README.md

---
license: mit
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-*
---
---
dataset_name: cats_dogs_dataset
dataset_summary: A dataset of resized (128x128) RGB images of cats and dogs for image classification tasks.
dataset_description: |
  This dataset consists of images of cats and dogs organized into training and testing sets. 
  The images have been resized to 128x128 pixels and converted to NumPy arrays for ease of use in machine learning models.
  The dataset includes labeled categories corresponding to each animal type.
  
  - **Train Set:** Contains images for training the model.
  - **Test Set:** Contains images for model evaluation.

  Each image is stored as a NumPy array with shape (128, 128, 3) and labels are provided as class indices.

dataset_features:
  - image: An `Array3D` of shape `(128, 128, 3)` representing an RGB image.
  - label: A `ClassLabel` corresponding to the category of the image.

dataset_splits:
  - train: Contains images used for training.
  - test: Contains images used for testing.

dataset_usage: |
  To load this dataset using the `datasets` library:
  
  ```python
  from datasets import load_dataset

  dataset = load_dataset("cats_dogs_dataset")

  # Access the train split
  train_dataset = dataset["train"]

  # Access an image and label
  sample = train_dataset[0]
  image = sample["image"]
  label = sample["label"]