CIFAR-Net: CIFAR-10 Image Classification

This repository contains the implementation of CIFAR-10 image classification using Python. The goal of this project is to classify these images into their respective categories using a convolutional neural network (CNN) architecture.

Overview

In this project, we have implemented a CNN model for image classification using the CIFAR-10 dataset. The key components of this repository include:

• cifar_script.sh: A batch file to automate the training, testing, and prediction processes.

Dataset

The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 classes, with 6,000 images per class. The classes are:

1. Airplane
2. Automobile
3. Bird
4. Cat
5. Deer
6. Dog
7. Frog
8. Horse
9. Ship
10. Truck

Usage

Training and Testing

To train and test the CIFAR-Net model, execute the following command:

./cifar_script.sh

This batch file automates the process of training the model on 50,000 images and testing it on 10,000 images from the CIFAR-10 dataset on SLURM.

Prediction

To make predictions using the trained model on custom images, you can modify the predict.py script or use it interactively:

python predict.py <path_to_custom_image>

Replace <path_to_custom_image> with the path to your custom image.