Skip to content

Latest commit

 

History

History
51 lines (40 loc) · 1.39 KB

README.md

File metadata and controls

51 lines (40 loc) · 1.39 KB
Raw

Image Classification using CNN

This project demonstrates image classification using a Convolutional Neural Network (CNN) on the CIFAR-10 dataset. The model is trained to classify images into one of 10 classes.

Features

  • Load Data: Load and preprocess CIFAR-10 image data.
  • Train Model: Train a CNN model on the CIFAR-10 training dataset.
  • Evaluate Model: Evaluate the trained model on the CIFAR-10 test dataset.
  • Jupyter Notebook: Interactive notebook for visualizing the image classification process.

Download Data

Download the CIFAR-10 dataset from the official website and extract it into the data folder. Alternatively, you can run the following script to automatically download and extract the dataset:

mkdir -p data
cd data
wget https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
tar -xvzf cifar-10-python.tar.gz
cd ..

Setup

  1. Clone the repository and install dependencies:
git clone https://github.com/SreeEswaran/Image-classification-using-CNN.git
cd Image-classification-using-CNN
  1. Install the depedencies 
    pip install -r requirements.txt

Usage

  1. Train the model 
    python scripts/train.py
  2. Evaluate the model 
    python scripts/evaluate.py

Interactive notebook

Open the Jupyter notebook:

jupyter notebook notebooks/Image_classification_using_CNN.ipynb