task_categories:image-classification size_categories:1K<n<10K source_datasets:original language:en license:cc0-1.0 region:us
---
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: 166451650.0
    num_examples: 8000
  - name: test
    num_bytes: 42101650.0
    num_examples: 2000
  download_size: 227859268
  dataset_size: 208553300.0
  size_in_bytes: 436412568.0
---

## Dataset Description
- **Homepage:** [Cat and Dog](https://www.kaggle.com/datasets/tongpython/cat-and-dog)
- **Download Size** 217.30 MiB
- **Generated Size** 198.89 MiB
- **Total Size** 416.20 MiB

### Dataset Summary

A dataset from [kaggle](https://www.kaggle.com/datasets/tongpython/cat-and-dog) 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 |


```python
>>> 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)}
```