👕 Fashion Forward: CNN-Based Garment Classification

Simple Deep Learning Prototype for Clothing Image Classification
Built with PyTorch, FashionMNIST, and CNN fundamentals

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📌 Overview

This project demonstrates how a Convolutional Neural Network (CNN) can be used to classify clothing images.

It is built as a prototype for automated garment categorization, inspired by real-world e-commerce use cases such as product tagging and inventory organization.

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🎯 Problem Statement

Manually categorizing clothing images is time-consuming and inconsistent.

This project shows how deep learning can automate this process by classifying images into predefined categories, forming a foundation for:

• Product categorization
• Improved search filtering
• Inventory organization

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🧠 Dataset

This project uses the FashionMNIST dataset.

Feature	Value
Image Type	Grayscale
Image Size	28 × 28
Classes	10
Examples	Shirt, Shoe, Bag, Dress

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🏗️ Model Architecture

The project utilizes a custom MultiClassImageClassifier designed for efficient image processing:

• Convolutional Layer: 1 input channel to 16 output channels with a $3 \times 3$ kernel.
• Activation: ReLU for non-linearity.
• Pooling: MaxPool2d ($2 \times 2$) for spatial dimension reduction.
• Classifier: Fully connected linear layer mapped to the specific number of garment classes.

[Image of Convolutional Neural Network architecture]

🔍 Layer Purpose

• Conv2D: Extracts visual features like edges and textures
• ReLU: Adds non-linearity to learn complex patterns
• MaxPooling: Reduces image size while keeping important features
• Fully Connected Layer: Produces final class predictions

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🚀 Features

• Custom CNN built with PyTorch
• Efficient data loading using DataLoader
• Evaluation using TorchMetrics
• Per-class performance analysis

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⚙️ How It Works

1. Load FashionMNIST dataset
2. Convert images into tensors
3. Train CNN using CrossEntropyLoss
4. Optimize using Adam optimizer
5. Evaluate using accuracy, precision, and recall

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📊 Evaluation

The model is evaluated using:

• Accuracy: Overall correctness
• Precision (per class): How many predicted items are correct
• Recall (per class): How many actual items are correctly identified

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🛠️ Installation

git clone https://github.com/YOUR_USERNAME/fashion-forward.git
cd fashion-forward