Datasets:
Tasks:
Image Classification
Modalities:
Image
Formats:
parquet
Languages:
English
Size:
10K - 100K
License:
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)} | |
``` |