Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
| 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](https://www.kaggle.com/datasets/aditmagotra/gameplay-images) | |
| - **Download Size** 2.50 GiB | |
| - **Generated Size** 1.68 GiB | |
| - **Total Size** 4.19 GiB | |
| A dataset from [kaggle](https://www.kaggle.com/datasets/aditmagotra/gameplay-images). | |
| 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 | |
| ```python | |
| >>> 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<br> | |
| generated: 1.68 GiB<br> | |
| 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': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=640x360>, | |
| '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 | |
| }) | |
| }) | |
| ``` | |