--- language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Gameplay Images size_categories: - 1K>> from datasets import load_dataset >>> dataset = load_dataset("Bingsu/Gameplay_Images") DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 10000 }) }) ``` ```python >>> 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: ```json { "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 } ``` ```python >>> dataset["train"][0] {'image': , 'label': 0} ``` ### Data Splits | | train | | ---------- | -------- | | # of data | 10000 | ### Note #### train_test_split ```python >>> 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 }) }) ```