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.
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.
1 maintainer
Release files for cifar10-tools 0.5.5
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| cifar10_tools-0.5.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 25.3 kB
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