README.md

metadata

language:
  - en
license:
  - cc0-1.0
pretty_name: Cat and Dog
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - image-classification
dataset_info:
  features:
    - name: image
      dtype: image
    - name: labels
      dtype:
        class_label:
          names:
            '0': cat
            '1': dog
  splits:
    - name: train
      num_bytes: 166451650
      num_examples: 8000
    - name: test
      num_bytes: 42101650
      num_examples: 2000
  download_size: 227859268
  dataset_size: 208553300
  size_in_bytes: 436412568

Dataset Description

• Homepage: Cat and Dog
• Download Size 217.30 MiB
• Generated Size 198.89 MiB
• Total Size 416.20 MiB

Dataset Summary

A dataset from kaggle with duplicate data removed.

Data Fields

The data instances have the following fields:

• image: A PIL.Image.Image object containing the image. 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].
• labels: an int classification label.

Class Label Mappings:

{
  "cat": 0,
  "dog": 1,
}

Data Splits

	train	test
# of examples	8000	2000

>>> from datasets import load_dataset

>>> dataset = load_dataset("Bingsu/Cat_and_Dog")
>>> dataset
DatasetDict({
    train: Dataset({
        features: ['image', 'labels'],
        num_rows: 8000
    })
    test: Dataset({
        features: ['image', 'labels'],
        num_rows: 2000
    })
})

>>> dataset["train"].features
{'image': Image(decode=True, id=None), 'labels': ClassLabel(num_classes=2, names=['cat', 'dog'], id=None)}