https://github.com/mohammadreza-mohammadi94/image-classification-convolutional-neural-network

Cat or Dog ? Image classification by using Convolutional Neural Network to classify cats & dogs images
https://github.com/mohammadreza-mohammadi94/image-classification-convolutional-neural-network

artificial-neural-networks cnn cnn-classification cnn-model neural-network python

Last synced: about 9 hours ago
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Cat or Dog ? Image classification by using Convolutional Neural Network to classify cats & dogs images

• Host: GitHub
• URL: https://github.com/mohammadreza-mohammadi94/image-classification-convolutional-neural-network
• Owner: mohammadreza-mohammadi94
• Created: 2024-07-12T17:02:16.000Z (4 months ago)
• Default Branch: master
• Last Pushed: 2024-07-12T17:11:18.000Z (4 months ago)
• Last Synced: 2024-11-07T01:14:50.168Z (about 9 hours ago)
• Topics: artificial-neural-networks, cnn, cnn-classification, cnn-model, neural-network, python
• Language: Python
• Homepage:
• Size: 4.98 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.MD

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README

        
# 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](https://www.kaggle.com/datasets/karakaggle/kaggle-cat-vs-dog-dataset). 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:**

    ```bash

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

    ```

2. **Navigate to the project directory:**

    ```bash

    cd Image_Classification_Convolutional_Neural_Network

    ```

3. **Download the dataset:**

   - Download the Kaggle Cat vs Dog dataset from [Kaggle](https://www.kaggle.com/datasets/karakaggle/kaggle-cat-vs-dog-dataset).

   - Extract the dataset files into a folder named `dataset` within the project directory.

4. **Open the Jupyter Notebook:**

    ```bash

    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:

```bash

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.