cifar10-tools 0.5.4

Tools for training neural networks on the CIFAR-10 task with PyTorch and TensorFlow

PyTorch: CIFAR-10 Demonstration

A progressive deep learning tutorial for image classification on the CIFAR-10 dataset using PyTorch. This project demonstrates the evolution from basic deep neural networks to optimized convolutional neural networks with data augmentation. It also provides a set of utility functions as a PyPI package for use in other projects.

View on PyPI | Documentation

Installation

Install the helper tools package locally in editable mode to use in this repository:

pip install -e .

Or install from PyPI to use in other projects:

pip install cifar10_tools

Project overview

This repository contains a series of Jupyter notebooks that progressively build more sophisticated neural network architectures for the CIFAR-10 image classification task. Each notebook builds upon concepts from the previous one, demonstrating key deep learning techniques.

Notebooks

Notebook	Description
01-DNN.ipynb	Deep Neural Network - Baseline fully-connected DNN classifier using nn.Sequential. Establishes a performance baseline with a simple architecture.
02-CNN.ipynb	Convolutional Neural Network - Introduction to CNNs with convolutional and pooling layers using nn.Sequential. Demonstrates the advantage of CNNs over DNNs for image tasks.
03-RGB-CNN.ipynb	RGB CNN - CNN classifier that utilizes full RGB color information instead of grayscale, improving feature extraction from color images.
04-optimized-CNN.ipynb	Hyperparameter Optimization - Uses Optuna for automated hyperparameter tuning to find optimal network architecture and training parameters.
05-augmented-CNN.ipynb	Data Augmentation - Trains the optimized CNN architecture with image augmentation techniques for improved generalization and robustness.

Requirements

• Python >=3.10, <3.13
• PyTorch >=2.0
• torchvision >=0.15
• numpy >=1.24

License

This project is licensed under the GPLv3 License - see the LICENSE file for details.