README.md

metadata

license: mit
task_categories:
  - image-classification
tags:
  - animals
  - computer-vision
  - image-classification
size_categories:
  - n<1K

Animals Dataset

Dataset Description

This dataset contains images of three animal categories: cats, dogs, and pandas.

Dataset Structure

The dataset is organized into training and testing splits:

Animals_dataset/
├── train/
│   ├── cats/
│   ├── dogs/
│   └── panda/
└── test/
    ├── cats/
    ├── dogs/
    └── panda/

Dataset Statistics

• Total Images: 600
• Training Images: 480 (80.0%)
• Testing Images: 120 (20.0%)

Class Distribution

Training Set:

• Cats: 160 images
• Dogs: 160 images
• Panda: 160 images

Testing Set:

• Cats: 40 images
• Dogs: 40 images
• Panda: 40 images

Usage

You can load this dataset using the Hugging Face datasets library:

from datasets import load_dataset

# Load the entire dataset
dataset = load_dataset("Melisa13/Animals_dataset")

# Access train and test splits
train_data = dataset['train']
test_data = dataset['test']

Or using custom code:

from huggingface_hub import hf_hub_download
from PIL import Image
import os

# Download a specific file
file_path = hf_hub_download(
    repo_id="Melisa13/Animals_dataset",
    filename="train/cats/cats_00001.jpg",
    repo_type="dataset"
)

# Load image
image = Image.open(file_path)

Dataset Creation

This dataset was split using scikit-learn's train_test_split with:

• Test size: 20.0%
• Random seed: 42

License

MIT License

Citation

If you use this dataset, please cite it appropriately.