Dog-Cat-Image-Classifier

Overview

This project is a deep learning-based image classifier designed to distinguish between dogs and cats with high accuracy. Using Convolutional Neural Networks (CNNs), the model is trained on a large dataset of labeled images and optimized for precise classification.

Dataset

The dataset used for training and validation is sourced from the FreeCodeCamp Dataset, which contains a diverse collection of dog and cat images. It is preprocessed to ensure optimal performance, including resizing, normalization, and augmentation techniques.

Technologies Used

• Python
• TensorFlow/Keras (for deep learning and CNN implementation)
• OpenCV (for image processing)
• Matplotlib & Seaborn (for visualization)
• NumPy & Pandas (for data handling)

Model Architecture

The CNN model is designed with multiple convolutional layers, batch normalization, pooling layers, and fully connected layers to effectively extract and learn image features. The architecture includes:

• Convolutional Layers with ReLU activation
• Max-Pooling Layers to reduce dimensionality
• Dropout Layers to prevent overfitting
• Dense Layers with a Softmax output for binary classification

Training & Evaluation

• The dataset is split into training and validation sets.
• The model is trained using the Adam optimizer and categorical cross-entropy loss function.
• Performance is evaluated using accuracy, precision, recall, and F1-score.
• The trained model achieves high accuracy on test images.

Installation & Usage

Prerequisites

Make sure you have the following installed:

• Python 3.x
• TensorFlow/Keras
• OpenCV
• NumPy, Pandas, Matplotlib

Installation

Clone this repository:

git clone https://github.com/yourusername/Dog-Cat-Image-Classifier.git
cd Dog-Cat-Image-Classifier