/ResNet

Primary LanguagePython

ResNet for CIFAR-10 Classification

Overview

This repository contains the implementation of a Residual Neural Network (ResNet) for the CIFAR-10 image classification task using PyTorch. The model achieved an accuracy of 94% on the test set.

Requirements

  • Python
  • PyTorch
  • Torchvision
  • NumPy

Dataset

The CIFAR-10 dataset consists of 60,000 32x32 color images in 10 different classes. It is divided into 50,000 training images and 10,000 test images.

Dataset Link: CIFAR-10

Model Architecture

The ResNet architecture used in this project is inspired by the original ResNet paper (He et al., 2015). The model is designed to handle the complexities of the CIFAR-10 dataset.