README.md

---
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:

```python
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:

```python
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.