clothes category

Project Description

This project is a Convolutional Neural Network (CNN) model built باستخدام PyTorch to classify fashion images from the FashionMNIST dataset.

The model is trained to recognize different types of clothing items such as T-shirts, shoes, bags, and more. It uses deep learning techniques to automatically extract features from images and accurately classify them into one of 10 categories.

Tools & Technologies

Python

PyTorch

Torchvision

NumPy

Matplotlib

Model Architecture

The CNN model consists of:

2 Convolutional layers (feature extraction)

MaxPooling layers (downsampling)

Fully Connected layers (classification)

ReLU activation function

How It Works

The dataset (FashionMNIST) is loaded and preprocessed.

Images are normalized and converted into tensors.

The CNN model is trained on training data.

The model learns patterns and features from images.

After training, the model is tested on unseen data.

The system outputs:

Predicted class

Accuracy of the model

Usage

Install required libraries (PyTorch, torchvision, etc.).

Run the Python script.

The model will:

Download the dataset automatically

Train for عدة epochs

Evaluate performance

Final output includes:

Training loss

Test accuracy

Visualization of predictions

Features

Image classification using deep learning

Automatic feature extraction (no manual features needed)

High accuracy on test data

Visualization of predictions vs actual labels

Easy to modify and extend

Use Cases

Learning deep learning and computer vision

Academic projects (AI / ML)

Image classification systems

Foundation for advanced models

Limitations

Uses a simple dataset (FashionMNIST)

Limited training epochs

No advanced tuning (hyperparameters)

Future Improvements

Increase training epochs for better accuracy

Use more complex datasets

Add data augmentation

Improve model architecture (deeper CNN)

Deploy as a web application