CIFAR10 - CNN

Deep Learning based Image Classification using PyTorch and Convolutional Neural Networks

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Project Overview

CIFAR10-CNN is a deep learning based image classification project built using PyTorch and Convolutional Neural Networks (CNNs) on the CIFAR-10 dataset.

The project demonstrates how CNN architectures automatically learn visual features such as edges, textures, patterns, and objects from images to perform accurate multi-class classification.

This repository showcases a complete deep learning workflow including:

• Dataset preprocessing
• Image normalization
• CNN architecture design
• Model training
• Performance evaluation
• Accuracy analysis

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Dataset Information

CIFAR-10 Dataset

The CIFAR-10 dataset contains:

• 60,000 RGB images
• 10 image categories
• Image size: 32×32 pixels

Classes

Label	Class
0	Airplane
1	Automobile
2	Bird
3	Cat
4	Deer
5	Dog
6	Frog
7	Horse
8	Ship
9	Truck

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CNN Architecture

Input Image (32×32×3)
        ↓
Conv2D Layer (32 Filters)
        ↓
ReLU Activation
        ↓
MaxPooling
        ↓
Conv2D Layer (64 Filters)
        ↓
ReLU Activation
        ↓
MaxPooling
        ↓
Conv2D Layer (128 Filters)
        ↓
ReLU Activation
        ↓
MaxPooling
        ↓
Fully Connected Layer
        ↓
Output Layer (10 Classes)

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Key Concepts Used

• Convolutional Neural Networks (CNN)
• Feature Extraction
• ReLU Activation
• MaxPooling
• Backpropagation
• CrossEntropy Loss
• Adam Optimizer
• Tensor Operations
• Batch Processing

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Tech Stack

Technology	Purpose
Python	Programming Language
PyTorch	Deep Learning Framework
TorchVision	Dataset Utilities
NumPy	Numerical Computing
Matplotlib	Visualization

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Model Training Details

Parameter	Value
Optimizer	Adam
Loss Function	CrossEntropyLoss
Epochs	10
Batch Size	64
Framework	PyTorch

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Model Performance

The CNN model achieved approximately:

75% Test Accuracy

on the CIFAR-10 dataset after training.

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Project Workflow

Dataset Loading
       ↓
Image Preprocessing
       ↓
Tensor Conversion
       ↓
CNN Feature Extraction
       ↓
Training & Backpropagation
       ↓
Prediction & Classification
       ↓
Accuracy Evaluation

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Installation

Clone Repository

git clone https://github.com/your-username/CIFAR10-CNN.git

Move into Project Directory

cd CIFAR10-CNN

Install Dependencies

pip install torch torchvision matplotlib numpy

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Run the Project

python main.py

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Why CNNs?

CNNs are specialized neural networks designed for image processing tasks.

They help:

• Learn spatial features
• Detect patterns automatically
• Reduce computation
• Improve image classification accuracy

CNNs are widely used in:

• Computer Vision
• Medical Imaging
• Autonomous Vehicles
• Facial Recognition
• Smart Surveillance

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Future Improvements

• Data Augmentation
• Transfer Learning
• Batch Normalization
• Dropout Regularization
• TensorBoard Integration
• Real-time Prediction Interface
• Model Deployment

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Applications

• Image Classification
• Computer Vision
• Object Recognition
• AI-based Vision Systems
• Smart Automation

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Repository Structure

├── main.py
├── README.md
├── requirements.txt
├── Dataset/
└── model/

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Learning Outcomes

This project helped in understanding:

• CNN architecture design
• Feature extraction in images
• Deep learning workflows
• Model optimization
• Practical implementation of PyTorch

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License

This project is open-source and available for educational and research purpose.

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Acknowledgement

Dataset:

• CIFAR-10 Dataset

Framework:

• PyTorch