead', 't', 'thead'] Number of Images {'train': 3879, 'valid': 383, 'test': 192} How to Use Install datasets: pip install datasets Load the dataset: from datasets import load_dataset ds = load_dataset("keremberke/csgo-object-detection", name="full") example = ds['train'][0] Roboflow Dataset Page https://universe.roboflow.com/asd-culfr/wlots/dataset/1 Citation @misc{ wlots_dataset… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/csgo-object-detection.

sourceHugging Faceupdated 4y agoView on Hugging Face
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Dataset Card

<div align="center"> <img width="640" alt="keremberke/csgo-object-detection" src="https://huggingface.co/datasets/keremberke/csgo-object-detection/resolve/main/thumbnail.jpg"> </div>

Dataset Labels

['ct', 'cthead', 't', 'thead']

Number of Images

json
{'train': 3879, 'valid': 383, 'test': 192}

How to Use

bash
pip install datasets
  • Load the dataset:
python
from datasets import load_dataset

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

Roboflow Dataset Page

https://universe.roboflow.com/asd-culfr/wlots/dataset/1

Citation

@misc{ wlots_dataset,
    title = { wlots Dataset },
    type = { Open Source Dataset },
    author = { asd },
    howpublished = { \\url{ https://universe.roboflow.com/asd-culfr/wlots } },
    url = { https://universe.roboflow.com/asd-culfr/wlots },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2022 },
    month = { may },
    note = { visited on 2023-01-27 },
}

License

CC BY 4.0

Dataset Summary

This dataset was exported via roboflow.com on December 28, 2022 at 8:08 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 4454 images. Ct-cthead-t-thead 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 416x416 (Fill (with center crop))

The following augmentation was applied to create 3 versions of each source image:

  • Random brigthness adjustment of between -15 and +15 percent