Bingsu/Gameplay_Images

Gameplay Images

A dataset from kaggle.

This is a dataset of 10 very famous video games in the world.

These include

• Among Us
• Apex Legends
• Fortnite
• Forza Horizon
• Free Fire
• Genshin Impact
• God of War
• Minecraft
• Roblox
• Terraria

There are 1000 images per class and all are sized 640 x 360. They are in the .png format.

This Dataset was made by saving frames every few seconds from famous gameplay videos on Youtube.

※ This dataset was uploaded in January 2022. Game content updated after that will not be included.

License

CC-BY-4.0

Dataset Structure

Data Instance

>>> from datasets import load_dataset

>>> dataset = load_dataset("Bingsu/Gameplay_Images")
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 10000
    })
})

>>> dataset["train"].features
{'image': Image(decode=True, id=None),
 'label': ClassLabel(num_classes=10, names=['Among Us', 'Apex Legends', 'Fortnite', 'Forza Horizon', 'Free Fire', 'Genshin Impact', 'God of War', 'Minecraft', 'Roblox', 'Terraria'], id=None)}

Data Size

download: 2.50 GiB
generated: 1.68 GiB
total: 4.19 GiB

Data Fields

• image: Image
  • A PIL.Image.Image object containing the image. size=640x360
  • Note that when accessing the image column: dataset[0]["image"] the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the "image" column, i.e. dataset[0]["image"] should always be preferred over dataset["image"][0].
• label: an int classification label.

Class Label Mappings:

{
    "Among Us": 0,
    "Apex Legends": 1,
    "Fortnite": 2,
    "Forza Horizon": 3,
    "Free Fire": 4,
    "Genshin Impact": 5,
    "God of War": 6,
    "Minecraft": 7,
    "Roblox": 8,
    "Terraria": 9
}

>>> dataset["train"][0]
{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=640x360>,
 'label': 0}

Data Splits

	train
# of data	10000

Note

train_test_split

>>> ds_new = dataset["train"].train_test_split(0.2, seed=42, stratify_by_column="label")
>>> ds_new
DatasetDict({
    train: Dataset({
        features: ['image', 'label'],
        num_rows: 8000
    })
    test: Dataset({
        features: ['image', 'label'],
        num_rows: 2000
    })
})