stefanshipinkoski / cifar-10-cnn

A Deep Learning project using Convolutional Neural Network (CNN) for classifying images from the CIFAR-10 dataset into 10 distinct categories.

CIFAR-10 Object Classification!

This is Deep Learning project using a Convolutional Neural Network (CNN) designed to classify images from the CIFAR-10 dataset into 10 distinct categories.

Overview

The CIFAR-10 dataset is a widely used benchmark in image classification tasks. It consists of 60,000 32x32 color images in 10 different classes, making it an ideal dataset for testing and training image classification models.

Features

• Convolutional Neural Network (CNN) architecture optimized for image classification.
• Trained on the CIFAR-10 dataset with 10 distinct classes.
• Evaluation of model performance using various classification metrics.
• Potential for further improvements and experimentation with Hyperparameters tuning.

Technologies Used:

• Python3.10

• TensorFlow

• Computer Vision

• CNN

Getting Started

1. Clone this repository to your local machine.

git clone https://github.com/stefanshipinkoski/cifar-10-cnn.git

2. Change the project directory.

cd cifar-10-cnn

3. Create a conda environment and install the required dependencies:

conda env create -f enviorment.yml

4. Activate the conda environment:

conda activate cifar-10-env

5. Run Jupyter Notebook in the directory

jupyter notebook

Contribution:

Any contributions to this project are welcomed!