/cifar_with_pruning

Primary LanguageJupyter Notebook

Usage

  1. Clone this repository.
  2. Open the Jupyter Notebook model_vgg.ipynb.
  3. Run the cells in the notebook to train and evaluate the VGG model on the CIFAR-10 dataset.

Overview

The notebook covers the following topics:

  • Importing required libraries
  • Loading and preprocessing the CIFAR-10 dataset
  • Defining the VGG model architecture
  • Training the model
  • Evaluating the model

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

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Custom VGG11-based Model with PyTorch and PyTorch Lightning

This repository contains a Jupyter Notebook (check_model_is_pruned.ipynb) that demonstrates how to implement, train, and evaluate a custom VGG11-based neural network model on the CIFAR-10 dataset.

Requirements

  • Python 3.x
  • PyTorch
  • PyTorch Lightning
  • TorchMetrics
  • torchvision

Installation

To install the required packages, run the following command:

pip install torch torchvision pytorch-lightning torchmetrics

Usage

  1. Clone this repository.
  2. Open the Jupyter Notebook check_model_is_pruned.ipynb.
  3. Run the cells in the notebook to train and evaluate the custom VGG11-based model.

Overview

The notebook includes:

  • Importing required libraries
  • Data loading and preprocessing using CIFAR-10 dataset
  • Model definition using a custom architecture based on VGG11
  • Training and evaluation using PyTorch Lightning

License

This project is licensed under the MIT License - see the LICENSE.md file for details.