Datasets:
| 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. | |