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.
- Python
- PyTorch
- Torchvision
- NumPy
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
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.