Datasets:
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---
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.
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