---
language:
- en
license: mit
tags:
- image-classification
- computer-vision
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
dataset_info:
  features:
  - name: image
    dtype:
      array3_d:
        shape:
        - 128
        - 128
        - 3
        dtype: uint8
  - name: label
    dtype:
      class_label:
        names:
          '0': cats
          '1': dogs
  splits:
  - name: train
    num_bytes: 921696000
    num_examples: 8000
  - name: test
    num_bytes: 230424000
    num_examples: 2000
  download_size: 487392383
  dataset_size: 1152120000
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# Cats and Dogs Image Classification Dataset

This dataset contains images of cats and dogs, intended for image classification tasks. It includes two classes: "cats" and "dogs".

## Dataset Structure

The dataset is structured into two splits:

* **train**: Contains 8000 images for training.
* **test**: Contains 2000 images for testing.

Images are stored in RGB format with a resolution of 128x128 pixels.

## Data Loading and Usage

The dataset can be loaded using the Hugging Face Datasets library:

```python
from datasets import load_dataset

dataset = load_dataset("cats_dogs_dataset")
This will return a DatasetDict object with the train and test splits.

Example Usage
Python

from datasets import load_dataset

dataset = load_dataset("cats_dogs_dataset")

# Access the first training example
example = dataset["train"]

# Print the image and label
print(example["image"])
print(example["label"])