Skip to content

Latest commit

 

History

History
64 lines (45 loc) · 2.55 KB

README.md

File metadata and controls

64 lines (45 loc) · 2.55 KB

Image Classification Using CNN on CIFAR-10

Project Overview

This project implements an image classification model using Convolutional Neural Networks (CNN) to classify images from the CIFAR-10 dataset. The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes, with 6,000 images per class. The classes include airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, and trucks.

Features

  • Data Preprocessing: Loading and normalizing the CIFAR-10 dataset.
  • CNN Model Architecture: Designing a CNN model with multiple convolutional and pooling layers.
  • Training: Training the model on the CIFAR-10 dataset using backpropagation.
  • Evaluation: Evaluating the model's performance on a test set.
  • Predictions: Making predictions on new images.

Technologies Used

  • Python: Core programming language for development.
  • TensorFlow/Keras: For building and training the CNN model.
  • NumPy: For numerical operations.
  • Matplotlib: For data visualization and displaying images.

Installation & Setup

Prerequisites

Make sure you have Python installed on your machine. You can download it from python.org.

Clone the Repository

  1. Clone the repository to your local machine: 
    git clone https://github.com/your-username/ImageClassification-CIFAR10.git
  2. Navigate to the project directory: 
    cd ImageClassification-CIFAR10

Install Dependencies

  1. Install the required Python packages using pip: 
    pip install -r requirements.txt

Usage

  1. Open the Jupyter Notebook in the project directory:

     jupyter notebook

  2. Open the CIFAR10_CNN.ipynb notebook.

  3. Follow the steps outlined in the notebook to preprocess data, build and train the CNN model, and evaluate its performance.

Example Usage To classify an image, load the image using the specified functions and input it into the trained model. The model will output the predicted class label.

Results

The model's performance is evaluated using metrics such as accuracy and loss. Visualizations of training and validation accuracy/loss over epochs are provided.

Future Enhancements

  • Experiment with different CNN architectures (e.g., ResNet, VGG).
  • Implement data augmentation techniques to improve model generalization.
  • Fine-tune hyperparameters for better performance.
  • Deploy the model as a web application for real-time predictions.

Contributing

  • Feel free to fork this project, make improvements, and submit a pull request. All contributions are welcome!