metadata

language:
  - en
license:
  - cc-by-4.0
multilinguality:
  - monolingual
pretty_name: Gameplay Images
size_categories:
  - 1K<n<10K
task_categories:
  - image-classification

Gameplay Images

Dataset Description

Homepage: kaggle
Download Size 2.50 GiB
Generated Size 1.68 GiB
Total Size 4.19 GiB

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
    })
})