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@@ -5,7 +5,7 @@ licenses:
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  - odbl-1.0
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  pretty_name: Human Action Recognition
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  size_categories:
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- - 1K<n<10K
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  source_datasets:
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  - original
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  task_categories:
@@ -70,4 +70,35 @@ The data instances have the following fields:
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  ### Data Splits
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  | | train | test |
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  |---------------|--------|-----:|
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- | # of examples | 12600 | 5400 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - odbl-1.0
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  pretty_name: Human Action Recognition
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  size_categories:
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+ - 10K<n<100K
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  source_datasets:
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  - original
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  task_categories:
 
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  ### Data Splits
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  | | train | test |
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  |---------------|--------|-----:|
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+ | # of examples | 12600 | 5400 |
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+
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+ ### Data Size
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+
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+ - download: 311.96 MiB
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+ - generated: 312.59 MiB
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+ - total: 624.55 MiB
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+
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+ ```pycon
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+ >>> from datasets import load_dataset
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+
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+ >>> ds = load_dataset("Bingsu/Human_Action_Recognition")
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+ >>> ds
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+ DatasetDict({
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+ test: Dataset({
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+ features: ['image', 'labels'],
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+ num_rows: 5400
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+ })
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+ train: Dataset({
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+ features: ['image', 'labels'],
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+ num_rows: 12600
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+ })
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+ })
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+
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+ >>> ds["train"].features
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+ {'image': Image(decode=True, id=None),
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+ 'labels': ClassLabel(num_classes=15, names=['calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop'], id=None)}
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+
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+ >>> ds["train"][0]
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+ {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=240x160>,
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+ 'labels': 11}
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+ ```