AIOmarRehan/AnimalsDataset

Cats, Dogs, and Snakes Dataset

Dataset Overview

The dataset contains images of three animal classes: Cats, Dogs, and Snakes. It is balanced and cleaned, designed for supervised image classification tasks.

Class	Number of Images	Description
Cats	1,000	Includes multiple breeds and poses
Dogs	1,000	Covers various breeds and backgrounds
Snakes	1,000	Includes multiple species and natural settings

Total Images: 3,000

Image Properties:

• Resolution: 224×224 pixels (resized for consistency)
• Color Mode: RGB
• Format: JPEG/PNG
• Cleaned: Duplicate, blurry, and irrelevant images removed

---

Data Split Recommendation

Set	Percentage	Number of Images
Training	70%	2,100
Validation	15%	450
Test	15%	450

---

Preprocessing

Images in the dataset have been standardized to support machine learning pipelines:

1. Resizing to 224×224 pixels.
2. Normalization of pixel values to [0,1] or mean subtraction for deep learning frameworks.
3. Label encoding: Integer encoding (0 = Cat, 1 = Dog, 2 = Snake) or one-hot encoding for model training.

---

Example: Loading and Using the Dataset (Python)

import os
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Path to dataset
dataset_path = "path/to/dataset"

# ImageDataGenerator for preprocessing
datagen = ImageDataGenerator(
    rescale=1./255,
    validation_split=0.15  # 15% for validation
)

# Load training data
train_generator = datagen.flow_from_directory(
    dataset_path,
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical',
    subset='training',
    shuffle=True
)

# Load validation data
validation_generator = datagen.flow_from_directory(
    dataset_path,
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical',
    subset='validation',
    shuffle=False
)

# Example: Iterate over one batch
images, labels = next(train_generator)
print(images.shape, labels.shape)  # (32, 224, 224, 3) (32, 3)

---

Key Features

• Balanced: Equal number of samples per class reduces bias.
• Cleaned: High-quality, relevant images improve model performance.
• Diverse: Covers multiple breeds, species, and environments to ensure generalization.
• Ready for ML: Preprocessed and easily integrated into popular deep learning frameworks.