CIFAR10_ImageClassification

This project involves building an image classification model using Xcode's ML Core tools and the CIFAR-10 dataset for training, testing, and validation.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Results
• Contributing
• License

Introduction

The goal of this project is to develop a machine learning model that can accurately classify images into one of the ten classes provided in the CIFAR-10 dataset. The model is built and trained using Xcode's ML Core tools.

Dataset

The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes, with 6,000 images per class. The dataset is divided into 50,000 training images and 10,000 testing images.

Dataset Citation:

HNG BiqC. (2021). CIFAR-10 Images Dataset. Kaggle. Retrieved from https://www.kaggle.com/datasets/hngbiquc/cifar10-images-dataset/data

Installation

1. Ensure you have Xcode installed.

2. Clone the repository:

   git clone https://github.com/dhruvel/CIFAR10_ImageClassification.git
   cd CIFAR10_ImageClassification

3. Rename the directory to have a .mlproj extension for it to be discoverable by Xcode:

   cd ..
   mv CIFAR10_ImageClassification CIFAR10_ImageClassification.mlproj

Usage

1. Prepare the dataset:

   • Download the CIFAR-10 dataset from Kaggle and place it in the appropriate directory.

2. Train the model further:

   • Open the .mlproj file in Xcode.
   • Download a Dataset from [Kaggle]{https://www.kaggle.com/} and train the model further to improve the accuracy
   • Run the training script to train the model on the CIFAR-10 dataset.

3. Test the model:

   • After training, use the testing script to evaluate the model's performance on the test set.

Results

Once the model is trained, you can visualize the results and evaluate the performance using the provided evaluation scripts. The results will be saved in the results directory.

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature/your-feature-name

3. Make your changes.
4. Commit your changes:

   git commit -m 'Add some feature'

5. Push to the branch:

   git push origin feature/your-feature-name

6. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Feel free to open an issue if you have any questions or suggestions.