Image Classification using Convolutional Neural Network (CNN)

This repository contains a Jupyter Notebook focused on image classification using Convolutional Neural Networks (CNN). The project utilizes the Kaggle Cat vs Dog dataset to train a CNN model to distinguish between images of cats and dogs.

Overview

The goal of this project is to build a deep learning model capable of accurately classifying whether an input image contains a cat or a dog. CNNs are particularly effective for image classification tasks due to their ability to capture spatial hierarchies and patterns in images.

Dataset

The dataset used in this project is the Kaggle Cat vs Dog dataset, which can be accessed here. It consists of thousands of labeled images of cats and dogs. The images vary in terms of breed, size, and orientation, providing a diverse set for training and testing the model.

Usage

1. Clone the repository:

   git clone https://github.com/mohammadreza-mohammadi94/Image_Classification_Convolutional_Neural_Network.git

2. Navigate to the project directory:

   cd Image_Classification_Convolutional_Neural_Network

3. Download the dataset:

   • Download the Kaggle Cat vs Dog dataset from Kaggle.
   • Extract the dataset files into a folder named dataset within the project directory.

4. Open the Jupyter Notebook:

   jupyter notebook Convolutional_Nerual_Network.ipynb

Key Steps

• Data Preprocessing: Loading images, resizing, and normalizing pixel values.
• CNN Architecture: Designing and building a convolutional neural network using TensorFlow/Keras.
• Model Training: Compiling the model, specifying loss function and optimizer, and training the model on the dataset.
• Evaluation: Assessing model performance using accuracy metrics and visualizing results.
• Prediction: Making predictions on new images using the trained model.

Dependencies

• Python
• Jupyter Notebook
• TensorFlow
• Keras

Install the required packages using:

pip install tensorflow matplotlib keras jupyter

Results

The project results in a trained CNN model that achieves high accuracy in classifying images of cats and dogs. Performance metrics and visualizations are provided to evaluate the model's effectiveness.

Conclusion

This project demonstrates the application of CNNs for image classification tasks using TensorFlow/Keras. The trained model can be further optimized or extended for more complex image recognition tasks or integrated into applications requiring automated image classification.