Cat vs Dog Classification using Convolutional Neural Networks

A deep learning project that classifies images of cats and dogs using CNN architecture built with TensorFlow and Keras. This implementation includes data preprocessing, model training, and evaluation capabilities.

Dataset and Model Overview

Dataset Specifications

• Source: Kaggle Dogs vs Cats dataset
• Total Images: 25,000
• Training Set: 20,000 images
• Test Set: 5,000 images
• Classes: Binary (Cats: 0, Dogs: 1)
• Image Format: JPG
• Input Size: 64x64x3 (RGB)

Model Architecture

model = Sequential([
    Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
    MaxPooling2D(pool_size=(2, 2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(pool_size=(2, 2)),
    Flatten(),
    Dense(128, activation='relu'),
    Dense(1, activation='sigmoid')
])

# Install required packages
pip install tensorflow numpy kagglehub pillow matplotlib

# Configure Kaggle credentials
export KAGGLE_USERNAME=your_username
export KAGGLE_KEY=your_api_key

import kagglehub
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Download dataset
path = kagglehub.dataset_download("tongpython/cat-and-dog")

# Create data generator with augmentation
train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

training_set = train_datagen.flow_from_directory(
    path + '/training_set',
    target_size=(64, 64),
    batch_size=32,
    class_mode='binary'
) 

I created cat-and-dog/single_prediction subfile for testing the my algorithm.