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 | |
}) | |
}) | |
``` | |