🧠 CIFAR-10 Classifier

A modular PyTorch project for experimenting with Convolutional Neural Networks (CNNs) on the CIFAR-10 image classification dataset.

📂 Project Structure

CIFAR10_Classifier/
├── images/
│   └── baseline/
│       ├── Model Performance.png
│       └── Training and Validation Metrics.png
├── models/
│   └── CIFAR_Baseline.pt
├── notebooks/
│   ├── cnn.ipynb
│   └── data_exploration.ipynb
├── README.md
├── requirements.txt
└── src/
    ├── dataset.py
    ├── engine.py
    ├── metrics.py
    ├── models.py
    └── plots.py

🛠️ Tech Stack

• Python
• PyTorch
• TorchVision
• TorchMetrics
• Pandas
• Matplotlib
• Jupyter Notebook

⚙️ Setup

Clone the repository:

git clone https://github.com/VRYeshwanth/CIFAR10_Classifier.git
cd CIFAR10_Classifier

Create a virtual environment:

python -m venv venv

Activate the virtual environment:

Windows

venv\Scripts\activate

Linux / MacOS

source venv/bin/activate

Install the dependencies:

pip install -r requirements.txt

Download the CIFAR-10 dataset and place the extracted training images and labels inside:

cifar-10/
├── train/
└── trainLabels.csv

📦 Dataset

The original CIFAR-10 training set was split into stratified training, validation, and test sets:

• Train: 40,000 images
• Validation: 10,000 images
• Test: 10,000 images

The processed dataset is available as a GitHub Release:

Download Dataset