victor/autotrain-donut-vs-croissant-1417653460
Image Classification
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image
imagewidth (px) 512
512
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class label 2
classes |
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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0croissant
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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1donut
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This dataset has been automatically processed by AutoTrain for project donut-vs-croissant.
The BCP-47 code for the dataset's language is unk.
A sample from this dataset looks as follows:
[
{
"image": "<512x512 RGB PIL image>",
"target": 0
},
{
"image": "<512x512 RGB PIL image>",
"target": 0
}
]
The dataset has the following fields (also called "features"):
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(num_classes=2, names=['croissant', 'donut'], id=None)"
}
This dataset is split into a train and validation split. The split sizes are as follow:
Split name | Num samples |
---|---|
train | 133 |
valid | 362 |